tensorflow基本教程10:RNN迴圈神經網路對於手寫體識別預測
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
#this is data
mnist=input_data.read_data_sets("MNIST_data",one_hot=True)
n_inputs=28#輸入一行,一行有28個數據
max_time=28#一共28行
lstm_size=100#隱層單元
n_classes=10#10個分類
batch_size=50#每批次50個樣本
n_batch=mnist.train.num_examples//batch_size#計算一共有多少個批次
#這裡的none表示第一個維度可以是任意的長度
x=tf.placeholder(tf.float32,[None,784])
#正確的標籤
y=tf.placeholder(tf.float32,[None,10])
#初始化權值
weights=tf.Variable(tf.truncated_normal([lstm_size,n_classes],stddev=0.1))
#初始化偏置
biases=tf.Variable(tf.constant(0.1,shape=[n_classes]))
#定義RNN網路
def RNN(X,weights,biases):
#inputs=[batch_size,max_time,n_inputs]
inputs=tf.reshape(X,[-1,max_time,n_inputs])
#定義lstm基本的cell
lstm_cell=tf.contrib.rnn.core_rnn_cell.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)
result=tf.nn.softmax(tf.matmul(final_state[1],weights)+biases)
return result
#計算RNN的返回結果
prediction=RNN(x,weights,biases)
cross_entropy=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction,labels=y))
#使用AdamOptimizer進行優化
train_step=tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
#結果存在一個布林型列表中
correct_prediction=tf.equal(tf.argmax(y,1),tf.argmax(prediction,1))#argmax返回一維張量中最大的值所在的位置
#求準確率
accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))#把correct_prediction變為float32型別
#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})
acc=sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels})
print("Iter"+str(epoch)+",Testing Accuracy="+str(acc))