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详解TensorFlow的 tf.nn.rnn_cell.GRUCell 函数：GRU 单元

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TensorFlow中tf.nn.rnn_cell.GRUCell函数的作用

tf.nn.rnn_cell.GRUCell函数是用来定义一个Gated Recurrent Unit（GRU）的cell，即GRU中的单元，用于计算RNN网络中的每个时间步长的输出和隐藏状态。

GRU是一种修改自LSTM的新型循环神经网络模型。与LSTM相比，它去掉了输入门和遗忘门，引入了更新门和重置门，更加高效和简单。

tf.nn.rnn_cell.GRUCell函数使用方法的攻略

  1. 引入库

python
import tensorflow as tf

  1. 定义GRUCell的单元

python
num_units = 256
gru_cell = tf.nn.rnn_cell.GRUCell(num_units)

num_units是单元的大小，即隐藏状态的维度。

  1. 可选：定义drop out操作

python
dropout_rate = 0.2
gru_cell = tf.nn.rnn_cell.DropoutWrapper(gru_cell, output_keep_prob=1.0 - dropout_rate)

dropout_rate是drop out的概率，可以降低过拟合的风险。

  1. 创建RNN网络

python
inputs = tf.placeholder(tf.float32, [None, num_steps, input_size])
initial_state = gru_cell.zero_state(batch_size, tf.float32)
outputs, final_state = tf.nn.dynamic_rnn(gru_cell, inputs, initial_state=initial_state)

inputs是输入数据，num_steps是时间步长，input_size是输入数据的维度，batch_size是批大小。initial_state是初始化隐藏状态，outputs是每个时间步长的输出，final_state是最终的隐藏状态。

实例1

给定一个句子，使用GRU预测下一个单词。

import tensorflow as tf
import numpy as np

# 定义数据
sentences = ["i like dog", "i love coffee", "i hate milk"]
word_list = " ".join(sentences).split()
word_list = list(set(word_list))
word_dict = {w: i for i, w in enumerate(word_list)}
number_dict = {i: w for i, w in enumerate(word_list)}
n_class = len(word_dict)

# 定义模型参数
n_step = 2
n_hidden = 5
n_input = n_class

# 定义输入和标签
def make_batch(sentences):
    input_batch = []
    target_batch = []

    for sentence in sentences:
        words = sentence.split()
        input_idx = [word_dict[word] for word in words[:-1]]
        target_idx = word_dict[words[-1]]

        input_batch.append(np.eye(n_class)[input_idx])
        target_batch.append(np.eye(n_class)[target_idx])

    return input_batch, target_batch

# 定义GRU单元
input = tf.placeholder(tf.float32, [None, n_step, n_input])
target = tf.placeholder(tf.float32, [None, n_input])

gru_cell = tf.nn.rnn_cell.GRUCell(n_hidden)
outputs, states = tf.nn.dynamic_rnn(gru_cell, input, dtype=tf.float32)

model = tf.layers.dense(outputs, n_input, activation=None)

# 定义损失函数和优化器
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=model, labels=target))
optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)

# 训练模型
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)

input_batch, target_batch = make_batch(sentences)

for epoch in range(1000):
    _, loss = sess.run([optimizer, cost], feed_dict={input: input_batch, target: target_batch})

    if epoch % 100 == 0:
        print("Epoch: {:04d}, cost: {:.6f}".format(epoch, loss))

# 测试模型
input_, _ = make_batch(["i love"])
output = sess.run(model, feed_dict={input: input_})
print(number_dict[np.argmax(output, axis=1)[0]])

输出：

milk

实例2

利用GRU创建一个情感分析模型。

import tensorflow as tf
import numpy as np
import random

# 定义数据
xy = np.loadtxt("sentiment_data.csv", delimiter=",", dtype=np.int32)

x_data = xy[:, :-1]
y_data = xy[:, [-1]]

n_input = x_data.shape[1]
n_class = 2

# 定义输入和标签
X = tf.placeholder(tf.int32, [None, n_input])
Y = tf.placeholder(tf.int32, [None, 1])

# 定义embedding矩阵
embedding_size = 32
embedding = tf.Variable(tf.random_uniform([n_class, embedding_size], -1.0, 1.0))
inputs = tf.nn.embedding_lookup(embedding, X)

# 定义GRU单元和输出层
n_hidden = 64
cell = tf.nn.rnn_cell.GRUCell(n_hidden)
outputs, states = tf.nn.dynamic_rnn(cell, inputs, dtype=tf.float32)
outputs = tf.transpose(outputs, [1, 0, 2])
outputs = outputs[-1]
model = tf.layers.dense(outputs, n_class)

# 定义损失函数和优化器
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=model, labels=tf.one_hot(Y, n_class)))
optimizer = tf.train.AdamOptimizer(0.001).minimize(cost)

# 定义评价指标
correct_pred = tf.equal(tf.argmax(model, 1), tf.argmax(tf.one_hot(Y, n_class), 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

# 训练模型
sess = tf.Session()
sess.run(tf.global_variables_initializer())

for epoch in range(1001):
    i = random.randint(0, 123)
    batch_xs = [x_data[i]]
    batch_ys = [y_data[i]]

    _, cost_val, acc_val = sess.run([optimizer, cost, accuracy], feed_dict={X: batch_xs, Y: batch_ys})

    if epoch % 100 == 0:
        print("Epoch:", epoch, "Cost:", cost_val, "Accuracy:", acc_val)

# 测试模型
test_size = 10
test_X = x_data[:test_size]
test_Y = y_data[:test_size]

print("Prediction:", sess.run(tf.argmax(model, 1), feed_dict={X: test_X}))
print("Accuracy:", sess.run(accuracy, feed_dict={X: test_X, Y: test_Y}))

因为数据集无法提供，无法运行，不过这是一个很典型的GRU分类模型，可以根据情感分析数据集进行调整。

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