吴裕雄 python 神经网络——TensorFlow 循环神经网络处理MNIST手写数字数据集

#加载TF并导入数据集
import tensorflow as tf
from tensorflow.contrib import rnn
from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets("E:\\MNIST_data\\", one_hot=True)

#设置训练的超参数，学习率 训练迭代最大次数，输入数据的个数
learning_rate= 0.001   #(learning_rate)
training_iters = 100000
batch_size = 128

# 神经网络参数
n_inputs = 28 #输出层的n
n_steps = 28 # 长度
n_hidden = 128 # 隐藏层的神经元个数
n_classes = 10 # MNIST的分类类别 (0-9)

# 定义输出数据及其权重
# 输入数据的占位符
x = tf.placeholder("float", [None, n_steps, n_inputs])
y = tf.placeholder("float", [None, n_classes])

# 定义权重
weights ={
    'in': tf.Variable(tf.random_normal([n_inputs, n_hidden])),
    'out': tf.Variable(tf.random_normal([n_hidden, n_classes]))
}

biases = {
    'in': tf.Variable(tf.random_normal([n_hidden,])),
    'out': tf.Variable(tf.random_normal([n_classes, ]))
}

#定义RNN模型
def RNN(X, weights, biases):
    #把输入的X转化成X (128 batch * 28 steps ,28 inputs)
    X = tf.reshape(X,[-1,n_inputs])

    #  进入隐藏层
    # X_in = (128 batch * 28 steps ,28 hidden)

    X_in = tf.matmul(X,weights['in']) + biases['in']
    # X_in = (128 batch * 28 steps ,28 hidden)
    X_in=tf.reshape(X_in,[-1,n_steps,n_hidden])
    #采用LSTM循环神经网络单元 basic LSTM Cell
    lstm_cell = tf.contrib.rnn.BasicLSTMCell(n_hidden, forget_bias=1.0,state_is_tuple=True)
    # 初始化为0  lstm 单元 由 h_cell，h_state两部分组成
    init_state=lstm_cell.zero_state(batch_size,dtype=tf.float32)

    # dynamic_rnn接受张量(batch ,steps,inputs)或者(steps,batch,inputs) 作为X_in
    outputs,final_state=tf.nn.dynamic_rnn(lstm_cell,X_in,initial_state=init_state,time_major=False)
    results=tf.matmul(final_state[1], weights['out']) + biases['out']
    return results

#定义损失函数和优化器，采用AdamOptimizer优化器
pred=RNN(x,weights,biases)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))
train_op= tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)

# 定义模型预测结果及准确率计算方法
correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

# 训练模型及评估模型

# 定义一个会话，启动图，每20次输出一次准确率
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    step = 0
    # 训练，达到最大迭代次数
    while step * batch_size < training_iters:
        batch_xs, batch_ys = mnist.train.next_batch(batch_size)
        # Reshape data to get 28 seq of 28 elements
        batch_xs = batch_xs.reshape((batch_size, n_steps, n_inputs))
        sess.run(train_op, feed_dict={x: batch_xs, y: batch_ys})
        if step % 20 == 0:
            print(sess.run(accuracy,feed_dict={x:batch_xs, y:batch_ys}))
        step +=1