multi_class_base.py

#!/usr/bin/env python
# -*- encoding: utf-8 -*-
'''
@File    :   multi_class_base.py
'''

# multi-task learning implementation of Kim's paper : Convolutional Neural Networks for Sentence Classification.

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, multi_size, vocab_size,
                 embedding_size, filter_sizes, num_filters, hidden_units, l2_reg_lambda):
        """

        :multi_size -- 多任务数量
        :filter_sizes -- 相当于n gram sizes，list；
        :num_filters -- 每个n gram filter输出特征数量，决定输出长度， int;
        :hidden_units -- softmax前的全连接层大小；
        """
        # Placeholders for input, output and dropout
        self.input_x = tf.placeholder(
            tf.int32, [None, sequence_length], name="input_x")

        self.input_y = []
        for i in range(multi_size):
            self.input_y.append(tf.placeholder(
                tf.float32, [None, num_classes], name="input_y"+str(i)))

        self.dropout_keep_prob = tf.placeholder(
            tf.float32, name="dropout_keep_prob")

        # Keeping track of l2 regularization loss (optional)
        l2_loss = []
        for i in range(multi_size):
            l2_loss.append(tf.constant(0.0, name="l2_loss"+str(i)))
        # Embedding layer
        with tf.device('/cpu:0'), tf.name_scope("embedding"):
            self.W = tf.Variable(
                tf.constant(0.0, shape=[vocab_size, embedding_size]),
                trainable=False, 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 = []
        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
        num_filters_total = num_filters * len(filter_sizes)
        self.h_pool = tf.concat(3, pooled_outputs)
        self.h_pool_flat = tf.reshape(self.h_pool, [-1, num_filters_total])

        # Add dropout
        with tf.name_scope("dropout"):
            self.h_drop = tf.nn.dropout(
                self.h_pool_flat, self.dropout_keep_prob)

        # Final (unnormalized) scores and predictions
        self.scores = []
        self.predictions = []
        self.loss = []
        self.accuracy = []

        for i in range(multi_size):
            with tf.name_scope("output" + str(i)):
                W = tf.get_variable(
                    "W"+str(i),
                    shape=[num_filters_total, hidden_units],
                    initializer=tf.contrib.layers.xavier_initializer())
                b = tf.Variable(tf.constant(
                    0.1, shape=[hidden_units]), name="b"+str(i))
                l2_loss[i] += tf.nn.l2_loss(W)
                l2_loss[i] += tf.nn.l2_loss(b)

                inference = tf.nn.softmax(tf.nn.bias_add(
                    tf.matmul(self.h_drop, W), b), name="softmax"+str(i))
                inference = tf.nn.dropout(inference, self.dropout_keep_prob)

                W2 = tf.get_variable(
                    "W2"+str(i),
                    shape=[hidden_units, num_classes],
                    initializer=tf.contrib.layers.xavier_initializer())
                b2 = tf.Variable(tf.constant(
                    0.1, shape=[num_classes]), name="b2"+str(i))
                l2_loss[i] += tf.nn.l2_loss(W2)
                l2_loss[i] += tf.nn.l2_loss(b2)

                self.scores.append(tf.nn.xw_plus_b(
                    inference, W2, b2, name="scores"+str(i)))
                self.predictions.append(tf.argmax(tf.nn.softmax(
                    self.scores[i]), 1, name="predictions"+str(i)))

        for i in range(multi_size):
            with tf.name_scope("loss"+str(i)):
                losses = tf.nn.softmax_cross_entropy_with_logits(
                    self.scores[i], self.input_y[i])
                self.loss.append(tf.reduce_mean(losses) +
                                 l2_reg_lambda * l2_loss[i])

        # Accuracy
        for i in range(multi_size):
            with tf.name_scope("accuracy"+str(i)):
                correct_predictions = tf.equal(
                    self.predictions[i], tf.argmax(self.input_y[i], 1))
                self.accuracy.append(tf.reduce_mean(
                    tf.cast(correct_predictions, "float"), name="accuracy"+str(i)))