x = tf.placeholder(tf.float32,[BATCH_SIZE,mnist_inference_6_4_1.IMAGE_SIZE,mnist_inference_6_4_1.IMAGE_SIZE,mnist_inference_6_4_1.NUM_CHANNELS],name='x-input')

y_ = tf.placeholder(tf.float32,[None,mnist_inference_6_4_1.OUTPUT_NODE],name='y-input')

regularizer = tf.contrib.layers.l2_regularizer(REGULARZTION_RATE)

y = mnist_inference_6_4_1.inference(x,True,regularizer)

global_step = tf.Variable(0,trainable=False)

variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY,global_step)

variable_averages_op = variable_averages.apply(tf.trainable_variables())

cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y,labels=tf.argmax(y_, 1))

cross_entropy_mean = tf.reduce_mean(cross_entropy)

loss = cross_entropy_mean + tf.add_n(tf.get_collection('losses'))

learning_rate = tf.train.exponential_decay(LEARNING_RATE_BASE,global_step,mnist.train.num_examples / BATCH_SIZE,LEARNING_RATE_DECAY)

train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss,global_step=global_step)

train_op = tf.group(train_step, variable_averages_op)

saver = tf.train.Saver()

net = layers.conv2d(input_data,32,[3,3])

with tf.Session() as sess:

tf.global_variables_initializer().run()

for i in range(TRAINING_STEPS):

xs,ys = mnist.train.next_batch(BATCH_SIZE)

reshaped_xs = np.reshape(xs,(BATCH_SIZE,mnist_inference_6_4_1.IMAGE_SIZE,mnist_inference_6_4_1.IMAGE_SIZE,mnist_inference_6_4_1.NUM_CHANNELS))

_,loss_value,step = sess.run([train_op,loss,global_step],feed_dict={x:reshaped_xs,y_:ys})

if i % 100 == 0:

print("After %d training step(s), loss is %g" %(step,loss_value))