MNIST 데이터 세트 2층 신경 네트워크 분류

MNIST 데이터 세트 2층 신경 네트워크 분류

>>> import tensorflow as tf
>>> from tensorflow.examples.tutorials.mnist import input_data
>>> mnist=input_data.read_data_sets("MNIST_data/",one_hot=True)
// mnist

>>> x=tf.placeholder(tf.float32,[None,784])// 
>>> y_=tf.placeholder(tf.float32,[None,10])// 
>>> x_image=tf.reshape(x,[-1,28,28,1])
// 784 28*28 

>>> def weight_variable(shape):
	initial=tf.truncated_normal(shape,stddev=0.1)
	return tf.Variable(initial)
// ，
>>> def bias_variable(shape):
	initial=tf.constant(0.1,shape=shape)
	return tf.Variable(initial)
// 
>>> def conv2d(x,W):
	return tf.nn.conv2d(x,W,strides=[1,1,1,1],padding='SAME')
// 
>>> def max_pool_2x2(x):
	return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')
// 

# 
>>> W_conv1=weight_variable([5,5,1,32])// 
>>> b_conv1=bias_variable([32])// 
>>> h_conv1=tf.nn.relu(conv2d(x_image,W_conv1)+b_conv1)
>// ， relu 
>>> h_pool1=max_pool_2x2(h_conv1)
// 

# 
>>> W_conv2=weight_variable([5,5,32,64])
>>> b_conv2=bias_variable([64])
>>> h_conv2=tf.nn.relu(conv2d(h_pool1,W_conv2)+b_conv2)
>>> h_pool2=max_pool_2x2(h_conv2)

# 
>>> W_fc1=weight_variable([7*7*64,1024])
>>> b_fc1=bias_variable([1024])
>>> h_pool2_flat=tf.reshape(h_pool2,[-1,7*7*64])
>>> h_fc1=tf.nn.relu(tf.matmul(h_pool2_flat,W_fc1)+b_fc1)

>>> keep_prob=tf.placeholder(tf.float32)
>>> h_fc1_drop=tf.nn.dropout(h_fc1,keep_prob)
// Dropout，keep_prob ， 0.5， 1

>>> W_fc2=weight_variable([1024,10])
>>> b_fc2=bias_variable([10])
>>> y_conv=tf.matmul(h_fc1_drop,W_fc2)+b_fc2
// h_fc1_drop 10 

>>> cross_entropy=tf.reduce_mean(
	tf.nn.softmax_cross_entropy_with_logits(labels=y_,logits=y_conv))
// softmax 
 tf.nn.softmax_cross_entropy_with_logits 
>>> train_step=tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
// Adam 

>>> correct_prediction=tf.equal(tf.argmax(y_conv,1),tf.argmax(y_,1))
>>> accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
// 

>>> sess=tf.InteractiveSession()// 
>>> sess.run(tf.global_variables_initializer())// 

>>> for i in range(20000):
	batch=mnist.train.next_batch(50)
// 20000 ， batch 50 

# 100 
>>> if i%100==0:
	train_accuracy=accuracy.eval(feed_dict={
		x:batch[0],y_:batch[1],keep_prob:1.0})
	print ("step %d,training accuracy %g" % (i,train_accuracy))
>>> train_step.run(feed_dict={x:batch[0],y_:batch[1],keep_prob:0.5})
// 

>>> print("test accuracy %g"%accuracy.eval(feed_dict={
	x:mnist.test.images, y_:mnist.test.labels,keep_prob:1.0}))
//