tensorflow1.1/循环神经网络手写数字啊识别

环境：tensorflow 1.1 , python 3 , matplotlib 2.02

#coding:utf-8"""tensorflow 1.1python 3 matplotlib 2.02"""import tensorflow as tfimport input_dataimport numpy as npimport matplotlib.pyplot as plt#设置随机种子tf.set_random_seed(100)np.random.seed(100)BATCH_SIZE = 64TIME_STEP = 28INPUT_SIZE = 28learning_rate = 0.01mnist = input_data.read_data_sets('mnist/',one_hot=True)test_x = mnist.test.images[:2000]test_y = mnist.test.labels[:2000]#查看图片plt.imshow(mnist.test.images[3].reshape((28,28)))plt.title('the picture is %i' %np.argmax(mnist.test.labels[3]),fontdict={'size':16,'color':'red'})plt.show()xs = tf.placeholder(tf.float32,[None,TIME_STEP*INPUT_SIZE])ys = tf.placeholder(tf.int32,[None,10])#输入神经网络前将形状(None,28*28) --->(None,28,28)x = tf.reshape(xs,[-1,TIME_STEP,INPUT_SIZE])#构建循环神经网络#tf.contrib.rnn.BasicLSTMCell()构建循环神经网络的cellrnn_cell = tf.contrib.rnn.BasicLSTMCell(num_units=64)#tf.nn.dynamic_rnn返回outputs和states,其中states包含主要state和次要state#输入神经网络的形状(batch,time_step,input)时间参数不在第一个维度,所以time_major=Falseoutputs,states = tf.nn.dynamic_rnn(rnn_cell,x,initial_state=None,dtype=tf.float32,time_major=False)#将最后一个time_step的输出作为输出output = tf.layers.dense(outputs[:,-1,:],10)#计算lossloss = tf.losses.softmax_cross_entropy(onehot_labels=ys,logits=output)train = tf.train.AdamOptimizer(learning_rate).minimize(loss)#计算accuracy,返回两个值acc和uodate_opaccuracy = tf.metrics.accuracy(labels=tf.argmax(ys,axis=1),predictions=tf.argmax(output,axis=1))[1]with tf.Session() as sess:    init = tf.group(tf.global_variables_initializer(),tf.local_variables_initializer())    sess.run(init)    for step in range(1000):        batch_x,batch_y = mnist.train.next_batch(BATCH_SIZE)        _,c = sess.run([train,loss],feed_dict={xs:batch_x,ys:batch_y})        if step % 100 == 0:            acc = sess.run(accuracy,feed_dict={xs:test_x,ys:test_y})            print('= = = = = = > > > > > >','epoch: ',int(step/100),'train loss : %.4f' %c,'test accuracy: %.3f' %acc)

结果