tensorflow lstm for mnist

# -*- coding:utf-8 -*-
import tensorflow as tf
import numpy as np
from tensorflow.contrib import rnn
from tensorflow.examples.tutorials.mnist import input_data

# 设置 GPU 按需增长
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)

""" 首先导入数据，看一下数据的形式"""
mnist = input_data.read_data_sets('../../MNIST_data', one_hot=True)
print(mnist.train.images.shape)
"""设置各种超参数"""
lr = 1e-3
input_size = 28  # 每个时刻的输入特征是28维的，就是每个时刻输入一行，一行有 28 个像素
timestep_size = 28  # 时序持续长度为28，即每做一次预测，需要先输入28行
hidden_size = 256  # 隐含层的数量
layer_num = 2  # LSTM layer 的层数
class_num = 10  # 最后输出分类类别数量，如果是回归预测的话应该是 1

_X = tf.placeholder(tf.float32, [None, 784])  # 输入占位符
y = tf.placeholder(tf.float32, [None, class_num])  # 输出占位符
# 在训练和测试的时候，我们想用不同的 batch_size.所以采用占位符的方式
batch_size = tf.placeholder(tf.int32, [])  # 注意类型必须为 tf.int32, batch_size = 128
keep_prob = tf.placeholder(tf.float32, [])  # dropout概率

"""开始搭建lstm"""
# 把784个点的字符信息还原成 28 * 28 的图片
# 下面几个步骤是实现 RNN / LSTM 的关键
####################################################################
# **步骤1：RNN 的输入shape = (batch_size, timestep_size, input_size)
X = tf.reshape(_X, [-1, 28, 28])


# # **步骤2：定义一层 LSTM_cell，只需要说明 hidden_size, 它会自动匹配输入的 X 的维度
# lstm_cell = rnn.BasicLSTMCell(num_units=hidden_size, forget_bias=1.0, state_is_tuple=True)

# # **步骤3：添加 dropout layer, 一般只设置 output_keep_prob
# lstm_cell = rnn.DropoutWrapper(cell=lstm_cell, input_keep_prob=1.0, output_keep_prob=keep_prob)


# # **步骤4：调用 MultiRNNCell 来实现多层 LSTM
# mlstm_cell = rnn.MultiRNNCell([lstm_cell] * layer_num, state_is_tuple=True)
# mlstm_cell = rnn.MultiRNNCell([lstm_cell for _ in range(layer_num)] , state_is_tuple=True)

# 在 tf 1.0.0 版本中，可以使用上面的 三个步骤创建多层 lstm， 但是在 tf 1.2.1 版本中，可以通过下面方式来创建
def lstm_cell():
    cell = rnn.LSTMCell(hidden_size, reuse=tf.get_variable_scope().reuse)
    return rnn.DropoutWrapper(cell, output_keep_prob=keep_prob)


mlstm_cell = tf.contrib.rnn.MultiRNNCell([lstm_cell() for _ in range(layer_num)], state_is_tuple=True)

# **步骤5：用全零来初始化state
init_state = mlstm_cell.zero_state(batch_size, dtype=tf.float32)

# **步骤6：方法一，调用 dynamic_rnn() 来让我们构建好的网络运行起来
# ** 当 time_major==False 时， outputs.shape = [batch_size, timestep_size, hidden_size]
# ** 所以，可以取 h_state = outputs[:, -1, :] 作为最后输出
# ** state.shape = [layer_num, 2, batch_size, hidden_size],
# ** 或者，可以取 h_state = state[-1][1] 作为最后输出
# ** 最后输出维度是 [batch_size, hidden_size]
outputs, state = tf.nn.dynamic_rnn(mlstm_cell, inputs=X, initial_state=init_state, time_major=False)
h_state = state[-1][1]

############################################################################
# 以下部分其实和之前写的多层 CNNs 来实现 MNIST 分类是一样的。
# 只是在测试的时候也要设置一样的 batch_size.

# 上面 LSTM 部分的输出会是一个 [hidden_size] 的tensor，我们要分类的话，还需要接一个 softmax 层
# 首先定义 softmax 的连接权重矩阵和偏置
# out_W = tf.placeholder(tf.float32, [hidden_size, class_num], name='out_Weights')
# out_bias = tf.placeholder(tf.float32, [class_num], name='out_bias')
# 开始训练和测试
W = tf.Variable(tf.truncated_normal([hidden_size, class_num], stddev=0.1), dtype=tf.float32)
bias = tf.Variable(tf.constant(0.1, shape=[class_num]), dtype=tf.float32)
y_pre = tf.nn.softmax(tf.matmul(h_state, W) + bias)

# 损失和评估函数
cross_entropy = -tf.reduce_mean(y * tf.log(y_pre))
train_op = tf.train.AdamOptimizer(lr).minimize(cross_entropy)

correct_prediction = tf.equal(tf.argmax(y_pre, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

sess.run(tf.global_variables_initializer())
for i in range(2000):
    _batch_size = 128
    batch = mnist.train.next_batch(_batch_size)
    if (i + 1) % 200 == 0:
        train_accuracy = sess.run(accuracy, feed_dict={
            _X: batch[0], y: batch[1], keep_prob: 1.0, batch_size: _batch_size})
        # 已经迭代完成的 epoch 数: mnist.train.epochs_completed
        print("Iter%d, step %d, training accuracy %g" % (mnist.train.epochs_completed, (i + 1), train_accuracy))

    sess.run(train_op, feed_dict={_X: batch[0], y: batch[1], keep_prob: 0.5, batch_size: _batch_size})

# 计算测试数据的准确率
print("test accuracy %g" % sess.run(accuracy, feed_dict={
    _X: mnist.test.images, y: mnist.test.labels, keep_prob: 1.0, batch_size: mnist.test.images.shape[0]}))