學(xué)習(xí)Tensorflow第一課：

from tensorflow.examples.tutorials.mnist import input_data  
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)  
import tensorflow as tf  
sess = tf.InteractiveSession()  
  
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])  
  
x_image = tf.reshape(x, [-1,28,28,1])  
  
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)  
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)  
  
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)  
  
cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv))  
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))  
sess.run(tf.initialize_all_variables())  
for i in range(20000):  
  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 %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}))  

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