#载入数据集 mnist=input_data.read_data_sets("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#隐藏单元 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)) #初始化偏置量 biases=tf.Variable(tf.constant(0.1,shape=[n_classes])) #定义RNN网络 def RNN(X,weights,biases): 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) 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}) acc=sess.run(accuracy,feed_dict={x:mnist.test.image,y:mnist.test.labels}) print("Iter"+str(epoch)+",Testing Accuracy="+str(acc))
