python实现基于门控卷积的语言模型训练

下面是一个简单的python实现基于门控卷积的语言模型训练的例子：

import numpy as np
import tensorflow as tf

# 定义模型超参数
embedding_size = 128
hidden_size = 256
batch_size = 64
num_epochs = 10

# 加载数据集
data = []  # 假设已经加载好了数据集
vocab_size = len(vocab)  # 假设已经计算好了词汇表大小

# 定义输入数据
input_data = tf.placeholder(tf.int32, [batch_size, None])
targets = tf.placeholder(tf.int32, [batch_size, None])

# 定义embedding层
embedding = tf.Variable(tf.random_uniform([vocab_size, embedding_size], -1.0, 1.0))
inputs = tf.nn.embedding_lookup(embedding, input_data)

# 定义门控卷积层
conv_size = 3
conv_output_size = hidden_size * 2
W_gate = tf.Variable(tf.truncated_normal([conv_size, embedding_size, conv_output_size], stddev=0.1))
W_conv = tf.Variable(tf.truncated_normal([conv_size, embedding_size, hidden_size], stddev=0.1))
b_gate = tf.Variable(tf.constant(0.1, shape=[conv_output_size]))
b_conv = tf.Variable(tf.constant(0.1, shape=[hidden_size]))
conv_input = tf.expand_dims(inputs, -1)
conv_gate = tf.nn.conv1d(conv_input, W_gate, stride=1, padding="SAME")
conv_gate = tf.nn.bias_add(conv_gate, b_gate)
conv_gate = tf.nn.sigmoid(conv_gate)
conv_output = tf.nn.conv1d(conv_input, W_conv, stride=1, padding="SAME")
conv_output = tf.nn.bias_add(conv_output, b_conv)
conv_output = tf.nn.tanh(conv_output)
conv_output = tf.squeeze(conv_output, -1)
conv_output = conv_gate * conv_output

# 定义LSTM层
lstm_cell = tf.contrib.rnn.BasicLSTMCell(hidden_size)
initial_state = lstm_cell.zero_state(batch_size, tf.float32)
outputs, final_state = tf.nn.dynamic_rnn(lstm_cell, conv_output, initial_state=initial_state)

# 定义输出层
softmax_W = tf.Variable(tf.truncated_normal([hidden_size, vocab_size], stddev=0.1))
softmax_b = tf.Variable(tf.constant(0.1, shape=[vocab_size]))
logits = tf.matmul(outputs, softmax_W) + softmax_b

# 定义损失函数和优化器
loss = tf.contrib.seq2seq.sequence_loss(logits, targets, tf.ones([batch_size, tf.shape(targets)[1]]))
optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss)

# 训练模型
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for epoch in range(num_epochs):
        for batch in range(num_batches):
            batch_inputs, batch_targets = generate_batch(data, batch_size)
            feed_dict = {input_data: batch_inputs, targets: batch_targets}
            _, loss_val = sess.run([optimizer, loss], feed_dict=feed_dict)
            print("Epoch: %d, Batch: %d, Loss: %f" % (epoch, batch, loss_val))