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tf.compat.v1.metrics.mean_relative_error Computes the mean relative error by normalizing with the given values.
tf.compat.v1.metrics.mean_relative_error(
labels, predictions, normalizer, weights=None, metrics_collections=None,
updates_collections=None, name=None
)
The mean_relative_error function creates two ... | tensorflow.compat.v1.metrics.mean_relative_error |
tf.compat.v1.metrics.mean_squared_error Computes the mean squared error between the labels and predictions.
tf.compat.v1.metrics.mean_squared_error(
labels, predictions, weights=None, metrics_collections=None,
updates_collections=None, name=None
)
The mean_squared_error function creates two local variables, t... | tensorflow.compat.v1.metrics.mean_squared_error |
tf.compat.v1.metrics.mean_tensor Computes the element-wise (weighted) mean of the given tensors.
tf.compat.v1.metrics.mean_tensor(
values, weights=None, metrics_collections=None, updates_collections=None,
name=None
)
In contrast to the mean function which returns a scalar with the mean, this function returns ... | tensorflow.compat.v1.metrics.mean_tensor |
tf.compat.v1.metrics.percentage_below Computes the percentage of values less than the given threshold.
tf.compat.v1.metrics.percentage_below(
values, threshold, weights=None, metrics_collections=None,
updates_collections=None, name=None
)
The percentage_below function creates two local variables, total and co... | tensorflow.compat.v1.metrics.percentage_below |
tf.compat.v1.metrics.precision Computes the precision of the predictions with respect to the labels.
tf.compat.v1.metrics.precision(
labels, predictions, weights=None, metrics_collections=None,
updates_collections=None, name=None
)
The precision function creates two local variables, true_positives and false_p... | tensorflow.compat.v1.metrics.precision |
tf.compat.v1.metrics.precision_at_k Computes precision@k of the predictions with respect to sparse labels.
tf.compat.v1.metrics.precision_at_k(
labels, predictions, k, class_id=None, weights=None, metrics_collections=None,
updates_collections=None, name=None
)
If class_id is specified, we calculate precision ... | tensorflow.compat.v1.metrics.precision_at_k |
tf.compat.v1.metrics.precision_at_thresholds Computes precision values for different thresholds on predictions.
tf.compat.v1.metrics.precision_at_thresholds(
labels, predictions, thresholds, weights=None, metrics_collections=None,
updates_collections=None, name=None
)
The precision_at_thresholds function crea... | tensorflow.compat.v1.metrics.precision_at_thresholds |
tf.compat.v1.metrics.precision_at_top_k Computes precision@k of the predictions with respect to sparse labels.
tf.compat.v1.metrics.precision_at_top_k(
labels, predictions_idx, k=None, class_id=None, weights=None,
metrics_collections=None, updates_collections=None, name=None
)
Differs from sparse_precision_at... | tensorflow.compat.v1.metrics.precision_at_top_k |
tf.compat.v1.metrics.recall Computes the recall of the predictions with respect to the labels.
tf.compat.v1.metrics.recall(
labels, predictions, weights=None, metrics_collections=None,
updates_collections=None, name=None
)
The recall function creates two local variables, true_positives and false_negatives, th... | tensorflow.compat.v1.metrics.recall |
tf.compat.v1.metrics.recall_at_k Computes recall@k of the predictions with respect to sparse labels.
tf.compat.v1.metrics.recall_at_k(
labels, predictions, k, class_id=None, weights=None, metrics_collections=None,
updates_collections=None, name=None
)
If class_id is specified, we calculate recall by consideri... | tensorflow.compat.v1.metrics.recall_at_k |
tf.compat.v1.metrics.recall_at_thresholds Computes various recall values for different thresholds on predictions.
tf.compat.v1.metrics.recall_at_thresholds(
labels, predictions, thresholds, weights=None, metrics_collections=None,
updates_collections=None, name=None
)
The recall_at_thresholds function creates ... | tensorflow.compat.v1.metrics.recall_at_thresholds |
tf.compat.v1.metrics.recall_at_top_k Computes recall@k of top-k predictions with respect to sparse labels.
tf.compat.v1.metrics.recall_at_top_k(
labels, predictions_idx, k=None, class_id=None, weights=None,
metrics_collections=None, updates_collections=None, name=None
)
Differs from recall_at_k in that predic... | tensorflow.compat.v1.metrics.recall_at_top_k |
tf.compat.v1.metrics.root_mean_squared_error Computes the root mean squared error between the labels and predictions.
tf.compat.v1.metrics.root_mean_squared_error(
labels, predictions, weights=None, metrics_collections=None,
updates_collections=None, name=None
)
The root_mean_squared_error function creates tw... | tensorflow.compat.v1.metrics.root_mean_squared_error |
tf.compat.v1.metrics.sensitivity_at_specificity Computes the specificity at a given sensitivity.
tf.compat.v1.metrics.sensitivity_at_specificity(
labels, predictions, specificity, weights=None, num_thresholds=200,
metrics_collections=None, updates_collections=None, name=None
)
The sensitivity_at_specificity f... | tensorflow.compat.v1.metrics.sensitivity_at_specificity |
tf.compat.v1.metrics.sparse_average_precision_at_k Renamed to average_precision_at_k, please use that method instead. (deprecated)
tf.compat.v1.metrics.sparse_average_precision_at_k(
labels, predictions, k, weights=None, metrics_collections=None,
updates_collections=None, name=None
)
Warning: THIS FUNCTION IS... | tensorflow.compat.v1.metrics.sparse_average_precision_at_k |
tf.compat.v1.metrics.sparse_precision_at_k Renamed to precision_at_k, please use that method instead. (deprecated)
tf.compat.v1.metrics.sparse_precision_at_k(
labels, predictions, k, class_id=None, weights=None, metrics_collections=None,
updates_collections=None, name=None
)
Warning: THIS FUNCTION IS DEPRECAT... | tensorflow.compat.v1.metrics.sparse_precision_at_k |
tf.compat.v1.metrics.specificity_at_sensitivity Computes the specificity at a given sensitivity.
tf.compat.v1.metrics.specificity_at_sensitivity(
labels, predictions, sensitivity, weights=None, num_thresholds=200,
metrics_collections=None, updates_collections=None, name=None
)
The specificity_at_sensitivity f... | tensorflow.compat.v1.metrics.specificity_at_sensitivity |
tf.compat.v1.metrics.true_negatives Sum the weights of true_negatives.
tf.compat.v1.metrics.true_negatives(
labels, predictions, weights=None, metrics_collections=None,
updates_collections=None, name=None
)
If weights is None, weights default to 1. Use weights of 0 to mask values.
Args
labels The gro... | tensorflow.compat.v1.metrics.true_negatives |
tf.compat.v1.metrics.true_negatives_at_thresholds Computes true negatives at provided threshold values.
tf.compat.v1.metrics.true_negatives_at_thresholds(
labels, predictions, thresholds, weights=None, metrics_collections=None,
updates_collections=None, name=None
)
If weights is None, weights default to 1. Us... | tensorflow.compat.v1.metrics.true_negatives_at_thresholds |
tf.compat.v1.metrics.true_positives Sum the weights of true_positives.
tf.compat.v1.metrics.true_positives(
labels, predictions, weights=None, metrics_collections=None,
updates_collections=None, name=None
)
If weights is None, weights default to 1. Use weights of 0 to mask values.
Args
labels The gro... | tensorflow.compat.v1.metrics.true_positives |
tf.compat.v1.metrics.true_positives_at_thresholds Computes true positives at provided threshold values.
tf.compat.v1.metrics.true_positives_at_thresholds(
labels, predictions, thresholds, weights=None, metrics_collections=None,
updates_collections=None, name=None
)
If weights is None, weights default to 1. Us... | tensorflow.compat.v1.metrics.true_positives_at_thresholds |
tf.compat.v1.min_max_variable_partitioner Partitioner to allocate minimum size per slice.
tf.compat.v1.min_max_variable_partitioner(
max_partitions=1, axis=0, min_slice_size=(256 << 10),
bytes_per_string_element=16
)
Returns a partitioner that partitions the variable of given shape and dtype such that each pa... | tensorflow.compat.v1.min_max_variable_partitioner |
Module: tf.compat.v1.mixed_precision Public API for tf.mixed_precision namespace. Modules experimental module: Public API for tf.mixed_precision.experimental namespace. Classes class DynamicLossScale: Loss scale that dynamically adjusts itself. class FixedLossScale: Loss scale with a fixed value. class LossScale: Base ... | tensorflow.compat.v1.mixed_precision |
tf.compat.v1.mixed_precision.disable_mixed_precision_graph_rewrite Disables the mixed precision graph rewrite. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.train.experimental.disable_mixed_precision_graph_rewrite
tf.compat.v1.mixed_precision.disable_mixed_precision_g... | tensorflow.compat.v1.mixed_precision.disable_mixed_precision_graph_rewrite |
tf.compat.v1.mixed_precision.enable_mixed_precision_graph_rewrite Enable mixed precision via a graph rewrite. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.train.experimental.enable_mixed_precision_graph_rewrite
tf.compat.v1.mixed_precision.enable_mixed_precision_grap... | tensorflow.compat.v1.mixed_precision.enable_mixed_precision_graph_rewrite |
Module: tf.compat.v1.mixed_precision.experimental Public API for tf.mixed_precision.experimental namespace. Classes class DynamicLossScale: Loss scale that dynamically adjusts itself. class FixedLossScale: Loss scale with a fixed value. class LossScale: Base class for all TF1 loss scales. | tensorflow.compat.v1.mixed_precision.experimental |
tf.compat.v1.mixed_precision.MixedPrecisionLossScaleOptimizer An optimizer that applies loss scaling. Inherits From: Optimizer View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.train.experimental.MixedPrecisionLossScaleOptimizer
tf.compat.v1.mixed_precision.MixedPrecision... | tensorflow.compat.v1.mixed_precision.mixedprecisionlossscaleoptimizer |
Module: tf.compat.v1.mlir Public API for tf.mlir namespace. Modules experimental module: Public API for tf.mlir.experimental namespace. | tensorflow.compat.v1.mlir |
Module: tf.compat.v1.mlir.experimental Public API for tf.mlir.experimental namespace. Functions convert_function(...): Import a ConcreteFunction and convert it to a textual MLIR module. convert_graph_def(...): Import a GraphDef and convert it to a textual MLIR module. | tensorflow.compat.v1.mlir.experimental |
tf.compat.v1.model_variables Returns all variables in the MODEL_VARIABLES collection.
tf.compat.v1.model_variables(
scope=None
)
Args
scope (Optional.) A string. If supplied, the resulting list is filtered to include only items whose name attribute matches scope using re.match. Items without a name attri... | tensorflow.compat.v1.model_variables |
tf.compat.v1.moving_average_variables Returns all variables that maintain their moving averages.
tf.compat.v1.moving_average_variables(
scope=None
)
If an ExponentialMovingAverage object is created and the apply() method is called on a list of variables, these variables will be added to the GraphKeys.MOVING_AVERA... | tensorflow.compat.v1.moving_average_variables |
tf.compat.v1.multinomial Draws samples from a multinomial distribution. (deprecated) View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.random.multinomial
tf.compat.v1.multinomial(
logits, num_samples, seed=None, name=None, output_dtype=None
)
Warning: THIS FUNCTION I... | tensorflow.compat.v1.multinomial |
tf.compat.v1.NameAttrList A ProtocolMessage
Attributes
attr repeated AttrEntry attr
name string name Child Classes class AttrEntry | tensorflow.compat.v1.nameattrlist |
tf.compat.v1.NameAttrList.AttrEntry A ProtocolMessage
Attributes
key string key
value AttrValue value | tensorflow.compat.v1.nameattrlist.attrentry |
Module: tf.compat.v1.nest Public API for tf.nest namespace. Functions assert_same_structure(...): Asserts that two structures are nested in the same way. flatten(...): Returns a flat list from a given nested structure. is_nested(...): Returns true if its input is a collections.abc.Sequence (except strings). map_structu... | tensorflow.compat.v1.nest |
Module: tf.compat.v1.nn Wrappers for primitive Neural Net (NN) Operations. Modules rnn_cell module: Module for constructing RNN Cells. Functions all_candidate_sampler(...): Generate the set of all classes. atrous_conv2d(...): Atrous convolution (a.k.a. convolution with holes or dilated convolution). atrous_conv2d_trans... | tensorflow.compat.v1.nn |
tf.compat.v1.nn.avg_pool Performs the average pooling on the input. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.nn.avg_pool2d
tf.compat.v1.nn.avg_pool(
value, ksize, strides, padding, data_format='NHWC', name=None,
input=None
)
Each entry in output is the m... | tensorflow.compat.v1.nn.avg_pool |
tf.compat.v1.nn.batch_norm_with_global_normalization Batch normalization.
tf.compat.v1.nn.batch_norm_with_global_normalization(
t=None, m=None, v=None, beta=None, gamma=None, variance_epsilon=None,
scale_after_normalization=None, name=None, input=None, mean=None, variance=None
)
This op is deprecated. See tf.... | tensorflow.compat.v1.nn.batch_norm_with_global_normalization |
tf.compat.v1.nn.bidirectional_dynamic_rnn Creates a dynamic version of bidirectional recurrent neural network. (deprecated)
tf.compat.v1.nn.bidirectional_dynamic_rnn(
cell_fw, cell_bw, inputs, sequence_length=None, initial_state_fw=None,
initial_state_bw=None, dtype=None, parallel_iterations=None, swap_memory=... | tensorflow.compat.v1.nn.bidirectional_dynamic_rnn |
tf.compat.v1.nn.conv1d Computes a 1-D convolution of input with rank >=3 and a 3-D filter. (deprecated argument values) (deprecated argument values)
tf.compat.v1.nn.conv1d(
value=None, filters=None, stride=None, padding=None, use_cudnn_on_gpu=None,
data_format=None, name=None, input=None, dilations=None
)
W... | tensorflow.compat.v1.nn.conv1d |
tf.compat.v1.nn.conv2d Computes a 2-D convolution given 4-D input and filter tensors.
tf.compat.v1.nn.conv2d(
input, filter=None, strides=None, padding=None, use_cudnn_on_gpu=True,
data_format='NHWC', dilations=[1, 1, 1, 1], name=None, filters=None
)
Given an input tensor of shape [batch, in_height, in_width,... | tensorflow.compat.v1.nn.conv2d |
tf.compat.v1.nn.conv2d_backprop_filter Computes the gradients of convolution with respect to the filter.
tf.compat.v1.nn.conv2d_backprop_filter(
input, filter_sizes, out_backprop, strides, padding, use_cudnn_on_gpu=True,
data_format='NHWC', dilations=[1, 1, 1, 1], name=None
)
Args
input A Tensor. Mus... | tensorflow.compat.v1.nn.conv2d_backprop_filter |
tf.compat.v1.nn.conv2d_backprop_input Computes the gradients of convolution with respect to the input.
tf.compat.v1.nn.conv2d_backprop_input(
input_sizes, filter=None, out_backprop=None, strides=None, padding=None,
use_cudnn_on_gpu=True, data_format='NHWC', dilations=[1, 1, 1, 1],
name=None, filters=None
)... | tensorflow.compat.v1.nn.conv2d_backprop_input |
tf.compat.v1.nn.conv2d_transpose The transpose of conv2d.
tf.compat.v1.nn.conv2d_transpose(
value=None, filter=None, output_shape=None, strides=None,
padding='SAME', data_format='NHWC', name=None, input=None,
filters=None, dilations=None
)
This operation is sometimes called "deconvolution" after (Zeiler e... | tensorflow.compat.v1.nn.conv2d_transpose |
tf.compat.v1.nn.conv3d Computes a 3-D convolution given 5-D input and filter tensors.
tf.compat.v1.nn.conv3d(
input, filter=None, strides=None, padding=None, data_format='NDHWC',
dilations=[1, 1, 1, 1, 1], name=None, filters=None
)
In signal processing, cross-correlation is a measure of similarity of two wave... | tensorflow.compat.v1.nn.conv3d |
tf.compat.v1.nn.conv3d_backprop_filter Computes the gradients of 3-D convolution with respect to the filter. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.nn.conv3d_backprop_filter_v2
tf.compat.v1.nn.conv3d_backprop_filter(
input, filter_sizes, out_backprop, strid... | tensorflow.compat.v1.nn.conv3d_backprop_filter |
tf.compat.v1.nn.conv3d_transpose The transpose of conv3d.
tf.compat.v1.nn.conv3d_transpose(
value, filter=None, output_shape=None, strides=None, padding='SAME',
data_format='NDHWC', name=None, input=None, filters=None,
dilations=None
)
This operation is sometimes called "deconvolution" after (Zeiler et al... | tensorflow.compat.v1.nn.conv3d_transpose |
tf.compat.v1.nn.convolution Computes sums of N-D convolutions (actually cross-correlation).
tf.compat.v1.nn.convolution(
input, filter, padding, strides=None, dilation_rate=None, name=None,
data_format=None, filters=None, dilations=None
)
This also supports either output striding via the optional strides para... | tensorflow.compat.v1.nn.convolution |
tf.compat.v1.nn.crelu Computes Concatenated ReLU.
tf.compat.v1.nn.crelu(
features, name=None, axis=-1
)
Concatenates a ReLU which selects only the positive part of the activation with a ReLU which selects only the negative part of the activation. Note that as a result this non-linearity doubles the depth of the a... | tensorflow.compat.v1.nn.crelu |
tf.compat.v1.nn.ctc_beam_search_decoder Performs beam search decoding on the logits given in input.
tf.compat.v1.nn.ctc_beam_search_decoder(
inputs, sequence_length, beam_width=100, top_paths=1, merge_repeated=True
)
Note: The ctc_greedy_decoder is a special case of the ctc_beam_search_decoder with top_paths=1 a... | tensorflow.compat.v1.nn.ctc_beam_search_decoder |
tf.compat.v1.nn.ctc_loss Computes the CTC (Connectionist Temporal Classification) Loss.
tf.compat.v1.nn.ctc_loss(
labels, inputs=None, sequence_length=None, preprocess_collapse_repeated=False,
ctc_merge_repeated=True, ignore_longer_outputs_than_inputs=False,
time_major=True, logits=None
)
This op implemen... | tensorflow.compat.v1.nn.ctc_loss |
tf.compat.v1.nn.ctc_loss_v2 Computes CTC (Connectionist Temporal Classification) loss.
tf.compat.v1.nn.ctc_loss_v2(
labels, logits, label_length, logit_length, logits_time_major=True, unique=None,
blank_index=None, name=None
)
This op implements the CTC loss as presented in (Graves et al., 2006). Notes: Same... | tensorflow.compat.v1.nn.ctc_loss_v2 |
tf.compat.v1.nn.depthwise_conv2d Depthwise 2-D convolution.
tf.compat.v1.nn.depthwise_conv2d(
input, filter, strides, padding, rate=None, name=None, data_format=None,
dilations=None
)
Given a 4D input tensor ('NHWC' or 'NCHW' data formats) and a filter tensor of shape [filter_height, filter_width, in_channels... | tensorflow.compat.v1.nn.depthwise_conv2d |
tf.compat.v1.nn.depthwise_conv2d_native Computes a 2-D depthwise convolution.
tf.compat.v1.nn.depthwise_conv2d_native(
input, filter, strides, padding, data_format='NHWC', dilations=[1, 1,
1, 1], name=None
)
Given an input tensor of shape [batch, in_height, in_width, in_channels] and a filter / kernel tensor ... | tensorflow.compat.v1.nn.depthwise_conv2d_native |
tf.compat.v1.nn.dilation2d Computes the grayscale dilation of 4-D input and 3-D filter tensors.
tf.compat.v1.nn.dilation2d(
input, filter=None, strides=None, rates=None, padding=None, name=None,
filters=None, dilations=None
)
The input tensor has shape [batch, in_height, in_width, depth] and the filter tensor... | tensorflow.compat.v1.nn.dilation2d |
tf.compat.v1.nn.dropout Computes dropout. (deprecated arguments)
tf.compat.v1.nn.dropout(
x, keep_prob=None, noise_shape=None, seed=None, name=None, rate=None
)
Warning: SOME ARGUMENTS ARE DEPRECATED: (keep_prob). They will be removed in a future version. Instructions for updating: Please use rate instead of keep... | tensorflow.compat.v1.nn.dropout |
tf.compat.v1.nn.dynamic_rnn Creates a recurrent neural network specified by RNNCell cell. (deprecated)
tf.compat.v1.nn.dynamic_rnn(
cell, inputs, sequence_length=None, initial_state=None, dtype=None,
parallel_iterations=None, swap_memory=False, time_major=False, scope=None
)
Warning: THIS FUNCTION IS DEPRECAT... | tensorflow.compat.v1.nn.dynamic_rnn |
tf.compat.v1.nn.embedding_lookup Looks up embeddings for the given ids from a list of tensors.
tf.compat.v1.nn.embedding_lookup(
params, ids, partition_strategy='mod', name=None,
validate_indices=True, max_norm=None
)
This function is used to perform parallel lookups on the list of tensors in params. It is a ... | tensorflow.compat.v1.nn.embedding_lookup |
tf.compat.v1.nn.embedding_lookup_sparse Looks up embeddings for the given ids and weights from a list of tensors.
tf.compat.v1.nn.embedding_lookup_sparse(
params, sp_ids, sp_weights, partition_strategy='mod', name=None,
combiner=None, max_norm=None
)
This op assumes that there is at least one id for each row ... | tensorflow.compat.v1.nn.embedding_lookup_sparse |
tf.compat.v1.nn.erosion2d Computes the grayscale erosion of 4-D value and 3-D kernel tensors.
tf.compat.v1.nn.erosion2d(
value, kernel, strides, rates, padding, name=None
)
The value tensor has shape [batch, in_height, in_width, depth] and the kernel tensor has shape [kernel_height, kernel_width, depth], i.e., ea... | tensorflow.compat.v1.nn.erosion2d |
tf.compat.v1.nn.fractional_avg_pool Performs fractional average pooling on the input. (deprecated)
tf.compat.v1.nn.fractional_avg_pool(
value, pooling_ratio, pseudo_random=False, overlapping=False,
deterministic=False, seed=0, seed2=0, name=None
)
Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a ... | tensorflow.compat.v1.nn.fractional_avg_pool |
tf.compat.v1.nn.fractional_max_pool Performs fractional max pooling on the input. (deprecated)
tf.compat.v1.nn.fractional_max_pool(
value, pooling_ratio, pseudo_random=False, overlapping=False,
deterministic=False, seed=0, seed2=0, name=None
)
Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a futu... | tensorflow.compat.v1.nn.fractional_max_pool |
tf.compat.v1.nn.fused_batch_norm Batch normalization.
tf.compat.v1.nn.fused_batch_norm(
x, scale, offset, mean=None, variance=None, epsilon=0.001,
data_format='NHWC', is_training=True, name=None,
exponential_avg_factor=1.0
)
See Source: Batch Normalization: Accelerating Deep Network Training by Reducing I... | tensorflow.compat.v1.nn.fused_batch_norm |
tf.compat.v1.nn.max_pool Performs the max pooling on the input.
tf.compat.v1.nn.max_pool(
value, ksize, strides, padding, data_format='NHWC', name=None,
input=None
)
Args
value A 4-D Tensor of the format specified by data_format.
ksize An int or list of ints that has length 1, 2 or 4. The size ... | tensorflow.compat.v1.nn.max_pool |
tf.compat.v1.nn.max_pool_with_argmax Performs max pooling on the input and outputs both max values and indices.
tf.compat.v1.nn.max_pool_with_argmax(
input, ksize, strides, padding, data_format='NHWC', Targmax=None,
name=None, output_dtype=None, include_batch_in_index=False
)
The indices in argmax are flatten... | tensorflow.compat.v1.nn.max_pool_with_argmax |
tf.compat.v1.nn.moments Calculate the mean and variance of x.
tf.compat.v1.nn.moments(
x, axes, shift=None, name=None, keep_dims=None, keepdims=None
)
The mean and variance are calculated by aggregating the contents of x across axes. If x is 1-D and axes = [0] this is just the mean and variance of a vector.
Note... | tensorflow.compat.v1.nn.moments |
tf.compat.v1.nn.nce_loss Computes and returns the noise-contrastive estimation training loss.
tf.compat.v1.nn.nce_loss(
weights, biases, labels, inputs, num_sampled, num_classes, num_true=1,
sampled_values=None, remove_accidental_hits=False,
partition_strategy='mod', name='nce_loss'
)
A common use case is... | tensorflow.compat.v1.nn.nce_loss |
tf.compat.v1.nn.pool Performs an N-D pooling operation.
tf.compat.v1.nn.pool(
input, window_shape, pooling_type, padding, dilation_rate=None, strides=None,
name=None, data_format=None, dilations=None
)
In the case that data_format does not start with "NC", computes for 0 <= b < batch_size, 0 <= x[i] < output_... | tensorflow.compat.v1.nn.pool |
tf.compat.v1.nn.quantized_avg_pool Produces the average pool of the input tensor for quantized types.
tf.compat.v1.nn.quantized_avg_pool(
input, min_input, max_input, ksize, strides, padding, name=None
)
Args
input A Tensor. Must be one of the following types: qint8, quint8, qint32, qint16, quint16. 4-D ... | tensorflow.compat.v1.nn.quantized_avg_pool |
tf.compat.v1.nn.quantized_conv2d Computes a 2D convolution given quantized 4D input and filter tensors.
tf.compat.v1.nn.quantized_conv2d(
input, filter, min_input, max_input, min_filter, max_filter, strides, padding,
out_type=tf.dtypes.qint32, dilations=[1, 1, 1, 1], name=None
)
The inputs are quantized tenso... | tensorflow.compat.v1.nn.quantized_conv2d |
tf.compat.v1.nn.quantized_max_pool Produces the max pool of the input tensor for quantized types.
tf.compat.v1.nn.quantized_max_pool(
input, min_input, max_input, ksize, strides, padding, name=None
)
Args
input A Tensor. Must be one of the following types: qint8, quint8, qint32, qint16, quint16. The 4D (... | tensorflow.compat.v1.nn.quantized_max_pool |
tf.compat.v1.nn.quantized_relu_x Computes Quantized Rectified Linear X: min(max(features, 0), max_value)
tf.compat.v1.nn.quantized_relu_x(
features, max_value, min_features, max_features, out_type=tf.dtypes.quint8,
name=None
)
Args
features A Tensor. Must be one of the following types: qint8, quint8,... | tensorflow.compat.v1.nn.quantized_relu_x |
tf.compat.v1.nn.raw_rnn Creates an RNN specified by RNNCell cell and loop function loop_fn.
tf.compat.v1.nn.raw_rnn(
cell, loop_fn, parallel_iterations=None, swap_memory=False, scope=None
)
Note: This method is still in testing, and the API may change.**
This function is a more primitive version of dynamic_rnn ... | tensorflow.compat.v1.nn.raw_rnn |
tf.compat.v1.nn.relu_layer Computes Relu(x * weight + biases).
tf.compat.v1.nn.relu_layer(
x, weights, biases, name=None
)
Args
x a 2D tensor. Dimensions typically: batch, in_units
weights a 2D tensor. Dimensions typically: in_units, out_units
biases a 1D tensor. Dimensions: out_units
nam... | tensorflow.compat.v1.nn.relu_layer |
Module: tf.compat.v1.nn.rnn_cell Module for constructing RNN Cells. Classes class BasicLSTMCell: DEPRECATED: Please use tf.compat.v1.nn.rnn_cell.LSTMCell instead. class BasicRNNCell: The most basic RNN cell. class DeviceWrapper: Operator that ensures an RNNCell runs on a particular device. class DropoutWrapper: Operato... | tensorflow.compat.v1.nn.rnn_cell |
tf.compat.v1.nn.rnn_cell.BasicLSTMCell DEPRECATED: Please use tf.compat.v1.nn.rnn_cell.LSTMCell instead. Inherits From: RNNCell, Layer, Layer, Module
tf.compat.v1.nn.rnn_cell.BasicLSTMCell(
num_units, forget_bias=1.0, state_is_tuple=True, activation=None, reuse=None,
name=None, dtype=None, **kwargs
)
Basic LS... | tensorflow.compat.v1.nn.rnn_cell.basiclstmcell |
tf.compat.v1.nn.rnn_cell.BasicRNNCell The most basic RNN cell. Inherits From: RNNCell, Layer, Layer, Module
tf.compat.v1.nn.rnn_cell.BasicRNNCell(
num_units, activation=None, reuse=None, name=None, dtype=None, **kwargs
)
Note that this cell is not optimized for performance. Please use tf.contrib.cudnn_rnn.CudnnRN... | tensorflow.compat.v1.nn.rnn_cell.basicrnncell |
tf.compat.v1.nn.rnn_cell.DeviceWrapper Operator that ensures an RNNCell runs on a particular device. Inherits From: RNNCell, Layer, Layer, Module
tf.compat.v1.nn.rnn_cell.DeviceWrapper(
*args, **kwargs
)
Args
cell An instance of RNNCell.
device A device string or function, for passing to tf.device.... | tensorflow.compat.v1.nn.rnn_cell.devicewrapper |
tf.compat.v1.nn.rnn_cell.DropoutWrapper Operator adding dropout to inputs and outputs of the given cell. Inherits From: RNNCell, Layer, Layer, Module
tf.compat.v1.nn.rnn_cell.DropoutWrapper(
*args, **kwargs
)
Args
cell an RNNCell, a projection to output_size is added to it.
input_keep_prob unit Ten... | tensorflow.compat.v1.nn.rnn_cell.dropoutwrapper |
tf.compat.v1.nn.rnn_cell.GRUCell Gated Recurrent Unit cell. Inherits From: RNNCell, Layer, Layer, Module
tf.compat.v1.nn.rnn_cell.GRUCell(
num_units, activation=None, reuse=None, kernel_initializer=None,
bias_initializer=None, name=None, dtype=None, **kwargs
)
Note that this cell is not optimized for performa... | tensorflow.compat.v1.nn.rnn_cell.grucell |
tf.compat.v1.nn.rnn_cell.LSTMCell Long short-term memory unit (LSTM) recurrent network cell. Inherits From: RNNCell, Layer, Layer, Module
tf.compat.v1.nn.rnn_cell.LSTMCell(
num_units, use_peepholes=False, cell_clip=None, initializer=None, num_proj=None,
proj_clip=None, num_unit_shards=None, num_proj_shards=Non... | tensorflow.compat.v1.nn.rnn_cell.lstmcell |
tf.compat.v1.nn.rnn_cell.LSTMStateTuple Tuple used by LSTM Cells for state_size, zero_state, and output state.
tf.compat.v1.nn.rnn_cell.LSTMStateTuple(
c, h
)
Stores two elements: (c, h), in that order. Where c is the hidden state and h is the output. Only used when state_is_tuple=True.
Attributes
c
... | tensorflow.compat.v1.nn.rnn_cell.lstmstatetuple |
tf.compat.v1.nn.rnn_cell.MultiRNNCell RNN cell composed sequentially of multiple simple cells. Inherits From: RNNCell, Layer, Layer, Module
tf.compat.v1.nn.rnn_cell.MultiRNNCell(
cells, state_is_tuple=True
)
Example: num_units = [128, 64]
cells = [BasicLSTMCell(num_units=n) for n in num_units]
stacked_rnn_cell = ... | tensorflow.compat.v1.nn.rnn_cell.multirnncell |
tf.compat.v1.nn.rnn_cell.ResidualWrapper RNNCell wrapper that ensures cell inputs are added to the outputs. Inherits From: RNNCell, Layer, Layer, Module
tf.compat.v1.nn.rnn_cell.ResidualWrapper(
*args, **kwargs
)
Args
cell An instance of RNNCell.
residual_fn (Optional) The function to map raw cell ... | tensorflow.compat.v1.nn.rnn_cell.residualwrapper |
tf.compat.v1.nn.rnn_cell.RNNCell Abstract object representing an RNN cell. Inherits From: Layer, Layer, Module
tf.compat.v1.nn.rnn_cell.RNNCell(
trainable=True, name=None, dtype=None, **kwargs
)
Every RNNCell must have the properties below and implement call with the signature (output, next_state) = call(input, s... | tensorflow.compat.v1.nn.rnn_cell.rnncell |
tf.compat.v1.nn.safe_embedding_lookup_sparse Lookup embedding results, accounting for invalid IDs and empty features.
tf.compat.v1.nn.safe_embedding_lookup_sparse(
embedding_weights, sparse_ids, sparse_weights=None, combiner='mean',
default_id=None, name=None, partition_strategy='div', max_norm=None
)
The par... | tensorflow.compat.v1.nn.safe_embedding_lookup_sparse |
tf.compat.v1.nn.sampled_softmax_loss Computes and returns the sampled softmax training loss.
tf.compat.v1.nn.sampled_softmax_loss(
weights, biases, labels, inputs, num_sampled, num_classes, num_true=1,
sampled_values=None, remove_accidental_hits=True,
partition_strategy='mod', name='sampled_softmax_loss',
... | tensorflow.compat.v1.nn.sampled_softmax_loss |
tf.compat.v1.nn.separable_conv2d 2-D convolution with separable filters.
tf.compat.v1.nn.separable_conv2d(
input, depthwise_filter, pointwise_filter, strides, padding, rate=None,
name=None, data_format=None, dilations=None
)
Performs a depthwise convolution that acts separately on channels followed by a point... | tensorflow.compat.v1.nn.separable_conv2d |
tf.compat.v1.nn.sigmoid_cross_entropy_with_logits Computes sigmoid cross entropy given logits.
tf.compat.v1.nn.sigmoid_cross_entropy_with_logits(
_sentinel=None, labels=None, logits=None, name=None
)
Measures the probability error in discrete classification tasks in which each class is independent and not mutuall... | tensorflow.compat.v1.nn.sigmoid_cross_entropy_with_logits |
tf.compat.v1.nn.softmax_cross_entropy_with_logits Computes softmax cross entropy between logits and labels. (deprecated)
tf.compat.v1.nn.softmax_cross_entropy_with_logits(
_sentinel=None, labels=None, logits=None, dim=-1, name=None, axis=None
)
Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future ... | tensorflow.compat.v1.nn.softmax_cross_entropy_with_logits |
tf.compat.v1.nn.softmax_cross_entropy_with_logits_v2 Computes softmax cross entropy between logits and labels. (deprecated arguments)
tf.compat.v1.nn.softmax_cross_entropy_with_logits_v2(
labels, logits, axis=None, name=None, dim=None
)
Warning: SOME ARGUMENTS ARE DEPRECATED: (dim). They will be removed in a futu... | tensorflow.compat.v1.nn.softmax_cross_entropy_with_logits_v2 |
tf.compat.v1.nn.sparse_softmax_cross_entropy_with_logits Computes sparse softmax cross entropy between logits and labels.
tf.compat.v1.nn.sparse_softmax_cross_entropy_with_logits(
_sentinel=None, labels=None, logits=None, name=None
)
Measures the probability error in discrete classification tasks in which the cla... | tensorflow.compat.v1.nn.sparse_softmax_cross_entropy_with_logits |
tf.compat.v1.nn.static_bidirectional_rnn Creates a bidirectional recurrent neural network. (deprecated)
tf.compat.v1.nn.static_bidirectional_rnn(
cell_fw, cell_bw, inputs, initial_state_fw=None, initial_state_bw=None,
dtype=None, sequence_length=None, scope=None
)
Warning: THIS FUNCTION IS DEPRECATED. It will... | tensorflow.compat.v1.nn.static_bidirectional_rnn |
tf.compat.v1.nn.static_rnn Creates a recurrent neural network specified by RNNCell cell. (deprecated)
tf.compat.v1.nn.static_rnn(
cell, inputs, initial_state=None, dtype=None, sequence_length=None, scope=None
)
Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating... | tensorflow.compat.v1.nn.static_rnn |
tf.compat.v1.nn.static_state_saving_rnn RNN that accepts a state saver for time-truncated RNN calculation. (deprecated)
tf.compat.v1.nn.static_state_saving_rnn(
cell, inputs, state_saver, state_name, sequence_length=None, scope=None
)
Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. I... | tensorflow.compat.v1.nn.static_state_saving_rnn |
tf.compat.v1.nn.sufficient_statistics Calculate the sufficient statistics for the mean and variance of x.
tf.compat.v1.nn.sufficient_statistics(
x, axes, shift=None, keep_dims=None, name=None, keepdims=None
)
These sufficient statistics are computed using the one pass algorithm on an input that's optionally shift... | tensorflow.compat.v1.nn.sufficient_statistics |
tf.compat.v1.nn.weighted_cross_entropy_with_logits Computes a weighted cross entropy. (deprecated arguments)
tf.compat.v1.nn.weighted_cross_entropy_with_logits(
labels=None, logits=None, pos_weight=None, name=None, targets=None
)
Warning: SOME ARGUMENTS ARE DEPRECATED: (targets). They will be removed in a future ... | tensorflow.compat.v1.nn.weighted_cross_entropy_with_logits |
tf.compat.v1.nn.weighted_moments Returns the frequency-weighted mean and variance of x.
tf.compat.v1.nn.weighted_moments(
x, axes, frequency_weights, name=None, keep_dims=None, keepdims=None
)
Args
x A tensor.
axes 1-d tensor of int32 values; these are the axes along which to compute mean and varia... | tensorflow.compat.v1.nn.weighted_moments |
tf.compat.v1.nn.xw_plus_b Computes matmul(x, weights) + biases.
tf.compat.v1.nn.xw_plus_b(
x, weights, biases, name=None
)
Args
x a 2D tensor. Dimensions typically: batch, in_units
weights a 2D tensor. Dimensions typically: in_units, out_units
biases a 1D tensor. Dimensions: out_units
nam... | tensorflow.compat.v1.nn.xw_plus_b |
tf.compat.v1.NodeDef A ProtocolMessage
Attributes
attr repeated AttrEntry attr
device string device
experimental_debug_info ExperimentalDebugInfo experimental_debug_info
input repeated string input
name string name
op string op Child Classes class AttrEntry class ExperimentalDe... | tensorflow.compat.v1.nodedef |
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