TextCNN.py

'''定义识别垃圾短信的神经网络'''

import tensorflow as tf
import numpy as np


class TextCNN(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=0.0):

        # 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)
        #1.构建中间层，单词转化成向量的形式
        # Embedding layer
        with tf.device('/cpu:0'), tf.name_scope("embedding"):
            self.W = tf.Variable(tf.random_uniform(
                [vocab_size, embedding_size], -1.0, 1.0),
                                 name="W")
            self.embedded_chars = tf.nn.embedding_lookup(self.W, self.input_x)
            #增加一个维度（变成4维）[None, sequence_length, embedding_size, 1]
            self.embedded_chars_expanded = tf.expand_dims(
                self.embedded_chars, -1)
#2.加入卷积层、激活层和池化层
# Create a convolution + maxpool layer for each filter size
        pooled_outputs = []
        for i, filter_size in enumerate(filter_sizes):
            with tf.name_scope("conv-maxpool-%s" % filter_size):
                # 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.append(pooled)

        # Combine all the pooled features
        #将三种filtersize的output拼接并拉平，用以全连接层
        num_filters_total = num_filters * len(filter_sizes)
        self.h_pool = tf.concat(pooled_outputs, 3)
        self.h_pool_flat = tf.reshape(self.h_pool, [-1, num_filters_total])
        #3.进行dropout
        # Add dropout
        with tf.name_scope("dropout"):
            self.h_drop = tf.nn.dropout(self.h_pool_flat,
                                        self.dropout_keep_prob)
#4.全连接层（output）
# 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")
#4.计算损失
# 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


#5.计算准确率
# 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")