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train | reduce_mean | Reduce the array along a given axis by mean value | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | def reduce_mean(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by mean value"""
new_attrs = translation_utils._fix_attribute_names(attrs, {'axes':'axis'})
return 'mean', new_attrs, inputs | def reduce_mean(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by mean value"""
new_attrs = translation_utils._fix_attribute_names(attrs, {'axes':'axis'})
return 'mean', new_attrs, inputs | [
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train | reduce_min | Reduce the array along a given axis by minimum value | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | def reduce_min(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by minimum value"""
new_attrs = translation_utils._fix_attribute_names(attrs, {'axes':'axis'})
return 'min', new_attrs, inputs | def reduce_min(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by minimum value"""
new_attrs = translation_utils._fix_attribute_names(attrs, {'axes':'axis'})
return 'min', new_attrs, inputs | [
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train | reduce_sum | Reduce the array along a given axis by sum value | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | def reduce_sum(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by sum value"""
new_attrs = translation_utils._fix_attribute_names(attrs, {'axes':'axis'})
return 'sum', new_attrs, inputs | def reduce_sum(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by sum value"""
new_attrs = translation_utils._fix_attribute_names(attrs, {'axes':'axis'})
return 'sum', new_attrs, inputs | [
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train | reduce_prod | Reduce the array along a given axis by product value | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | def reduce_prod(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by product value"""
new_attrs = translation_utils._fix_attribute_names(attrs, {'axes':'axis'})
return 'prod', new_attrs, inputs | def reduce_prod(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by product value"""
new_attrs = translation_utils._fix_attribute_names(attrs, {'axes':'axis'})
return 'prod', new_attrs, inputs | [
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train | reduce_log_sum | Reduce the array along a given axis by log sum value | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | def reduce_log_sum(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by log sum value"""
keep_dims = True if 'keepdims' not in attrs else attrs.get('keepdims')
sum_op = symbol.sum(inputs[0], axis=attrs.get('axes'),
keepdims=keep_dims)
log_sym = symbol.log(sum_op)
return log_sym, attrs, inputs | def reduce_log_sum(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by log sum value"""
keep_dims = True if 'keepdims' not in attrs else attrs.get('keepdims')
sum_op = symbol.sum(inputs[0], axis=attrs.get('axes'),
keepdims=keep_dims)
log_sym = symbol.log(sum_op)
return log_sym, attrs, inputs | [
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train | reduce_log_sum_exp | Reduce the array along a given axis by log sum exp value | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | def reduce_log_sum_exp(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by log sum exp value"""
keep_dims = True if 'keepdims' not in attrs else attrs.get('keepdims')
exp_op = symbol.exp(inputs[0])
sum_op = symbol.sum(exp_op, axis=attrs.get('axes'),
keepdims=keep_dims)
log_sym = symbol.log(sum_op)
return log_sym, attrs, inputs | def reduce_log_sum_exp(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by log sum exp value"""
keep_dims = True if 'keepdims' not in attrs else attrs.get('keepdims')
exp_op = symbol.exp(inputs[0])
sum_op = symbol.sum(exp_op, axis=attrs.get('axes'),
keepdims=keep_dims)
log_sym = symbol.log(sum_op)
return log_sym, attrs, inputs | [
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train | reduce_sum_square | Reduce the array along a given axis by sum square value | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | def reduce_sum_square(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by sum square value"""
square_op = symbol.square(inputs[0])
sum_op = symbol.sum(square_op, axis=attrs.get('axes'),
keepdims=attrs.get('keepdims'))
return sum_op, attrs, inputs | def reduce_sum_square(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by sum square value"""
square_op = symbol.square(inputs[0])
sum_op = symbol.sum(square_op, axis=attrs.get('axes'),
keepdims=attrs.get('keepdims'))
return sum_op, attrs, inputs | [
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train | reduce_l1 | Reduce input tensor by l1 normalization. | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | def reduce_l1(attrs, inputs, proto_obj):
"""Reduce input tensor by l1 normalization."""
new_attrs = translation_utils._fix_attribute_names(attrs, {'axes':'axis'})
new_attrs = translation_utils._add_extra_attributes(new_attrs,
{'ord' : 1})
return 'norm', new_attrs, inputs | def reduce_l1(attrs, inputs, proto_obj):
"""Reduce input tensor by l1 normalization."""
new_attrs = translation_utils._fix_attribute_names(attrs, {'axes':'axis'})
new_attrs = translation_utils._add_extra_attributes(new_attrs,
{'ord' : 1})
return 'norm', new_attrs, inputs | [
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train | reduce_l2 | Reduce input tensor by l2 normalization. | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | def reduce_l2(attrs, inputs, proto_obj):
"""Reduce input tensor by l2 normalization."""
new_attrs = translation_utils._fix_attribute_names(attrs, {'axes':'axis'})
return 'norm', new_attrs, inputs | def reduce_l2(attrs, inputs, proto_obj):
"""Reduce input tensor by l2 normalization."""
new_attrs = translation_utils._fix_attribute_names(attrs, {'axes':'axis'})
return 'norm', new_attrs, inputs | [
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train | avg_pooling | Average pooling | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | def avg_pooling(attrs, inputs, proto_obj):
""" Average pooling"""
new_attrs = translation_utils._fix_attribute_names(attrs,
{'kernel_shape': 'kernel',
'strides': 'stride',
'pads': 'pad',
})
new_attrs = translation_utils._add_extra_attributes(new_attrs,
{'pooling_convention': 'valid'
})
new_op = translation_utils._fix_pooling('avg', inputs, new_attrs)
return new_op, new_attrs, inputs | def avg_pooling(attrs, inputs, proto_obj):
""" Average pooling"""
new_attrs = translation_utils._fix_attribute_names(attrs,
{'kernel_shape': 'kernel',
'strides': 'stride',
'pads': 'pad',
})
new_attrs = translation_utils._add_extra_attributes(new_attrs,
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})
new_op = translation_utils._fix_pooling('avg', inputs, new_attrs)
return new_op, new_attrs, inputs | [
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train | lp_pooling | LP Pooling | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | def lp_pooling(attrs, inputs, proto_obj):
"""LP Pooling"""
p_value = attrs.get('p', 2)
new_attrs = translation_utils._fix_attribute_names(attrs,
{'kernel_shape': 'kernel',
'strides': 'stride',
'pads': 'pad'
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new_attrs = translation_utils._remove_attributes(new_attrs, ['p'])
new_attrs = translation_utils._add_extra_attributes(new_attrs,
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})
new_op = translation_utils._fix_pooling('lp', inputs, new_attrs)
return new_op, new_attrs, inputs | def lp_pooling(attrs, inputs, proto_obj):
"""LP Pooling"""
p_value = attrs.get('p', 2)
new_attrs = translation_utils._fix_attribute_names(attrs,
{'kernel_shape': 'kernel',
'strides': 'stride',
'pads': 'pad'
})
new_attrs = translation_utils._remove_attributes(new_attrs, ['p'])
new_attrs = translation_utils._add_extra_attributes(new_attrs,
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new_op = translation_utils._fix_pooling('lp', inputs, new_attrs)
return new_op, new_attrs, inputs | [
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train | max_roi_pooling | Max ROI Pooling. | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | def max_roi_pooling(attrs, inputs, proto_obj):
"""Max ROI Pooling."""
new_attrs = translation_utils._fix_attribute_names(attrs,
{'pooled_shape': 'pooled_size',
'spatial_scale': 'spatial_scale'
})
return 'ROIPooling', new_attrs, inputs | def max_roi_pooling(attrs, inputs, proto_obj):
"""Max ROI Pooling."""
new_attrs = translation_utils._fix_attribute_names(attrs,
{'pooled_shape': 'pooled_size',
'spatial_scale': 'spatial_scale'
})
return 'ROIPooling', new_attrs, inputs | [
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train | depthtospace | Rearranges data from depth into blocks of spatial data. | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | def depthtospace(attrs, inputs, proto_obj):
"""Rearranges data from depth into blocks of spatial data."""
new_attrs = translation_utils._fix_attribute_names(attrs, {'blocksize':'block_size'})
return "depth_to_space", new_attrs, inputs | def depthtospace(attrs, inputs, proto_obj):
"""Rearranges data from depth into blocks of spatial data."""
new_attrs = translation_utils._fix_attribute_names(attrs, {'blocksize':'block_size'})
return "depth_to_space", new_attrs, inputs | [
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train | spacetodepth | Rearranges blocks of spatial data into depth. | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | def spacetodepth(attrs, inputs, proto_obj):
"""Rearranges blocks of spatial data into depth."""
new_attrs = translation_utils._fix_attribute_names(attrs, {'blocksize':'block_size'})
return "space_to_depth", new_attrs, inputs | def spacetodepth(attrs, inputs, proto_obj):
"""Rearranges blocks of spatial data into depth."""
new_attrs = translation_utils._fix_attribute_names(attrs, {'blocksize':'block_size'})
return "space_to_depth", new_attrs, inputs | [
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train | hardmax | Returns batched one-hot vectors. | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | def hardmax(attrs, inputs, proto_obj):
"""Returns batched one-hot vectors."""
input_tensor_data = proto_obj.model_metadata.get('input_tensor_data')[0]
input_shape = input_tensor_data[1]
axis = int(attrs.get('axis', 1))
axis = axis if axis >= 0 else len(input_shape) + axis
if axis == len(input_shape) - 1:
amax = symbol.argmax(inputs[0], axis=-1)
one_hot = symbol.one_hot(amax, depth=input_shape[-1])
return one_hot, attrs, inputs
# since reshape doesn't take a tensor for shape,
# computing with np.prod. This needs to be changed to
# to use mx.sym.prod() when mx.sym.reshape() is fixed.
# (https://github.com/apache/incubator-mxnet/issues/10789)
new_shape = (int(np.prod(input_shape[:axis])),
int(np.prod(input_shape[axis:])))
reshape_op = symbol.reshape(inputs[0], new_shape)
amax = symbol.argmax(reshape_op, axis=-1)
one_hot = symbol.one_hot(amax, depth=new_shape[-1])
hardmax_op = symbol.reshape(one_hot, input_shape)
return hardmax_op, attrs, inputs | def hardmax(attrs, inputs, proto_obj):
"""Returns batched one-hot vectors."""
input_tensor_data = proto_obj.model_metadata.get('input_tensor_data')[0]
input_shape = input_tensor_data[1]
axis = int(attrs.get('axis', 1))
axis = axis if axis >= 0 else len(input_shape) + axis
if axis == len(input_shape) - 1:
amax = symbol.argmax(inputs[0], axis=-1)
one_hot = symbol.one_hot(amax, depth=input_shape[-1])
return one_hot, attrs, inputs
# since reshape doesn't take a tensor for shape,
# computing with np.prod. This needs to be changed to
# to use mx.sym.prod() when mx.sym.reshape() is fixed.
# (https://github.com/apache/incubator-mxnet/issues/10789)
new_shape = (int(np.prod(input_shape[:axis])),
int(np.prod(input_shape[axis:])))
reshape_op = symbol.reshape(inputs[0], new_shape)
amax = symbol.argmax(reshape_op, axis=-1)
one_hot = symbol.one_hot(amax, depth=new_shape[-1])
hardmax_op = symbol.reshape(one_hot, input_shape)
return hardmax_op, attrs, inputs | [
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train | lpnormalization | ONNX does not have eps attribute, so cannot map it to L2normalization in MXNet
without that, it works as norm operator discussion in PR:
https://github.com/onnx/onnx/pull/1330 | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | def lpnormalization(attrs, inputs, proto_obj):
"""ONNX does not have eps attribute, so cannot map it to L2normalization in MXNet
without that, it works as norm operator discussion in PR:
https://github.com/onnx/onnx/pull/1330"""
new_attrs = translation_utils._fix_attribute_names(attrs, {'p': 'ord'})
axis = int(attrs.get("axis", -1))
new_attrs.update(axis=axis)
return 'norm', new_attrs, inputs | def lpnormalization(attrs, inputs, proto_obj):
"""ONNX does not have eps attribute, so cannot map it to L2normalization in MXNet
without that, it works as norm operator discussion in PR:
https://github.com/onnx/onnx/pull/1330"""
new_attrs = translation_utils._fix_attribute_names(attrs, {'p': 'ord'})
axis = int(attrs.get("axis", -1))
new_attrs.update(axis=axis)
return 'norm', new_attrs, inputs | [
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train | download_mp4 | download mp4s | example/gluon/lipnet/utils/download_data.py | def download_mp4(from_idx, to_idx, _params):
"""
download mp4s
"""
succ = set()
fail = set()
for idx in range(from_idx, to_idx):
name = 's' + str(idx)
save_folder = '{src_path}/{nm}'.format(src_path=_params['src_path'], nm=name)
if idx == 0 or os.path.isdir(save_folder):
continue
script = "http://spandh.dcs.shef.ac.uk/gridcorpus/{nm}/video/{nm}.mpg_vcd.zip".format( \
nm=name)
down_sc = 'cd {src_path} && curl {script} --output {nm}.mpg_vcd.zip && \
unzip {nm}.mpg_vcd.zip'.format(script=script,
nm=name,
src_path=_params['src_path'])
try:
print(down_sc)
os.system(down_sc)
succ.add(idx)
except OSError as error:
print(error)
fail.add(idx)
return (succ, fail) | def download_mp4(from_idx, to_idx, _params):
"""
download mp4s
"""
succ = set()
fail = set()
for idx in range(from_idx, to_idx):
name = 's' + str(idx)
save_folder = '{src_path}/{nm}'.format(src_path=_params['src_path'], nm=name)
if idx == 0 or os.path.isdir(save_folder):
continue
script = "http://spandh.dcs.shef.ac.uk/gridcorpus/{nm}/video/{nm}.mpg_vcd.zip".format( \
nm=name)
down_sc = 'cd {src_path} && curl {script} --output {nm}.mpg_vcd.zip && \
unzip {nm}.mpg_vcd.zip'.format(script=script,
nm=name,
src_path=_params['src_path'])
try:
print(down_sc)
os.system(down_sc)
succ.add(idx)
except OSError as error:
print(error)
fail.add(idx)
return (succ, fail) | [
"download",
"mp4s"
] | apache/incubator-mxnet | python | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/example/gluon/lipnet/utils/download_data.py#L28-L52 | [
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train | download_align | download aligns | example/gluon/lipnet/utils/download_data.py | def download_align(from_idx, to_idx, _params):
"""
download aligns
"""
succ = set()
fail = set()
for idx in range(from_idx, to_idx):
name = 's' + str(idx)
if idx == 0:
continue
script = "http://spandh.dcs.shef.ac.uk/gridcorpus/{nm}/align/{nm}.tar".format(nm=name)
down_sc = 'cd {align_path} && wget {script} && \
tar -xvf {nm}.tar'.format(script=script,
nm=name,
align_path=_params['align_path'])
try:
print(down_sc)
os.system(down_sc)
succ.add(idx)
except OSError as error:
print(error)
fail.add(idx)
return (succ, fail) | def download_align(from_idx, to_idx, _params):
"""
download aligns
"""
succ = set()
fail = set()
for idx in range(from_idx, to_idx):
name = 's' + str(idx)
if idx == 0:
continue
script = "http://spandh.dcs.shef.ac.uk/gridcorpus/{nm}/align/{nm}.tar".format(nm=name)
down_sc = 'cd {align_path} && wget {script} && \
tar -xvf {nm}.tar'.format(script=script,
nm=name,
align_path=_params['align_path'])
try:
print(down_sc)
os.system(down_sc)
succ.add(idx)
except OSError as error:
print(error)
fail.add(idx)
return (succ, fail) | [
"download",
"aligns"
] | apache/incubator-mxnet | python | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/example/gluon/lipnet/utils/download_data.py#L55-L77 | [
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train | run_ut_py3_qemu | Run unit tests in the emulator and copy the results back to the host through the mounted
volume in /mxnet | ci/docker/qemu/runtime_functions.py | def run_ut_py3_qemu():
"""Run unit tests in the emulator and copy the results back to the host through the mounted
volume in /mxnet"""
from vmcontrol import VM
with VM() as vm:
qemu_provision(vm.ssh_port)
logging.info("execute tests")
qemu_ssh(vm.ssh_port, "./runtime_functions.py", "run_ut_python3_qemu_internal")
qemu_rsync_to_host(vm.ssh_port, "*.xml", "mxnet")
logging.info("copied to host")
logging.info("tests finished, vm shutdown.")
vm.shutdown() | def run_ut_py3_qemu():
"""Run unit tests in the emulator and copy the results back to the host through the mounted
volume in /mxnet"""
from vmcontrol import VM
with VM() as vm:
qemu_provision(vm.ssh_port)
logging.info("execute tests")
qemu_ssh(vm.ssh_port, "./runtime_functions.py", "run_ut_python3_qemu_internal")
qemu_rsync_to_host(vm.ssh_port, "*.xml", "mxnet")
logging.info("copied to host")
logging.info("tests finished, vm shutdown.")
vm.shutdown() | [
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train | run_ut_python3_qemu_internal | this runs inside the vm | ci/docker/qemu/runtime_functions.py | def run_ut_python3_qemu_internal():
"""this runs inside the vm"""
pkg = glob.glob('mxnet_dist/*.whl')[0]
logging.info("=== NOW Running inside QEMU ===")
logging.info("PIP Installing %s", pkg)
check_call(['sudo', 'pip3', 'install', pkg])
logging.info("PIP Installing mxnet/test_requirements.txt")
check_call(['sudo', 'pip3', 'install', '-r', 'mxnet/test_requirements.txt'])
logging.info("Running tests in mxnet/tests/python/unittest/")
check_call(['nosetests', '--with-timer', '--with-xunit', '--xunit-file', 'nosetests_unittest.xml', '--verbose', 'mxnet/tests/python/unittest/test_engine.py']) | def run_ut_python3_qemu_internal():
"""this runs inside the vm"""
pkg = glob.glob('mxnet_dist/*.whl')[0]
logging.info("=== NOW Running inside QEMU ===")
logging.info("PIP Installing %s", pkg)
check_call(['sudo', 'pip3', 'install', pkg])
logging.info("PIP Installing mxnet/test_requirements.txt")
check_call(['sudo', 'pip3', 'install', '-r', 'mxnet/test_requirements.txt'])
logging.info("Running tests in mxnet/tests/python/unittest/")
check_call(['nosetests', '--with-timer', '--with-xunit', '--xunit-file', 'nosetests_unittest.xml', '--verbose', 'mxnet/tests/python/unittest/test_engine.py']) | [
"this",
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] | apache/incubator-mxnet | python | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/ci/docker/qemu/runtime_functions.py#L74-L83 | [
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train | _get_subword_units | Return subword-units presentation, given a word/token. | example/nce-loss/text8_data.py | def _get_subword_units(token, gram):
"""Return subword-units presentation, given a word/token.
"""
if token == '</s>': # special token for padding purpose.
return [token]
t = '#' + token + '#'
return [t[i:i + gram] for i in range(0, len(t) - gram + 1)] | def _get_subword_units(token, gram):
"""Return subword-units presentation, given a word/token.
"""
if token == '</s>': # special token for padding purpose.
return [token]
t = '#' + token + '#'
return [t[i:i + gram] for i in range(0, len(t) - gram + 1)] | [
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] | apache/incubator-mxnet | python | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/example/nce-loss/text8_data.py#L68-L74 | [
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train | fit | Train the model using Caffe operator in MXNet | example/caffe/train_model.py | def fit(args, network, data_loader, eval_metrics=None, batch_end_callback=None):
"""Train the model using Caffe operator in MXNet"""
# kvstore
kv = mx.kvstore.create(args.kv_store)
# logging
head = '%(asctime)-15s Node[' + str(kv.rank) + '] %(message)s'
if 'log_file' in args and args.log_file is not None:
log_file = args.log_file
log_dir = args.log_dir
log_file_full_name = os.path.join(log_dir, log_file)
if not os.path.exists(log_dir):
os.mkdir(log_dir)
logger = logging.getLogger()
handler = logging.FileHandler(log_file_full_name)
formatter = logging.Formatter(head)
handler.setFormatter(formatter)
logger.addHandler(handler)
logger.setLevel(logging.DEBUG)
logger.info('start with arguments %s', args)
else:
logging.basicConfig(level=logging.DEBUG, format=head)
logging.info('start with arguments %s', args)
# load model
model_prefix = args.model_prefix
if model_prefix is not None:
model_prefix += "-%d" % (kv.rank)
model_args = {}
if args.load_epoch is not None:
assert model_prefix is not None
tmp = mx.model.FeedForward.load(model_prefix, args.load_epoch)
model_args = {'arg_params' : tmp.arg_params,
'aux_params' : tmp.aux_params,
'begin_epoch' : args.load_epoch}
# save model
save_model_prefix = args.save_model_prefix
if save_model_prefix is None:
save_model_prefix = model_prefix
checkpoint = None if save_model_prefix is None else mx.callback.do_checkpoint(save_model_prefix)
# data
(train, val) = data_loader(args, kv)
# train
devs = mx.cpu() if args.gpus is None else [
mx.gpu(int(i)) for i in args.gpus.split(',')]
epoch_size = args.num_examples / args.batch_size
if args.kv_store == 'dist_sync':
epoch_size /= kv.num_workers
model_args['epoch_size'] = epoch_size
if 'lr_factor' in args and args.lr_factor < 1:
model_args['lr_scheduler'] = mx.lr_scheduler.FactorScheduler(
step=max(int(epoch_size * args.lr_factor_epoch), 1),
factor=args.lr_factor)
if 'clip_gradient' in args and args.clip_gradient is not None:
model_args['clip_gradient'] = args.clip_gradient
# disable kvstore for single device
if 'local' in kv.type and (
args.gpus is None or len(args.gpus.split(',')) is 1):
kv = None
mod = mx.mod.Module(network, context=devs)
if eval_metrics is None:
eval_metrics = ['accuracy']
# TopKAccuracy only allows top_k > 1
for top_k in [5, 10, 20]:
eval_metrics.append(mx.metric.create('top_k_accuracy', top_k=top_k))
if batch_end_callback is not None:
if not isinstance(batch_end_callback, list):
batch_end_callback = [batch_end_callback]
else:
batch_end_callback = []
batch_end_callback.append(mx.callback.Speedometer(args.batch_size, 50))
mod.fit(train_data=train, eval_metric=eval_metrics, eval_data=val, optimizer='sgd',
optimizer_params={'learning_rate':args.lr, 'momentum': 0.9, 'wd': 0.00001},
num_epoch=args.num_epochs, batch_end_callback=batch_end_callback,
initializer=mx.init.Xavier(factor_type="in", magnitude=2.34),
kvstore=kv, epoch_end_callback=checkpoint, **model_args) | def fit(args, network, data_loader, eval_metrics=None, batch_end_callback=None):
"""Train the model using Caffe operator in MXNet"""
# kvstore
kv = mx.kvstore.create(args.kv_store)
# logging
head = '%(asctime)-15s Node[' + str(kv.rank) + '] %(message)s'
if 'log_file' in args and args.log_file is not None:
log_file = args.log_file
log_dir = args.log_dir
log_file_full_name = os.path.join(log_dir, log_file)
if not os.path.exists(log_dir):
os.mkdir(log_dir)
logger = logging.getLogger()
handler = logging.FileHandler(log_file_full_name)
formatter = logging.Formatter(head)
handler.setFormatter(formatter)
logger.addHandler(handler)
logger.setLevel(logging.DEBUG)
logger.info('start with arguments %s', args)
else:
logging.basicConfig(level=logging.DEBUG, format=head)
logging.info('start with arguments %s', args)
# load model
model_prefix = args.model_prefix
if model_prefix is not None:
model_prefix += "-%d" % (kv.rank)
model_args = {}
if args.load_epoch is not None:
assert model_prefix is not None
tmp = mx.model.FeedForward.load(model_prefix, args.load_epoch)
model_args = {'arg_params' : tmp.arg_params,
'aux_params' : tmp.aux_params,
'begin_epoch' : args.load_epoch}
# save model
save_model_prefix = args.save_model_prefix
if save_model_prefix is None:
save_model_prefix = model_prefix
checkpoint = None if save_model_prefix is None else mx.callback.do_checkpoint(save_model_prefix)
# data
(train, val) = data_loader(args, kv)
# train
devs = mx.cpu() if args.gpus is None else [
mx.gpu(int(i)) for i in args.gpus.split(',')]
epoch_size = args.num_examples / args.batch_size
if args.kv_store == 'dist_sync':
epoch_size /= kv.num_workers
model_args['epoch_size'] = epoch_size
if 'lr_factor' in args and args.lr_factor < 1:
model_args['lr_scheduler'] = mx.lr_scheduler.FactorScheduler(
step=max(int(epoch_size * args.lr_factor_epoch), 1),
factor=args.lr_factor)
if 'clip_gradient' in args and args.clip_gradient is not None:
model_args['clip_gradient'] = args.clip_gradient
# disable kvstore for single device
if 'local' in kv.type and (
args.gpus is None or len(args.gpus.split(',')) is 1):
kv = None
mod = mx.mod.Module(network, context=devs)
if eval_metrics is None:
eval_metrics = ['accuracy']
# TopKAccuracy only allows top_k > 1
for top_k in [5, 10, 20]:
eval_metrics.append(mx.metric.create('top_k_accuracy', top_k=top_k))
if batch_end_callback is not None:
if not isinstance(batch_end_callback, list):
batch_end_callback = [batch_end_callback]
else:
batch_end_callback = []
batch_end_callback.append(mx.callback.Speedometer(args.batch_size, 50))
mod.fit(train_data=train, eval_metric=eval_metrics, eval_data=val, optimizer='sgd',
optimizer_params={'learning_rate':args.lr, 'momentum': 0.9, 'wd': 0.00001},
num_epoch=args.num_epochs, batch_end_callback=batch_end_callback,
initializer=mx.init.Xavier(factor_type="in", magnitude=2.34),
kvstore=kv, epoch_end_callback=checkpoint, **model_args) | [
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train | Atari8080Preprocessor.preprocess | Preprocess a 210x160x3 uint8 frame into a 6400 (80x80) (1 x input_size)
float vector. | example/reinforcement-learning/parallel_actor_critic/envs.py | def preprocess(self, img):
"""
Preprocess a 210x160x3 uint8 frame into a 6400 (80x80) (1 x input_size)
float vector.
"""
# Crop, down-sample, erase background and set foreground to 1.
# See https://gist.github.com/karpathy/a4166c7fe253700972fcbc77e4ea32c5
img = img[35:195]
img = img[::2, ::2, 0]
img[img == 144] = 0
img[img == 109] = 0
img[img != 0] = 1
curr = np.expand_dims(img.astype(np.float).ravel(), axis=0)
# Subtract the last preprocessed image.
diff = (curr - self.prev if self.prev is not None
else np.zeros((1, curr.shape[1])))
self.prev = curr
return diff | def preprocess(self, img):
"""
Preprocess a 210x160x3 uint8 frame into a 6400 (80x80) (1 x input_size)
float vector.
"""
# Crop, down-sample, erase background and set foreground to 1.
# See https://gist.github.com/karpathy/a4166c7fe253700972fcbc77e4ea32c5
img = img[35:195]
img = img[::2, ::2, 0]
img[img == 144] = 0
img[img == 109] = 0
img[img != 0] = 1
curr = np.expand_dims(img.astype(np.float).ravel(), axis=0)
# Subtract the last preprocessed image.
diff = (curr - self.prev if self.prev is not None
else np.zeros((1, curr.shape[1])))
self.prev = curr
return diff | [
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train | _new_empty_handle | Returns a new empty handle.
Empty handle can be used to hold a result.
Returns
-------
handle
A new empty `NDArray` handle. | python/mxnet/ndarray/ndarray.py | def _new_empty_handle():
"""Returns a new empty handle.
Empty handle can be used to hold a result.
Returns
-------
handle
A new empty `NDArray` handle.
"""
hdl = NDArrayHandle()
check_call(_LIB.MXNDArrayCreateNone(ctypes.byref(hdl)))
return hdl | def _new_empty_handle():
"""Returns a new empty handle.
Empty handle can be used to hold a result.
Returns
-------
handle
A new empty `NDArray` handle.
"""
hdl = NDArrayHandle()
check_call(_LIB.MXNDArrayCreateNone(ctypes.byref(hdl)))
return hdl | [
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train | _new_alloc_handle | Return a new handle with specified shape and context.
Empty handle is only used to hold results.
Returns
-------
handle
A new empty `NDArray` handle. | python/mxnet/ndarray/ndarray.py | def _new_alloc_handle(shape, ctx, delay_alloc, dtype=mx_real_t):
"""Return a new handle with specified shape and context.
Empty handle is only used to hold results.
Returns
-------
handle
A new empty `NDArray` handle.
"""
hdl = NDArrayHandle()
check_call(_LIB.MXNDArrayCreateEx(
c_array_buf(mx_uint, native_array('I', shape)),
mx_uint(len(shape)),
ctypes.c_int(ctx.device_typeid),
ctypes.c_int(ctx.device_id),
ctypes.c_int(int(delay_alloc)),
ctypes.c_int(int(_DTYPE_NP_TO_MX[np.dtype(dtype).type])),
ctypes.byref(hdl)))
return hdl | def _new_alloc_handle(shape, ctx, delay_alloc, dtype=mx_real_t):
"""Return a new handle with specified shape and context.
Empty handle is only used to hold results.
Returns
-------
handle
A new empty `NDArray` handle.
"""
hdl = NDArrayHandle()
check_call(_LIB.MXNDArrayCreateEx(
c_array_buf(mx_uint, native_array('I', shape)),
mx_uint(len(shape)),
ctypes.c_int(ctx.device_typeid),
ctypes.c_int(ctx.device_id),
ctypes.c_int(int(delay_alloc)),
ctypes.c_int(int(_DTYPE_NP_TO_MX[np.dtype(dtype).type])),
ctypes.byref(hdl)))
return hdl | [
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train | _get_indexing_dispatch_code | Returns a dispatch code for calling basic or advanced indexing functions. | python/mxnet/ndarray/ndarray.py | def _get_indexing_dispatch_code(key):
"""Returns a dispatch code for calling basic or advanced indexing functions."""
if isinstance(key, (NDArray, np.ndarray)):
return _NDARRAY_ADVANCED_INDEXING
elif isinstance(key, list):
# TODO(junwu): Add support for nested lists besides integer list
for i in key:
if not isinstance(i, integer_types):
raise TypeError('Indexing NDArray only supports a list of integers as index'
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% (str(i), str(type(i))))
return _NDARRAY_ADVANCED_INDEXING
elif isinstance(key, (integer_types, py_slice)):
return _NDARRAY_BASIC_INDEXING
elif isinstance(key, tuple):
for idx in key:
if isinstance(idx, (NDArray, np.ndarray, list, tuple)):
return _NDARRAY_ADVANCED_INDEXING
elif not isinstance(idx, (py_slice, integer_types)):
raise ValueError("NDArray does not support slicing with key %s of type %s."
% (str(idx), str(type(idx))))
return _NDARRAY_BASIC_INDEXING
else:
return _NDARRAY_UNSUPPORTED_INDEXING | def _get_indexing_dispatch_code(key):
"""Returns a dispatch code for calling basic or advanced indexing functions."""
if isinstance(key, (NDArray, np.ndarray)):
return _NDARRAY_ADVANCED_INDEXING
elif isinstance(key, list):
# TODO(junwu): Add support for nested lists besides integer list
for i in key:
if not isinstance(i, integer_types):
raise TypeError('Indexing NDArray only supports a list of integers as index'
' when key is of list type, received element=%s of type=%s'
% (str(i), str(type(i))))
return _NDARRAY_ADVANCED_INDEXING
elif isinstance(key, (integer_types, py_slice)):
return _NDARRAY_BASIC_INDEXING
elif isinstance(key, tuple):
for idx in key:
if isinstance(idx, (NDArray, np.ndarray, list, tuple)):
return _NDARRAY_ADVANCED_INDEXING
elif not isinstance(idx, (py_slice, integer_types)):
raise ValueError("NDArray does not support slicing with key %s of type %s."
% (str(idx), str(type(idx))))
return _NDARRAY_BASIC_INDEXING
else:
return _NDARRAY_UNSUPPORTED_INDEXING | [
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train | _get_index_range | Given start, stop, step and array length, return
absolute values of start, stop, and step for generating index range.
The returned values have been compensated by adding length if they
are less than zero for all the cases but slice(None, None, -1).
Note that the returned value of stop is not necessarily >= 0, since
absolute stop is -1 in the case of slice(None, None, -1). | python/mxnet/ndarray/ndarray.py | def _get_index_range(start, stop, length, step=1):
"""Given start, stop, step and array length, return
absolute values of start, stop, and step for generating index range.
The returned values have been compensated by adding length if they
are less than zero for all the cases but slice(None, None, -1).
Note that the returned value of stop is not necessarily >= 0, since
absolute stop is -1 in the case of slice(None, None, -1)."""
if step == 0:
raise ValueError('step size cannot be zero')
if length < 0:
raise ValueError('array length cannot be less than zero')
if step is None:
step = 1
if start is None:
if step > 0:
start = 0
else:
start = length - 1
elif start < 0:
start += length
if start < 0:
raise IndexError('Slicing start %d exceeds limit of %d' % (start-length, length))
elif start >= length:
raise IndexError('Slicing start %d exceeds limit of %d' % (start, length))
if stop is None:
if step > 0:
stop = length
else:
# this supports case such as ::-1
# stop = -1 here refers to the element before index 0,
# instead of the last element in the array
stop = -1
elif stop < 0:
stop += length
if stop < 0:
raise IndexError('Slicing stop %d exceeds limit of %d' % (stop-length, length))
elif stop > length:
raise IndexError('Slicing stop %d exceeds limit of %d' % (stop, length))
return start, stop, step | def _get_index_range(start, stop, length, step=1):
"""Given start, stop, step and array length, return
absolute values of start, stop, and step for generating index range.
The returned values have been compensated by adding length if they
are less than zero for all the cases but slice(None, None, -1).
Note that the returned value of stop is not necessarily >= 0, since
absolute stop is -1 in the case of slice(None, None, -1)."""
if step == 0:
raise ValueError('step size cannot be zero')
if length < 0:
raise ValueError('array length cannot be less than zero')
if step is None:
step = 1
if start is None:
if step > 0:
start = 0
else:
start = length - 1
elif start < 0:
start += length
if start < 0:
raise IndexError('Slicing start %d exceeds limit of %d' % (start-length, length))
elif start >= length:
raise IndexError('Slicing start %d exceeds limit of %d' % (start, length))
if stop is None:
if step > 0:
stop = length
else:
# this supports case such as ::-1
# stop = -1 here refers to the element before index 0,
# instead of the last element in the array
stop = -1
elif stop < 0:
stop += length
if stop < 0:
raise IndexError('Slicing stop %d exceeds limit of %d' % (stop-length, length))
elif stop > length:
raise IndexError('Slicing stop %d exceeds limit of %d' % (stop, length))
return start, stop, step | [
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train | _get_oshape_of_gather_nd_op | Given data and index shapes, get the output `NDArray` shape.
This basically implements the infer shape logic of op gather_nd. | python/mxnet/ndarray/ndarray.py | def _get_oshape_of_gather_nd_op(dshape, ishape):
"""Given data and index shapes, get the output `NDArray` shape.
This basically implements the infer shape logic of op gather_nd."""
assert len(dshape) > 0 and len(ishape) > 0
oshape = list(ishape[1:])
if ishape[0] < len(dshape):
oshape.extend(dshape[ishape[0]:])
return tuple(oshape) | def _get_oshape_of_gather_nd_op(dshape, ishape):
"""Given data and index shapes, get the output `NDArray` shape.
This basically implements the infer shape logic of op gather_nd."""
assert len(dshape) > 0 and len(ishape) > 0
oshape = list(ishape[1:])
if ishape[0] < len(dshape):
oshape.extend(dshape[ishape[0]:])
return tuple(oshape) | [
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train | _get_dim_size | Given start, stop, and stop, calculate the number of elements
of this slice. | python/mxnet/ndarray/ndarray.py | def _get_dim_size(start, stop, step):
"""Given start, stop, and stop, calculate the number of elements
of this slice."""
assert step != 0
if step > 0:
assert start < stop
dim_size = (stop - start - 1) // step + 1
else:
assert stop < start
dim_size = (start - stop - 1) // (-step) + 1
return dim_size | def _get_dim_size(start, stop, step):
"""Given start, stop, and stop, calculate the number of elements
of this slice."""
assert step != 0
if step > 0:
assert start < stop
dim_size = (stop - start - 1) // step + 1
else:
assert stop < start
dim_size = (start - stop - 1) // (-step) + 1
return dim_size | [
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train | _get_broadcast_shape | Given two shapes that are not identical, find the shape
that both input shapes can broadcast to. | python/mxnet/ndarray/ndarray.py | def _get_broadcast_shape(shape1, shape2):
"""Given two shapes that are not identical, find the shape
that both input shapes can broadcast to."""
if shape1 == shape2:
return shape1
length1 = len(shape1)
length2 = len(shape2)
if length1 > length2:
shape = list(shape1)
else:
shape = list(shape2)
i = max(length1, length2) - 1
for a, b in zip(shape1[::-1], shape2[::-1]):
if a != 1 and b != 1 and a != b:
raise ValueError('shape1=%s is not broadcastable to shape2=%s' % (shape1, shape2))
shape[i] = max(a, b)
i -= 1
return tuple(shape) | def _get_broadcast_shape(shape1, shape2):
"""Given two shapes that are not identical, find the shape
that both input shapes can broadcast to."""
if shape1 == shape2:
return shape1
length1 = len(shape1)
length2 = len(shape2)
if length1 > length2:
shape = list(shape1)
else:
shape = list(shape2)
i = max(length1, length2) - 1
for a, b in zip(shape1[::-1], shape2[::-1]):
if a != 1 and b != 1 and a != b:
raise ValueError('shape1=%s is not broadcastable to shape2=%s' % (shape1, shape2))
shape[i] = max(a, b)
i -= 1
return tuple(shape) | [
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train | ones | Returns a new array filled with all ones, with the given shape and type.
Parameters
----------
shape : int or tuple of int or list of int
The shape of the empty array.
ctx : Context, optional
An optional device context.
Defaults to the current default context (``mxnet.context.current_context()``).
dtype : str or numpy.dtype, optional
An optional value type (default is `float32`).
out : NDArray, optional
The output NDArray (default is `None`).
Returns
-------
NDArray
A new array of the specified shape filled with all ones.
Examples
--------
>>> mx.nd.ones(1).asnumpy()
array([ 1.], dtype=float32)
>>> mx.nd.ones((1,2), mx.gpu(0))
<NDArray 1x2 @gpu(0)>
>>> mx.nd.ones((1,2), dtype='float16').asnumpy()
array([[ 1., 1.]], dtype=float16) | python/mxnet/ndarray/ndarray.py | def ones(shape, ctx=None, dtype=None, **kwargs):
"""Returns a new array filled with all ones, with the given shape and type.
Parameters
----------
shape : int or tuple of int or list of int
The shape of the empty array.
ctx : Context, optional
An optional device context.
Defaults to the current default context (``mxnet.context.current_context()``).
dtype : str or numpy.dtype, optional
An optional value type (default is `float32`).
out : NDArray, optional
The output NDArray (default is `None`).
Returns
-------
NDArray
A new array of the specified shape filled with all ones.
Examples
--------
>>> mx.nd.ones(1).asnumpy()
array([ 1.], dtype=float32)
>>> mx.nd.ones((1,2), mx.gpu(0))
<NDArray 1x2 @gpu(0)>
>>> mx.nd.ones((1,2), dtype='float16').asnumpy()
array([[ 1., 1.]], dtype=float16)
"""
# pylint: disable= unused-argument
if ctx is None:
ctx = current_context()
dtype = mx_real_t if dtype is None else dtype
# pylint: disable= no-member, protected-access
return _internal._ones(shape=shape, ctx=ctx, dtype=dtype, **kwargs) | def ones(shape, ctx=None, dtype=None, **kwargs):
"""Returns a new array filled with all ones, with the given shape and type.
Parameters
----------
shape : int or tuple of int or list of int
The shape of the empty array.
ctx : Context, optional
An optional device context.
Defaults to the current default context (``mxnet.context.current_context()``).
dtype : str or numpy.dtype, optional
An optional value type (default is `float32`).
out : NDArray, optional
The output NDArray (default is `None`).
Returns
-------
NDArray
A new array of the specified shape filled with all ones.
Examples
--------
>>> mx.nd.ones(1).asnumpy()
array([ 1.], dtype=float32)
>>> mx.nd.ones((1,2), mx.gpu(0))
<NDArray 1x2 @gpu(0)>
>>> mx.nd.ones((1,2), dtype='float16').asnumpy()
array([[ 1., 1.]], dtype=float16)
"""
# pylint: disable= unused-argument
if ctx is None:
ctx = current_context()
dtype = mx_real_t if dtype is None else dtype
# pylint: disable= no-member, protected-access
return _internal._ones(shape=shape, ctx=ctx, dtype=dtype, **kwargs) | [
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train | full | Returns a new array of given shape and type, filled with the given value `val`.
Parameters
--------
shape : int or tuple of int
The shape of the new array.
val : scalar
Fill value.
ctx : Context, optional
Device context (default is the current default context).
dtype : `str` or `numpy.dtype`, optional
The data type of the returned `NDArray`. The default datatype is `float32`.
out : NDArray, optional
The output NDArray (default is `None`).
Returns
-------
NDArray
`NDArray` filled with `val`, with the given shape, ctx, and dtype.
Examples
--------
>>> mx.nd.full(1, 2.0).asnumpy()
array([ 2.], dtype=float32)
>>> mx.nd.full((1, 2), 2.0, mx.gpu(0))
<NDArray 1x2 @gpu(0)>
>>> mx.nd.full((1, 2), 2.0, dtype='float16').asnumpy()
array([[ 2., 2.]], dtype=float16) | python/mxnet/ndarray/ndarray.py | def full(shape, val, ctx=None, dtype=mx_real_t, out=None):
"""Returns a new array of given shape and type, filled with the given value `val`.
Parameters
--------
shape : int or tuple of int
The shape of the new array.
val : scalar
Fill value.
ctx : Context, optional
Device context (default is the current default context).
dtype : `str` or `numpy.dtype`, optional
The data type of the returned `NDArray`. The default datatype is `float32`.
out : NDArray, optional
The output NDArray (default is `None`).
Returns
-------
NDArray
`NDArray` filled with `val`, with the given shape, ctx, and dtype.
Examples
--------
>>> mx.nd.full(1, 2.0).asnumpy()
array([ 2.], dtype=float32)
>>> mx.nd.full((1, 2), 2.0, mx.gpu(0))
<NDArray 1x2 @gpu(0)>
>>> mx.nd.full((1, 2), 2.0, dtype='float16').asnumpy()
array([[ 2., 2.]], dtype=float16)
"""
out = empty(shape, ctx, dtype) if out is None else out
out[:] = val
return out | def full(shape, val, ctx=None, dtype=mx_real_t, out=None):
"""Returns a new array of given shape and type, filled with the given value `val`.
Parameters
--------
shape : int or tuple of int
The shape of the new array.
val : scalar
Fill value.
ctx : Context, optional
Device context (default is the current default context).
dtype : `str` or `numpy.dtype`, optional
The data type of the returned `NDArray`. The default datatype is `float32`.
out : NDArray, optional
The output NDArray (default is `None`).
Returns
-------
NDArray
`NDArray` filled with `val`, with the given shape, ctx, and dtype.
Examples
--------
>>> mx.nd.full(1, 2.0).asnumpy()
array([ 2.], dtype=float32)
>>> mx.nd.full((1, 2), 2.0, mx.gpu(0))
<NDArray 1x2 @gpu(0)>
>>> mx.nd.full((1, 2), 2.0, dtype='float16').asnumpy()
array([[ 2., 2.]], dtype=float16)
"""
out = empty(shape, ctx, dtype) if out is None else out
out[:] = val
return out | [
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train | array | Creates an array from any object exposing the array interface.
Parameters
----------
source_array : array_like
An object exposing the array interface, an object whose `__array__`
method returns an array, or any (nested) sequence.
ctx : Context, optional
Device context (default is the current default context).
dtype : str or numpy.dtype, optional
The data type of the output array. The default dtype is ``source_array.dtype``
if `source_array` is an `NDArray`, `float32` otherwise.
Returns
-------
NDArray
An `NDArray` with the same contents as the `source_array`. | python/mxnet/ndarray/ndarray.py | def array(source_array, ctx=None, dtype=None):
"""Creates an array from any object exposing the array interface.
Parameters
----------
source_array : array_like
An object exposing the array interface, an object whose `__array__`
method returns an array, or any (nested) sequence.
ctx : Context, optional
Device context (default is the current default context).
dtype : str or numpy.dtype, optional
The data type of the output array. The default dtype is ``source_array.dtype``
if `source_array` is an `NDArray`, `float32` otherwise.
Returns
-------
NDArray
An `NDArray` with the same contents as the `source_array`.
"""
if isinstance(source_array, NDArray):
dtype = source_array.dtype if dtype is None else dtype
else:
dtype = mx_real_t if dtype is None else dtype
if not isinstance(source_array, np.ndarray):
try:
source_array = np.array(source_array, dtype=dtype)
except:
raise TypeError('source_array must be array like object')
arr = empty(source_array.shape, ctx, dtype)
arr[:] = source_array
return arr | def array(source_array, ctx=None, dtype=None):
"""Creates an array from any object exposing the array interface.
Parameters
----------
source_array : array_like
An object exposing the array interface, an object whose `__array__`
method returns an array, or any (nested) sequence.
ctx : Context, optional
Device context (default is the current default context).
dtype : str or numpy.dtype, optional
The data type of the output array. The default dtype is ``source_array.dtype``
if `source_array` is an `NDArray`, `float32` otherwise.
Returns
-------
NDArray
An `NDArray` with the same contents as the `source_array`.
"""
if isinstance(source_array, NDArray):
dtype = source_array.dtype if dtype is None else dtype
else:
dtype = mx_real_t if dtype is None else dtype
if not isinstance(source_array, np.ndarray):
try:
source_array = np.array(source_array, dtype=dtype)
except:
raise TypeError('source_array must be array like object')
arr = empty(source_array.shape, ctx, dtype)
arr[:] = source_array
return arr | [
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train | moveaxis | Moves the `source` axis into the `destination` position
while leaving the other axes in their original order
Parameters
----------
tensor : mx.nd.array
The array which axes should be reordered
source : int or sequence of int
Original position of the axes to move. Can be negative but must be unique.
destination : int or sequence of int
Destination position for each of the original axes. Can be negative but must be unique.
Returns
-------
result : mx.nd.array
Array with moved axes.
Examples
--------
>>> X = mx.nd.array([[1, 2, 3], [4, 5, 6]])
>>> mx.nd.moveaxis(X, 0, 1).shape
(3L, 2L)
>>> X = mx.nd.zeros((3, 4, 5))
>>> mx.nd.moveaxis(X, [0, 1], [-1, -2]).shape
(5, 4, 3) | python/mxnet/ndarray/ndarray.py | def moveaxis(tensor, source, destination):
"""Moves the `source` axis into the `destination` position
while leaving the other axes in their original order
Parameters
----------
tensor : mx.nd.array
The array which axes should be reordered
source : int or sequence of int
Original position of the axes to move. Can be negative but must be unique.
destination : int or sequence of int
Destination position for each of the original axes. Can be negative but must be unique.
Returns
-------
result : mx.nd.array
Array with moved axes.
Examples
--------
>>> X = mx.nd.array([[1, 2, 3], [4, 5, 6]])
>>> mx.nd.moveaxis(X, 0, 1).shape
(3L, 2L)
>>> X = mx.nd.zeros((3, 4, 5))
>>> mx.nd.moveaxis(X, [0, 1], [-1, -2]).shape
(5, 4, 3)
"""
try:
source = np.core.numeric.normalize_axis_tuple(source, tensor.ndim)
except IndexError:
raise ValueError('Source should verify 0 <= source < tensor.ndim'
'Got %d' % source)
try:
destination = np.core.numeric.normalize_axis_tuple(destination, tensor.ndim)
except IndexError:
raise ValueError('Destination should verify 0 <= destination < tensor.ndim (%d).'
% tensor.ndim, 'Got %d' % destination)
if len(source) != len(destination):
raise ValueError('`source` and `destination` arguments must have '
'the same number of elements')
order = [n for n in range(tensor.ndim) if n not in source]
for dest, src in sorted(zip(destination, source)):
order.insert(dest, src)
return op.transpose(tensor, order) | def moveaxis(tensor, source, destination):
"""Moves the `source` axis into the `destination` position
while leaving the other axes in their original order
Parameters
----------
tensor : mx.nd.array
The array which axes should be reordered
source : int or sequence of int
Original position of the axes to move. Can be negative but must be unique.
destination : int or sequence of int
Destination position for each of the original axes. Can be negative but must be unique.
Returns
-------
result : mx.nd.array
Array with moved axes.
Examples
--------
>>> X = mx.nd.array([[1, 2, 3], [4, 5, 6]])
>>> mx.nd.moveaxis(X, 0, 1).shape
(3L, 2L)
>>> X = mx.nd.zeros((3, 4, 5))
>>> mx.nd.moveaxis(X, [0, 1], [-1, -2]).shape
(5, 4, 3)
"""
try:
source = np.core.numeric.normalize_axis_tuple(source, tensor.ndim)
except IndexError:
raise ValueError('Source should verify 0 <= source < tensor.ndim'
'Got %d' % source)
try:
destination = np.core.numeric.normalize_axis_tuple(destination, tensor.ndim)
except IndexError:
raise ValueError('Destination should verify 0 <= destination < tensor.ndim (%d).'
% tensor.ndim, 'Got %d' % destination)
if len(source) != len(destination):
raise ValueError('`source` and `destination` arguments must have '
'the same number of elements')
order = [n for n in range(tensor.ndim) if n not in source]
for dest, src in sorted(zip(destination, source)):
order.insert(dest, src)
return op.transpose(tensor, order) | [
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train | arange | Returns evenly spaced values within a given interval.
Values are generated within the half-open interval [`start`, `stop`). In other
words, the interval includes `start` but excludes `stop`. The function is
similar to the built-in Python function `range` and to `numpy.arange`,
but returns an `NDArray`.
Parameters
----------
start : number, optional
Start of interval. The default start value is 0.
stop : number
End of interval.
step : number, optional
Spacing between values. The default step size is 1.
repeat : int, optional
Number of times to repeat each element. The default repeat count is 1.
infer_range : boolean, optional
When set to True, infer the stop position from the start, step,
repeat, and output tensor size.
ctx : Context, optional
Device context. Default context is the current default context.
dtype : str or numpy.dtype, optional
The data type of the `NDArray`. The default datatype is `np.float32`.
Returns
-------
NDArray
`NDArray` of evenly spaced values in the specified range.
Examples
--------
>>> mx.nd.arange(3).asnumpy()
array([ 0., 1., 2.], dtype=float32)
>>> mx.nd.arange(2, 6).asnumpy()
array([ 2., 3., 4., 5.], dtype=float32)
>>> mx.nd.arange(2, 6, step=2).asnumpy()
array([ 2., 4.], dtype=float32)
>>> mx.nd.arange(2, 6, step=1.5, repeat=2).asnumpy()
array([ 2. , 2. , 3.5, 3.5, 5. , 5. ], dtype=float32)
>>> mx.nd.arange(2, 6, step=2, repeat=3, dtype='int32').asnumpy()
array([2, 2, 2, 4, 4, 4], dtype=int32) | python/mxnet/ndarray/ndarray.py | def arange(start, stop=None, step=1.0, repeat=1, infer_range=None, ctx=None, dtype=mx_real_t):
"""Returns evenly spaced values within a given interval.
Values are generated within the half-open interval [`start`, `stop`). In other
words, the interval includes `start` but excludes `stop`. The function is
similar to the built-in Python function `range` and to `numpy.arange`,
but returns an `NDArray`.
Parameters
----------
start : number, optional
Start of interval. The default start value is 0.
stop : number
End of interval.
step : number, optional
Spacing between values. The default step size is 1.
repeat : int, optional
Number of times to repeat each element. The default repeat count is 1.
infer_range : boolean, optional
When set to True, infer the stop position from the start, step,
repeat, and output tensor size.
ctx : Context, optional
Device context. Default context is the current default context.
dtype : str or numpy.dtype, optional
The data type of the `NDArray`. The default datatype is `np.float32`.
Returns
-------
NDArray
`NDArray` of evenly spaced values in the specified range.
Examples
--------
>>> mx.nd.arange(3).asnumpy()
array([ 0., 1., 2.], dtype=float32)
>>> mx.nd.arange(2, 6).asnumpy()
array([ 2., 3., 4., 5.], dtype=float32)
>>> mx.nd.arange(2, 6, step=2).asnumpy()
array([ 2., 4.], dtype=float32)
>>> mx.nd.arange(2, 6, step=1.5, repeat=2).asnumpy()
array([ 2. , 2. , 3.5, 3.5, 5. , 5. ], dtype=float32)
>>> mx.nd.arange(2, 6, step=2, repeat=3, dtype='int32').asnumpy()
array([2, 2, 2, 4, 4, 4], dtype=int32)
"""
if infer_range is not None:
warnings.warn('`infer_range` argument has been deprecated',
DeprecationWarning)
if ctx is None:
ctx = current_context()
return _internal._arange(start=start, stop=stop, step=step, repeat=repeat,
infer_range=False, dtype=dtype, ctx=str(ctx)) | def arange(start, stop=None, step=1.0, repeat=1, infer_range=None, ctx=None, dtype=mx_real_t):
"""Returns evenly spaced values within a given interval.
Values are generated within the half-open interval [`start`, `stop`). In other
words, the interval includes `start` but excludes `stop`. The function is
similar to the built-in Python function `range` and to `numpy.arange`,
but returns an `NDArray`.
Parameters
----------
start : number, optional
Start of interval. The default start value is 0.
stop : number
End of interval.
step : number, optional
Spacing between values. The default step size is 1.
repeat : int, optional
Number of times to repeat each element. The default repeat count is 1.
infer_range : boolean, optional
When set to True, infer the stop position from the start, step,
repeat, and output tensor size.
ctx : Context, optional
Device context. Default context is the current default context.
dtype : str or numpy.dtype, optional
The data type of the `NDArray`. The default datatype is `np.float32`.
Returns
-------
NDArray
`NDArray` of evenly spaced values in the specified range.
Examples
--------
>>> mx.nd.arange(3).asnumpy()
array([ 0., 1., 2.], dtype=float32)
>>> mx.nd.arange(2, 6).asnumpy()
array([ 2., 3., 4., 5.], dtype=float32)
>>> mx.nd.arange(2, 6, step=2).asnumpy()
array([ 2., 4.], dtype=float32)
>>> mx.nd.arange(2, 6, step=1.5, repeat=2).asnumpy()
array([ 2. , 2. , 3.5, 3.5, 5. , 5. ], dtype=float32)
>>> mx.nd.arange(2, 6, step=2, repeat=3, dtype='int32').asnumpy()
array([2, 2, 2, 4, 4, 4], dtype=int32)
"""
if infer_range is not None:
warnings.warn('`infer_range` argument has been deprecated',
DeprecationWarning)
if ctx is None:
ctx = current_context()
return _internal._arange(start=start, stop=stop, step=step, repeat=repeat,
infer_range=False, dtype=dtype, ctx=str(ctx)) | [
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train | _ufunc_helper | Helper function for element-wise operation.
The function will perform numpy-like broadcasting if needed and call different functions.
Parameters
--------
lhs : NDArray or numeric value
Left-hand side operand.
rhs : NDArray or numeric value
Right-hand operand,
fn_array : function
Function to be called if both lhs and rhs are of ``NDArray`` type.
fn_scalar : function
Function to be called if both lhs and rhs are numeric values.
lfn_scalar : function
Function to be called if lhs is ``NDArray`` while rhs is numeric value
rfn_scalar : function
Function to be called if lhs is numeric value while rhs is ``NDArray``;
if none is provided, then the function is commutative, so rfn_scalar is equal to lfn_scalar
Returns
--------
NDArray
result array | python/mxnet/ndarray/ndarray.py | def _ufunc_helper(lhs, rhs, fn_array, fn_scalar, lfn_scalar, rfn_scalar=None):
""" Helper function for element-wise operation.
The function will perform numpy-like broadcasting if needed and call different functions.
Parameters
--------
lhs : NDArray or numeric value
Left-hand side operand.
rhs : NDArray or numeric value
Right-hand operand,
fn_array : function
Function to be called if both lhs and rhs are of ``NDArray`` type.
fn_scalar : function
Function to be called if both lhs and rhs are numeric values.
lfn_scalar : function
Function to be called if lhs is ``NDArray`` while rhs is numeric value
rfn_scalar : function
Function to be called if lhs is numeric value while rhs is ``NDArray``;
if none is provided, then the function is commutative, so rfn_scalar is equal to lfn_scalar
Returns
--------
NDArray
result array
"""
if isinstance(lhs, numeric_types):
if isinstance(rhs, numeric_types):
return fn_scalar(lhs, rhs)
else:
if rfn_scalar is None:
# commutative function
return lfn_scalar(rhs, float(lhs))
else:
return rfn_scalar(rhs, float(lhs))
elif isinstance(rhs, numeric_types):
return lfn_scalar(lhs, float(rhs))
elif isinstance(rhs, NDArray):
return fn_array(lhs, rhs)
else:
raise TypeError('type %s not supported' % str(type(rhs))) | def _ufunc_helper(lhs, rhs, fn_array, fn_scalar, lfn_scalar, rfn_scalar=None):
""" Helper function for element-wise operation.
The function will perform numpy-like broadcasting if needed and call different functions.
Parameters
--------
lhs : NDArray or numeric value
Left-hand side operand.
rhs : NDArray or numeric value
Right-hand operand,
fn_array : function
Function to be called if both lhs and rhs are of ``NDArray`` type.
fn_scalar : function
Function to be called if both lhs and rhs are numeric values.
lfn_scalar : function
Function to be called if lhs is ``NDArray`` while rhs is numeric value
rfn_scalar : function
Function to be called if lhs is numeric value while rhs is ``NDArray``;
if none is provided, then the function is commutative, so rfn_scalar is equal to lfn_scalar
Returns
--------
NDArray
result array
"""
if isinstance(lhs, numeric_types):
if isinstance(rhs, numeric_types):
return fn_scalar(lhs, rhs)
else:
if rfn_scalar is None:
# commutative function
return lfn_scalar(rhs, float(lhs))
else:
return rfn_scalar(rhs, float(lhs))
elif isinstance(rhs, numeric_types):
return lfn_scalar(lhs, float(rhs))
elif isinstance(rhs, NDArray):
return fn_array(lhs, rhs)
else:
raise TypeError('type %s not supported' % str(type(rhs))) | [
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train | add | Returns element-wise sum of the input arrays with broadcasting.
Equivalent to ``lhs + rhs``, ``mx.nd.broadcast_add(lhs, rhs)`` and
``mx.nd.broadcast_plus(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be added.
rhs : scalar or mxnet.ndarray.array
Second array to be added.
If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
The element-wise sum of the input arrays.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> (x+2).asnumpy()
array([[ 3., 3., 3.],
[ 3., 3., 3.]], dtype=float32)
>>> (x+y).asnumpy()
array([[ 1., 1., 1.],
[ 2., 2., 2.]], dtype=float32)
>>> mx.nd.add(x,y).asnumpy()
array([[ 1., 1., 1.],
[ 2., 2., 2.]], dtype=float32)
>>> (z + y).asnumpy()
array([[ 0., 1.],
[ 1., 2.]], dtype=float32) | python/mxnet/ndarray/ndarray.py | def add(lhs, rhs):
"""Returns element-wise sum of the input arrays with broadcasting.
Equivalent to ``lhs + rhs``, ``mx.nd.broadcast_add(lhs, rhs)`` and
``mx.nd.broadcast_plus(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be added.
rhs : scalar or mxnet.ndarray.array
Second array to be added.
If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
The element-wise sum of the input arrays.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> (x+2).asnumpy()
array([[ 3., 3., 3.],
[ 3., 3., 3.]], dtype=float32)
>>> (x+y).asnumpy()
array([[ 1., 1., 1.],
[ 2., 2., 2.]], dtype=float32)
>>> mx.nd.add(x,y).asnumpy()
array([[ 1., 1., 1.],
[ 2., 2., 2.]], dtype=float32)
>>> (z + y).asnumpy()
array([[ 0., 1.],
[ 1., 2.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_add,
operator.add,
_internal._plus_scalar,
None) | def add(lhs, rhs):
"""Returns element-wise sum of the input arrays with broadcasting.
Equivalent to ``lhs + rhs``, ``mx.nd.broadcast_add(lhs, rhs)`` and
``mx.nd.broadcast_plus(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be added.
rhs : scalar or mxnet.ndarray.array
Second array to be added.
If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
The element-wise sum of the input arrays.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> (x+2).asnumpy()
array([[ 3., 3., 3.],
[ 3., 3., 3.]], dtype=float32)
>>> (x+y).asnumpy()
array([[ 1., 1., 1.],
[ 2., 2., 2.]], dtype=float32)
>>> mx.nd.add(x,y).asnumpy()
array([[ 1., 1., 1.],
[ 2., 2., 2.]], dtype=float32)
>>> (z + y).asnumpy()
array([[ 0., 1.],
[ 1., 2.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_add,
operator.add,
_internal._plus_scalar,
None) | [
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"-",
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"input",
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"with",
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"."
] | apache/incubator-mxnet | python | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/python/mxnet/ndarray/ndarray.py#L2663-L2721 | [
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",",
"None... | 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7 |
train | subtract | Returns element-wise difference of the input arrays with broadcasting.
Equivalent to ``lhs - rhs``, ``mx.nd.broadcast_sub(lhs, rhs)`` and
``mx.nd.broadcast_minus(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be subtracted.
rhs : scalar or mxnet.ndarray.array
Second array to be subtracted.
If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
The element-wise difference of the input arrays.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> (x-2).asnumpy()
array([[-1., -1., -1.],
[-1., -1., -1.]], dtype=float32)
>>> (x-y).asnumpy()
array([[ 1., 1., 1.],
[ 0., 0., 0.]], dtype=float32)
>>> mx.nd.subtract(x,y).asnumpy()
array([[ 1., 1., 1.],
[ 0., 0., 0.]], dtype=float32)
>>> (z-y).asnumpy()
array([[ 0., 1.],
[-1., 0.]], dtype=float32) | python/mxnet/ndarray/ndarray.py | def subtract(lhs, rhs):
"""Returns element-wise difference of the input arrays with broadcasting.
Equivalent to ``lhs - rhs``, ``mx.nd.broadcast_sub(lhs, rhs)`` and
``mx.nd.broadcast_minus(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be subtracted.
rhs : scalar or mxnet.ndarray.array
Second array to be subtracted.
If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
The element-wise difference of the input arrays.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> (x-2).asnumpy()
array([[-1., -1., -1.],
[-1., -1., -1.]], dtype=float32)
>>> (x-y).asnumpy()
array([[ 1., 1., 1.],
[ 0., 0., 0.]], dtype=float32)
>>> mx.nd.subtract(x,y).asnumpy()
array([[ 1., 1., 1.],
[ 0., 0., 0.]], dtype=float32)
>>> (z-y).asnumpy()
array([[ 0., 1.],
[-1., 0.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_sub,
operator.sub,
_internal._minus_scalar,
_internal._rminus_scalar) | def subtract(lhs, rhs):
"""Returns element-wise difference of the input arrays with broadcasting.
Equivalent to ``lhs - rhs``, ``mx.nd.broadcast_sub(lhs, rhs)`` and
``mx.nd.broadcast_minus(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be subtracted.
rhs : scalar or mxnet.ndarray.array
Second array to be subtracted.
If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
The element-wise difference of the input arrays.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> (x-2).asnumpy()
array([[-1., -1., -1.],
[-1., -1., -1.]], dtype=float32)
>>> (x-y).asnumpy()
array([[ 1., 1., 1.],
[ 0., 0., 0.]], dtype=float32)
>>> mx.nd.subtract(x,y).asnumpy()
array([[ 1., 1., 1.],
[ 0., 0., 0.]], dtype=float32)
>>> (z-y).asnumpy()
array([[ 0., 1.],
[-1., 0.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_sub,
operator.sub,
_internal._minus_scalar,
_internal._rminus_scalar) | [
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"wise",
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"of",
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"input",
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"with",
"broadcasting",
"."
] | apache/incubator-mxnet | python | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/python/mxnet/ndarray/ndarray.py#L2725-L2783 | [
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",",
... | 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7 |
train | multiply | Returns element-wise product of the input arrays with broadcasting.
Equivalent to ``lhs * rhs`` and ``mx.nd.broadcast_mul(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be multiplied.
rhs : scalar or mxnet.ndarray.array
Second array to be multiplied.
If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
The element-wise multiplication of the input arrays.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> (x*2).asnumpy()
array([[ 2., 2., 2.],
[ 2., 2., 2.]], dtype=float32)
>>> (x*y).asnumpy()
array([[ 0., 0., 0.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.multiply(x, y).asnumpy()
array([[ 0., 0., 0.],
[ 1., 1., 1.]], dtype=float32)
>>> (z*y).asnumpy()
array([[ 0., 0.],
[ 0., 1.]], dtype=float32) | python/mxnet/ndarray/ndarray.py | def multiply(lhs, rhs):
"""Returns element-wise product of the input arrays with broadcasting.
Equivalent to ``lhs * rhs`` and ``mx.nd.broadcast_mul(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be multiplied.
rhs : scalar or mxnet.ndarray.array
Second array to be multiplied.
If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
The element-wise multiplication of the input arrays.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> (x*2).asnumpy()
array([[ 2., 2., 2.],
[ 2., 2., 2.]], dtype=float32)
>>> (x*y).asnumpy()
array([[ 0., 0., 0.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.multiply(x, y).asnumpy()
array([[ 0., 0., 0.],
[ 1., 1., 1.]], dtype=float32)
>>> (z*y).asnumpy()
array([[ 0., 0.],
[ 0., 1.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_mul,
operator.mul,
_internal._mul_scalar,
None) | def multiply(lhs, rhs):
"""Returns element-wise product of the input arrays with broadcasting.
Equivalent to ``lhs * rhs`` and ``mx.nd.broadcast_mul(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be multiplied.
rhs : scalar or mxnet.ndarray.array
Second array to be multiplied.
If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
The element-wise multiplication of the input arrays.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> (x*2).asnumpy()
array([[ 2., 2., 2.],
[ 2., 2., 2.]], dtype=float32)
>>> (x*y).asnumpy()
array([[ 0., 0., 0.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.multiply(x, y).asnumpy()
array([[ 0., 0., 0.],
[ 1., 1., 1.]], dtype=float32)
>>> (z*y).asnumpy()
array([[ 0., 0.],
[ 0., 1.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_mul,
operator.mul,
_internal._mul_scalar,
None) | [
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"wise",
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] | apache/incubator-mxnet | python | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/python/mxnet/ndarray/ndarray.py#L2787-L2844 | [
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"... | 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7 |
train | divide | Returns element-wise division of the input arrays with broadcasting.
Equivalent to ``lhs / rhs`` and ``mx.nd.broadcast_div(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array in division.
rhs : scalar or mxnet.ndarray.array
Second array in division.
The arrays to be divided. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
The element-wise division of the input arrays.
Examples
--------
>>> x = mx.nd.ones((2,3))*6
>>> y = mx.nd.ones((2,1))*2
>>> x.asnumpy()
array([[ 6., 6., 6.],
[ 6., 6., 6.]], dtype=float32)
>>> y.asnumpy()
array([[ 2.],
[ 2.]], dtype=float32)
>>> x/2
<NDArray 2x3 @cpu(0)>
>>> (x/3).asnumpy()
array([[ 2., 2., 2.],
[ 2., 2., 2.]], dtype=float32)
>>> (x/y).asnumpy()
array([[ 3., 3., 3.],
[ 3., 3., 3.]], dtype=float32)
>>> mx.nd.divide(x,y).asnumpy()
array([[ 3., 3., 3.],
[ 3., 3., 3.]], dtype=float32) | python/mxnet/ndarray/ndarray.py | def divide(lhs, rhs):
"""Returns element-wise division of the input arrays with broadcasting.
Equivalent to ``lhs / rhs`` and ``mx.nd.broadcast_div(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array in division.
rhs : scalar or mxnet.ndarray.array
Second array in division.
The arrays to be divided. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
The element-wise division of the input arrays.
Examples
--------
>>> x = mx.nd.ones((2,3))*6
>>> y = mx.nd.ones((2,1))*2
>>> x.asnumpy()
array([[ 6., 6., 6.],
[ 6., 6., 6.]], dtype=float32)
>>> y.asnumpy()
array([[ 2.],
[ 2.]], dtype=float32)
>>> x/2
<NDArray 2x3 @cpu(0)>
>>> (x/3).asnumpy()
array([[ 2., 2., 2.],
[ 2., 2., 2.]], dtype=float32)
>>> (x/y).asnumpy()
array([[ 3., 3., 3.],
[ 3., 3., 3.]], dtype=float32)
>>> mx.nd.divide(x,y).asnumpy()
array([[ 3., 3., 3.],
[ 3., 3., 3.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_div,
operator.truediv,
_internal._div_scalar,
_internal._rdiv_scalar) | def divide(lhs, rhs):
"""Returns element-wise division of the input arrays with broadcasting.
Equivalent to ``lhs / rhs`` and ``mx.nd.broadcast_div(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array in division.
rhs : scalar or mxnet.ndarray.array
Second array in division.
The arrays to be divided. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
The element-wise division of the input arrays.
Examples
--------
>>> x = mx.nd.ones((2,3))*6
>>> y = mx.nd.ones((2,1))*2
>>> x.asnumpy()
array([[ 6., 6., 6.],
[ 6., 6., 6.]], dtype=float32)
>>> y.asnumpy()
array([[ 2.],
[ 2.]], dtype=float32)
>>> x/2
<NDArray 2x3 @cpu(0)>
>>> (x/3).asnumpy()
array([[ 2., 2., 2.],
[ 2., 2., 2.]], dtype=float32)
>>> (x/y).asnumpy()
array([[ 3., 3., 3.],
[ 3., 3., 3.]], dtype=float32)
>>> mx.nd.divide(x,y).asnumpy()
array([[ 3., 3., 3.],
[ 3., 3., 3.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_div,
operator.truediv,
_internal._div_scalar,
_internal._rdiv_scalar) | [
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"-",
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"arrays",
"with",
"broadcasting",
"."
] | apache/incubator-mxnet | python | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/python/mxnet/ndarray/ndarray.py#L2848-L2901 | [
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... | 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7 |
train | modulo | Returns element-wise modulo of the input arrays with broadcasting.
Equivalent to ``lhs % rhs`` and ``mx.nd.broadcast_mod(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array in modulo.
rhs : scalar or mxnet.ndarray.array
Second array in modulo.
The arrays to be taken modulo. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
The element-wise modulo of the input arrays.
Examples
--------
>>> x = mx.nd.ones((2,3))*6
>>> y = mx.nd.ones((2,1))*4
>>> x.asnumpy()
array([[ 6., 6., 6.],
[ 6., 6., 6.]], dtype=float32)
>>> y.asnumpy()
array([[ 4.],
[ 4.]], dtype=float32)
>>> x%5
<NDArray 2x3 @cpu(0)>
>>> (x%5).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> (x%y).asnumpy()
array([[ 2., 2., 2.],
[ 2., 2., 2.]], dtype=float32)
>>> mx.nd.modulo(x,y).asnumpy()
array([[ 2., 2., 2.],
[ 2., 2., 2.]], dtype=float32) | python/mxnet/ndarray/ndarray.py | def modulo(lhs, rhs):
"""Returns element-wise modulo of the input arrays with broadcasting.
Equivalent to ``lhs % rhs`` and ``mx.nd.broadcast_mod(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array in modulo.
rhs : scalar or mxnet.ndarray.array
Second array in modulo.
The arrays to be taken modulo. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
The element-wise modulo of the input arrays.
Examples
--------
>>> x = mx.nd.ones((2,3))*6
>>> y = mx.nd.ones((2,1))*4
>>> x.asnumpy()
array([[ 6., 6., 6.],
[ 6., 6., 6.]], dtype=float32)
>>> y.asnumpy()
array([[ 4.],
[ 4.]], dtype=float32)
>>> x%5
<NDArray 2x3 @cpu(0)>
>>> (x%5).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> (x%y).asnumpy()
array([[ 2., 2., 2.],
[ 2., 2., 2.]], dtype=float32)
>>> mx.nd.modulo(x,y).asnumpy()
array([[ 2., 2., 2.],
[ 2., 2., 2.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_mod,
operator.mod,
_internal._mod_scalar,
_internal._rmod_scalar) | def modulo(lhs, rhs):
"""Returns element-wise modulo of the input arrays with broadcasting.
Equivalent to ``lhs % rhs`` and ``mx.nd.broadcast_mod(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array in modulo.
rhs : scalar or mxnet.ndarray.array
Second array in modulo.
The arrays to be taken modulo. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
The element-wise modulo of the input arrays.
Examples
--------
>>> x = mx.nd.ones((2,3))*6
>>> y = mx.nd.ones((2,1))*4
>>> x.asnumpy()
array([[ 6., 6., 6.],
[ 6., 6., 6.]], dtype=float32)
>>> y.asnumpy()
array([[ 4.],
[ 4.]], dtype=float32)
>>> x%5
<NDArray 2x3 @cpu(0)>
>>> (x%5).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> (x%y).asnumpy()
array([[ 2., 2., 2.],
[ 2., 2., 2.]], dtype=float32)
>>> mx.nd.modulo(x,y).asnumpy()
array([[ 2., 2., 2.],
[ 2., 2., 2.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_mod,
operator.mod,
_internal._mod_scalar,
_internal._rmod_scalar) | [
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] | apache/incubator-mxnet | python | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/python/mxnet/ndarray/ndarray.py#L2905-L2958 | [
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"_i... | 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7 |
train | power | Returns result of first array elements raised to powers from second array, element-wise
with broadcasting.
Equivalent to ``base ** exp`` and ``mx.nd.broadcast_power(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
base : scalar or NDArray
The base array
exp : scalar or NDArray
The exponent array. If ``base.shape != exp.shape``, they must be
broadcastable to a common shape.
Returns
--------
NDArray
The bases in x raised to the exponents in y.
Examples
--------
>>> x = mx.nd.ones((2,3))*2
>>> y = mx.nd.arange(1,3).reshape((2,1))
>>> z = mx.nd.arange(1,3).reshape((2,1))
>>> x.asnumpy()
array([[ 2., 2., 2.],
[ 2., 2., 2.]], dtype=float32)
>>> y.asnumpy()
array([[ 1.],
[ 2.]], dtype=float32)
>>> z.asnumpy()
array([[ 1.],
[ 2.]], dtype=float32)
>>> (x**2).asnumpy()
array([[ 4., 4., 4.],
[ 4., 4., 4.]], dtype=float32)
>>> (x**y).asnumpy()
array([[ 2., 2., 2.],
[ 4., 4., 4.]], dtype=float32)
>>> mx.nd.power(x,y).asnumpy()
array([[ 2., 2., 2.],
[ 4., 4., 4.]], dtype=float32)
>>> (z**y).asnumpy()
array([[ 1.],
[ 4.]], dtype=float32) | python/mxnet/ndarray/ndarray.py | def power(base, exp):
"""Returns result of first array elements raised to powers from second array, element-wise
with broadcasting.
Equivalent to ``base ** exp`` and ``mx.nd.broadcast_power(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
base : scalar or NDArray
The base array
exp : scalar or NDArray
The exponent array. If ``base.shape != exp.shape``, they must be
broadcastable to a common shape.
Returns
--------
NDArray
The bases in x raised to the exponents in y.
Examples
--------
>>> x = mx.nd.ones((2,3))*2
>>> y = mx.nd.arange(1,3).reshape((2,1))
>>> z = mx.nd.arange(1,3).reshape((2,1))
>>> x.asnumpy()
array([[ 2., 2., 2.],
[ 2., 2., 2.]], dtype=float32)
>>> y.asnumpy()
array([[ 1.],
[ 2.]], dtype=float32)
>>> z.asnumpy()
array([[ 1.],
[ 2.]], dtype=float32)
>>> (x**2).asnumpy()
array([[ 4., 4., 4.],
[ 4., 4., 4.]], dtype=float32)
>>> (x**y).asnumpy()
array([[ 2., 2., 2.],
[ 4., 4., 4.]], dtype=float32)
>>> mx.nd.power(x,y).asnumpy()
array([[ 2., 2., 2.],
[ 4., 4., 4.]], dtype=float32)
>>> (z**y).asnumpy()
array([[ 1.],
[ 4.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
base,
exp,
op.broadcast_power,
operator.pow,
_internal._power_scalar,
_internal._rpower_scalar) | def power(base, exp):
"""Returns result of first array elements raised to powers from second array, element-wise
with broadcasting.
Equivalent to ``base ** exp`` and ``mx.nd.broadcast_power(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
base : scalar or NDArray
The base array
exp : scalar or NDArray
The exponent array. If ``base.shape != exp.shape``, they must be
broadcastable to a common shape.
Returns
--------
NDArray
The bases in x raised to the exponents in y.
Examples
--------
>>> x = mx.nd.ones((2,3))*2
>>> y = mx.nd.arange(1,3).reshape((2,1))
>>> z = mx.nd.arange(1,3).reshape((2,1))
>>> x.asnumpy()
array([[ 2., 2., 2.],
[ 2., 2., 2.]], dtype=float32)
>>> y.asnumpy()
array([[ 1.],
[ 2.]], dtype=float32)
>>> z.asnumpy()
array([[ 1.],
[ 2.]], dtype=float32)
>>> (x**2).asnumpy()
array([[ 4., 4., 4.],
[ 4., 4., 4.]], dtype=float32)
>>> (x**y).asnumpy()
array([[ 2., 2., 2.],
[ 4., 4., 4.]], dtype=float32)
>>> mx.nd.power(x,y).asnumpy()
array([[ 2., 2., 2.],
[ 4., 4., 4.]], dtype=float32)
>>> (z**y).asnumpy()
array([[ 1.],
[ 4.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
base,
exp,
op.broadcast_power,
operator.pow,
_internal._power_scalar,
_internal._rpower_scalar) | [
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... | 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7 |
train | maximum | Returns element-wise maximum of the input arrays with broadcasting.
Equivalent to ``mx.nd.broadcast_maximum(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be compared.
rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
The element-wise maximum of the input arrays.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> mx.nd.maximum(x, 2).asnumpy()
array([[ 2., 2., 2.],
[ 2., 2., 2.]], dtype=float32)
>>> mx.nd.maximum(x, y).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.maximum(y, z).asnumpy()
array([[ 0., 1.],
[ 1., 1.]], dtype=float32) | python/mxnet/ndarray/ndarray.py | def maximum(lhs, rhs):
"""Returns element-wise maximum of the input arrays with broadcasting.
Equivalent to ``mx.nd.broadcast_maximum(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be compared.
rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
The element-wise maximum of the input arrays.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> mx.nd.maximum(x, 2).asnumpy()
array([[ 2., 2., 2.],
[ 2., 2., 2.]], dtype=float32)
>>> mx.nd.maximum(x, y).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.maximum(y, z).asnumpy()
array([[ 0., 1.],
[ 1., 1.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_maximum,
lambda x, y: x if x > y else y,
_internal._maximum_scalar,
None) | def maximum(lhs, rhs):
"""Returns element-wise maximum of the input arrays with broadcasting.
Equivalent to ``mx.nd.broadcast_maximum(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be compared.
rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
The element-wise maximum of the input arrays.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> mx.nd.maximum(x, 2).asnumpy()
array([[ 2., 2., 2.],
[ 2., 2., 2.]], dtype=float32)
>>> mx.nd.maximum(x, y).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.maximum(y, z).asnumpy()
array([[ 0., 1.],
[ 1., 1.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_maximum,
lambda x, y: x if x > y else y,
_internal._maximum_scalar,
None) | [
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"."
] | apache/incubator-mxnet | python | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/python/mxnet/ndarray/ndarray.py#L3024-L3077 | [
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">",
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"else... | 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7 |
train | minimum | Returns element-wise minimum of the input arrays with broadcasting.
Equivalent to ``mx.nd.broadcast_minimum(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be compared.
rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
The element-wise minimum of the input arrays.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> mx.nd.minimum(x, 2).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.minimum(x, y).asnumpy()
array([[ 0., 0., 0.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.minimum(z, y).asnumpy()
array([[ 0., 0.],
[ 0., 1.]], dtype=float32) | python/mxnet/ndarray/ndarray.py | def minimum(lhs, rhs):
"""Returns element-wise minimum of the input arrays with broadcasting.
Equivalent to ``mx.nd.broadcast_minimum(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be compared.
rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
The element-wise minimum of the input arrays.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> mx.nd.minimum(x, 2).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.minimum(x, y).asnumpy()
array([[ 0., 0., 0.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.minimum(z, y).asnumpy()
array([[ 0., 0.],
[ 0., 1.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_minimum,
lambda x, y: x if x < y else y,
_internal._minimum_scalar,
None) | def minimum(lhs, rhs):
"""Returns element-wise minimum of the input arrays with broadcasting.
Equivalent to ``mx.nd.broadcast_minimum(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be compared.
rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
The element-wise minimum of the input arrays.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> mx.nd.minimum(x, 2).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.minimum(x, y).asnumpy()
array([[ 0., 0., 0.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.minimum(z, y).asnumpy()
array([[ 0., 0.],
[ 0., 1.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_minimum,
lambda x, y: x if x < y else y,
_internal._minimum_scalar,
None) | [
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"input",
"arrays",
"with",
"broadcasting",
"."
] | apache/incubator-mxnet | python | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/python/mxnet/ndarray/ndarray.py#L3081-L3134 | [
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"else... | 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7 |
train | equal | Returns the result of element-wise **equal to** (==) comparison operation with
broadcasting.
For each element in input arrays, return 1(true) if corresponding elements are same,
otherwise return 0(false).
Equivalent to ``lhs == rhs`` and ``mx.nd.broadcast_equal(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be compared.
rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
Output array of boolean values.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> (x == 1).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> (x == y).asnumpy()
array([[ 0., 0., 0.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.equal(x,y).asnumpy()
array([[ 0., 0., 0.],
[ 1., 1., 1.]], dtype=float32)
>>> (z == y).asnumpy()
array([[ 1., 0.],
[ 0., 1.]], dtype=float32) | python/mxnet/ndarray/ndarray.py | def equal(lhs, rhs):
"""Returns the result of element-wise **equal to** (==) comparison operation with
broadcasting.
For each element in input arrays, return 1(true) if corresponding elements are same,
otherwise return 0(false).
Equivalent to ``lhs == rhs`` and ``mx.nd.broadcast_equal(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be compared.
rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
Output array of boolean values.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> (x == 1).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> (x == y).asnumpy()
array([[ 0., 0., 0.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.equal(x,y).asnumpy()
array([[ 0., 0., 0.],
[ 1., 1., 1.]], dtype=float32)
>>> (z == y).asnumpy()
array([[ 1., 0.],
[ 0., 1.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_equal,
lambda x, y: 1 if x == y else 0,
_internal._equal_scalar,
None) | def equal(lhs, rhs):
"""Returns the result of element-wise **equal to** (==) comparison operation with
broadcasting.
For each element in input arrays, return 1(true) if corresponding elements are same,
otherwise return 0(false).
Equivalent to ``lhs == rhs`` and ``mx.nd.broadcast_equal(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be compared.
rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
Output array of boolean values.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> (x == 1).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> (x == y).asnumpy()
array([[ 0., 0., 0.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.equal(x,y).asnumpy()
array([[ 0., 0., 0.],
[ 1., 1., 1.]], dtype=float32)
>>> (z == y).asnumpy()
array([[ 1., 0.],
[ 0., 1.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_equal,
lambda x, y: 1 if x == y else 0,
_internal._equal_scalar,
None) | [
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train | not_equal | Returns the result of element-wise **not equal to** (!=) comparison operation
with broadcasting.
For each element in input arrays, return 1(true) if corresponding elements are different,
otherwise return 0(false).
Equivalent to ``lhs != rhs`` and ``mx.nd.broadcast_not_equal(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be compared.
rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
Output array of boolean values.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> (z == y).asnumpy()
array([[ 1., 0.],
[ 0., 1.]], dtype=float32)
>>> (x != 1).asnumpy()
array([[ 0., 0., 0.],
[ 0., 0., 0.]], dtype=float32)
>>> (x != y).asnumpy()
array([[ 1., 1., 1.],
[ 0., 0., 0.]], dtype=float32)
>>> mx.nd.not_equal(x, y).asnumpy()
array([[ 1., 1., 1.],
[ 0., 0., 0.]], dtype=float32)
>>> (z != y).asnumpy()
array([[ 0., 1.],
[ 1., 0.]], dtype=float32) | python/mxnet/ndarray/ndarray.py | def not_equal(lhs, rhs):
"""Returns the result of element-wise **not equal to** (!=) comparison operation
with broadcasting.
For each element in input arrays, return 1(true) if corresponding elements are different,
otherwise return 0(false).
Equivalent to ``lhs != rhs`` and ``mx.nd.broadcast_not_equal(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be compared.
rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
Output array of boolean values.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> (z == y).asnumpy()
array([[ 1., 0.],
[ 0., 1.]], dtype=float32)
>>> (x != 1).asnumpy()
array([[ 0., 0., 0.],
[ 0., 0., 0.]], dtype=float32)
>>> (x != y).asnumpy()
array([[ 1., 1., 1.],
[ 0., 0., 0.]], dtype=float32)
>>> mx.nd.not_equal(x, y).asnumpy()
array([[ 1., 1., 1.],
[ 0., 0., 0.]], dtype=float32)
>>> (z != y).asnumpy()
array([[ 0., 1.],
[ 1., 0.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_not_equal,
lambda x, y: 1 if x != y else 0,
_internal._not_equal_scalar,
None) | def not_equal(lhs, rhs):
"""Returns the result of element-wise **not equal to** (!=) comparison operation
with broadcasting.
For each element in input arrays, return 1(true) if corresponding elements are different,
otherwise return 0(false).
Equivalent to ``lhs != rhs`` and ``mx.nd.broadcast_not_equal(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be compared.
rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
Output array of boolean values.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> (z == y).asnumpy()
array([[ 1., 0.],
[ 0., 1.]], dtype=float32)
>>> (x != 1).asnumpy()
array([[ 0., 0., 0.],
[ 0., 0., 0.]], dtype=float32)
>>> (x != y).asnumpy()
array([[ 1., 1., 1.],
[ 0., 0., 0.]], dtype=float32)
>>> mx.nd.not_equal(x, y).asnumpy()
array([[ 1., 1., 1.],
[ 0., 0., 0.]], dtype=float32)
>>> (z != y).asnumpy()
array([[ 0., 1.],
[ 1., 0.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_not_equal,
lambda x, y: 1 if x != y else 0,
_internal._not_equal_scalar,
None) | [
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... | 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7 |
train | greater | Returns the result of element-wise **greater than** (>) comparison operation
with broadcasting.
For each element in input arrays, return 1(true) if lhs elements are greater than rhs,
otherwise return 0(false).
Equivalent to ``lhs > rhs`` and ``mx.nd.broadcast_greater(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be compared.
rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
Output array of boolean values.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> (x > 1).asnumpy()
array([[ 0., 0., 0.],
[ 0., 0., 0.]], dtype=float32)
>>> (x > y).asnumpy()
array([[ 1., 1., 1.],
[ 0., 0., 0.]], dtype=float32)
>>> mx.nd.greater(x, y).asnumpy()
array([[ 1., 1., 1.],
[ 0., 0., 0.]], dtype=float32)
>>> (z > y).asnumpy()
array([[ 0., 1.],
[ 0., 0.]], dtype=float32) | python/mxnet/ndarray/ndarray.py | def greater(lhs, rhs):
"""Returns the result of element-wise **greater than** (>) comparison operation
with broadcasting.
For each element in input arrays, return 1(true) if lhs elements are greater than rhs,
otherwise return 0(false).
Equivalent to ``lhs > rhs`` and ``mx.nd.broadcast_greater(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be compared.
rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
Output array of boolean values.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> (x > 1).asnumpy()
array([[ 0., 0., 0.],
[ 0., 0., 0.]], dtype=float32)
>>> (x > y).asnumpy()
array([[ 1., 1., 1.],
[ 0., 0., 0.]], dtype=float32)
>>> mx.nd.greater(x, y).asnumpy()
array([[ 1., 1., 1.],
[ 0., 0., 0.]], dtype=float32)
>>> (z > y).asnumpy()
array([[ 0., 1.],
[ 0., 0.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_greater,
lambda x, y: 1 if x > y else 0,
_internal._greater_scalar,
_internal._lesser_scalar) | def greater(lhs, rhs):
"""Returns the result of element-wise **greater than** (>) comparison operation
with broadcasting.
For each element in input arrays, return 1(true) if lhs elements are greater than rhs,
otherwise return 0(false).
Equivalent to ``lhs > rhs`` and ``mx.nd.broadcast_greater(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be compared.
rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
Output array of boolean values.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> (x > 1).asnumpy()
array([[ 0., 0., 0.],
[ 0., 0., 0.]], dtype=float32)
>>> (x > y).asnumpy()
array([[ 1., 1., 1.],
[ 0., 0., 0.]], dtype=float32)
>>> mx.nd.greater(x, y).asnumpy()
array([[ 1., 1., 1.],
[ 0., 0., 0.]], dtype=float32)
>>> (z > y).asnumpy()
array([[ 0., 1.],
[ 0., 0.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_greater,
lambda x, y: 1 if x > y else 0,
_internal._greater_scalar,
_internal._lesser_scalar) | [
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"else... | 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7 |
train | greater_equal | Returns the result of element-wise **greater than or equal to** (>=) comparison
operation with broadcasting.
For each element in input arrays, return 1(true) if lhs elements are greater than equal to rhs,
otherwise return 0(false).
Equivalent to ``lhs >= rhs`` and ``mx.nd.broadcast_greater_equal(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be compared.
rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
Output array of boolean values.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> (x >= 1).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> (x >= y).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.greater_equal(x, y).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> (z >= y).asnumpy()
array([[ 1., 1.],
[ 0., 1.]], dtype=float32) | python/mxnet/ndarray/ndarray.py | def greater_equal(lhs, rhs):
"""Returns the result of element-wise **greater than or equal to** (>=) comparison
operation with broadcasting.
For each element in input arrays, return 1(true) if lhs elements are greater than equal to rhs,
otherwise return 0(false).
Equivalent to ``lhs >= rhs`` and ``mx.nd.broadcast_greater_equal(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be compared.
rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
Output array of boolean values.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> (x >= 1).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> (x >= y).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.greater_equal(x, y).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> (z >= y).asnumpy()
array([[ 1., 1.],
[ 0., 1.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_greater_equal,
lambda x, y: 1 if x >= y else 0,
_internal._greater_equal_scalar,
_internal._lesser_equal_scalar) | def greater_equal(lhs, rhs):
"""Returns the result of element-wise **greater than or equal to** (>=) comparison
operation with broadcasting.
For each element in input arrays, return 1(true) if lhs elements are greater than equal to rhs,
otherwise return 0(false).
Equivalent to ``lhs >= rhs`` and ``mx.nd.broadcast_greater_equal(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be compared.
rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
Output array of boolean values.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> (x >= 1).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> (x >= y).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.greater_equal(x, y).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> (z >= y).asnumpy()
array([[ 1., 1.],
[ 0., 1.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_greater_equal,
lambda x, y: 1 if x >= y else 0,
_internal._greater_equal_scalar,
_internal._lesser_equal_scalar) | [
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... | 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7 |
train | lesser | Returns the result of element-wise **lesser than** (<) comparison operation
with broadcasting.
For each element in input arrays, return 1(true) if lhs elements are less than rhs,
otherwise return 0(false).
Equivalent to ``lhs < rhs`` and ``mx.nd.broadcast_lesser(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be compared.
rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
Output array of boolean values.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> (x < 1).asnumpy()
array([[ 0., 0., 0.],
[ 0., 0., 0.]], dtype=float32)
>>> (x < y).asnumpy()
array([[ 0., 0., 0.],
[ 0., 0., 0.]], dtype=float32)
>>> mx.nd.lesser(x, y).asnumpy()
array([[ 0., 0., 0.],
[ 0., 0., 0.]], dtype=float32)
>>> (z < y).asnumpy()
array([[ 0., 0.],
[ 1., 0.]], dtype=float32) | python/mxnet/ndarray/ndarray.py | def lesser(lhs, rhs):
"""Returns the result of element-wise **lesser than** (<) comparison operation
with broadcasting.
For each element in input arrays, return 1(true) if lhs elements are less than rhs,
otherwise return 0(false).
Equivalent to ``lhs < rhs`` and ``mx.nd.broadcast_lesser(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be compared.
rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
Output array of boolean values.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> (x < 1).asnumpy()
array([[ 0., 0., 0.],
[ 0., 0., 0.]], dtype=float32)
>>> (x < y).asnumpy()
array([[ 0., 0., 0.],
[ 0., 0., 0.]], dtype=float32)
>>> mx.nd.lesser(x, y).asnumpy()
array([[ 0., 0., 0.],
[ 0., 0., 0.]], dtype=float32)
>>> (z < y).asnumpy()
array([[ 0., 0.],
[ 1., 0.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_lesser,
lambda x, y: 1 if x < y else 0,
_internal._lesser_scalar,
_internal._greater_scalar) | def lesser(lhs, rhs):
"""Returns the result of element-wise **lesser than** (<) comparison operation
with broadcasting.
For each element in input arrays, return 1(true) if lhs elements are less than rhs,
otherwise return 0(false).
Equivalent to ``lhs < rhs`` and ``mx.nd.broadcast_lesser(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be compared.
rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
Output array of boolean values.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> (x < 1).asnumpy()
array([[ 0., 0., 0.],
[ 0., 0., 0.]], dtype=float32)
>>> (x < y).asnumpy()
array([[ 0., 0., 0.],
[ 0., 0., 0.]], dtype=float32)
>>> mx.nd.lesser(x, y).asnumpy()
array([[ 0., 0., 0.],
[ 0., 0., 0.]], dtype=float32)
>>> (z < y).asnumpy()
array([[ 0., 0.],
[ 1., 0.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_lesser,
lambda x, y: 1 if x < y else 0,
_internal._lesser_scalar,
_internal._greater_scalar) | [
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] | apache/incubator-mxnet | python | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/python/mxnet/ndarray/ndarray.py#L3397-L3457 | [
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"else",... | 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7 |
train | lesser_equal | Returns the result of element-wise **lesser than or equal to** (<=) comparison
operation with broadcasting.
For each element in input arrays, return 1(true) if lhs elements are
lesser than equal to rhs, otherwise return 0(false).
Equivalent to ``lhs <= rhs`` and ``mx.nd.broadcast_lesser_equal(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be compared.
rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
Output array of boolean values.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> (x <= 1).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> (x <= y).asnumpy()
array([[ 0., 0., 0.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.lesser_equal(x, y).asnumpy()
array([[ 0., 0., 0.],
[ 1., 1., 1.]], dtype=float32)
>>> (z <= y).asnumpy()
array([[ 1., 0.],
[ 1., 1.]], dtype=float32) | python/mxnet/ndarray/ndarray.py | def lesser_equal(lhs, rhs):
"""Returns the result of element-wise **lesser than or equal to** (<=) comparison
operation with broadcasting.
For each element in input arrays, return 1(true) if lhs elements are
lesser than equal to rhs, otherwise return 0(false).
Equivalent to ``lhs <= rhs`` and ``mx.nd.broadcast_lesser_equal(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be compared.
rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
Output array of boolean values.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> (x <= 1).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> (x <= y).asnumpy()
array([[ 0., 0., 0.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.lesser_equal(x, y).asnumpy()
array([[ 0., 0., 0.],
[ 1., 1., 1.]], dtype=float32)
>>> (z <= y).asnumpy()
array([[ 1., 0.],
[ 1., 1.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_lesser_equal,
lambda x, y: 1 if x <= y else 0,
_internal._lesser_equal_scalar,
_internal._greater_equal_scalar) | def lesser_equal(lhs, rhs):
"""Returns the result of element-wise **lesser than or equal to** (<=) comparison
operation with broadcasting.
For each element in input arrays, return 1(true) if lhs elements are
lesser than equal to rhs, otherwise return 0(false).
Equivalent to ``lhs <= rhs`` and ``mx.nd.broadcast_lesser_equal(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be compared.
rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
Output array of boolean values.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> (x <= 1).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> (x <= y).asnumpy()
array([[ 0., 0., 0.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.lesser_equal(x, y).asnumpy()
array([[ 0., 0., 0.],
[ 1., 1., 1.]], dtype=float32)
>>> (z <= y).asnumpy()
array([[ 1., 0.],
[ 1., 1.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_lesser_equal,
lambda x, y: 1 if x <= y else 0,
_internal._lesser_equal_scalar,
_internal._greater_equal_scalar) | [
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train | logical_and | Returns the result of element-wise **logical and** comparison
operation with broadcasting.
For each element in input arrays, return 1(true) if lhs elements and rhs elements
are true, otherwise return 0(false).
Equivalent to ``lhs and rhs`` and ``mx.nd.broadcast_logical_and(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First input of the function.
rhs : scalar or mxnet.ndarray.array
Second input of the function. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
Output array of boolean values.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> mx.nd.logical_and(x, 1).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.logical_and(x, y).asnumpy()
array([[ 0., 0., 0.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.logical_and(z, y).asnumpy()
array([[ 0., 0.],
[ 0., 1.]], dtype=float32) | python/mxnet/ndarray/ndarray.py | def logical_and(lhs, rhs):
"""Returns the result of element-wise **logical and** comparison
operation with broadcasting.
For each element in input arrays, return 1(true) if lhs elements and rhs elements
are true, otherwise return 0(false).
Equivalent to ``lhs and rhs`` and ``mx.nd.broadcast_logical_and(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First input of the function.
rhs : scalar or mxnet.ndarray.array
Second input of the function. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
Output array of boolean values.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> mx.nd.logical_and(x, 1).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.logical_and(x, y).asnumpy()
array([[ 0., 0., 0.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.logical_and(z, y).asnumpy()
array([[ 0., 0.],
[ 0., 1.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_logical_and,
lambda x, y: 1 if x and y else 0,
_internal._logical_and_scalar,
None) | def logical_and(lhs, rhs):
"""Returns the result of element-wise **logical and** comparison
operation with broadcasting.
For each element in input arrays, return 1(true) if lhs elements and rhs elements
are true, otherwise return 0(false).
Equivalent to ``lhs and rhs`` and ``mx.nd.broadcast_logical_and(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First input of the function.
rhs : scalar or mxnet.ndarray.array
Second input of the function. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
Output array of boolean values.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> mx.nd.logical_and(x, 1).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.logical_and(x, y).asnumpy()
array([[ 0., 0., 0.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.logical_and(z, y).asnumpy()
array([[ 0., 0.],
[ 0., 1.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_logical_and,
lambda x, y: 1 if x and y else 0,
_internal._logical_and_scalar,
None) | [
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train | logical_or | Returns the result of element-wise **logical or** comparison
operation with broadcasting.
For each element in input arrays, return 1(true) if lhs elements or rhs elements
are true, otherwise return 0(false).
Equivalent to ``lhs or rhs`` and ``mx.nd.broadcast_logical_or(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First input of the function.
rhs : scalar or mxnet.ndarray.array
Second input of the function. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
Output array of boolean values.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> mx.nd.logical_or(x, 1).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.logical_or(x, y).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.logical_or(z, y).asnumpy()
array([[ 0., 1.],
[ 1., 1.]], dtype=float32) | python/mxnet/ndarray/ndarray.py | def logical_or(lhs, rhs):
"""Returns the result of element-wise **logical or** comparison
operation with broadcasting.
For each element in input arrays, return 1(true) if lhs elements or rhs elements
are true, otherwise return 0(false).
Equivalent to ``lhs or rhs`` and ``mx.nd.broadcast_logical_or(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First input of the function.
rhs : scalar or mxnet.ndarray.array
Second input of the function. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
Output array of boolean values.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> mx.nd.logical_or(x, 1).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.logical_or(x, y).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.logical_or(z, y).asnumpy()
array([[ 0., 1.],
[ 1., 1.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_logical_or,
lambda x, y: 1 if x or y else 0,
_internal._logical_or_scalar,
None) | def logical_or(lhs, rhs):
"""Returns the result of element-wise **logical or** comparison
operation with broadcasting.
For each element in input arrays, return 1(true) if lhs elements or rhs elements
are true, otherwise return 0(false).
Equivalent to ``lhs or rhs`` and ``mx.nd.broadcast_logical_or(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First input of the function.
rhs : scalar or mxnet.ndarray.array
Second input of the function. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
Output array of boolean values.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> mx.nd.logical_or(x, 1).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.logical_or(x, y).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.logical_or(z, y).asnumpy()
array([[ 0., 1.],
[ 1., 1.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_logical_or,
lambda x, y: 1 if x or y else 0,
_internal._logical_or_scalar,
None) | [
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... | 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7 |
train | logical_xor | Returns the result of element-wise **logical xor** comparison
operation with broadcasting.
For each element in input arrays, return 1(true) if lhs elements or rhs elements
are true, otherwise return 0(false).
Equivalent to ``bool(lhs) ^ bool(rhs)`` and ``mx.nd.broadcast_logical_xor(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First input of the function.
rhs : scalar or mxnet.ndarray.array
Second input of the function. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
Output array of boolean values.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> mx.nd.logical_xor(x, y).asnumpy()
array([[ 1., 1., 1.],
[ 0., 0., 0.]], dtype=float32) | python/mxnet/ndarray/ndarray.py | def logical_xor(lhs, rhs):
"""Returns the result of element-wise **logical xor** comparison
operation with broadcasting.
For each element in input arrays, return 1(true) if lhs elements or rhs elements
are true, otherwise return 0(false).
Equivalent to ``bool(lhs) ^ bool(rhs)`` and ``mx.nd.broadcast_logical_xor(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First input of the function.
rhs : scalar or mxnet.ndarray.array
Second input of the function. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
Output array of boolean values.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> mx.nd.logical_xor(x, y).asnumpy()
array([[ 1., 1., 1.],
[ 0., 0., 0.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_logical_xor,
lambda x, y: 1 if bool(x) ^ bool(y) else 0,
_internal._logical_xor_scalar,
None) | def logical_xor(lhs, rhs):
"""Returns the result of element-wise **logical xor** comparison
operation with broadcasting.
For each element in input arrays, return 1(true) if lhs elements or rhs elements
are true, otherwise return 0(false).
Equivalent to ``bool(lhs) ^ bool(rhs)`` and ``mx.nd.broadcast_logical_xor(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First input of the function.
rhs : scalar or mxnet.ndarray.array
Second input of the function. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
Output array of boolean values.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> mx.nd.logical_xor(x, y).asnumpy()
array([[ 1., 1., 1.],
[ 0., 0., 0.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_logical_xor,
lambda x, y: 1 if bool(x) ^ bool(y) else 0,
_internal._logical_xor_scalar,
None) | [
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train | concatenate | DEPRECATED, use ``concat`` instead
Parameters
----------
arrays : list of `NDArray`
Arrays to be concatenate. They must have identical shape except
the first dimension. They also must have the same data type.
axis : int
The axis along which to concatenate.
always_copy : bool
Default `True`. When not `True`, if the arrays only contain one
`NDArray`, that element will be returned directly, avoid copying.
Returns
-------
NDArray
An `NDArray` that lives on the same context as `arrays[0].context`. | python/mxnet/ndarray/ndarray.py | def concatenate(arrays, axis=0, always_copy=True):
"""DEPRECATED, use ``concat`` instead
Parameters
----------
arrays : list of `NDArray`
Arrays to be concatenate. They must have identical shape except
the first dimension. They also must have the same data type.
axis : int
The axis along which to concatenate.
always_copy : bool
Default `True`. When not `True`, if the arrays only contain one
`NDArray`, that element will be returned directly, avoid copying.
Returns
-------
NDArray
An `NDArray` that lives on the same context as `arrays[0].context`.
"""
assert isinstance(arrays, list)
assert len(arrays) > 0
assert isinstance(arrays[0], NDArray)
if not always_copy and len(arrays) == 1:
return arrays[0]
shape_axis = arrays[0].shape[axis]
shape_rest1 = arrays[0].shape[0:axis]
shape_rest2 = arrays[0].shape[axis+1:]
dtype = arrays[0].dtype
for arr in arrays[1:]:
shape_axis += arr.shape[axis]
assert shape_rest1 == arr.shape[0:axis]
assert shape_rest2 == arr.shape[axis+1:]
assert dtype == arr.dtype
ret_shape = shape_rest1 + (shape_axis,) + shape_rest2
ret = empty(ret_shape, ctx=arrays[0].context, dtype=dtype)
idx = 0
begin = [0 for _ in ret_shape]
end = list(ret_shape)
for arr in arrays:
if axis == 0:
ret[idx:idx+arr.shape[0]] = arr
else:
begin[axis] = idx
end[axis] = idx+arr.shape[axis]
# pylint: disable=no-member,protected-access
_internal._crop_assign(ret, arr, out=ret,
begin=tuple(begin),
end=tuple(end))
# pylint: enable=no-member,protected-access
idx += arr.shape[axis]
return ret | def concatenate(arrays, axis=0, always_copy=True):
"""DEPRECATED, use ``concat`` instead
Parameters
----------
arrays : list of `NDArray`
Arrays to be concatenate. They must have identical shape except
the first dimension. They also must have the same data type.
axis : int
The axis along which to concatenate.
always_copy : bool
Default `True`. When not `True`, if the arrays only contain one
`NDArray`, that element will be returned directly, avoid copying.
Returns
-------
NDArray
An `NDArray` that lives on the same context as `arrays[0].context`.
"""
assert isinstance(arrays, list)
assert len(arrays) > 0
assert isinstance(arrays[0], NDArray)
if not always_copy and len(arrays) == 1:
return arrays[0]
shape_axis = arrays[0].shape[axis]
shape_rest1 = arrays[0].shape[0:axis]
shape_rest2 = arrays[0].shape[axis+1:]
dtype = arrays[0].dtype
for arr in arrays[1:]:
shape_axis += arr.shape[axis]
assert shape_rest1 == arr.shape[0:axis]
assert shape_rest2 == arr.shape[axis+1:]
assert dtype == arr.dtype
ret_shape = shape_rest1 + (shape_axis,) + shape_rest2
ret = empty(ret_shape, ctx=arrays[0].context, dtype=dtype)
idx = 0
begin = [0 for _ in ret_shape]
end = list(ret_shape)
for arr in arrays:
if axis == 0:
ret[idx:idx+arr.shape[0]] = arr
else:
begin[axis] = idx
end[axis] = idx+arr.shape[axis]
# pylint: disable=no-member,protected-access
_internal._crop_assign(ret, arr, out=ret,
begin=tuple(begin),
end=tuple(end))
# pylint: enable=no-member,protected-access
idx += arr.shape[axis]
return ret | [
"DEPRECATED",
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train | imdecode | DEPRECATED, use mx.img instead
Parameters
----------
str_img : str
Binary image data
clip_rect : iterable of 4 int
Clip decoded image to rectangle (x0, y0, x1, y1).
out : NDArray
Output buffer. Can be 3 dimensional (c, h, w) or 4 dimensional (n, c, h, w).
index : int
Output decoded image to i-th slice of 4 dimensional buffer.
channels : int
Number of channels to output. Decode to grey scale when channels = 1.
mean : NDArray
Subtract mean from decode image before outputing. | python/mxnet/ndarray/ndarray.py | def imdecode(str_img, clip_rect=(0, 0, 0, 0), out=None, index=0, channels=3, mean=None):
"""DEPRECATED, use mx.img instead
Parameters
----------
str_img : str
Binary image data
clip_rect : iterable of 4 int
Clip decoded image to rectangle (x0, y0, x1, y1).
out : NDArray
Output buffer. Can be 3 dimensional (c, h, w) or 4 dimensional (n, c, h, w).
index : int
Output decoded image to i-th slice of 4 dimensional buffer.
channels : int
Number of channels to output. Decode to grey scale when channels = 1.
mean : NDArray
Subtract mean from decode image before outputing.
"""
# pylint: disable= no-member, protected-access, too-many-arguments
if mean is None:
mean = NDArray(_new_empty_handle())
if out is None:
return _internal._imdecode(mean, index,
clip_rect[0],
clip_rect[1],
clip_rect[2],
clip_rect[3],
channels,
len(str_img),
str_img=str_img)
else:
return _internal._imdecode(mean, index,
clip_rect[0],
clip_rect[1],
clip_rect[2],
clip_rect[3],
channels,
len(str_img),
str_img=str_img,
out=out) | def imdecode(str_img, clip_rect=(0, 0, 0, 0), out=None, index=0, channels=3, mean=None):
"""DEPRECATED, use mx.img instead
Parameters
----------
str_img : str
Binary image data
clip_rect : iterable of 4 int
Clip decoded image to rectangle (x0, y0, x1, y1).
out : NDArray
Output buffer. Can be 3 dimensional (c, h, w) or 4 dimensional (n, c, h, w).
index : int
Output decoded image to i-th slice of 4 dimensional buffer.
channels : int
Number of channels to output. Decode to grey scale when channels = 1.
mean : NDArray
Subtract mean from decode image before outputing.
"""
# pylint: disable= no-member, protected-access, too-many-arguments
if mean is None:
mean = NDArray(_new_empty_handle())
if out is None:
return _internal._imdecode(mean, index,
clip_rect[0],
clip_rect[1],
clip_rect[2],
clip_rect[3],
channels,
len(str_img),
str_img=str_img)
else:
return _internal._imdecode(mean, index,
clip_rect[0],
clip_rect[1],
clip_rect[2],
clip_rect[3],
channels,
len(str_img),
str_img=str_img,
out=out) | [
"DEPRECATED",
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] | apache/incubator-mxnet | python | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/python/mxnet/ndarray/ndarray.py#L3763-L3802 | [
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train | zeros | Returns a new array filled with all zeros, with the given shape and type.
Parameters
----------
shape : int or tuple of int
The shape of the empty array.
ctx : Context, optional
An optional device context (default is the current default context).
dtype : str or numpy.dtype, optional
An optional value type (default is `float32`).
out : NDArray, optional
The output NDArray (default is `None`).
Returns
-------
NDArray
A created array
Examples
--------
>>> mx.nd.zeros(1).asnumpy()
array([ 0.], dtype=float32)
>>> mx.nd.zeros((1,2), mx.gpu(0))
<NDArray 1x2 @gpu(0)>
>>> mx.nd.zeros((1,2), mx.gpu(0), 'float16').asnumpy()
array([[ 0., 0.]], dtype=float16) | python/mxnet/ndarray/ndarray.py | def zeros(shape, ctx=None, dtype=None, **kwargs):
"""Returns a new array filled with all zeros, with the given shape and type.
Parameters
----------
shape : int or tuple of int
The shape of the empty array.
ctx : Context, optional
An optional device context (default is the current default context).
dtype : str or numpy.dtype, optional
An optional value type (default is `float32`).
out : NDArray, optional
The output NDArray (default is `None`).
Returns
-------
NDArray
A created array
Examples
--------
>>> mx.nd.zeros(1).asnumpy()
array([ 0.], dtype=float32)
>>> mx.nd.zeros((1,2), mx.gpu(0))
<NDArray 1x2 @gpu(0)>
>>> mx.nd.zeros((1,2), mx.gpu(0), 'float16').asnumpy()
array([[ 0., 0.]], dtype=float16)
"""
# pylint: disable= unused-argument
if ctx is None:
ctx = current_context()
dtype = mx_real_t if dtype is None else dtype
# pylint: disable= no-member, protected-access
return _internal._zeros(shape=shape, ctx=ctx, dtype=dtype, **kwargs) | def zeros(shape, ctx=None, dtype=None, **kwargs):
"""Returns a new array filled with all zeros, with the given shape and type.
Parameters
----------
shape : int or tuple of int
The shape of the empty array.
ctx : Context, optional
An optional device context (default is the current default context).
dtype : str or numpy.dtype, optional
An optional value type (default is `float32`).
out : NDArray, optional
The output NDArray (default is `None`).
Returns
-------
NDArray
A created array
Examples
--------
>>> mx.nd.zeros(1).asnumpy()
array([ 0.], dtype=float32)
>>> mx.nd.zeros((1,2), mx.gpu(0))
<NDArray 1x2 @gpu(0)>
>>> mx.nd.zeros((1,2), mx.gpu(0), 'float16').asnumpy()
array([[ 0., 0.]], dtype=float16)
"""
# pylint: disable= unused-argument
if ctx is None:
ctx = current_context()
dtype = mx_real_t if dtype is None else dtype
# pylint: disable= no-member, protected-access
return _internal._zeros(shape=shape, ctx=ctx, dtype=dtype, **kwargs) | [
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train | eye | Return a 2-D array with ones on the diagonal and zeros elsewhere.
Parameters
----------
N: int
Number of rows in the output.
M: int, optional
Number of columns in the output. If 0, defaults to N.
k: int, optional
Index of the diagonal: 0 (the default) refers to the main diagonal,
a positive value refers to an upper diagonal,
and a negative value to a lower diagonal.
ctx: Context, optional
An optional device context (default is the current default context)
dtype: str or numpy.dtype, optional
An optional value type (default is `float32`)
Returns
-------
NDArray
A created array
Examples
--------
>>> mx.nd.eye(2)
[[ 1. 0.]
[ 0. 1.]]
<NDArray 2x2 @cpu(0)>
>>> mx.nd.eye(2, 3, 1)
[[ 0. 1. 0.]
[ 0. 0. 1.]]
<NDArray 2x3 @cpu(0)> | python/mxnet/ndarray/ndarray.py | def eye(N, M=0, k=0, ctx=None, dtype=None, **kwargs):
"""Return a 2-D array with ones on the diagonal and zeros elsewhere.
Parameters
----------
N: int
Number of rows in the output.
M: int, optional
Number of columns in the output. If 0, defaults to N.
k: int, optional
Index of the diagonal: 0 (the default) refers to the main diagonal,
a positive value refers to an upper diagonal,
and a negative value to a lower diagonal.
ctx: Context, optional
An optional device context (default is the current default context)
dtype: str or numpy.dtype, optional
An optional value type (default is `float32`)
Returns
-------
NDArray
A created array
Examples
--------
>>> mx.nd.eye(2)
[[ 1. 0.]
[ 0. 1.]]
<NDArray 2x2 @cpu(0)>
>>> mx.nd.eye(2, 3, 1)
[[ 0. 1. 0.]
[ 0. 0. 1.]]
<NDArray 2x3 @cpu(0)>
"""
# pylint: disable= unused-argument
if ctx is None:
ctx = current_context()
dtype = mx_real_t if dtype is None else dtype
# pylint: disable= no-member, protected-access
return _internal._eye(N=N, M=M, k=k, ctx=ctx, dtype=dtype, **kwargs) | def eye(N, M=0, k=0, ctx=None, dtype=None, **kwargs):
"""Return a 2-D array with ones on the diagonal and zeros elsewhere.
Parameters
----------
N: int
Number of rows in the output.
M: int, optional
Number of columns in the output. If 0, defaults to N.
k: int, optional
Index of the diagonal: 0 (the default) refers to the main diagonal,
a positive value refers to an upper diagonal,
and a negative value to a lower diagonal.
ctx: Context, optional
An optional device context (default is the current default context)
dtype: str or numpy.dtype, optional
An optional value type (default is `float32`)
Returns
-------
NDArray
A created array
Examples
--------
>>> mx.nd.eye(2)
[[ 1. 0.]
[ 0. 1.]]
<NDArray 2x2 @cpu(0)>
>>> mx.nd.eye(2, 3, 1)
[[ 0. 1. 0.]
[ 0. 0. 1.]]
<NDArray 2x3 @cpu(0)>
"""
# pylint: disable= unused-argument
if ctx is None:
ctx = current_context()
dtype = mx_real_t if dtype is None else dtype
# pylint: disable= no-member, protected-access
return _internal._eye(N=N, M=M, k=k, ctx=ctx, dtype=dtype, **kwargs) | [
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train | empty | Returns a new array of given shape and type, without initializing entries.
Parameters
----------
shape : int or tuple of int
The shape of the empty array.
ctx : Context, optional
An optional device context (default is the current default context).
dtype : str or numpy.dtype, optional
An optional value type (default is `float32`).
Returns
-------
NDArray
A created array. | python/mxnet/ndarray/ndarray.py | def empty(shape, ctx=None, dtype=None):
"""Returns a new array of given shape and type, without initializing entries.
Parameters
----------
shape : int or tuple of int
The shape of the empty array.
ctx : Context, optional
An optional device context (default is the current default context).
dtype : str or numpy.dtype, optional
An optional value type (default is `float32`).
Returns
-------
NDArray
A created array.
"""
if isinstance(shape, int):
shape = (shape, )
if ctx is None:
ctx = current_context()
if dtype is None:
dtype = mx_real_t
return NDArray(handle=_new_alloc_handle(shape, ctx, False, dtype)) | def empty(shape, ctx=None, dtype=None):
"""Returns a new array of given shape and type, without initializing entries.
Parameters
----------
shape : int or tuple of int
The shape of the empty array.
ctx : Context, optional
An optional device context (default is the current default context).
dtype : str or numpy.dtype, optional
An optional value type (default is `float32`).
Returns
-------
NDArray
A created array.
"""
if isinstance(shape, int):
shape = (shape, )
if ctx is None:
ctx = current_context()
if dtype is None:
dtype = mx_real_t
return NDArray(handle=_new_alloc_handle(shape, ctx, False, dtype)) | [
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train | histogram | Compute the histogram of the input data.
Parameters
----------
a : NDArray
Input data. The histogram is computed over the flattened array.
bins : int or sequence of scalars
If bins is an int, it defines the number of equal-width bins in the
given range (10, by default). If bins is a sequence, it defines the bin edges,
including the rightmost edge, allowing for non-uniform bin widths.
range : (float, float), optional
The lower and upper range of the bins. If not provided, range is simply (a.min(), a.max()).
Values outside the range are ignored. The first element of the range must be less than or
equal to the second. range affects the automatic bin computation as well, the range will
be equally divided by the number of bins.
Returns
-------
NDArray
A created array. | python/mxnet/ndarray/ndarray.py | def histogram(a, bins=10, range=None):
"""Compute the histogram of the input data.
Parameters
----------
a : NDArray
Input data. The histogram is computed over the flattened array.
bins : int or sequence of scalars
If bins is an int, it defines the number of equal-width bins in the
given range (10, by default). If bins is a sequence, it defines the bin edges,
including the rightmost edge, allowing for non-uniform bin widths.
range : (float, float), optional
The lower and upper range of the bins. If not provided, range is simply (a.min(), a.max()).
Values outside the range are ignored. The first element of the range must be less than or
equal to the second. range affects the automatic bin computation as well, the range will
be equally divided by the number of bins.
Returns
-------
NDArray
A created array.
"""
# pylint: disable= no-member, protected-access
if isinstance(bins, NDArray):
return _internal._histogram(data=a, bins=bins)
elif isinstance(bins, integer_types):
if range is None:
warnings.warn("range is not specified, using numpy's result "
"to ensure consistency with numpy")
res, bin_bounds = np.histogram(a.asnumpy(), bins=bins)
return array(res), array(bin_bounds)
return _internal._histogram(data=a, bin_cnt=bins, range=range)
raise ValueError("bins argument should be either an integer or an NDArray") | def histogram(a, bins=10, range=None):
"""Compute the histogram of the input data.
Parameters
----------
a : NDArray
Input data. The histogram is computed over the flattened array.
bins : int or sequence of scalars
If bins is an int, it defines the number of equal-width bins in the
given range (10, by default). If bins is a sequence, it defines the bin edges,
including the rightmost edge, allowing for non-uniform bin widths.
range : (float, float), optional
The lower and upper range of the bins. If not provided, range is simply (a.min(), a.max()).
Values outside the range are ignored. The first element of the range must be less than or
equal to the second. range affects the automatic bin computation as well, the range will
be equally divided by the number of bins.
Returns
-------
NDArray
A created array.
"""
# pylint: disable= no-member, protected-access
if isinstance(bins, NDArray):
return _internal._histogram(data=a, bins=bins)
elif isinstance(bins, integer_types):
if range is None:
warnings.warn("range is not specified, using numpy's result "
"to ensure consistency with numpy")
res, bin_bounds = np.histogram(a.asnumpy(), bins=bins)
return array(res), array(bin_bounds)
return _internal._histogram(data=a, bin_cnt=bins, range=range)
raise ValueError("bins argument should be either an integer or an NDArray") | [
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train | split_v2 | Split an array into multiple sub-arrays.
Parameters
----------
ary : NDArray
Array to be divided into sub-arrays.
indices_or_sections : int or tuple of ints
If `indices_or_sections` is an integer, N, the array will be divided
into N equal arrays along `axis`. If such a split is not possible,
an error is raised.
If `indices_or_sections` is a 1-D array of sorted integers, the entries
indicate where along `axis` the array is split. For example,
``[2, 3]`` would, for ``axis=0``, result in
- ary[:2]
- ary[2:3]
- ary[3:]
If an index exceeds the dimension of the array along `axis`,
an empty sub-array is returned correspondingly.
axis : int, optional
The axis along which to split, default is 0.
squeeze_axis: boolean, optional
Whether to squeeze the axis of sub-arrays or not, only useful when size
of the sub-arrays are 1 on the `axis`. Default is False.
Returns
-------
NDArray
A created array. | python/mxnet/ndarray/ndarray.py | def split_v2(ary, indices_or_sections, axis=0, squeeze_axis=False):
"""Split an array into multiple sub-arrays.
Parameters
----------
ary : NDArray
Array to be divided into sub-arrays.
indices_or_sections : int or tuple of ints
If `indices_or_sections` is an integer, N, the array will be divided
into N equal arrays along `axis`. If such a split is not possible,
an error is raised.
If `indices_or_sections` is a 1-D array of sorted integers, the entries
indicate where along `axis` the array is split. For example,
``[2, 3]`` would, for ``axis=0``, result in
- ary[:2]
- ary[2:3]
- ary[3:]
If an index exceeds the dimension of the array along `axis`,
an empty sub-array is returned correspondingly.
axis : int, optional
The axis along which to split, default is 0.
squeeze_axis: boolean, optional
Whether to squeeze the axis of sub-arrays or not, only useful when size
of the sub-arrays are 1 on the `axis`. Default is False.
Returns
-------
NDArray
A created array.
"""
indices = []
axis_size = ary.shape[axis]
if isinstance(indices_or_sections, int):
sections = indices_or_sections
if axis_size % sections:
raise ValueError('array split does not result in an equal division')
section_size = int(axis_size / sections)
indices = [i * section_size for i in range(sections)]
elif isinstance(indices_or_sections, tuple):
indices = [0] + list(indices_or_sections)
else:
raise ValueError('indices_or_sections must either int or tuple of ints')
return _internal._split_v2(ary, indices, axis, squeeze_axis) | def split_v2(ary, indices_or_sections, axis=0, squeeze_axis=False):
"""Split an array into multiple sub-arrays.
Parameters
----------
ary : NDArray
Array to be divided into sub-arrays.
indices_or_sections : int or tuple of ints
If `indices_or_sections` is an integer, N, the array will be divided
into N equal arrays along `axis`. If such a split is not possible,
an error is raised.
If `indices_or_sections` is a 1-D array of sorted integers, the entries
indicate where along `axis` the array is split. For example,
``[2, 3]`` would, for ``axis=0``, result in
- ary[:2]
- ary[2:3]
- ary[3:]
If an index exceeds the dimension of the array along `axis`,
an empty sub-array is returned correspondingly.
axis : int, optional
The axis along which to split, default is 0.
squeeze_axis: boolean, optional
Whether to squeeze the axis of sub-arrays or not, only useful when size
of the sub-arrays are 1 on the `axis`. Default is False.
Returns
-------
NDArray
A created array.
"""
indices = []
axis_size = ary.shape[axis]
if isinstance(indices_or_sections, int):
sections = indices_or_sections
if axis_size % sections:
raise ValueError('array split does not result in an equal division')
section_size = int(axis_size / sections)
indices = [i * section_size for i in range(sections)]
elif isinstance(indices_or_sections, tuple):
indices = [0] + list(indices_or_sections)
else:
raise ValueError('indices_or_sections must either int or tuple of ints')
return _internal._split_v2(ary, indices, axis, squeeze_axis) | [
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train | to_dlpack_for_read | Returns a reference view of NDArray that represents as DLManagedTensor until
all previous write operations on the current array are finished.
Parameters
----------
data: NDArray
input data.
Returns
-------
PyCapsule (the pointer of DLManagedTensor)
a reference view of NDArray that represents as DLManagedTensor.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.to_dlpack_for_read(x)
>>> type(y)
<class 'PyCapsule'>
>>> z = mx.nd.from_dlpack(y)
>>> z
[[1. 1. 1.]
[1. 1. 1.]]
<NDArray 2x3 @cpu(0)> | python/mxnet/ndarray/ndarray.py | def to_dlpack_for_read(data):
"""Returns a reference view of NDArray that represents as DLManagedTensor until
all previous write operations on the current array are finished.
Parameters
----------
data: NDArray
input data.
Returns
-------
PyCapsule (the pointer of DLManagedTensor)
a reference view of NDArray that represents as DLManagedTensor.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.to_dlpack_for_read(x)
>>> type(y)
<class 'PyCapsule'>
>>> z = mx.nd.from_dlpack(y)
>>> z
[[1. 1. 1.]
[1. 1. 1.]]
<NDArray 2x3 @cpu(0)>
"""
data.wait_to_read()
dlpack = DLPackHandle()
check_call(_LIB.MXNDArrayToDLPack(data.handle, ctypes.byref(dlpack)))
return ctypes.pythonapi.PyCapsule_New(dlpack, _c_str_dltensor, _c_dlpack_deleter) | def to_dlpack_for_read(data):
"""Returns a reference view of NDArray that represents as DLManagedTensor until
all previous write operations on the current array are finished.
Parameters
----------
data: NDArray
input data.
Returns
-------
PyCapsule (the pointer of DLManagedTensor)
a reference view of NDArray that represents as DLManagedTensor.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.to_dlpack_for_read(x)
>>> type(y)
<class 'PyCapsule'>
>>> z = mx.nd.from_dlpack(y)
>>> z
[[1. 1. 1.]
[1. 1. 1.]]
<NDArray 2x3 @cpu(0)>
"""
data.wait_to_read()
dlpack = DLPackHandle()
check_call(_LIB.MXNDArrayToDLPack(data.handle, ctypes.byref(dlpack)))
return ctypes.pythonapi.PyCapsule_New(dlpack, _c_str_dltensor, _c_dlpack_deleter) | [
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train | to_dlpack_for_write | Returns a reference view of NDArray that represents as DLManagedTensor until
all previous read/write operations on the current array are finished.
Parameters
----------
data: NDArray
input data.
Returns
-------
PyCapsule (the pointer of DLManagedTensor)
a reference view of NDArray that represents as DLManagedTensor.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> w = mx.nd.to_dlpack_for_write(x)
>>> type(w)
<class 'PyCapsule'>
>>> u = mx.nd.from_dlpack(w)
>>> u += 1
>>> x
[[2. 2. 2.]
[2. 2. 2.]]
<NDArray 2x3 @cpu(0)> | python/mxnet/ndarray/ndarray.py | def to_dlpack_for_write(data):
"""Returns a reference view of NDArray that represents as DLManagedTensor until
all previous read/write operations on the current array are finished.
Parameters
----------
data: NDArray
input data.
Returns
-------
PyCapsule (the pointer of DLManagedTensor)
a reference view of NDArray that represents as DLManagedTensor.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> w = mx.nd.to_dlpack_for_write(x)
>>> type(w)
<class 'PyCapsule'>
>>> u = mx.nd.from_dlpack(w)
>>> u += 1
>>> x
[[2. 2. 2.]
[2. 2. 2.]]
<NDArray 2x3 @cpu(0)>
"""
check_call(_LIB.MXNDArrayWaitToWrite(data.handle))
dlpack = DLPackHandle()
check_call(_LIB.MXNDArrayToDLPack(data.handle, ctypes.byref(dlpack)))
return ctypes.pythonapi.PyCapsule_New(dlpack, _c_str_dltensor, _c_dlpack_deleter) | def to_dlpack_for_write(data):
"""Returns a reference view of NDArray that represents as DLManagedTensor until
all previous read/write operations on the current array are finished.
Parameters
----------
data: NDArray
input data.
Returns
-------
PyCapsule (the pointer of DLManagedTensor)
a reference view of NDArray that represents as DLManagedTensor.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> w = mx.nd.to_dlpack_for_write(x)
>>> type(w)
<class 'PyCapsule'>
>>> u = mx.nd.from_dlpack(w)
>>> u += 1
>>> x
[[2. 2. 2.]
[2. 2. 2.]]
<NDArray 2x3 @cpu(0)>
"""
check_call(_LIB.MXNDArrayWaitToWrite(data.handle))
dlpack = DLPackHandle()
check_call(_LIB.MXNDArrayToDLPack(data.handle, ctypes.byref(dlpack)))
return ctypes.pythonapi.PyCapsule_New(dlpack, _c_str_dltensor, _c_dlpack_deleter) | [
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train | from_dlpack | Returns a NDArray backed by a dlpack tensor.
Parameters
----------
dlpack: PyCapsule (the pointer of DLManagedTensor)
input data
Returns
-------
NDArray
a NDArray backed by a dlpack tensor
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.to_dlpack_for_read(x)
>>> type(y)
<class 'PyCapsule'>
>>> z = mx.nd.from_dlpack(y)
>>> type(z)
<class 'mxnet.ndarray.ndarray.NDArray'>
>>> z
[[ 1. 1. 1.]
[ 1. 1. 1.]]
<NDArray 2x3 @cpu(0)>
>>> w = mx.nd.to_dlpack_for_write(x)
>>> type(w)
<class 'PyCapsule'>
>>> u = mx.nd.from_dlpack(w)
>>> u += 1
>>> x
[[2. 2. 2.]
[2. 2. 2.]]
<NDArray 2x3 @cpu(0)> | python/mxnet/ndarray/ndarray.py | def from_dlpack(dlpack):
"""Returns a NDArray backed by a dlpack tensor.
Parameters
----------
dlpack: PyCapsule (the pointer of DLManagedTensor)
input data
Returns
-------
NDArray
a NDArray backed by a dlpack tensor
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.to_dlpack_for_read(x)
>>> type(y)
<class 'PyCapsule'>
>>> z = mx.nd.from_dlpack(y)
>>> type(z)
<class 'mxnet.ndarray.ndarray.NDArray'>
>>> z
[[ 1. 1. 1.]
[ 1. 1. 1.]]
<NDArray 2x3 @cpu(0)>
>>> w = mx.nd.to_dlpack_for_write(x)
>>> type(w)
<class 'PyCapsule'>
>>> u = mx.nd.from_dlpack(w)
>>> u += 1
>>> x
[[2. 2. 2.]
[2. 2. 2.]]
<NDArray 2x3 @cpu(0)>
"""
handle = NDArrayHandle()
dlpack = ctypes.py_object(dlpack)
assert ctypes.pythonapi.PyCapsule_IsValid(dlpack, _c_str_dltensor), ValueError(
'Invalid DLPack Tensor. DLTensor capsules can be consumed only once.')
dlpack_handle = ctypes.c_void_p(ctypes.pythonapi.PyCapsule_GetPointer(dlpack, _c_str_dltensor))
check_call(_LIB.MXNDArrayFromDLPack(dlpack_handle, ctypes.byref(handle)))
# Rename PyCapsule (DLPack)
ctypes.pythonapi.PyCapsule_SetName(dlpack, _c_str_used_dltensor)
# delete the deleter of the old dlpack
ctypes.pythonapi.PyCapsule_SetDestructor(dlpack, None)
return NDArray(handle=handle) | def from_dlpack(dlpack):
"""Returns a NDArray backed by a dlpack tensor.
Parameters
----------
dlpack: PyCapsule (the pointer of DLManagedTensor)
input data
Returns
-------
NDArray
a NDArray backed by a dlpack tensor
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.to_dlpack_for_read(x)
>>> type(y)
<class 'PyCapsule'>
>>> z = mx.nd.from_dlpack(y)
>>> type(z)
<class 'mxnet.ndarray.ndarray.NDArray'>
>>> z
[[ 1. 1. 1.]
[ 1. 1. 1.]]
<NDArray 2x3 @cpu(0)>
>>> w = mx.nd.to_dlpack_for_write(x)
>>> type(w)
<class 'PyCapsule'>
>>> u = mx.nd.from_dlpack(w)
>>> u += 1
>>> x
[[2. 2. 2.]
[2. 2. 2.]]
<NDArray 2x3 @cpu(0)>
"""
handle = NDArrayHandle()
dlpack = ctypes.py_object(dlpack)
assert ctypes.pythonapi.PyCapsule_IsValid(dlpack, _c_str_dltensor), ValueError(
'Invalid DLPack Tensor. DLTensor capsules can be consumed only once.')
dlpack_handle = ctypes.c_void_p(ctypes.pythonapi.PyCapsule_GetPointer(dlpack, _c_str_dltensor))
check_call(_LIB.MXNDArrayFromDLPack(dlpack_handle, ctypes.byref(handle)))
# Rename PyCapsule (DLPack)
ctypes.pythonapi.PyCapsule_SetName(dlpack, _c_str_used_dltensor)
# delete the deleter of the old dlpack
ctypes.pythonapi.PyCapsule_SetDestructor(dlpack, None)
return NDArray(handle=handle) | [
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train | from_numpy | Returns an MXNet's NDArray backed by Numpy's ndarray.
Parameters
----------
ndarray: numpy.ndarray
input data
zero_copy: bool
Whether we use DLPack's zero-copy conversion to convert to MXNet's NDArray.
This is only available for c-contiguous arrays, i.e. array.flags[C_CONTIGUOUS] == True.
Returns
-------
NDArray
a NDArray backed by a dlpack tensor | python/mxnet/ndarray/ndarray.py | def from_numpy(ndarray, zero_copy=True):
"""Returns an MXNet's NDArray backed by Numpy's ndarray.
Parameters
----------
ndarray: numpy.ndarray
input data
zero_copy: bool
Whether we use DLPack's zero-copy conversion to convert to MXNet's NDArray.
This is only available for c-contiguous arrays, i.e. array.flags[C_CONTIGUOUS] == True.
Returns
-------
NDArray
a NDArray backed by a dlpack tensor
"""
def _make_manager_ctx(obj):
pyobj = ctypes.py_object(obj)
void_p = ctypes.c_void_p.from_buffer(pyobj)
ctypes.pythonapi.Py_IncRef(pyobj)
return void_p
def _make_dl_tensor(array):
if str(array.dtype) not in DLDataType.TYPE_MAP:
raise ValueError(str(array.dtype) + " is not supported.")
dl_tensor = DLTensor()
dl_tensor.data = array.ctypes.data_as(ctypes.c_void_p)
dl_tensor.ctx = DLContext(1, 0)
dl_tensor.ndim = array.ndim
dl_tensor.dtype = DLDataType.TYPE_MAP[str(array.dtype)]
dl_tensor.shape = array.ctypes.shape_as(ctypes.c_int64)
dl_tensor.strides = None
dl_tensor.byte_offset = 0
return dl_tensor
def _make_dl_managed_tensor(array):
c_obj = DLManagedTensor()
c_obj.dl_tensor = _make_dl_tensor(array)
c_obj.manager_ctx = _make_manager_ctx(array)
c_obj.deleter = dl_managed_tensor_deleter
return c_obj
if not zero_copy:
return array(ndarray, dtype=ndarray.dtype)
if not ndarray.flags['C_CONTIGUOUS']:
raise ValueError("Only c-contiguous arrays are supported for zero-copy")
c_obj = _make_dl_managed_tensor(ndarray)
address = ctypes.addressof(c_obj)
address = ctypes.cast(address, ctypes.c_void_p)
handle = NDArrayHandle()
check_call(_LIB.MXNDArrayFromDLPack(address, ctypes.byref(handle)))
return NDArray(handle=handle) | def from_numpy(ndarray, zero_copy=True):
"""Returns an MXNet's NDArray backed by Numpy's ndarray.
Parameters
----------
ndarray: numpy.ndarray
input data
zero_copy: bool
Whether we use DLPack's zero-copy conversion to convert to MXNet's NDArray.
This is only available for c-contiguous arrays, i.e. array.flags[C_CONTIGUOUS] == True.
Returns
-------
NDArray
a NDArray backed by a dlpack tensor
"""
def _make_manager_ctx(obj):
pyobj = ctypes.py_object(obj)
void_p = ctypes.c_void_p.from_buffer(pyobj)
ctypes.pythonapi.Py_IncRef(pyobj)
return void_p
def _make_dl_tensor(array):
if str(array.dtype) not in DLDataType.TYPE_MAP:
raise ValueError(str(array.dtype) + " is not supported.")
dl_tensor = DLTensor()
dl_tensor.data = array.ctypes.data_as(ctypes.c_void_p)
dl_tensor.ctx = DLContext(1, 0)
dl_tensor.ndim = array.ndim
dl_tensor.dtype = DLDataType.TYPE_MAP[str(array.dtype)]
dl_tensor.shape = array.ctypes.shape_as(ctypes.c_int64)
dl_tensor.strides = None
dl_tensor.byte_offset = 0
return dl_tensor
def _make_dl_managed_tensor(array):
c_obj = DLManagedTensor()
c_obj.dl_tensor = _make_dl_tensor(array)
c_obj.manager_ctx = _make_manager_ctx(array)
c_obj.deleter = dl_managed_tensor_deleter
return c_obj
if not zero_copy:
return array(ndarray, dtype=ndarray.dtype)
if not ndarray.flags['C_CONTIGUOUS']:
raise ValueError("Only c-contiguous arrays are supported for zero-copy")
c_obj = _make_dl_managed_tensor(ndarray)
address = ctypes.addressof(c_obj)
address = ctypes.cast(address, ctypes.c_void_p)
handle = NDArrayHandle()
check_call(_LIB.MXNDArrayFromDLPack(address, ctypes.byref(handle)))
return NDArray(handle=handle) | [
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train | NDArray._get_index_nd | Returns an index array for use in scatter_nd and gather_nd. | python/mxnet/ndarray/ndarray.py | def _get_index_nd(self, key):
"""Returns an index array for use in scatter_nd and gather_nd."""
def _is_advanced_index(index):
"""The definition of advanced index here includes integers as well, while
integers are considered as basic index type when the key contains only
slices and integers."""
return not isinstance(index, py_slice)
if isinstance(key, (NDArray, np.ndarray, list, integer_types, py_slice)):
key = (key,)
assert isinstance(key, tuple),\
'index=%s must be a NDArray, or np.ndarray, or list, or tuple ' \
' type to use advanced indexing, received type=%s' % (str(key), str(type(key)))
assert len(key) > 0, "Cannot slice with empty indices"
shape = self.shape
assert len(shape) >= len(key),\
"Slicing dimensions exceeds array dimensions, %d vs %d" % (len(key), len(shape))
indices = []
dtype = 'int32' # index data type passed to gather_nd op
need_broadcast = (len(key) != 1)
advanced_indices = [] # include list, NDArray, np.ndarray, integer
basic_indices = [] # include only slices
advanced_index_bshape = None # final advanced index shape
for i, idx_i in enumerate(key):
is_advanced_index = True
if isinstance(idx_i, (np.ndarray, list, tuple)):
idx_i = array(idx_i, ctx=self.context, dtype=dtype)
advanced_indices.append(i)
elif isinstance(idx_i, py_slice):
start, stop, step = _get_index_range(idx_i.start, idx_i.stop, shape[i], idx_i.step)
idx_i = arange(start, stop, step, ctx=self.context, dtype=dtype)
basic_indices.append(i)
is_advanced_index = False
elif isinstance(idx_i, integer_types):
start, stop, step = _get_index_range(idx_i, idx_i+1, shape[i], 1)
idx_i = arange(start, stop, step, ctx=self.context, dtype=dtype)
advanced_indices.append(i)
elif isinstance(idx_i, NDArray):
if dtype != idx_i.dtype:
idx_i = idx_i.astype(dtype)
advanced_indices.append(i)
else:
raise IndexError('Indexing NDArray with index=%s of type=%s is not supported'
% (str(key), str(type(key))))
if is_advanced_index:
if advanced_index_bshape is None:
advanced_index_bshape = idx_i.shape
elif advanced_index_bshape != idx_i.shape:
need_broadcast = True
advanced_index_bshape = _get_broadcast_shape(advanced_index_bshape, idx_i.shape)
indices.append(idx_i)
# Get final index shape for gather_nd. See the following reference
# for determining the output array shape.
# https://docs.scipy.org/doc/numpy-1.13.0/reference/arrays.indexing.html#combining-advanced-and-basic-indexing # pylint: disable=line-too-long
if len(advanced_indices) == 0:
raise ValueError('Advanced index tuple must contain at least one of the following types:'
' list, tuple, NDArray, np.ndarray, integer, received index=%s' % key)
# determine the output array's shape by checking whether advanced_indices are all adjacent
# or separated by slices
advanced_indices_adjacent = True
for i in range(0, len(advanced_indices)-1):
if advanced_indices[i] + 1 != advanced_indices[i+1]:
advanced_indices_adjacent = False
break
index_bshape_list = [] # index broadcasted shape
if advanced_indices_adjacent:
for i in range(0, advanced_indices[0]):
index_bshape_list.extend(indices[i].shape)
if not need_broadcast and indices[i].shape != advanced_index_bshape:
need_broadcast = True
index_bshape_list.extend(advanced_index_bshape)
for i in range(advanced_indices[-1]+1, len(indices)):
if not need_broadcast and indices[i].shape != advanced_index_bshape:
need_broadcast = True
index_bshape_list.extend(indices[i].shape)
else:
index_bshape_list.extend(advanced_index_bshape)
for i in basic_indices:
index_bshape_list.extend(indices[i].shape)
if not need_broadcast and indices[i].shape != advanced_index_bshape:
need_broadcast = True
index_bshape = tuple(index_bshape_list)
# Need to broadcast all ndarrays in indices to the final shape.
# For example, suppose an array has shape=(5, 6, 7, 8) and
# key=(slice(1, 5), [[1, 2]], slice(2, 5), [1]).
# Since key[1] and key[3] are two advanced indices here and they are
# separated by basic indices key[0] and key[2], the output shape
# is (1, 2, 4, 3), where the first two elements come from the shape
# that key[1] and key[3] should broadcast to, which is (1, 2), and
# the last two elements come from the shape of two basic indices.
# In order to broadcast all basic and advanced indices to the output shape,
# we need to reshape them based on their axis. For example, to broadcast key[0],
# with shape=(4,), we first need to reshape it into (1, 1, 4, 1), and then
# broadcast the reshaped array to (1, 2, 4, 3); to broadcast key[1], we first
# reshape it into (1, 2, 1, 1), then broadcast the reshaped array to (1, 2, 4, 3).
if need_broadcast:
broadcasted_indices = []
idx_rshape = [1] * len(index_bshape)
if advanced_indices_adjacent:
advanced_index_bshape_start = advanced_indices[0] # start index of advanced_index_bshape in index_shape
advanced_index_bshape_stop = advanced_index_bshape_start + len(advanced_index_bshape)
for i, idx in enumerate(key):
if _is_advanced_index(idx):
k = advanced_index_bshape_stop
# find the reshaped shape for indices[i]
for dim_size in indices[i].shape[::-1]:
k -= 1
idx_rshape[k] = dim_size
else:
if i < advanced_indices[0]: # slice is on the left side of advanced indices
idx_rshape[i] = indices[i].shape[0]
elif i > advanced_indices[-1]: # slice is on the right side of advanced indices
idx_rshape[i-len(key)] = indices[i].shape[0]
else:
raise ValueError('basic index i=%d cannot be between advanced index i=%d and i=%d'
% (i, advanced_indices[0], advanced_indices[-1]))
# broadcast current index to the final shape
broadcasted_indices.append(indices[i].reshape(tuple(idx_rshape)).broadcast_to(index_bshape))
# reset idx_rshape to ones
for j, _ in enumerate(idx_rshape):
idx_rshape[j] = 1
else:
basic_index_offset = len(advanced_index_bshape)
for i, idx in enumerate(key):
if _is_advanced_index(idx):
k = len(advanced_index_bshape)
for dim_size in indices[i].shape[::-1]:
k -= 1
idx_rshape[k] = dim_size
else:
idx_rshape[basic_index_offset] = indices[i].shape[0]
basic_index_offset += 1
# broadcast current index to the final shape
broadcasted_indices.append(indices[i].reshape(tuple(idx_rshape)).broadcast_to(index_bshape))
# reset idx_rshape to ones
for j, _ in enumerate(idx_rshape):
idx_rshape[j] = 1
indices = broadcasted_indices
return op.stack(*indices) | def _get_index_nd(self, key):
"""Returns an index array for use in scatter_nd and gather_nd."""
def _is_advanced_index(index):
"""The definition of advanced index here includes integers as well, while
integers are considered as basic index type when the key contains only
slices and integers."""
return not isinstance(index, py_slice)
if isinstance(key, (NDArray, np.ndarray, list, integer_types, py_slice)):
key = (key,)
assert isinstance(key, tuple),\
'index=%s must be a NDArray, or np.ndarray, or list, or tuple ' \
' type to use advanced indexing, received type=%s' % (str(key), str(type(key)))
assert len(key) > 0, "Cannot slice with empty indices"
shape = self.shape
assert len(shape) >= len(key),\
"Slicing dimensions exceeds array dimensions, %d vs %d" % (len(key), len(shape))
indices = []
dtype = 'int32' # index data type passed to gather_nd op
need_broadcast = (len(key) != 1)
advanced_indices = [] # include list, NDArray, np.ndarray, integer
basic_indices = [] # include only slices
advanced_index_bshape = None # final advanced index shape
for i, idx_i in enumerate(key):
is_advanced_index = True
if isinstance(idx_i, (np.ndarray, list, tuple)):
idx_i = array(idx_i, ctx=self.context, dtype=dtype)
advanced_indices.append(i)
elif isinstance(idx_i, py_slice):
start, stop, step = _get_index_range(idx_i.start, idx_i.stop, shape[i], idx_i.step)
idx_i = arange(start, stop, step, ctx=self.context, dtype=dtype)
basic_indices.append(i)
is_advanced_index = False
elif isinstance(idx_i, integer_types):
start, stop, step = _get_index_range(idx_i, idx_i+1, shape[i], 1)
idx_i = arange(start, stop, step, ctx=self.context, dtype=dtype)
advanced_indices.append(i)
elif isinstance(idx_i, NDArray):
if dtype != idx_i.dtype:
idx_i = idx_i.astype(dtype)
advanced_indices.append(i)
else:
raise IndexError('Indexing NDArray with index=%s of type=%s is not supported'
% (str(key), str(type(key))))
if is_advanced_index:
if advanced_index_bshape is None:
advanced_index_bshape = idx_i.shape
elif advanced_index_bshape != idx_i.shape:
need_broadcast = True
advanced_index_bshape = _get_broadcast_shape(advanced_index_bshape, idx_i.shape)
indices.append(idx_i)
# Get final index shape for gather_nd. See the following reference
# for determining the output array shape.
# https://docs.scipy.org/doc/numpy-1.13.0/reference/arrays.indexing.html#combining-advanced-and-basic-indexing # pylint: disable=line-too-long
if len(advanced_indices) == 0:
raise ValueError('Advanced index tuple must contain at least one of the following types:'
' list, tuple, NDArray, np.ndarray, integer, received index=%s' % key)
# determine the output array's shape by checking whether advanced_indices are all adjacent
# or separated by slices
advanced_indices_adjacent = True
for i in range(0, len(advanced_indices)-1):
if advanced_indices[i] + 1 != advanced_indices[i+1]:
advanced_indices_adjacent = False
break
index_bshape_list = [] # index broadcasted shape
if advanced_indices_adjacent:
for i in range(0, advanced_indices[0]):
index_bshape_list.extend(indices[i].shape)
if not need_broadcast and indices[i].shape != advanced_index_bshape:
need_broadcast = True
index_bshape_list.extend(advanced_index_bshape)
for i in range(advanced_indices[-1]+1, len(indices)):
if not need_broadcast and indices[i].shape != advanced_index_bshape:
need_broadcast = True
index_bshape_list.extend(indices[i].shape)
else:
index_bshape_list.extend(advanced_index_bshape)
for i in basic_indices:
index_bshape_list.extend(indices[i].shape)
if not need_broadcast and indices[i].shape != advanced_index_bshape:
need_broadcast = True
index_bshape = tuple(index_bshape_list)
# Need to broadcast all ndarrays in indices to the final shape.
# For example, suppose an array has shape=(5, 6, 7, 8) and
# key=(slice(1, 5), [[1, 2]], slice(2, 5), [1]).
# Since key[1] and key[3] are two advanced indices here and they are
# separated by basic indices key[0] and key[2], the output shape
# is (1, 2, 4, 3), where the first two elements come from the shape
# that key[1] and key[3] should broadcast to, which is (1, 2), and
# the last two elements come from the shape of two basic indices.
# In order to broadcast all basic and advanced indices to the output shape,
# we need to reshape them based on their axis. For example, to broadcast key[0],
# with shape=(4,), we first need to reshape it into (1, 1, 4, 1), and then
# broadcast the reshaped array to (1, 2, 4, 3); to broadcast key[1], we first
# reshape it into (1, 2, 1, 1), then broadcast the reshaped array to (1, 2, 4, 3).
if need_broadcast:
broadcasted_indices = []
idx_rshape = [1] * len(index_bshape)
if advanced_indices_adjacent:
advanced_index_bshape_start = advanced_indices[0] # start index of advanced_index_bshape in index_shape
advanced_index_bshape_stop = advanced_index_bshape_start + len(advanced_index_bshape)
for i, idx in enumerate(key):
if _is_advanced_index(idx):
k = advanced_index_bshape_stop
# find the reshaped shape for indices[i]
for dim_size in indices[i].shape[::-1]:
k -= 1
idx_rshape[k] = dim_size
else:
if i < advanced_indices[0]: # slice is on the left side of advanced indices
idx_rshape[i] = indices[i].shape[0]
elif i > advanced_indices[-1]: # slice is on the right side of advanced indices
idx_rshape[i-len(key)] = indices[i].shape[0]
else:
raise ValueError('basic index i=%d cannot be between advanced index i=%d and i=%d'
% (i, advanced_indices[0], advanced_indices[-1]))
# broadcast current index to the final shape
broadcasted_indices.append(indices[i].reshape(tuple(idx_rshape)).broadcast_to(index_bshape))
# reset idx_rshape to ones
for j, _ in enumerate(idx_rshape):
idx_rshape[j] = 1
else:
basic_index_offset = len(advanced_index_bshape)
for i, idx in enumerate(key):
if _is_advanced_index(idx):
k = len(advanced_index_bshape)
for dim_size in indices[i].shape[::-1]:
k -= 1
idx_rshape[k] = dim_size
else:
idx_rshape[basic_index_offset] = indices[i].shape[0]
basic_index_offset += 1
# broadcast current index to the final shape
broadcasted_indices.append(indices[i].reshape(tuple(idx_rshape)).broadcast_to(index_bshape))
# reset idx_rshape to ones
for j, _ in enumerate(idx_rshape):
idx_rshape[j] = 1
indices = broadcasted_indices
return op.stack(*indices) | [
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train | NDArray._prepare_value_nd | Given value and vshape, create an `NDArray` from value with the same
context and dtype as the current one and broadcast it to vshape. | python/mxnet/ndarray/ndarray.py | def _prepare_value_nd(self, value, vshape):
"""Given value and vshape, create an `NDArray` from value with the same
context and dtype as the current one and broadcast it to vshape."""
if isinstance(value, numeric_types):
value_nd = full(shape=vshape, val=value, ctx=self.context, dtype=self.dtype)
elif isinstance(value, NDArray):
value_nd = value.as_in_context(self.context)
if value_nd.dtype != self.dtype:
value_nd = value_nd.astype(self.dtype)
else:
try:
value_nd = array(value, ctx=self.context, dtype=self.dtype)
except:
raise TypeError('NDArray does not support assignment with non-array-like'
' object %s of type %s' % (str(value), str(type(value))))
if value_nd.shape != vshape:
value_nd = value_nd.broadcast_to(vshape)
return value_nd | def _prepare_value_nd(self, value, vshape):
"""Given value and vshape, create an `NDArray` from value with the same
context and dtype as the current one and broadcast it to vshape."""
if isinstance(value, numeric_types):
value_nd = full(shape=vshape, val=value, ctx=self.context, dtype=self.dtype)
elif isinstance(value, NDArray):
value_nd = value.as_in_context(self.context)
if value_nd.dtype != self.dtype:
value_nd = value_nd.astype(self.dtype)
else:
try:
value_nd = array(value, ctx=self.context, dtype=self.dtype)
except:
raise TypeError('NDArray does not support assignment with non-array-like'
' object %s of type %s' % (str(value), str(type(value))))
if value_nd.shape != vshape:
value_nd = value_nd.broadcast_to(vshape)
return value_nd | [
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train | NDArray._set_nd_basic_indexing | This function is called by __setitem__ when key is a basic index, i.e.
an integer, or a slice, or a tuple of integers and slices. No restrictions
on the values of slices' steps. | python/mxnet/ndarray/ndarray.py | def _set_nd_basic_indexing(self, key, value):
"""This function is called by __setitem__ when key is a basic index, i.e.
an integer, or a slice, or a tuple of integers and slices. No restrictions
on the values of slices' steps."""
shape = self.shape
if isinstance(key, integer_types):
if key < 0:
key += shape[0]
if key < 0 or key >= shape[0]:
if key < 0:
key -= shape[0]
raise IndexError('index %d is out of bounds for axis 0 with size %d'
% (key, shape[0]))
key = py_slice(key, key+1) # key must be >= 0 here
if isinstance(key, py_slice):
assign_to_self = key.step is None or key.step == 1
assign_to_self &= key.start is None or key.start == 0
assign_to_self &= key.stop is None or key.stop == shape[0]
if assign_to_self: # trivial case, assign value to self
if isinstance(value, NDArray):
if value.handle is not self.handle:
if value.shape != shape:
value = value.broadcast_to(shape)
value.copyto(self)
elif isinstance(value, numeric_types):
_internal._full(shape=shape, ctx=self.context,
dtype=self.dtype, value=float(value), out=self)
elif isinstance(value, (np.ndarray, np.generic)):
if isinstance(value, np.generic) or value.shape != shape:
value = np.broadcast_to(value, shape)
self._sync_copyfrom(value)
else: # value might be a list or a tuple
value_nd = self._prepare_value_nd(value, shape)
value_nd.copyto(self)
return
else: # non-trivial case, use _slice_assign or _slice_assign_scalar
key = (key,)
assert isinstance(key, tuple), "key=%s must be a tuple of slices and integers" % str(key)
assert len(key) <= len(shape), "Indexing dimensions exceed array dimensions, %d vs %d"\
% (len(key), len(shape))
begin = []
end = []
steps = []
oshape = [] # output shape of slice using key
vshape = [] # value shape of data[key]
for i, slice_i in enumerate(key):
dim_size = 1
if isinstance(slice_i, py_slice):
begin.append(slice_i.start)
end.append(slice_i.stop)
steps.append(slice_i.step)
start, stop, step = _get_index_range(slice_i.start, slice_i.stop,
shape[i], slice_i.step)
dim_size = _get_dim_size(start, stop, step)
vshape.append(dim_size)
elif isinstance(slice_i, integer_types):
begin.append(slice_i)
end.append(slice_i+1 if slice_i != -1 else self.shape[i])
steps.append(1)
else:
raise ValueError("basic indexing does not support index=%s of type=%s"
% (str(slice_i), str(type(slice_i))))
oshape.append(dim_size)
oshape.extend(shape[len(key):])
vshape.extend(shape[len(key):])
# if key contains all integers, vshape should be (1,)
if len(vshape) == 0:
vshape.append(1)
oshape = tuple(oshape)
vshape = tuple(vshape)
if isinstance(value, numeric_types):
_internal._slice_assign_scalar(self, out=self, begin=begin, end=end,
step=steps, scalar=float(value))
else:
value_nd = self._prepare_value_nd(value, vshape)
if vshape != oshape:
value_nd = value_nd.reshape(oshape)
_internal._slice_assign(self, value_nd, begin, end, steps, out=self) | def _set_nd_basic_indexing(self, key, value):
"""This function is called by __setitem__ when key is a basic index, i.e.
an integer, or a slice, or a tuple of integers and slices. No restrictions
on the values of slices' steps."""
shape = self.shape
if isinstance(key, integer_types):
if key < 0:
key += shape[0]
if key < 0 or key >= shape[0]:
if key < 0:
key -= shape[0]
raise IndexError('index %d is out of bounds for axis 0 with size %d'
% (key, shape[0]))
key = py_slice(key, key+1) # key must be >= 0 here
if isinstance(key, py_slice):
assign_to_self = key.step is None or key.step == 1
assign_to_self &= key.start is None or key.start == 0
assign_to_self &= key.stop is None or key.stop == shape[0]
if assign_to_self: # trivial case, assign value to self
if isinstance(value, NDArray):
if value.handle is not self.handle:
if value.shape != shape:
value = value.broadcast_to(shape)
value.copyto(self)
elif isinstance(value, numeric_types):
_internal._full(shape=shape, ctx=self.context,
dtype=self.dtype, value=float(value), out=self)
elif isinstance(value, (np.ndarray, np.generic)):
if isinstance(value, np.generic) or value.shape != shape:
value = np.broadcast_to(value, shape)
self._sync_copyfrom(value)
else: # value might be a list or a tuple
value_nd = self._prepare_value_nd(value, shape)
value_nd.copyto(self)
return
else: # non-trivial case, use _slice_assign or _slice_assign_scalar
key = (key,)
assert isinstance(key, tuple), "key=%s must be a tuple of slices and integers" % str(key)
assert len(key) <= len(shape), "Indexing dimensions exceed array dimensions, %d vs %d"\
% (len(key), len(shape))
begin = []
end = []
steps = []
oshape = [] # output shape of slice using key
vshape = [] # value shape of data[key]
for i, slice_i in enumerate(key):
dim_size = 1
if isinstance(slice_i, py_slice):
begin.append(slice_i.start)
end.append(slice_i.stop)
steps.append(slice_i.step)
start, stop, step = _get_index_range(slice_i.start, slice_i.stop,
shape[i], slice_i.step)
dim_size = _get_dim_size(start, stop, step)
vshape.append(dim_size)
elif isinstance(slice_i, integer_types):
begin.append(slice_i)
end.append(slice_i+1 if slice_i != -1 else self.shape[i])
steps.append(1)
else:
raise ValueError("basic indexing does not support index=%s of type=%s"
% (str(slice_i), str(type(slice_i))))
oshape.append(dim_size)
oshape.extend(shape[len(key):])
vshape.extend(shape[len(key):])
# if key contains all integers, vshape should be (1,)
if len(vshape) == 0:
vshape.append(1)
oshape = tuple(oshape)
vshape = tuple(vshape)
if isinstance(value, numeric_types):
_internal._slice_assign_scalar(self, out=self, begin=begin, end=end,
step=steps, scalar=float(value))
else:
value_nd = self._prepare_value_nd(value, vshape)
if vshape != oshape:
value_nd = value_nd.reshape(oshape)
_internal._slice_assign(self, value_nd, begin, end, steps, out=self) | [
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train | NDArray._set_nd_advanced_indexing | This function is called by __setitem__ when key is an advanced index. | python/mxnet/ndarray/ndarray.py | def _set_nd_advanced_indexing(self, key, value):
"""This function is called by __setitem__ when key is an advanced index."""
indices = self._get_index_nd(key)
vshape = _get_oshape_of_gather_nd_op(self.shape, indices.shape)
value_nd = self._prepare_value_nd(value, vshape)
_internal._scatter_set_nd(lhs=self, rhs=value_nd, indices=indices,
shape=self.shape, out=self) | def _set_nd_advanced_indexing(self, key, value):
"""This function is called by __setitem__ when key is an advanced index."""
indices = self._get_index_nd(key)
vshape = _get_oshape_of_gather_nd_op(self.shape, indices.shape)
value_nd = self._prepare_value_nd(value, vshape)
_internal._scatter_set_nd(lhs=self, rhs=value_nd, indices=indices,
shape=self.shape, out=self) | [
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train | NDArray._get_nd_basic_indexing | This function is called when key is a slice, or an integer,
or a tuple of slices or integers | python/mxnet/ndarray/ndarray.py | def _get_nd_basic_indexing(self, key):
"""This function is called when key is a slice, or an integer,
or a tuple of slices or integers"""
shape = self.shape
if isinstance(key, integer_types):
if key > shape[0] - 1:
raise IndexError(
'index {} is out of bounds for axis 0 with size {}'.format(
key, shape[0]))
return self._at(key)
elif isinstance(key, py_slice):
if key.step is not None and key.step != 1:
if key.step == 0:
raise ValueError("slice step cannot be zero")
return op.slice(self, begin=(key.start,), end=(key.stop,), step=(key.step,))
elif key.start is not None or key.stop is not None:
return self._slice(key.start, key.stop)
else:
return self
if not isinstance(key, tuple):
raise ValueError('index=%s must be a slice, or an ineger, or a tuple'
' of slices and integers to use basic indexing, received type=%s'
% (str(key), str(type(key))))
assert len(key) != 0, 'basic index cannot be an empty tuple'
begin = []
end = []
step = []
kept_axes = [] # axes where slice_i is a slice
i = -1
for i, slice_i in enumerate(key):
if isinstance(slice_i, integer_types):
begin.append(slice_i)
end.append(slice_i+1 if slice_i != -1 else self.shape[i])
step.append(1)
elif isinstance(slice_i, py_slice):
if slice_i.step == 0:
raise ValueError('basic index=%s cannot have slice=%s with step = 0'
% (str(key), str(slice_i)))
begin.append(slice_i.start)
end.append(slice_i.stop)
step.append(slice_i.step)
kept_axes.append(i)
else:
raise ValueError('basic_indexing does not support slicing with '
'index=%s of type=%s.' % (str(slice_i), str(type(slice_i))))
kept_axes.extend(range(i+1, len(shape)))
sliced_nd = op.slice(self, begin, end, step)
if len(kept_axes) == len(shape):
return sliced_nd
# squeeze sliced_shape to remove the axes indexed by integers
oshape = []
sliced_shape = sliced_nd.shape
for axis in kept_axes:
oshape.append(sliced_shape[axis])
# if key is a tuple of integers, still need to keep 1 dim
# while in Numpy, the output will become an value instead of an ndarray
if len(oshape) == 0:
oshape.append(1)
oshape = tuple(oshape)
assert np.prod(oshape) == np.prod(sliced_shape), 'oshape=%s has different size'\
' than sliced_shape=%s'\
% (oshape, sliced_shape)
return sliced_nd.reshape(oshape) | def _get_nd_basic_indexing(self, key):
"""This function is called when key is a slice, or an integer,
or a tuple of slices or integers"""
shape = self.shape
if isinstance(key, integer_types):
if key > shape[0] - 1:
raise IndexError(
'index {} is out of bounds for axis 0 with size {}'.format(
key, shape[0]))
return self._at(key)
elif isinstance(key, py_slice):
if key.step is not None and key.step != 1:
if key.step == 0:
raise ValueError("slice step cannot be zero")
return op.slice(self, begin=(key.start,), end=(key.stop,), step=(key.step,))
elif key.start is not None or key.stop is not None:
return self._slice(key.start, key.stop)
else:
return self
if not isinstance(key, tuple):
raise ValueError('index=%s must be a slice, or an ineger, or a tuple'
' of slices and integers to use basic indexing, received type=%s'
% (str(key), str(type(key))))
assert len(key) != 0, 'basic index cannot be an empty tuple'
begin = []
end = []
step = []
kept_axes = [] # axes where slice_i is a slice
i = -1
for i, slice_i in enumerate(key):
if isinstance(slice_i, integer_types):
begin.append(slice_i)
end.append(slice_i+1 if slice_i != -1 else self.shape[i])
step.append(1)
elif isinstance(slice_i, py_slice):
if slice_i.step == 0:
raise ValueError('basic index=%s cannot have slice=%s with step = 0'
% (str(key), str(slice_i)))
begin.append(slice_i.start)
end.append(slice_i.stop)
step.append(slice_i.step)
kept_axes.append(i)
else:
raise ValueError('basic_indexing does not support slicing with '
'index=%s of type=%s.' % (str(slice_i), str(type(slice_i))))
kept_axes.extend(range(i+1, len(shape)))
sliced_nd = op.slice(self, begin, end, step)
if len(kept_axes) == len(shape):
return sliced_nd
# squeeze sliced_shape to remove the axes indexed by integers
oshape = []
sliced_shape = sliced_nd.shape
for axis in kept_axes:
oshape.append(sliced_shape[axis])
# if key is a tuple of integers, still need to keep 1 dim
# while in Numpy, the output will become an value instead of an ndarray
if len(oshape) == 0:
oshape.append(1)
oshape = tuple(oshape)
assert np.prod(oshape) == np.prod(sliced_shape), 'oshape=%s has different size'\
' than sliced_shape=%s'\
% (oshape, sliced_shape)
return sliced_nd.reshape(oshape) | [
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train | NDArray._sync_copyfrom | Performs a synchronized copy from the `source_array` to the current array.
This is called through ``x[:] = source_array``, where the `source_array`
is a `numpy.ndarray` or array-like object.
This function blocks until all the pending read/write operations with respect
to the current `NDArray` are finished and carry out the copy operation to the
current NDArray.
Parameters
----------
source_array : array_like
The data source we would like to copy from.
Example
-------
>>> a = mx.nd.array([1, 2])
>>> a.asnumpy()
array([ 1., 2.], dtype=float32)
>>> a[:] = np.array([3, 4])
>> a.asnumpy()
array([ 3., 4.], dtype=float32) | python/mxnet/ndarray/ndarray.py | def _sync_copyfrom(self, source_array):
"""Performs a synchronized copy from the `source_array` to the current array.
This is called through ``x[:] = source_array``, where the `source_array`
is a `numpy.ndarray` or array-like object.
This function blocks until all the pending read/write operations with respect
to the current `NDArray` are finished and carry out the copy operation to the
current NDArray.
Parameters
----------
source_array : array_like
The data source we would like to copy from.
Example
-------
>>> a = mx.nd.array([1, 2])
>>> a.asnumpy()
array([ 1., 2.], dtype=float32)
>>> a[:] = np.array([3, 4])
>> a.asnumpy()
array([ 3., 4.], dtype=float32)
"""
if not isinstance(source_array, np.ndarray):
try:
source_array = np.array(source_array, dtype=self.dtype)
except:
raise TypeError('array must consist of array-like data,' +
'type %s is not supported' % str(type(array)))
source_array = np.asarray(source_array, dtype=self.dtype, order='C')
if source_array.shape != self.shape:
raise ValueError('Shape inconsistent: expected %s vs got %s'%(
str(source_array.shape), str(self.shape)))
check_call(_LIB.MXNDArraySyncCopyFromCPU(
self.handle,
source_array.ctypes.data_as(ctypes.c_void_p),
ctypes.c_size_t(source_array.size))) | def _sync_copyfrom(self, source_array):
"""Performs a synchronized copy from the `source_array` to the current array.
This is called through ``x[:] = source_array``, where the `source_array`
is a `numpy.ndarray` or array-like object.
This function blocks until all the pending read/write operations with respect
to the current `NDArray` are finished and carry out the copy operation to the
current NDArray.
Parameters
----------
source_array : array_like
The data source we would like to copy from.
Example
-------
>>> a = mx.nd.array([1, 2])
>>> a.asnumpy()
array([ 1., 2.], dtype=float32)
>>> a[:] = np.array([3, 4])
>> a.asnumpy()
array([ 3., 4.], dtype=float32)
"""
if not isinstance(source_array, np.ndarray):
try:
source_array = np.array(source_array, dtype=self.dtype)
except:
raise TypeError('array must consist of array-like data,' +
'type %s is not supported' % str(type(array)))
source_array = np.asarray(source_array, dtype=self.dtype, order='C')
if source_array.shape != self.shape:
raise ValueError('Shape inconsistent: expected %s vs got %s'%(
str(source_array.shape), str(self.shape)))
check_call(_LIB.MXNDArraySyncCopyFromCPU(
self.handle,
source_array.ctypes.data_as(ctypes.c_void_p),
ctypes.c_size_t(source_array.size))) | [
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train | NDArray._slice | Returns a sliced NDArray that shares memory with the current one.
This is called through ``x[start:stop]``.
Parameters
----------
start : int
Starting inclusive index of slice in the first dim.
stop : int
Finishing exclusive index of slice in the first dim.
Returns
-------
`NDArray` sharing the memory with the current one sliced from
start to stop in the first dim.
Examples:
>>> a = mx.nd.array([[1,2], [3, 4], [5, 6], [7, 8]])
>>> a[1:2].asnumpy()
array([[ 3., 4.]], dtype=float32)
>>> a[1:1].asnumpy()
array([], shape=(0, 2), dtype=float32) | python/mxnet/ndarray/ndarray.py | def _slice(self, start, stop):
"""Returns a sliced NDArray that shares memory with the current one.
This is called through ``x[start:stop]``.
Parameters
----------
start : int
Starting inclusive index of slice in the first dim.
stop : int
Finishing exclusive index of slice in the first dim.
Returns
-------
`NDArray` sharing the memory with the current one sliced from
start to stop in the first dim.
Examples:
>>> a = mx.nd.array([[1,2], [3, 4], [5, 6], [7, 8]])
>>> a[1:2].asnumpy()
array([[ 3., 4.]], dtype=float32)
>>> a[1:1].asnumpy()
array([], shape=(0, 2), dtype=float32)
"""
handle = NDArrayHandle()
start, stop, _ = _get_index_range(start, stop, self.shape[0])
check_call(_LIB.MXNDArraySlice(
self.handle, mx_uint(start), mx_uint(stop), ctypes.byref(handle)))
return NDArray(handle=handle, writable=self.writable) | def _slice(self, start, stop):
"""Returns a sliced NDArray that shares memory with the current one.
This is called through ``x[start:stop]``.
Parameters
----------
start : int
Starting inclusive index of slice in the first dim.
stop : int
Finishing exclusive index of slice in the first dim.
Returns
-------
`NDArray` sharing the memory with the current one sliced from
start to stop in the first dim.
Examples:
>>> a = mx.nd.array([[1,2], [3, 4], [5, 6], [7, 8]])
>>> a[1:2].asnumpy()
array([[ 3., 4.]], dtype=float32)
>>> a[1:1].asnumpy()
array([], shape=(0, 2), dtype=float32)
"""
handle = NDArrayHandle()
start, stop, _ = _get_index_range(start, stop, self.shape[0])
check_call(_LIB.MXNDArraySlice(
self.handle, mx_uint(start), mx_uint(stop), ctypes.byref(handle)))
return NDArray(handle=handle, writable=self.writable) | [
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train | NDArray._at | Returns a view of the array sliced at `idx` in the first dim.
This is called through ``x[idx]``.
Parameters
----------
idx : int
index for slicing the `NDArray` in the first dim.
Returns
-------
NDArray
`NDArray` sharing the memory with the current one sliced at `idx` in the first dim.
Examples
--------
>>> a = mx.nd.array([[1,2], [3, 4]])
>>> a[1].asnumpy()
array([ 3., 4.], dtype=float32)
>>> b = mx.nd.array([1, 2, 3, 4])
>>> b[0].asnumpy()
array([ 1.], dtype=float32) | python/mxnet/ndarray/ndarray.py | def _at(self, idx):
"""Returns a view of the array sliced at `idx` in the first dim.
This is called through ``x[idx]``.
Parameters
----------
idx : int
index for slicing the `NDArray` in the first dim.
Returns
-------
NDArray
`NDArray` sharing the memory with the current one sliced at `idx` in the first dim.
Examples
--------
>>> a = mx.nd.array([[1,2], [3, 4]])
>>> a[1].asnumpy()
array([ 3., 4.], dtype=float32)
>>> b = mx.nd.array([1, 2, 3, 4])
>>> b[0].asnumpy()
array([ 1.], dtype=float32)
"""
handle = NDArrayHandle()
if idx < 0:
length = self.shape[0]
idx += length
if idx < 0:
raise IndexError('index %d is out of bounds for axis 0 with size %d'
% (idx-length, length))
check_call(_LIB.MXNDArrayAt(
self.handle, mx_uint(idx), ctypes.byref(handle)))
return NDArray(handle=handle, writable=self.writable) | def _at(self, idx):
"""Returns a view of the array sliced at `idx` in the first dim.
This is called through ``x[idx]``.
Parameters
----------
idx : int
index for slicing the `NDArray` in the first dim.
Returns
-------
NDArray
`NDArray` sharing the memory with the current one sliced at `idx` in the first dim.
Examples
--------
>>> a = mx.nd.array([[1,2], [3, 4]])
>>> a[1].asnumpy()
array([ 3., 4.], dtype=float32)
>>> b = mx.nd.array([1, 2, 3, 4])
>>> b[0].asnumpy()
array([ 1.], dtype=float32)
"""
handle = NDArrayHandle()
if idx < 0:
length = self.shape[0]
idx += length
if idx < 0:
raise IndexError('index %d is out of bounds for axis 0 with size %d'
% (idx-length, length))
check_call(_LIB.MXNDArrayAt(
self.handle, mx_uint(idx), ctypes.byref(handle)))
return NDArray(handle=handle, writable=self.writable) | [
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train | NDArray.reshape | Returns a **view** of this array with a new shape without altering any data.
Parameters
----------
shape : tuple of int, or n ints
The new shape should not change the array size, namely
``np.prod(new_shape)`` should be equal to ``np.prod(self.shape)``.
Some dimensions of the shape can take special values from the set {0, -1, -2, -3, -4}.
The significance of each is explained below:
- ``0`` copy this dimension from the input to the output shape.
Example::
- input shape = (2,3,4), shape = (4,0,2), output shape = (4,3,2)
- input shape = (2,3,4), shape = (2,0,0), output shape = (2,3,4)
- ``-1`` infers the dimension of the output shape by using the remainder of the
input dimensions keeping the size of the new array same as that of the input array.
At most one dimension of shape can be -1.
Example::
- input shape = (2,3,4), shape = (6,1,-1), output shape = (6,1,4)
- input shape = (2,3,4), shape = (3,-1,8), output shape = (3,1,8)
- input shape = (2,3,4), shape=(-1,), output shape = (24,)
- ``-2`` copy all/remainder of the input dimensions to the output shape.
Example::
- input shape = (2,3,4), shape = (-2,), output shape = (2,3,4)
- input shape = (2,3,4), shape = (2,-2), output shape = (2,3,4)
- input shape = (2,3,4), shape = (-2,1,1), output shape = (2,3,4,1,1)
- ``-3`` use the product of two consecutive dimensions of the input shape as the
output dimension.
Example::
- input shape = (2,3,4), shape = (-3,4), output shape = (6,4)
- input shape = (2,3,4,5), shape = (-3,-3), output shape = (6,20)
- input shape = (2,3,4), shape = (0,-3), output shape = (2,12)
- input shape = (2,3,4), shape = (-3,-2), output shape = (6,4)
- ``-4`` split one dimension of the input into two dimensions passed subsequent to
-4 in shape (can contain -1).
Example::
- input shape = (2,3,4), shape = (-4,1,2,-2), output shape =(1,2,3,4)
- input shape = (2,3,4), shape = (2,-4,-1,3,-2), output shape = (2,1,3,4)
- If the argument `reverse` is set to 1, then the special values are inferred from right
to left.
Example::
- without reverse=1, for input shape = (10,5,4), shape = (-1,0), output shape would be \
(40,5).
- with reverse=1, output shape will be (50,4).
reverse : bool, default False
If true then the special values are inferred from right to left. Only supported as
keyword argument.
Returns
-------
NDArray
An array with desired shape that shares data with this array.
Examples
--------
>>> x = mx.nd.arange(0,6).reshape(2,3)
>>> x.asnumpy()
array([[ 0., 1., 2.],
[ 3., 4., 5.]], dtype=float32)
>>> y = x.reshape(3,2)
>>> y.asnumpy()
array([[ 0., 1.],
[ 2., 3.],
[ 4., 5.]], dtype=float32)
>>> y = x.reshape(3,-1)
>>> y.asnumpy()
array([[ 0., 1.],
[ 2., 3.],
[ 4., 5.]], dtype=float32)
>>> y = x.reshape(3,2)
>>> y.asnumpy()
array([[ 0., 1.],
[ 2., 3.],
[ 4., 5.]], dtype=float32)
>>> y = x.reshape(-3)
>>> y.asnumpy()
array([ 0. 1. 2. 3. 4. 5.], dtype=float32)
>>> y[:] = -1
>>> x.asnumpy()
array([[-1., -1., -1.],
[-1., -1., -1.]], dtype=float32) | python/mxnet/ndarray/ndarray.py | def reshape(self, *shape, **kwargs):
"""Returns a **view** of this array with a new shape without altering any data.
Parameters
----------
shape : tuple of int, or n ints
The new shape should not change the array size, namely
``np.prod(new_shape)`` should be equal to ``np.prod(self.shape)``.
Some dimensions of the shape can take special values from the set {0, -1, -2, -3, -4}.
The significance of each is explained below:
- ``0`` copy this dimension from the input to the output shape.
Example::
- input shape = (2,3,4), shape = (4,0,2), output shape = (4,3,2)
- input shape = (2,3,4), shape = (2,0,0), output shape = (2,3,4)
- ``-1`` infers the dimension of the output shape by using the remainder of the
input dimensions keeping the size of the new array same as that of the input array.
At most one dimension of shape can be -1.
Example::
- input shape = (2,3,4), shape = (6,1,-1), output shape = (6,1,4)
- input shape = (2,3,4), shape = (3,-1,8), output shape = (3,1,8)
- input shape = (2,3,4), shape=(-1,), output shape = (24,)
- ``-2`` copy all/remainder of the input dimensions to the output shape.
Example::
- input shape = (2,3,4), shape = (-2,), output shape = (2,3,4)
- input shape = (2,3,4), shape = (2,-2), output shape = (2,3,4)
- input shape = (2,3,4), shape = (-2,1,1), output shape = (2,3,4,1,1)
- ``-3`` use the product of two consecutive dimensions of the input shape as the
output dimension.
Example::
- input shape = (2,3,4), shape = (-3,4), output shape = (6,4)
- input shape = (2,3,4,5), shape = (-3,-3), output shape = (6,20)
- input shape = (2,3,4), shape = (0,-3), output shape = (2,12)
- input shape = (2,3,4), shape = (-3,-2), output shape = (6,4)
- ``-4`` split one dimension of the input into two dimensions passed subsequent to
-4 in shape (can contain -1).
Example::
- input shape = (2,3,4), shape = (-4,1,2,-2), output shape =(1,2,3,4)
- input shape = (2,3,4), shape = (2,-4,-1,3,-2), output shape = (2,1,3,4)
- If the argument `reverse` is set to 1, then the special values are inferred from right
to left.
Example::
- without reverse=1, for input shape = (10,5,4), shape = (-1,0), output shape would be \
(40,5).
- with reverse=1, output shape will be (50,4).
reverse : bool, default False
If true then the special values are inferred from right to left. Only supported as
keyword argument.
Returns
-------
NDArray
An array with desired shape that shares data with this array.
Examples
--------
>>> x = mx.nd.arange(0,6).reshape(2,3)
>>> x.asnumpy()
array([[ 0., 1., 2.],
[ 3., 4., 5.]], dtype=float32)
>>> y = x.reshape(3,2)
>>> y.asnumpy()
array([[ 0., 1.],
[ 2., 3.],
[ 4., 5.]], dtype=float32)
>>> y = x.reshape(3,-1)
>>> y.asnumpy()
array([[ 0., 1.],
[ 2., 3.],
[ 4., 5.]], dtype=float32)
>>> y = x.reshape(3,2)
>>> y.asnumpy()
array([[ 0., 1.],
[ 2., 3.],
[ 4., 5.]], dtype=float32)
>>> y = x.reshape(-3)
>>> y.asnumpy()
array([ 0. 1. 2. 3. 4. 5.], dtype=float32)
>>> y[:] = -1
>>> x.asnumpy()
array([[-1., -1., -1.],
[-1., -1., -1.]], dtype=float32)
"""
if len(shape) == 1 and isinstance(shape[0], (list, tuple)):
shape = shape[0]
elif not shape:
shape = kwargs.get('shape')
assert shape, "Shape must be provided."
if not all(k in ['shape', 'reverse'] for k in kwargs):
raise TypeError(
"Got unknown keywords in reshape: {}. " \
"Accepted keyword arguments are 'shape' and 'reverse'.".format(
', '.join([k for k in kwargs if k not in ['shape', 'reverse']])))
reverse = kwargs.get('reverse', False)
handle = NDArrayHandle()
# Actual reshape
check_call(_LIB.MXNDArrayReshape64(self.handle,
len(shape),
c_array(ctypes.c_int64, shape),
reverse,
ctypes.byref(handle)))
return NDArray(handle=handle, writable=self.writable) | def reshape(self, *shape, **kwargs):
"""Returns a **view** of this array with a new shape without altering any data.
Parameters
----------
shape : tuple of int, or n ints
The new shape should not change the array size, namely
``np.prod(new_shape)`` should be equal to ``np.prod(self.shape)``.
Some dimensions of the shape can take special values from the set {0, -1, -2, -3, -4}.
The significance of each is explained below:
- ``0`` copy this dimension from the input to the output shape.
Example::
- input shape = (2,3,4), shape = (4,0,2), output shape = (4,3,2)
- input shape = (2,3,4), shape = (2,0,0), output shape = (2,3,4)
- ``-1`` infers the dimension of the output shape by using the remainder of the
input dimensions keeping the size of the new array same as that of the input array.
At most one dimension of shape can be -1.
Example::
- input shape = (2,3,4), shape = (6,1,-1), output shape = (6,1,4)
- input shape = (2,3,4), shape = (3,-1,8), output shape = (3,1,8)
- input shape = (2,3,4), shape=(-1,), output shape = (24,)
- ``-2`` copy all/remainder of the input dimensions to the output shape.
Example::
- input shape = (2,3,4), shape = (-2,), output shape = (2,3,4)
- input shape = (2,3,4), shape = (2,-2), output shape = (2,3,4)
- input shape = (2,3,4), shape = (-2,1,1), output shape = (2,3,4,1,1)
- ``-3`` use the product of two consecutive dimensions of the input shape as the
output dimension.
Example::
- input shape = (2,3,4), shape = (-3,4), output shape = (6,4)
- input shape = (2,3,4,5), shape = (-3,-3), output shape = (6,20)
- input shape = (2,3,4), shape = (0,-3), output shape = (2,12)
- input shape = (2,3,4), shape = (-3,-2), output shape = (6,4)
- ``-4`` split one dimension of the input into two dimensions passed subsequent to
-4 in shape (can contain -1).
Example::
- input shape = (2,3,4), shape = (-4,1,2,-2), output shape =(1,2,3,4)
- input shape = (2,3,4), shape = (2,-4,-1,3,-2), output shape = (2,1,3,4)
- If the argument `reverse` is set to 1, then the special values are inferred from right
to left.
Example::
- without reverse=1, for input shape = (10,5,4), shape = (-1,0), output shape would be \
(40,5).
- with reverse=1, output shape will be (50,4).
reverse : bool, default False
If true then the special values are inferred from right to left. Only supported as
keyword argument.
Returns
-------
NDArray
An array with desired shape that shares data with this array.
Examples
--------
>>> x = mx.nd.arange(0,6).reshape(2,3)
>>> x.asnumpy()
array([[ 0., 1., 2.],
[ 3., 4., 5.]], dtype=float32)
>>> y = x.reshape(3,2)
>>> y.asnumpy()
array([[ 0., 1.],
[ 2., 3.],
[ 4., 5.]], dtype=float32)
>>> y = x.reshape(3,-1)
>>> y.asnumpy()
array([[ 0., 1.],
[ 2., 3.],
[ 4., 5.]], dtype=float32)
>>> y = x.reshape(3,2)
>>> y.asnumpy()
array([[ 0., 1.],
[ 2., 3.],
[ 4., 5.]], dtype=float32)
>>> y = x.reshape(-3)
>>> y.asnumpy()
array([ 0. 1. 2. 3. 4. 5.], dtype=float32)
>>> y[:] = -1
>>> x.asnumpy()
array([[-1., -1., -1.],
[-1., -1., -1.]], dtype=float32)
"""
if len(shape) == 1 and isinstance(shape[0], (list, tuple)):
shape = shape[0]
elif not shape:
shape = kwargs.get('shape')
assert shape, "Shape must be provided."
if not all(k in ['shape', 'reverse'] for k in kwargs):
raise TypeError(
"Got unknown keywords in reshape: {}. " \
"Accepted keyword arguments are 'shape' and 'reverse'.".format(
', '.join([k for k in kwargs if k not in ['shape', 'reverse']])))
reverse = kwargs.get('reverse', False)
handle = NDArrayHandle()
# Actual reshape
check_call(_LIB.MXNDArrayReshape64(self.handle,
len(shape),
c_array(ctypes.c_int64, shape),
reverse,
ctypes.byref(handle)))
return NDArray(handle=handle, writable=self.writable) | [
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train | NDArray.broadcast_to | Broadcasts the input array to a new shape.
Broadcasting is only allowed on axes with size 1. The new shape cannot change
the number of dimensions.
For example, you could broadcast from shape (2, 1) to (2, 3), but not from
shape (2, 3) to (2, 3, 3).
Parameters
----------
shape : tuple of int
The shape of the desired array.
Returns
-------
NDArray
A NDArray with the desired shape that is not sharing data with this
array, even if the new shape is the same as ``self.shape``.
Examples
--------
>>> x = mx.nd.arange(0,3).reshape((1,3,1))
>>> x.asnumpy()
array([[[ 0.],
[ 1.],
[ 2.]]], dtype=float32)
>>> y = x.broadcast_to((2,3,3))
>>> y.asnumpy()
array([[[ 0., 0., 0.],
[ 1., 1., 1.],
[ 2., 2., 2.]],
<BLANKLINE>
[[ 0., 0., 0.],
[ 1., 1., 1.],
[ 2., 2., 2.]]], dtype=float32) | python/mxnet/ndarray/ndarray.py | def broadcast_to(self, shape):
"""Broadcasts the input array to a new shape.
Broadcasting is only allowed on axes with size 1. The new shape cannot change
the number of dimensions.
For example, you could broadcast from shape (2, 1) to (2, 3), but not from
shape (2, 3) to (2, 3, 3).
Parameters
----------
shape : tuple of int
The shape of the desired array.
Returns
-------
NDArray
A NDArray with the desired shape that is not sharing data with this
array, even if the new shape is the same as ``self.shape``.
Examples
--------
>>> x = mx.nd.arange(0,3).reshape((1,3,1))
>>> x.asnumpy()
array([[[ 0.],
[ 1.],
[ 2.]]], dtype=float32)
>>> y = x.broadcast_to((2,3,3))
>>> y.asnumpy()
array([[[ 0., 0., 0.],
[ 1., 1., 1.],
[ 2., 2., 2.]],
<BLANKLINE>
[[ 0., 0., 0.],
[ 1., 1., 1.],
[ 2., 2., 2.]]], dtype=float32)
"""
cur_shape = self.shape
err_str = 'operands could not be broadcast together with remapped shapes' \
'[original->remapped]: {} and requested shape {}'.format(cur_shape, shape)
if len(shape) < len(cur_shape):
raise ValueError(err_str)
cur_shape = (1,) * (len(shape) - len(cur_shape)) + cur_shape
cur_shape_arr = np.array(cur_shape)
broadcasting_axes = np.nonzero(cur_shape_arr != np.array(shape))
if (cur_shape_arr[broadcasting_axes] != 1).any():
raise ValueError(err_str)
if cur_shape != self.shape:
return op.broadcast_to(self.reshape(cur_shape), shape=shape)
else:
return op.broadcast_to(self, shape=tuple(shape)) | def broadcast_to(self, shape):
"""Broadcasts the input array to a new shape.
Broadcasting is only allowed on axes with size 1. The new shape cannot change
the number of dimensions.
For example, you could broadcast from shape (2, 1) to (2, 3), but not from
shape (2, 3) to (2, 3, 3).
Parameters
----------
shape : tuple of int
The shape of the desired array.
Returns
-------
NDArray
A NDArray with the desired shape that is not sharing data with this
array, even if the new shape is the same as ``self.shape``.
Examples
--------
>>> x = mx.nd.arange(0,3).reshape((1,3,1))
>>> x.asnumpy()
array([[[ 0.],
[ 1.],
[ 2.]]], dtype=float32)
>>> y = x.broadcast_to((2,3,3))
>>> y.asnumpy()
array([[[ 0., 0., 0.],
[ 1., 1., 1.],
[ 2., 2., 2.]],
<BLANKLINE>
[[ 0., 0., 0.],
[ 1., 1., 1.],
[ 2., 2., 2.]]], dtype=float32)
"""
cur_shape = self.shape
err_str = 'operands could not be broadcast together with remapped shapes' \
'[original->remapped]: {} and requested shape {}'.format(cur_shape, shape)
if len(shape) < len(cur_shape):
raise ValueError(err_str)
cur_shape = (1,) * (len(shape) - len(cur_shape)) + cur_shape
cur_shape_arr = np.array(cur_shape)
broadcasting_axes = np.nonzero(cur_shape_arr != np.array(shape))
if (cur_shape_arr[broadcasting_axes] != 1).any():
raise ValueError(err_str)
if cur_shape != self.shape:
return op.broadcast_to(self.reshape(cur_shape), shape=shape)
else:
return op.broadcast_to(self, shape=tuple(shape)) | [
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] | apache/incubator-mxnet | python | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/python/mxnet/ndarray/ndarray.py#L1710-L1759 | [
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train | NDArray.shape | Tuple of array dimensions.
Examples
--------
>>> x = mx.nd.array([1, 2, 3, 4])
>>> x.shape
(4L,)
>>> y = mx.nd.zeros((2, 3, 4))
>>> y.shape
(2L, 3L, 4L) | python/mxnet/ndarray/ndarray.py | def shape(self):
"""Tuple of array dimensions.
Examples
--------
>>> x = mx.nd.array([1, 2, 3, 4])
>>> x.shape
(4L,)
>>> y = mx.nd.zeros((2, 3, 4))
>>> y.shape
(2L, 3L, 4L)
"""
ndim = mx_int()
pdata = ctypes.POINTER(mx_int)()
check_call(_LIB.MXNDArrayGetShapeEx(
self.handle, ctypes.byref(ndim), ctypes.byref(pdata)))
if ndim.value == -1:
return None
else:
return tuple(pdata[:ndim.value]) | def shape(self):
"""Tuple of array dimensions.
Examples
--------
>>> x = mx.nd.array([1, 2, 3, 4])
>>> x.shape
(4L,)
>>> y = mx.nd.zeros((2, 3, 4))
>>> y.shape
(2L, 3L, 4L)
"""
ndim = mx_int()
pdata = ctypes.POINTER(mx_int)()
check_call(_LIB.MXNDArrayGetShapeEx(
self.handle, ctypes.byref(ndim), ctypes.byref(pdata)))
if ndim.value == -1:
return None
else:
return tuple(pdata[:ndim.value]) | [
"Tuple",
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"."
] | apache/incubator-mxnet | python | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/python/mxnet/ndarray/ndarray.py#L1836-L1855 | [
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train | NDArray.context | Device context of the array.
Examples
--------
>>> x = mx.nd.array([1, 2, 3, 4])
>>> x.context
cpu(0)
>>> type(x.context)
<class 'mxnet.context.Context'>
>>> y = mx.nd.zeros((2,3), mx.gpu(0))
>>> y.context
gpu(0) | python/mxnet/ndarray/ndarray.py | def context(self):
"""Device context of the array.
Examples
--------
>>> x = mx.nd.array([1, 2, 3, 4])
>>> x.context
cpu(0)
>>> type(x.context)
<class 'mxnet.context.Context'>
>>> y = mx.nd.zeros((2,3), mx.gpu(0))
>>> y.context
gpu(0)
"""
dev_typeid = ctypes.c_int()
dev_id = ctypes.c_int()
check_call(_LIB.MXNDArrayGetContext(
self.handle, ctypes.byref(dev_typeid), ctypes.byref(dev_id)))
return Context(Context.devtype2str[dev_typeid.value], dev_id.value) | def context(self):
"""Device context of the array.
Examples
--------
>>> x = mx.nd.array([1, 2, 3, 4])
>>> x.context
cpu(0)
>>> type(x.context)
<class 'mxnet.context.Context'>
>>> y = mx.nd.zeros((2,3), mx.gpu(0))
>>> y.context
gpu(0)
"""
dev_typeid = ctypes.c_int()
dev_id = ctypes.c_int()
check_call(_LIB.MXNDArrayGetContext(
self.handle, ctypes.byref(dev_typeid), ctypes.byref(dev_id)))
return Context(Context.devtype2str[dev_typeid.value], dev_id.value) | [
"Device",
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] | apache/incubator-mxnet | python | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/python/mxnet/ndarray/ndarray.py#L1879-L1897 | [
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train | NDArray.dtype | Data-type of the array's elements.
Returns
-------
numpy.dtype
This NDArray's data type.
Examples
--------
>>> x = mx.nd.zeros((2,3))
>>> x.dtype
<type 'numpy.float32'>
>>> y = mx.nd.zeros((2,3), dtype='int32')
>>> y.dtype
<type 'numpy.int32'> | python/mxnet/ndarray/ndarray.py | def dtype(self):
"""Data-type of the array's elements.
Returns
-------
numpy.dtype
This NDArray's data type.
Examples
--------
>>> x = mx.nd.zeros((2,3))
>>> x.dtype
<type 'numpy.float32'>
>>> y = mx.nd.zeros((2,3), dtype='int32')
>>> y.dtype
<type 'numpy.int32'>
"""
mx_dtype = ctypes.c_int()
check_call(_LIB.MXNDArrayGetDType(
self.handle, ctypes.byref(mx_dtype)))
return _DTYPE_MX_TO_NP[mx_dtype.value] | def dtype(self):
"""Data-type of the array's elements.
Returns
-------
numpy.dtype
This NDArray's data type.
Examples
--------
>>> x = mx.nd.zeros((2,3))
>>> x.dtype
<type 'numpy.float32'>
>>> y = mx.nd.zeros((2,3), dtype='int32')
>>> y.dtype
<type 'numpy.int32'>
"""
mx_dtype = ctypes.c_int()
check_call(_LIB.MXNDArrayGetDType(
self.handle, ctypes.byref(mx_dtype)))
return _DTYPE_MX_TO_NP[mx_dtype.value] | [
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train | NDArray._fresh_grad | Whether this array's corresponding gradient array
(registered via `autograd.mark_variables`) has been
updated by `autograd.backward` since last reset.
`_fresh_grad` need to be manually set to False
after consuming gradient (usually after updating this
array). | python/mxnet/ndarray/ndarray.py | def _fresh_grad(self):
"""Whether this array's corresponding gradient array
(registered via `autograd.mark_variables`) has been
updated by `autograd.backward` since last reset.
`_fresh_grad` need to be manually set to False
after consuming gradient (usually after updating this
array).
"""
out = ctypes.c_int()
check_call(_LIB.MXNDArrayGetGradState(self.handle, ctypes.byref(out)))
return out.value | def _fresh_grad(self):
"""Whether this array's corresponding gradient array
(registered via `autograd.mark_variables`) has been
updated by `autograd.backward` since last reset.
`_fresh_grad` need to be manually set to False
after consuming gradient (usually after updating this
array).
"""
out = ctypes.c_int()
check_call(_LIB.MXNDArrayGetGradState(self.handle, ctypes.byref(out)))
return out.value | [
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] | apache/incubator-mxnet | python | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/python/mxnet/ndarray/ndarray.py#L1957-L1968 | [
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train | NDArray.asnumpy | Returns a ``numpy.ndarray`` object with value copied from this array.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = x.asnumpy()
>>> type(y)
<type 'numpy.ndarray'>
>>> y
array([[ 1., 1., 1.],
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>>> z = mx.nd.ones((2,3), dtype='int32')
>>> z.asnumpy()
array([[1, 1, 1],
[1, 1, 1]], dtype=int32) | python/mxnet/ndarray/ndarray.py | def asnumpy(self):
"""Returns a ``numpy.ndarray`` object with value copied from this array.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = x.asnumpy()
>>> type(y)
<type 'numpy.ndarray'>
>>> y
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> z = mx.nd.ones((2,3), dtype='int32')
>>> z.asnumpy()
array([[1, 1, 1],
[1, 1, 1]], dtype=int32)
"""
data = np.empty(self.shape, dtype=self.dtype)
check_call(_LIB.MXNDArraySyncCopyToCPU(
self.handle,
data.ctypes.data_as(ctypes.c_void_p),
ctypes.c_size_t(data.size)))
return data | def asnumpy(self):
"""Returns a ``numpy.ndarray`` object with value copied from this array.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = x.asnumpy()
>>> type(y)
<type 'numpy.ndarray'>
>>> y
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> z = mx.nd.ones((2,3), dtype='int32')
>>> z.asnumpy()
array([[1, 1, 1],
[1, 1, 1]], dtype=int32)
"""
data = np.empty(self.shape, dtype=self.dtype)
check_call(_LIB.MXNDArraySyncCopyToCPU(
self.handle,
data.ctypes.data_as(ctypes.c_void_p),
ctypes.c_size_t(data.size)))
return data | [
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train | NDArray.astype | Returns a copy of the array after casting to a specified type.
Parameters
----------
dtype : numpy.dtype or str
The type of the returned array.
copy : bool
Default `True`. By default, astype always returns a newly
allocated ndarray on the same context. If this is set to
`False`, and the dtype requested is the same as the ndarray's
dtype, the ndarray is returned instead of a copy.
Returns
-------
NDArray, CSRNDArray or RowSparseNDArray
The copied array after casting to the specified type, or
the same array if copy=False and dtype is the same as the input
array.
Examples
--------
>>> x = mx.nd.zeros((2,3), dtype='float32')
>>> y = x.astype('int32')
>>> y.dtype
<type 'numpy.int32'> | python/mxnet/ndarray/ndarray.py | def astype(self, dtype, copy=True):
"""Returns a copy of the array after casting to a specified type.
Parameters
----------
dtype : numpy.dtype or str
The type of the returned array.
copy : bool
Default `True`. By default, astype always returns a newly
allocated ndarray on the same context. If this is set to
`False`, and the dtype requested is the same as the ndarray's
dtype, the ndarray is returned instead of a copy.
Returns
-------
NDArray, CSRNDArray or RowSparseNDArray
The copied array after casting to the specified type, or
the same array if copy=False and dtype is the same as the input
array.
Examples
--------
>>> x = mx.nd.zeros((2,3), dtype='float32')
>>> y = x.astype('int32')
>>> y.dtype
<type 'numpy.int32'>
"""
if not copy and np.dtype(dtype) == self.dtype:
return self
res = empty(self.shape, ctx=self.context, dtype=dtype)
self.copyto(res)
return res | def astype(self, dtype, copy=True):
"""Returns a copy of the array after casting to a specified type.
Parameters
----------
dtype : numpy.dtype or str
The type of the returned array.
copy : bool
Default `True`. By default, astype always returns a newly
allocated ndarray on the same context. If this is set to
`False`, and the dtype requested is the same as the ndarray's
dtype, the ndarray is returned instead of a copy.
Returns
-------
NDArray, CSRNDArray or RowSparseNDArray
The copied array after casting to the specified type, or
the same array if copy=False and dtype is the same as the input
array.
Examples
--------
>>> x = mx.nd.zeros((2,3), dtype='float32')
>>> y = x.astype('int32')
>>> y.dtype
<type 'numpy.int32'>
"""
if not copy and np.dtype(dtype) == self.dtype:
return self
res = empty(self.shape, ctx=self.context, dtype=dtype)
self.copyto(res)
return res | [
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train | NDArray.copyto | Copies the value of this array to another array.
If ``other`` is a ``NDArray`` object, then ``other.shape`` and
``self.shape`` should be the same. This function copies the value from
``self`` to ``other``.
If ``other`` is a context, a new ``NDArray`` will be first created on
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The destination array or context.
Returns
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NDArray, CSRNDArray or RowSparseNDArray
The copied array. If ``other`` is an ``NDArray``, then the return value
and ``other`` will point to the same ``NDArray``.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.zeros((2,3), mx.gpu(0))
>>> z = x.copyto(y)
>>> z is y
True
>>> y.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.copyto(mx.gpu(0))
<NDArray 2x3 @gpu(0)> | python/mxnet/ndarray/ndarray.py | def copyto(self, other):
"""Copies the value of this array to another array.
If ``other`` is a ``NDArray`` object, then ``other.shape`` and
``self.shape`` should be the same. This function copies the value from
``self`` to ``other``.
If ``other`` is a context, a new ``NDArray`` will be first created on
the target context, and the value of ``self`` is copied.
Parameters
----------
other : NDArray or Context
The destination array or context.
Returns
-------
NDArray, CSRNDArray or RowSparseNDArray
The copied array. If ``other`` is an ``NDArray``, then the return value
and ``other`` will point to the same ``NDArray``.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.zeros((2,3), mx.gpu(0))
>>> z = x.copyto(y)
>>> z is y
True
>>> y.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.copyto(mx.gpu(0))
<NDArray 2x3 @gpu(0)>
"""
if isinstance(other, NDArray):
if other.handle is self.handle:
warnings.warn('You are attempting to copy an array to itself', RuntimeWarning)
return False
return _internal._copyto(self, out=other)
elif isinstance(other, Context):
hret = NDArray(_new_alloc_handle(self.shape, other, True, self.dtype))
return _internal._copyto(self, out=hret)
else:
raise TypeError('copyto does not support type ' + str(type(other))) | def copyto(self, other):
"""Copies the value of this array to another array.
If ``other`` is a ``NDArray`` object, then ``other.shape`` and
``self.shape`` should be the same. This function copies the value from
``self`` to ``other``.
If ``other`` is a context, a new ``NDArray`` will be first created on
the target context, and the value of ``self`` is copied.
Parameters
----------
other : NDArray or Context
The destination array or context.
Returns
-------
NDArray, CSRNDArray or RowSparseNDArray
The copied array. If ``other`` is an ``NDArray``, then the return value
and ``other`` will point to the same ``NDArray``.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.zeros((2,3), mx.gpu(0))
>>> z = x.copyto(y)
>>> z is y
True
>>> y.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.copyto(mx.gpu(0))
<NDArray 2x3 @gpu(0)>
"""
if isinstance(other, NDArray):
if other.handle is self.handle:
warnings.warn('You are attempting to copy an array to itself', RuntimeWarning)
return False
return _internal._copyto(self, out=other)
elif isinstance(other, Context):
hret = NDArray(_new_alloc_handle(self.shape, other, True, self.dtype))
return _internal._copyto(self, out=hret)
else:
raise TypeError('copyto does not support type ' + str(type(other))) | [
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train | NDArray.as_in_context | Returns an array on the target device with the same value as this array.
If the target context is the same as ``self.context``, then ``self`` is
returned. Otherwise, a copy is made.
Parameters
----------
context : Context
The target context.
Returns
-------
NDArray, CSRNDArray or RowSparseNDArray
The target array.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = x.as_in_context(mx.cpu())
>>> y is x
True
>>> z = x.as_in_context(mx.gpu(0))
>>> z is x
False | python/mxnet/ndarray/ndarray.py | def as_in_context(self, context):
"""Returns an array on the target device with the same value as this array.
If the target context is the same as ``self.context``, then ``self`` is
returned. Otherwise, a copy is made.
Parameters
----------
context : Context
The target context.
Returns
-------
NDArray, CSRNDArray or RowSparseNDArray
The target array.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = x.as_in_context(mx.cpu())
>>> y is x
True
>>> z = x.as_in_context(mx.gpu(0))
>>> z is x
False
"""
if self.context == context:
return self
return self.copyto(context) | def as_in_context(self, context):
"""Returns an array on the target device with the same value as this array.
If the target context is the same as ``self.context``, then ``self`` is
returned. Otherwise, a copy is made.
Parameters
----------
context : Context
The target context.
Returns
-------
NDArray, CSRNDArray or RowSparseNDArray
The target array.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = x.as_in_context(mx.cpu())
>>> y is x
True
>>> z = x.as_in_context(mx.gpu(0))
>>> z is x
False
"""
if self.context == context:
return self
return self.copyto(context) | [
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train | NDArray.attach_grad | Attach a gradient buffer to this NDArray, so that `backward`
can compute gradient with respect to it.
Parameters
----------
grad_req : {'write', 'add', 'null'}
How gradient will be accumulated.
- 'write': gradient will be overwritten on every backward.
- 'add': gradient will be added to existing value on every backward.
- 'null': do not compute gradient for this NDArray.
stype : str, optional
The storage type of the gradient array. Defaults to the same stype of this NDArray. | python/mxnet/ndarray/ndarray.py | def attach_grad(self, grad_req='write', stype=None):
"""Attach a gradient buffer to this NDArray, so that `backward`
can compute gradient with respect to it.
Parameters
----------
grad_req : {'write', 'add', 'null'}
How gradient will be accumulated.
- 'write': gradient will be overwritten on every backward.
- 'add': gradient will be added to existing value on every backward.
- 'null': do not compute gradient for this NDArray.
stype : str, optional
The storage type of the gradient array. Defaults to the same stype of this NDArray.
"""
from . import zeros as _zeros
if stype is not None:
grad = _zeros(self.shape, stype=stype)
else:
grad = op.zeros_like(self) # pylint: disable=undefined-variable
grad_req = _GRAD_REQ_MAP[grad_req]
check_call(_LIB.MXAutogradMarkVariables(
1, ctypes.pointer(self.handle),
ctypes.pointer(mx_uint(grad_req)),
ctypes.pointer(grad.handle))) | def attach_grad(self, grad_req='write', stype=None):
"""Attach a gradient buffer to this NDArray, so that `backward`
can compute gradient with respect to it.
Parameters
----------
grad_req : {'write', 'add', 'null'}
How gradient will be accumulated.
- 'write': gradient will be overwritten on every backward.
- 'add': gradient will be added to existing value on every backward.
- 'null': do not compute gradient for this NDArray.
stype : str, optional
The storage type of the gradient array. Defaults to the same stype of this NDArray.
"""
from . import zeros as _zeros
if stype is not None:
grad = _zeros(self.shape, stype=stype)
else:
grad = op.zeros_like(self) # pylint: disable=undefined-variable
grad_req = _GRAD_REQ_MAP[grad_req]
check_call(_LIB.MXAutogradMarkVariables(
1, ctypes.pointer(self.handle),
ctypes.pointer(mx_uint(grad_req)),
ctypes.pointer(grad.handle))) | [
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train | NDArray.grad | Returns gradient buffer attached to this NDArray. | python/mxnet/ndarray/ndarray.py | def grad(self):
"""Returns gradient buffer attached to this NDArray."""
from . import _ndarray_cls
hdl = NDArrayHandle()
check_call(_LIB.MXNDArrayGetGrad(self.handle, ctypes.byref(hdl)))
if hdl.value is None:
return None
return _ndarray_cls(hdl) | def grad(self):
"""Returns gradient buffer attached to this NDArray."""
from . import _ndarray_cls
hdl = NDArrayHandle()
check_call(_LIB.MXNDArrayGetGrad(self.handle, ctypes.byref(hdl)))
if hdl.value is None:
return None
return _ndarray_cls(hdl) | [
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train | NDArray.detach | Returns a new NDArray, detached from the current graph. | python/mxnet/ndarray/ndarray.py | def detach(self):
"""Returns a new NDArray, detached from the current graph."""
from . import _ndarray_cls
hdl = NDArrayHandle()
check_call(_LIB.MXNDArrayDetach(self.handle, ctypes.byref(hdl)))
return _ndarray_cls(hdl) | def detach(self):
"""Returns a new NDArray, detached from the current graph."""
from . import _ndarray_cls
hdl = NDArrayHandle()
check_call(_LIB.MXNDArrayDetach(self.handle, ctypes.byref(hdl)))
return _ndarray_cls(hdl) | [
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train | NDArray.backward | Compute the gradients of this NDArray w.r.t variables.
Parameters
----------
out_grad : NDArray, optional
Gradient with respect to head.
retain_graph : bool, optional
Whether to retain the computaion graph for another backward
pass on the same graph. By default the computaion history
is cleared.
train_mode : bool, optional
Whether to compute gradient for training or inference. | python/mxnet/ndarray/ndarray.py | def backward(self, out_grad=None, retain_graph=False, train_mode=True):
"""Compute the gradients of this NDArray w.r.t variables.
Parameters
----------
out_grad : NDArray, optional
Gradient with respect to head.
retain_graph : bool, optional
Whether to retain the computaion graph for another backward
pass on the same graph. By default the computaion history
is cleared.
train_mode : bool, optional
Whether to compute gradient for training or inference.
"""
if out_grad is None:
ograd_handles = [NDArrayHandle(0)]
else:
ograd_handles = [out_grad.handle]
check_call(_LIB.MXAutogradBackwardEx(
1, c_handle_array([self]),
c_array(NDArrayHandle, ograd_handles),
0,
ctypes.c_void_p(0),
ctypes.c_int(retain_graph),
ctypes.c_int(0),
ctypes.c_int(train_mode),
ctypes.c_void_p(0),
ctypes.c_void_p(0))) | def backward(self, out_grad=None, retain_graph=False, train_mode=True):
"""Compute the gradients of this NDArray w.r.t variables.
Parameters
----------
out_grad : NDArray, optional
Gradient with respect to head.
retain_graph : bool, optional
Whether to retain the computaion graph for another backward
pass on the same graph. By default the computaion history
is cleared.
train_mode : bool, optional
Whether to compute gradient for training or inference.
"""
if out_grad is None:
ograd_handles = [NDArrayHandle(0)]
else:
ograd_handles = [out_grad.handle]
check_call(_LIB.MXAutogradBackwardEx(
1, c_handle_array([self]),
c_array(NDArrayHandle, ograd_handles),
0,
ctypes.c_void_p(0),
ctypes.c_int(retain_graph),
ctypes.c_int(0),
ctypes.c_int(train_mode),
ctypes.c_void_p(0),
ctypes.c_void_p(0))) | [
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train | Align.build | Build the align array | example/gluon/lipnet/utils/align.py | def build(self, align_path):
"""
Build the align array
"""
file = open(align_path, 'r')
lines = file.readlines()
file.close()
# words: list([op, ed, word])
words = []
for line in lines:
_op, _ed, word = line.strip().split(' ')
if word not in Align.skip_list:
words.append((int(_op), int(_ed), word))
self.words = words
self.n_words = len(words)
self.sentence_str = " ".join([w[2] for w in self.words])
self.sentence_length = len(self.sentence_str) | def build(self, align_path):
"""
Build the align array
"""
file = open(align_path, 'r')
lines = file.readlines()
file.close()
# words: list([op, ed, word])
words = []
for line in lines:
_op, _ed, word = line.strip().split(' ')
if word not in Align.skip_list:
words.append((int(_op), int(_ed), word))
self.words = words
self.n_words = len(words)
self.sentence_str = " ".join([w[2] for w in self.words])
self.sentence_length = len(self.sentence_str) | [
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train | Align.sentence | Get sentence | example/gluon/lipnet/utils/align.py | def sentence(self, padding=75):
"""
Get sentence
"""
vec = word_to_vector(self.sentence_str)
vec += [-1] * (padding - self.sentence_length)
return np.array(vec, dtype=np.int32) | def sentence(self, padding=75):
"""
Get sentence
"""
vec = word_to_vector(self.sentence_str)
vec += [-1] * (padding - self.sentence_length)
return np.array(vec, dtype=np.int32) | [
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train | Align.word | Get words | example/gluon/lipnet/utils/align.py | def word(self, _id, padding=75):
"""
Get words
"""
word = self.words[_id][2]
vec = word_to_vector(word)
vec += [-1] * (padding - len(vec))
return np.array(vec, dtype=np.int32) | def word(self, _id, padding=75):
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Get words
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vec += [-1] * (padding - len(vec))
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train | Align.word_frame_pos | Get the position of words | example/gluon/lipnet/utils/align.py | def word_frame_pos(self, _id):
"""
Get the position of words
"""
left = int(self.words[_id][0]/1000)
right = max(left+1, int(self.words[_id][1]/1000))
return (left, right) | def word_frame_pos(self, _id):
"""
Get the position of words
"""
left = int(self.words[_id][0]/1000)
right = max(left+1, int(self.words[_id][1]/1000))
return (left, right) | [
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train | CustomModule.prepare_sparse_params | Prepares the module for processing a data batch by pulling row_sparse
parameters from kvstore to all devices based on rowids.
Parameters
----------
param_rowids : dict of str to NDArray of list of NDArrays | example/rnn/large_word_lm/custom_module.py | def prepare_sparse_params(self, param_rowids):
'''Prepares the module for processing a data batch by pulling row_sparse
parameters from kvstore to all devices based on rowids.
Parameters
----------
param_rowids : dict of str to NDArray of list of NDArrays
'''
if not self._kvstore:
return
assert(isinstance(param_rowids, dict))
for param_name, rowids in param_rowids.items():
if isinstance(rowids, (tuple, list)):
rowids_1d = []
for r in rowids:
rowids_1d.append(r.reshape((-1,)).astype(np.int64))
rowid = mx.nd.concat(*rowids_1d, dim=0)
else:
rowid = rowids
param_idx = self._exec_group.param_names.index(param_name)
param_val = self._exec_group.param_arrays[param_idx]
self._kvstore.row_sparse_pull(param_name, param_val, row_ids=rowid,
priority=-param_idx) | def prepare_sparse_params(self, param_rowids):
'''Prepares the module for processing a data batch by pulling row_sparse
parameters from kvstore to all devices based on rowids.
Parameters
----------
param_rowids : dict of str to NDArray of list of NDArrays
'''
if not self._kvstore:
return
assert(isinstance(param_rowids, dict))
for param_name, rowids in param_rowids.items():
if isinstance(rowids, (tuple, list)):
rowids_1d = []
for r in rowids:
rowids_1d.append(r.reshape((-1,)).astype(np.int64))
rowid = mx.nd.concat(*rowids_1d, dim=0)
else:
rowid = rowids
param_idx = self._exec_group.param_names.index(param_name)
param_val = self._exec_group.param_arrays[param_idx]
self._kvstore.row_sparse_pull(param_name, param_val, row_ids=rowid,
priority=-param_idx) | [
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train | CustomModule.save_params | Saves model parameters to file.
Parameters
----------
fname : str
Path to output param file.
Examples
--------
>>> # An example of saving module parameters.
>>> mod.save_params('myfile') | example/rnn/large_word_lm/custom_module.py | def save_params(self, fname):
"""Saves model parameters to file.
Parameters
----------
fname : str
Path to output param file.
Examples
--------
>>> # An example of saving module parameters.
>>> mod.save_params('myfile')
"""
arg_params, aux_params = self.get_params_from_kv(self._arg_params, self._aux_params)
save_dict = {('arg:%s' % k) : v.as_in_context(mx.cpu()) for k, v in arg_params.items()}
save_dict.update({('aux:%s' % k) : v.as_in_context(mx.cpu()) for k, v in aux_params.items()})
mx.nd.save(fname, save_dict) | def save_params(self, fname):
"""Saves model parameters to file.
Parameters
----------
fname : str
Path to output param file.
Examples
--------
>>> # An example of saving module parameters.
>>> mod.save_params('myfile')
"""
arg_params, aux_params = self.get_params_from_kv(self._arg_params, self._aux_params)
save_dict = {('arg:%s' % k) : v.as_in_context(mx.cpu()) for k, v in arg_params.items()}
save_dict.update({('aux:%s' % k) : v.as_in_context(mx.cpu()) for k, v in aux_params.items()})
mx.nd.save(fname, save_dict) | [
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train | CustomModule.get_params_from_kv | Copy data from kvstore to `arg_params` and `aux_params`.
Parameters
----------
arg_params : list of NDArray
Target parameter arrays.
aux_params : list of NDArray
Target aux arrays.
Notes
-----
- This function will inplace update the NDArrays in arg_params and aux_params. | example/rnn/large_word_lm/custom_module.py | def get_params_from_kv(self, arg_params, aux_params):
""" Copy data from kvstore to `arg_params` and `aux_params`.
Parameters
----------
arg_params : list of NDArray
Target parameter arrays.
aux_params : list of NDArray
Target aux arrays.
Notes
-----
- This function will inplace update the NDArrays in arg_params and aux_params.
"""
assert(self._kvstore is not None)
for name, block in zip(self._exec_group.param_names, self._exec_group.param_arrays):
assert(isinstance(block, list))
if block[0].stype == 'row_sparse':
row_ids = mx.nd.arange(start=0, stop=block[0].shape[0], dtype='int64')
self._kvstore.row_sparse_pull(name, arg_params[name], row_ids=row_ids)
else:
assert(block[0].stype == 'default')
self._kvstore.pull(name, out=arg_params[name])
if len(aux_params) > 0:
raise NotImplementedError()
return arg_params, aux_params | def get_params_from_kv(self, arg_params, aux_params):
""" Copy data from kvstore to `arg_params` and `aux_params`.
Parameters
----------
arg_params : list of NDArray
Target parameter arrays.
aux_params : list of NDArray
Target aux arrays.
Notes
-----
- This function will inplace update the NDArrays in arg_params and aux_params.
"""
assert(self._kvstore is not None)
for name, block in zip(self._exec_group.param_names, self._exec_group.param_arrays):
assert(isinstance(block, list))
if block[0].stype == 'row_sparse':
row_ids = mx.nd.arange(start=0, stop=block[0].shape[0], dtype='int64')
self._kvstore.row_sparse_pull(name, arg_params[name], row_ids=row_ids)
else:
assert(block[0].stype == 'default')
self._kvstore.pull(name, out=arg_params[name])
if len(aux_params) > 0:
raise NotImplementedError()
return arg_params, aux_params | [
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train | CustomModule.clip_by_global_norm_per_ctx | Clips gradient norm.
The norm is computed over all gradients together, as if they were
concatenated into a single vector. Gradients are modified in-place.
The method is first used in
`[ICML2013] On the difficulty of training recurrent neural networks`
Note that the gradients are concatenated per context in this implementation.
Examples
--------
An example of using clip_grad_norm to clip the gradient before updating the parameters::
>>> #Get the gradient via back-propagation
>>> net.forward_backward(data_batch=data_batch)
>>> norm_val = net.clip_by_global_norm(max_norm=2.0, param_names='w0')
>>> net.update() | example/rnn/large_word_lm/custom_module.py | def clip_by_global_norm_per_ctx(self, max_norm=1.0, param_names=None):
"""Clips gradient norm.
The norm is computed over all gradients together, as if they were
concatenated into a single vector. Gradients are modified in-place.
The method is first used in
`[ICML2013] On the difficulty of training recurrent neural networks`
Note that the gradients are concatenated per context in this implementation.
Examples
--------
An example of using clip_grad_norm to clip the gradient before updating the parameters::
>>> #Get the gradient via back-propagation
>>> net.forward_backward(data_batch=data_batch)
>>> norm_val = net.clip_by_global_norm(max_norm=2.0, param_names='w0')
>>> net.update()
"""
assert self.binded and self.params_initialized and self.optimizer_initialized
num_ctx = len(self._exec_group.grad_arrays[0])
grad_array_per_ctx = [[] for i in range(num_ctx)]
assert(param_names is not None)
for param_name in param_names:
param_idx = self._exec_group.param_names.index(param_name)
grad_val = self._exec_group.grad_arrays[param_idx]
assert(len(grad_val) == num_ctx)
for i in range(num_ctx):
grad_array_per_ctx[i].append(grad_val[i])
norm_vals = []
for i in range(num_ctx):
mx.gluon.utils.clip_global_norm(grad_array_per_ctx[i], max_norm) | def clip_by_global_norm_per_ctx(self, max_norm=1.0, param_names=None):
"""Clips gradient norm.
The norm is computed over all gradients together, as if they were
concatenated into a single vector. Gradients are modified in-place.
The method is first used in
`[ICML2013] On the difficulty of training recurrent neural networks`
Note that the gradients are concatenated per context in this implementation.
Examples
--------
An example of using clip_grad_norm to clip the gradient before updating the parameters::
>>> #Get the gradient via back-propagation
>>> net.forward_backward(data_batch=data_batch)
>>> norm_val = net.clip_by_global_norm(max_norm=2.0, param_names='w0')
>>> net.update()
"""
assert self.binded and self.params_initialized and self.optimizer_initialized
num_ctx = len(self._exec_group.grad_arrays[0])
grad_array_per_ctx = [[] for i in range(num_ctx)]
assert(param_names is not None)
for param_name in param_names:
param_idx = self._exec_group.param_names.index(param_name)
grad_val = self._exec_group.grad_arrays[param_idx]
assert(len(grad_val) == num_ctx)
for i in range(num_ctx):
grad_array_per_ctx[i].append(grad_val[i])
norm_vals = []
for i in range(num_ctx):
mx.gluon.utils.clip_global_norm(grad_array_per_ctx[i], max_norm) | [
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train | CustomModule.rescale_grad | Rescale the gradient of provided parameters by a certain scale | example/rnn/large_word_lm/custom_module.py | def rescale_grad(self, scale=None, param_name=None):
""" Rescale the gradient of provided parameters by a certain scale """
if scale is None or param_name is None:
return
param_idx = self._exec_group.param_names.index(param_name)
grad_vals = self._exec_group.grad_arrays[param_idx]
for grad in grad_vals:
grad[:] *= scale | def rescale_grad(self, scale=None, param_name=None):
""" Rescale the gradient of provided parameters by a certain scale """
if scale is None or param_name is None:
return
param_idx = self._exec_group.param_names.index(param_name)
grad_vals = self._exec_group.grad_arrays[param_idx]
for grad in grad_vals:
grad[:] *= scale | [
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train | factorization_machine_model | builds factorization machine network with proper formulation:
y = w_0 \sum(x_i w_i) + 0.5(\sum\sum<v_i,v_j>x_ix_j - \sum<v_iv_i>x_i^2) | example/sparse/factorization_machine/model.py | def factorization_machine_model(factor_size, num_features,
lr_mult_config, wd_mult_config, init_config):
""" builds factorization machine network with proper formulation:
y = w_0 \sum(x_i w_i) + 0.5(\sum\sum<v_i,v_j>x_ix_j - \sum<v_iv_i>x_i^2)
"""
x = mx.symbol.Variable("data", stype='csr')
# factor, linear and bias terms
v = mx.symbol.Variable("v", shape=(num_features, factor_size), stype='row_sparse',
init=init_config['v'], lr_mult=lr_mult_config['v'],
wd_mult=wd_mult_config['v'])
w = mx.symbol.Variable('w', shape=(num_features, 1), stype='row_sparse',
init=init_config['w'], lr_mult=lr_mult_config['w'],
wd_mult=wd_mult_config['w'])
w0 = mx.symbol.Variable('w0', shape=(1,), init=init_config['w0'],
lr_mult=lr_mult_config['w0'], wd_mult=wd_mult_config['w0'])
w1 = mx.symbol.broadcast_add(mx.symbol.dot(x, w), w0)
# squared terms for subtracting self interactions
v_s = mx.symbol._internal._square_sum(data=v, axis=1, keepdims=True)
x_s = x.square()
bd_sum = mx.sym.dot(x_s, v_s)
# interactions
w2 = mx.symbol.dot(x, v)
w2_squared = 0.5 * mx.symbol.square(data=w2)
# putting everything together
w_all = mx.symbol.Concat(w1, w2_squared, dim=1)
sum1 = w_all.sum(axis=1, keepdims=True)
sum2 = -0.5 * bd_sum
model = sum1 + sum2
y = mx.symbol.Variable("softmax_label")
model = mx.symbol.LogisticRegressionOutput(data=model, label=y)
return model | def factorization_machine_model(factor_size, num_features,
lr_mult_config, wd_mult_config, init_config):
""" builds factorization machine network with proper formulation:
y = w_0 \sum(x_i w_i) + 0.5(\sum\sum<v_i,v_j>x_ix_j - \sum<v_iv_i>x_i^2)
"""
x = mx.symbol.Variable("data", stype='csr')
# factor, linear and bias terms
v = mx.symbol.Variable("v", shape=(num_features, factor_size), stype='row_sparse',
init=init_config['v'], lr_mult=lr_mult_config['v'],
wd_mult=wd_mult_config['v'])
w = mx.symbol.Variable('w', shape=(num_features, 1), stype='row_sparse',
init=init_config['w'], lr_mult=lr_mult_config['w'],
wd_mult=wd_mult_config['w'])
w0 = mx.symbol.Variable('w0', shape=(1,), init=init_config['w0'],
lr_mult=lr_mult_config['w0'], wd_mult=wd_mult_config['w0'])
w1 = mx.symbol.broadcast_add(mx.symbol.dot(x, w), w0)
# squared terms for subtracting self interactions
v_s = mx.symbol._internal._square_sum(data=v, axis=1, keepdims=True)
x_s = x.square()
bd_sum = mx.sym.dot(x_s, v_s)
# interactions
w2 = mx.symbol.dot(x, v)
w2_squared = 0.5 * mx.symbol.square(data=w2)
# putting everything together
w_all = mx.symbol.Concat(w1, w2_squared, dim=1)
sum1 = w_all.sum(axis=1, keepdims=True)
sum2 = -0.5 * bd_sum
model = sum1 + sum2
y = mx.symbol.Variable("softmax_label")
model = mx.symbol.LogisticRegressionOutput(data=model, label=y)
return model | [
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"x... | apache/incubator-mxnet | python | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/example/sparse/factorization_machine/model.py#L20-L54 | [
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train | batchify | Reshape data into (num_example, batch_size) | example/rnn/word_lm/data.py | def batchify(data, batch_size):
"""Reshape data into (num_example, batch_size)"""
nbatch = data.shape[0] // batch_size
data = data[:nbatch * batch_size]
data = data.reshape((batch_size, nbatch)).T
return data | def batchify(data, batch_size):
"""Reshape data into (num_example, batch_size)"""
nbatch = data.shape[0] // batch_size
data = data[:nbatch * batch_size]
data = data.reshape((batch_size, nbatch)).T
return data | [
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] | apache/incubator-mxnet | python | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/example/rnn/word_lm/data.py#L72-L77 | [
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train | Corpus.tokenize | Tokenizes a text file. | example/rnn/word_lm/data.py | def tokenize(self, path):
"""Tokenizes a text file."""
assert os.path.exists(path)
# Add words to the dictionary
with open(path, 'r') as f:
tokens = 0
for line in f:
words = line.split() + ['<eos>']
tokens += len(words)
for word in words:
self.dictionary.add_word(word)
# Tokenize file content
with open(path, 'r') as f:
ids = np.zeros((tokens,), dtype='int32')
token = 0
for line in f:
words = line.split() + ['<eos>']
for word in words:
ids[token] = self.dictionary.word2idx[word]
token += 1
return mx.nd.array(ids, dtype='int32') | def tokenize(self, path):
"""Tokenizes a text file."""
assert os.path.exists(path)
# Add words to the dictionary
with open(path, 'r') as f:
tokens = 0
for line in f:
words = line.split() + ['<eos>']
tokens += len(words)
for word in words:
self.dictionary.add_word(word)
# Tokenize file content
with open(path, 'r') as f:
ids = np.zeros((tokens,), dtype='int32')
token = 0
for line in f:
words = line.split() + ['<eos>']
for word in words:
ids[token] = self.dictionary.word2idx[word]
token += 1
return mx.nd.array(ids, dtype='int32') | [
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] | apache/incubator-mxnet | python | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/example/rnn/word_lm/data.py#L48-L70 | [
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train | _build_doc | Build docstring for symbolic functions. | python/mxnet/symbol_doc.py | def _build_doc(func_name,
desc,
arg_names,
arg_types,
arg_desc,
key_var_num_args=None,
ret_type=None):
"""Build docstring for symbolic functions."""
param_str = _build_param_doc(arg_names, arg_types, arg_desc)
if key_var_num_args:
desc += '\nThis function support variable length of positional input.'
doc_str = ('%s\n\n' +
'%s\n' +
'name : string, optional.\n' +
' Name of the resulting symbol.\n\n' +
'Returns\n' +
'-------\n' +
'Symbol\n' +
' The result symbol.')
doc_str = doc_str % (desc, param_str)
extra_doc = "\n" + '\n'.join([x.__doc__ for x in type.__subclasses__(SymbolDoc)
if x.__name__ == '%sDoc' % func_name])
doc_str += _re.sub(_re.compile(" "), "", extra_doc)
doc_str = _re.sub('NDArray-or-Symbol', 'Symbol', doc_str)
return doc_str | def _build_doc(func_name,
desc,
arg_names,
arg_types,
arg_desc,
key_var_num_args=None,
ret_type=None):
"""Build docstring for symbolic functions."""
param_str = _build_param_doc(arg_names, arg_types, arg_desc)
if key_var_num_args:
desc += '\nThis function support variable length of positional input.'
doc_str = ('%s\n\n' +
'%s\n' +
'name : string, optional.\n' +
' Name of the resulting symbol.\n\n' +
'Returns\n' +
'-------\n' +
'Symbol\n' +
' The result symbol.')
doc_str = doc_str % (desc, param_str)
extra_doc = "\n" + '\n'.join([x.__doc__ for x in type.__subclasses__(SymbolDoc)
if x.__name__ == '%sDoc' % func_name])
doc_str += _re.sub(_re.compile(" "), "", extra_doc)
doc_str = _re.sub('NDArray-or-Symbol', 'Symbol', doc_str)
return doc_str | [
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"... | 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7 |
train | SymbolDoc.get_output_shape | Get user friendly information of the output shapes. | python/mxnet/symbol_doc.py | def get_output_shape(sym, **input_shapes):
"""Get user friendly information of the output shapes."""
_, s_outputs, _ = sym.infer_shape(**input_shapes)
return dict(zip(sym.list_outputs(), s_outputs)) | def get_output_shape(sym, **input_shapes):
"""Get user friendly information of the output shapes."""
_, s_outputs, _ = sym.infer_shape(**input_shapes)
return dict(zip(sym.list_outputs(), s_outputs)) | [
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"... | 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7 |
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