text stringlengths 1 93.6k |
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attention_pool = tf.reduce_sum(scores * passage,1)
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_, state = cell(attention_pool, initial_state)
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inputs = [passage, state]
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p2_logits = attention(inputs, Params.attn_size, weights_p, memory_len = passage_len, scope = "attention", reuse = True)
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return tf.stack((p1_logits,p2_logits),1)
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def attention_rnn(inputs, inputs_len, units, attn_cell, bidirection = True, scope = "gated_attention_rnn", is_training = True):
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with tf.variable_scope(scope):
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if bidirection:
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outputs = bidirectional_GRU(inputs,
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inputs_len,
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cell = attn_cell,
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scope = scope + "_bidirectional",
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output = 0,
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is_training = is_training)
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else:
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outputs, _ = tf.nn.dynamic_rnn(attn_cell, inputs,
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sequence_length = inputs_len,
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dtype=tf.float32)
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return outputs
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def question_pooling(memory, units, weights, memory_len = None, scope = "question_pooling"):
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with tf.variable_scope(scope):
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shapes = memory.get_shape().as_list()
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V_r = tf.get_variable("question_param", shape = (Params.max_q_len, units), initializer = tf.contrib.layers.xavier_initializer(), dtype = tf.float32)
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inputs_ = [memory, V_r]
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attn = attention(inputs_, units, weights, memory_len = memory_len, scope = "question_attention_pooling")
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attn = tf.expand_dims(attn, -1)
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return tf.reduce_sum(attn * memory, 1)
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def gated_attention(memory, inputs, states, units, params, self_matching = False, memory_len = None, scope="gated_attention"):
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with tf.variable_scope(scope):
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weights, W_g = params
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inputs_ = [memory, inputs]
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states = tf.reshape(states,(Params.batch_size,Params.attn_size))
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if not self_matching:
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inputs_.append(states)
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scores = attention(inputs_, units, weights, memory_len = memory_len)
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scores = tf.expand_dims(scores,-1)
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attention_pool = tf.reduce_sum(scores * memory, 1)
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inputs = tf.concat((inputs,attention_pool),axis = 1)
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g_t = tf.sigmoid(tf.matmul(inputs,W_g))
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return g_t * inputs
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def mask_attn_score(score, memory_sequence_length, score_mask_value = -1e8):
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score_mask = tf.sequence_mask(
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memory_sequence_length, maxlen=score.shape[1])
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score_mask_values = score_mask_value * tf.ones_like(score)
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return tf.where(score_mask, score, score_mask_values)
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def attention(inputs, units, weights, scope = "attention", memory_len = None, reuse = None):
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with tf.variable_scope(scope, reuse = reuse):
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outputs_ = []
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weights, v = weights
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for i, (inp,w) in enumerate(zip(inputs,weights)):
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shapes = inp.shape.as_list()
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inp = tf.reshape(inp, (-1, shapes[-1]))
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if w is None:
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w = tf.get_variable("w_%d"%i, dtype = tf.float32, shape = [shapes[-1],Params.attn_size], initializer = tf.contrib.layers.xavier_initializer())
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outputs = tf.matmul(inp, w)
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# Hardcoded attention output reshaping. Equation (4), (8), (9) and (11) in the original paper.
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if len(shapes) > 2:
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outputs = tf.reshape(outputs, (shapes[0], shapes[1], -1))
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elif len(shapes) == 2 and shapes[0] is Params.batch_size:
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outputs = tf.reshape(outputs, (shapes[0],1,-1))
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else:
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outputs = tf.reshape(outputs, (1, shapes[0],-1))
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outputs_.append(outputs)
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outputs = sum(outputs_)
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if Params.bias:
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b = tf.get_variable("b", shape = outputs.shape[-1], dtype = tf.float32, initializer = tf.contrib.layers.xavier_initializer())
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outputs += b
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scores = tf.reduce_sum(tf.tanh(outputs) * v, [-1])
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if memory_len is not None:
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scores = mask_attn_score(scores, memory_len)
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return tf.nn.softmax(scores) # all attention output is softmaxed now
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def cross_entropy(output, target):
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cross_entropy = target * tf.log(output + 1e-8)
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cross_entropy = -tf.reduce_sum(cross_entropy, 2) # sum across passage timestep
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cross_entropy = tf.reduce_mean(cross_entropy, 1) # average across pointer networks output
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return tf.reduce_mean(cross_entropy) # average across batch size
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def total_params():
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total_parameters = 0
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for variable in tf.trainable_variables():
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shape = variable.get_shape()
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variable_parametes = 1
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for dim in shape:
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variable_parametes *= dim.value
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total_parameters += variable_parametes
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print("Total number of trainable parameters: {}".format(total_parameters))
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# <FILESEP>
|
import torch
|
import pandas as pd
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from train_util import AddEgoIds, extract_param, add_arange_ids, get_loaders, evaluate_homo, evaluate_hetero
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from training import get_model
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from torch_geometric.nn import to_hetero, summary
|
import wandb
|
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