cnn_model.py

import tensorflow as tf


class TCNNConfig(object):

    embedding_dim = 64
    seq_length = 600
    num_classes = 5
    num_filters = 256
    kernel_size = 5
    vocab_size = 2048+1024

    hidden_dim = 128

    dropout_keep_prob = 0.5
    learning_rate = 1e-3

    batch_size = 16
    num_epochs = 32

    print_per_batch = 64
    save_per_batch = 8


class TextCNN(object):

    def __init__(self, config):
        self.config = config

        self.input_x = tf.placeholder(tf.int32, [None, self.config.seq_length], name='input_x')
        self.input_y = tf.placeholder(tf.float32, [None, self.config.num_classes], name='input_y')
        self.keep_prob = tf.placeholder(tf.float32, name='keep_prob')

        self.cnn()

    def cnn(self):
        with tf.device('/cpu:0'):
            embedding = tf.get_variable('embedding', [self.config.vocab_size, self.config.embedding_dim])
            embedding_inputs = tf.nn.embedding_lookup(embedding, self.input_x)

        with tf.name_scope("cnn"):
            conv = tf.layers.conv1d(embedding_inputs, self.config.num_filters, self.config.kernel_size, name='conv')
            gmp = tf.reduce_max(conv, reduction_indices=[1], name='gmp')

        with tf.name_scope("score"):
            fc = tf.layers.dense(gmp, self.config.hidden_dim, name='fc1')
            fc = tf.contrib.layers.dropout(fc, self.keep_prob)
            fc = tf.nn.relu(fc)

            self.logits = tf.layers.dense(fc, self.config.num_classes, name='fc2')
            self.y_pred_cls = tf.argmax(tf.nn.softmax(self.logits), 1)

        with tf.name_scope("optimize"):
            cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=self.logits, labels=self.input_y)
            self.loss = tf.reduce_mean(cross_entropy)
            self.optim = tf.train.AdamOptimizer(learning_rate=self.config.learning_rate).minimize(self.loss)

        with tf.name_scope("accuracy"):
            correct_pred = tf.equal(tf.argmax(self.input_y, 1), self.y_pred_cls)
            self.acc = tf.reduce_mean(tf.cast(correct_pred, tf.float32))