TensorFlow 권 적 신경 망 실현

# -*- coding: utf-8 -*-
'''        MNIST  '''

#########  MNIST  ########
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
mnist = input_data.read_data_sets('MNIST_data/', one_hot=True)

#     InteractiveSession
sess = tf.InteractiveSession()


#########                  ，             ########
#                  （           ，     0.1）
def weight_variable(shape):
 initial = tf.truncated_normal(shape, stddev=0.1)
 return tf.Variable(initial)
#        ReLU，            （0.1）        （dead neurons）
def bias_variable(shape):
 initial = tf.constant(0.1, shape=shape)
 return tf.Variable(initial)


########   、           ，        ########
# tf.nn.conv2d TensorFlow  2     
def conv2d(x, W):
 return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
#   2*2     
def max_pool_2x2(x):
 return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME')


########               placeholder########
# x   ，y_   label。      1D  2D。  tensor     tf.reshape
x = tf.placeholder(tf.float32, shape=[None, 784])
y_ = tf.placeholder(tf.float32, shape=[None, 10])
x_image = tf.reshape(x,[-1,28,28,1])


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

#      （        ，    64，        64   ）
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)

#     。     1024。  ReLU    
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)

#        ，       Dropout 
keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

#    。    softmax ，  softmax regression  。      。
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)


########      ########
#   loss function cross entropy，     Adam，             1e-4
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_*tf.log(y_conv),reduction_indices=[1]))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

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


########      ########
#        
tf.global_variables_initializer().run()

#   （     Dropout kepp_prob   0.5。mini-batch 50，  2000     ，      5 ）
#      100   ，          keep_prob   1，           
for i in range(1000):
 batch = mnist.train.next_batch(50)
 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 %.4f"%(i, train_accuracy)
 train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
#         ，             ，          
print "       MNIST      : %g"%accuracy.eval(feed_dict={
  x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0})