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twolayerCNN.py
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twolayerCNN.py
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#!/usr/bin/python3
# -*- coding: utf-8 -*-
# @Time : 1/18/2018 7:05 PM
# @Author : Leyang
import tensorflow as tf
import numpy as np
class twolayerCNN(object):
"""
A CNN for text classification.
Uses an embedding layer, followed by a convolutional, max-pooling and softmax layer.
"""
def __init__(
self, sequence_length, num_classes, vocab_size,
embedding_size, filter_sizes, num_filters, l2_reg_lambda,learning_rate):
# Placeholders for input, output and dropout
self.input_x = tf.placeholder(tf.int32, [None, sequence_length], name="input_x")
self.input_y = tf.placeholder(tf.float32, [None, num_classes], name="input_y")
self.dropout_keep_prob = tf.placeholder(tf.float32, name="dropout_keep_prob")
# Keeping track of l2 regularization loss (optional)
l2_loss = tf.constant(0.0)
# Embedding layer
with tf.device('/cpu:0'), tf.name_scope("embedding"):
self.W = tf.Variable(
tf.random_uniform([vocab_size, embedding_size], -0.25, 0.25),trainable=True,
name="W")
self.embedded_chars = tf.nn.embedding_lookup(self.W, self.input_x)
self.embedded_chars_expanded = tf.expand_dims(self.embedded_chars, -1)
# Create a convolution + maxpool layer for each filter size
pooled_outputs_1 = []
for i, filter_size in enumerate(filter_sizes):
with tf.name_scope("conv-maxpool-1-%s" % filter_size):
# 1st Convolution Layer
filter_shape = [filter_size, embedding_size, 1, num_filters]
W = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.1), name="W")
b = tf.Variable(tf.constant(0.1, shape=[num_filters]), name="b")
conv = tf.nn.conv2d(
self.embedded_chars_expanded,
W,
strides=[1, 1, 1, 1],
padding="VALID",
name="conv")
# Apply nonlinearity
h = tf.nn.relu(tf.nn.bias_add(conv, b), name="relu")
# Maxpooling over the outputs
pooled = tf.nn.max_pool(
h,
ksize=[1, sequence_length - filter_size + 1, 1, 1],
strides=[1, 1, 1, 1],
padding='VALID',
name="pool")
pooled_outputs_1.append(pooled)
# Combine all the pooled features
num_filters_total = num_filters * len(filter_sizes)
self.h_pool_1 = tf.concat(pooled_outputs_1, 3)
#self.h_pool_flat_1 = tf.reshape(self.h_pool_1, [-1, num_filters_total])
#reshape(batch, width, height and channel.)
self.h_pool_flat_1 = tf.reshape(self.h_pool_1, [-1, num_filters_total, 1, 1])
#2nd convolution layer
pooled_outputs_2 = []
for i ,filter_size in enumerate(filter_sizes):
with tf.name_scope("con-maxpool-2-%s" %filter_size):
filter_shape = [filter_size, 1, 1, num_filters]
#input and output channel are 1 and num_filters
W = tf.Variable(tf.truncated_normal(filter_shape,stddev = 0.1), name = "W")
b = tf.Variable(tf.constant(0.1,shape = [num_filters]) , name = "b")
conv = tf.nn.conv2d(
self.h_pool_flat_1,
W,
strides = [1,1,1,1],
padding = "VALID",
name = "conv"
)
#nonlinearity
h = tf.nn.relu(tf.nn.bias_add(conv , b) , name ="relu")
# Maxpooling over the outputs
pooled = tf.nn.max_pool(
h,
ksize=[1, sequence_length - filter_size + 1, 1, 1],
strides=[1, 1, 1, 1],
padding='VALID',
name="pool")
pooled_outputs_2.append(pooled)
#Combine all the pooled features
self.h_pool_2 = tf.concat(pooled_outputs_1, 3)
self.h_pool_flat_2 = tf.reshape(self.h_pool_2, [-1, num_filters_total])
# Add dropout
with tf.name_scope("dropout"):
self.h_drop = tf.nn.dropout(self.h_pool_flat_2, self.dropout_keep_prob)
# Final (unnormalized) scores and predictions
with tf.name_scope("output"):
W = tf.get_variable(
"W",
shape=[num_filters_total, num_classes],
initializer=tf.contrib.layers.xavier_initializer())
b = tf.Variable(tf.constant(0.1, shape=[num_classes]), name="b")
l2_loss += tf.nn.l2_loss(W)
l2_loss += tf.nn.l2_loss(b)
self.scores = tf.nn.xw_plus_b(self.h_drop, W, b, name="scores")
self.predictions = tf.argmax(self.scores, 1, name="predictions")
# CalculateMean cross-entropy loss
with tf.name_scope("loss"):
losses = tf.nn.softmax_cross_entropy_with_logits(logits=self.scores, labels=self.input_y)
self.loss = tf.reduce_mean(losses) + l2_reg_lambda * l2_loss
# optimizer = tf.train.AdamOptimizer(learning_rate)
# grads_and_vars = optimizer.compute_gradients(self.loss)
# self.train_op = optimizer.apply_gradients(grads_and_vars)
# 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, "float"), name="accuracy")
self.y = tf.argmax(self.input_y, 1)