tf3.py

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import numpy as np
import matplotlib.pyplot as plt

tf.set_random_seed(1)
np.random.seed(1)

BATCH_SIZE=50
LR=0.001
mnist=input_data.read_data_sets('./mnist',one_hot=True)

test_x=mnist.test.images[:2000]
test_y=mnist.test.labels[:2000]

tf_x=tf.placeholder(tf.float32,[None,28*28])/255
image=tf.reshape(tf_x,[-1,28,28,1])
tf_y=tf.placeholder(tf.int32,[None,10])

conv1=tf.layers.conv2d(inputs=image,filters=16,kernel_size=5,strides=1,padding='same',activation=tf.nn.relu)
pool1=tf.layers.max_pooling2d(conv1,pool_size=2,strides=2)
conv2=tf.layers.conv2d(pool1,32,5,1,'same',activation=tf.nn.relu)
pool2=tf.layers.max_pooling2d(conv2,2,2)
flat=tf.reshape(pool2,[-1,7*7*32])
output=tf.layers.dense(flat,10)

loss=tf.losses.softmax_cross_entropy(onehot_labels=tf_y,logits=output)
train_op=tf.train.AdamOptimizer(LR).minimize(loss)

accuracy=tf.metrics.accuracy(labels=tf.argmax(tf_y,axis=1),predictions=tf.argmax(output,axis=1),)[1]

sess=tf.Session()
init_op=tf.group(tf.global_variables_initializer(),tf.local_variables_initializer())
sess.run(init_op)

for step in range(600):
	b_x,b_y=mnist.train.next_batch(BATCH_SIZE)
	_,loss_=sess.run([train_op,loss],{tf_x:b_x,tf_y:b_y})
	if step%50==0:
		acc_,flat_representation=sess.run([accuracy,flat],{tf_x:test_x,tf_y:test_y})
		
		print('step',step,'| train loss:%.4f' %loss_,'| test acc:%.2f' %acc_,)

test_output=sess.run(output,{tf_x:test_x[:10]})
pred_y=np.argmax(test_output,1)
print(pred_y,'prediction number')
print(np.argmax(test_y[:10],1),'real number')