Tensorflow递归神经网络学习练习

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

#载入数据集
mnist = input_data.read_data_sets("F:\TensorflowProject\MNIST_data",one_hot=True)

# 输入图片是28*28
n_inputs = 28 #输入一行，一行有28个数据
max_time = 28 #一共28行
lstm_size = 100 #隐层单元
n_classes = 10 # 10个分类
batch_size = 50 #每批次50个样本
n_batch = mnist.train.num_examples // batch_size #计算一共有多少个批次

#这里的none表示第一个维度可以是任意的长度
x = tf.placeholder(tf.float32,[None,784])
#正确的标签
y = tf.placeholder(tf.float32,[None,10])

#初始化权值
weights = tf.Variable(tf.truncated_normal([lstm_size, n_classes], stddev=0.1))
#初始化偏置值
biases = tf.Variable(tf.constant(0.1, shape=[n_classes]))

#定义RNN网络
def RNN(X,weights,biases):
# inputs=[batch_size, max_time, n_inputs]
inputs = tf.reshape(X,[-1,max_time,n_inputs])
#定义LSTM基本CELL
#lstm_cell = tf.contrib.rnn.core_rnn_cell.BasicLSTMCell(lstm_size)
lstm_cell = rnn.BasicLSTMCell(lstm_size)
# final_state[0]是cell state
# final_state[1]是hidden_state
outputs,final_state = tf.nn.dynamic_rnn(lstm_cell,inputs,dtype=tf.float32)
results = tf.nn.softmax(tf.matmul(final_state[1],weights) + biases)
return results

#计算RNN的返回结果
prediction= RNN(x, weights, biases)
#损失函数
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction,labels=y))
#使用AdamOptimizer进行优化
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
#结果存放在一个布尔型列表中
correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(prediction,1))#argmax返回一维张量中最大的值所在的位置
#求准确率
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))#把correct_prediction变为float32类型
#初始化
init = tf.global_variables_initializer()

with tf.Session() as sess:
　　sess.run(init)
　　for epoch in range(6):
　　　　for batch in range(n_batch):
　　　　　　batch_xs,batch_ys = mnist.train.next_batch(batch_size)
　　　　　　sess.run(train_step,feed_dict={x:batch_xs,y:batch_ys})

　　　　test_acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels})
　　　　print ("Iter " + str(epoch) + ", Testing Accuracy= " + str(test_acc ))

#############运行结果

Extracting F:\TensorflowProject\MNIST_data\train-images-idx3-ubyte.gz

Extracting F:\TensorflowProject\MNIST_data\train-labels-idx1-ubyte.gz

Extracting F:\TensorflowProject\MNIST_data\t10k-images-idx3-ubyte.gz

Extracting F:\TensorflowProject\MNIST_data\t10k-labels-idx1-ubyte.gz

WARNING:tensorflow:From <ipython-input-5-efe314a2932e>:40: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.

Instructions for updating:

Future major versions of TensorFlow will allow gradients to flow

into the labels input on backprop by default.

See tf.nn.softmax_cross_entropy_with_logits_v2.

Iter 0, Testing Accuracy= 0.7372

Iter 1, Testing Accuracy= 0.8334

Iter 2, Testing Accuracy= 0.908

Iter 3, Testing Accuracy= 0.9182

Iter 4, Testing Accuracy= 0.9172

Iter 5, Testing Accuracy= 0.9266

##############第二次运行

Iter  0  ,Testing Accuracy= 0.744

Iter  1  ,Testing Accuracy= 0.8028

Iter  2  ,Testing Accuracy= 0.8803

Iter  3  ,Testing Accuracy= 0.9115

Iter  4  ,Testing Accuracy= 0.9262

Iter  5  ,Testing Accuracy= 0.9303

巴特西

Tensorflow递归神经网络学习练习

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