莫烦TensorFlow_11 MNIST优化使用CNN

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

#number 1 to 10 data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

def compute_accuracy(v_xs, v_ys):
  global prediction
  y_pre = sess.run(prediction, feed_dict={xs:v_xs, keep_prob:1})
  correct_prediction = tf.equal(tf.argmax(y_pre, 1), tf.argmax(v_ys, 1))
  accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
  result = sess.run(accuracy, feed_dict={xs:v_xs, ys:v_ys, keep_prob:1})
  return result

def weight_variable(shape):
  initial = tf.truncated_normal(shape, stddev=0.1) # initial variables with normal distribution
  return tf.Variable(initial)

def bias_variable(shape):
  initial = tf.constant(0.1, shape=shape)
  return tf.Variable(initial)

def conv2d(x, W):
  #strides [1, x_movement, y_movement, 1]
  #Must have strides[0] = strides[3] = 1
  return tf.nn.conv2d(x, W, strides=[1,1,1,1], padding = 'SAME')

def max_pool_2x2(x):
  #strides [1, x_movement, y_movement, 1]
  #Must have strides[0] = strides[3] = 1
  return tf.nn.max_pool(x, ksize=[1,2,2,1], strides = [1,2,2,1], padding = 'SAME')

#define placeholder for inputs to network
xs = tf.placeholder(tf.float32, [None, 784])
ys = tf.placeholder(tf.float32, [None, 10])
keep_prob = tf.placeholder(tf.float32)
x_image = tf.reshape(xs, [-1, 28, 28, 1])
#print(x_image.shape) #[n_sample, 28, 28, 1]

## conv1 layer ##
W_conv1 = weight_variable([5,5,1,32])#patch 5x5, in in size 1, out size 32
b_conv1 = bias_variable([32])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) # output size 28x28x32
h_pool1 = max_pool_2x2(h_conv1)# output size 14x14x32

## conv2 layer ##
W_conv2 = weight_variable([5,5,32, 64])#patch 5x5, in in size 32, out size 64
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) # output size 14x14x64
h_pool2 = max_pool_2x2(h_conv2)# output size 7x7x64

## func1 layer ##
W_fc1 = weight_variable([7*7*64, 1024])
b_fc1 = bias_variable([1024])
 # [n_sample, 7,7,64] ->> [n_sample, 7*7*64]
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)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

## func2 layer ##
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])

prediction = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)

# the error between prediction and real data
cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys*tf.log(prediction),
					      　　reduction_indices=[1]))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

sess = tf.Session()
sess.run(tf.global_variables_initializer())

for i in range(1000):
  batch_xs, batch_ys = mnist.train.next_batch(100)
  sess.run(train_step, feed_dict = {xs:batch_xs, ys:batch_ys, keep_prob:0.8})
  if i% 50 == 0:
    print(compute_accuracy(mnist.test.images, mnist.test.labels))

　　

两层卷积层

训练速度慢了，但是精度提高了