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text_cnn_wy_trans_pf.py
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text_cnn_wy_trans_pf.py
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import tensorflow as tf
import numpy as np
import os,sys
BASEDIR = os.path.dirname(os.path.abspath(__file__))
sys.path.append(BASEDIR)
from attention import multihead_attention
class TextCNN:
def __init__(self, sequence_length, num_classes,pos_vocab_size, pos_embedding_size,
text_embedding_size,filter_sizes, num_heads, num_filters, l2_reg_lambda=0.0):
# Placeholders for input, output and dropout
self.text_embedded_chars = tf.placeholder(tf.float32, shape=[None, sequence_length, 768], name='text_embedded_chars')
self.input_p1 = tf.placeholder(tf.int32, shape=[None, sequence_length], name='input_p1')
self.input_p2 = tf.placeholder(tf.int32, shape=[None, sequence_length], name='input_p2')
self.input_y = tf.placeholder(tf.float32, shape=[None, num_classes], name='input_y') #[20 19]
self.dropout_keep_prob = tf.placeholder(tf.float32, name='dropout_keep_prob')
self.emb_dropout_keep_prob = tf.placeholder(tf.float32, name='emb_dropout_keep_prob')
initializer = tf.keras.initializers.glorot_normal
# Embedding layer
# with tf.device('/device:GPU:0'), tf.variable_scope("text-embedding"):
# # self.W_text = tf.Variable(tf.random_uniform([text_vocab_size, text_embedding_size], -0.25, 0.25), name="W_text")
# # self.text_embedded_chars = tf.nn.embedding_lookup(self.W_text, self.input_text) #[800 90 300]
# # self.text_embedded_chars = server_bert.get_sentence_embedding(self.input_text) #[800 90 768]
# # self.text_embedded_chars_trans = transformer.transformerencoder(self.text_embedded_chars)
# self.text_embedded_chars_change = tf.layers.dense(self.text_embedded_chars, units=300,activation=tf.nn.relu,use_bias=True, trainable=True) #[800 90 300]
# print("change:",self.text_embedded_chars_change.get_shape())# (?, 90, 300)
# self.text_embedded_chars_expanded = tf.expand_dims(self.text_embedded_chars_change, -1) #[800 90 300 1]
# print(self.text_embedded_chars_expanded.get_shape())
with tf.device('/cpu:0'), tf.variable_scope("position-embedding"):
self.W_pos = tf.get_variable("W_pos", [pos_vocab_size, pos_embedding_size], initializer=initializer())
self.p1_embedded_chars = tf.nn.embedding_lookup(self.W_pos, self.input_p1)
self.p2_embedded_chars = tf.nn.embedding_lookup(self.W_pos, self.input_p2)
self.p1_embedded_chars_expanded = tf.expand_dims(self.p1_embedded_chars, -1) #[800 90 50 1]
self.p2_embedded_chars_expanded = tf.expand_dims(self.p2_embedded_chars, -1)
# self.embedded_chars_expanded = tf.concat([self.text_embedded_chars_expanded,
# self.p1_embedded_chars_expanded,
# self.p2_embedded_chars_expanded], 2) #[800 90 400 1]
_embedding_size = text_embedding_size
self.text_shape=tf.shape(self.text_embedded_chars)
# self.text_expand_shape=tf.shape(self.text_embedded_chars_expanded)
# self.pos_expand_shape=tf.shape(self.p1_embedded_chars_expanded)
# self.embedd_shape=tf.shape(self.text_embedded_chars_change)
# self.embedding_size_shape=tf.shape(_embedding_size)
# Dropout for Word Embedding
with tf.variable_scope('dropout-embeddings'):
self.embedded_chars = tf.nn.dropout(self.text_embedded_chars, self.emb_dropout_keep_prob)
# self-attention
with tf.variable_scope("self-attention"):
self.self_attn_output, self.self_alphas = multihead_attention(self.embedded_chars, self.embedded_chars,
num_units=768, num_heads=num_heads)
# print("attention shape:", self.self_attn.get_shape) #(?, 90 ,300)
self.self_attn = tf.layers.dense(self.self_attn_output, units=300,
activation=tf.nn.relu, use_bias=True,
trainable=True) # [800 90 300]
print("change:", self.self_attn.get_shape()) # (?, 90, 300)
self.self_atten_change =tf.expand_dims(self.self_attn, -1) #[800 90 300 1]
# Create a convolution + maxpool layer for each filter size
pooled_outputs = []
for i, filter_size in enumerate(filter_sizes):
with tf.variable_scope("conv-maxpool-%s" % filter_size):
# Convolution Layer
conv = tf.layers.conv2d(self.self_atten_change, num_filters, [filter_size, _embedding_size],
kernel_initializer=initializer(), activation=tf.nn.relu,
name="conv") # num_filter=128,filter_size=2,3,4,5
print(conv.get_shape()) # (?,89,1, 128);(?88,1,128)(?87,1,128)(?86 1 128)
# Maxpooling over the outputs
pooled = tf.nn.max_pool(conv, ksize=[1, sequence_length - filter_size + 1, 1, 1],
strides=[1, 1, 1, 1], padding='VALID', name="pool")
print(pooled.get_shape()) # (?, 1, 1, 128)
pooled_outputs.append(pooled)
# Combine all the pooled features
num_filters_total = num_filters * len(filter_sizes)
# print(pooled_outputs.get_shape())
print(np.array(pooled_outputs).shape) #（4，）
self.h_pool = tf.concat(pooled_outputs, 3)
# print(self.h_pool.get_shape()) #(?,1,1,512)
self.h_pool_flat = tf.reshape(self.h_pool, [-1, num_filters_total])
# print(self.h_pool_flat.get_shape())#(?,512)
# Add dropout
with tf.variable_scope("dropout"):
self.h_drop = tf.nn.dropout(self.h_pool_flat, self.dropout_keep_prob)
# Final scores and predictions
with tf.variable_scope("output"):
self.logits = tf.layers.dense(self.h_drop, num_classes, kernel_initializer=initializer())
print(self.logits.get_shape()) #(?,19)
self.predictions = tf.argmax(self.logits, 1, name="predictions")
# Calculate mean cross-entropy loss
with tf.variable_scope("loss"):
losses = tf.nn.softmax_cross_entropy_with_logits_v2(logits=self.logits, labels=self.input_y)
self.l2 = tf.add_n([tf.nn.l2_loss(v) for v in tf.trainable_variables()])
self.loss = tf.reduce_mean(losses) + l2_reg_lambda * self.l2
# Accuracy
with tf.name_scope("accuracy"):
correct_predictions = tf.equal(self.predictions, tf.argmax(self.input_y, 1))
self.accuracy = tf.reduce_mean(tf.cast(correct_predictions, tf.float32), name="accuracy")