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| import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("./MNIST_data/",one_hot=True)
sess = tf.InteractiveSession()
x = tf.placeholder('float',[None,784]) y_= tf.placeholder('float',[None,10])
W = tf.Variable(tf.zeros([784,10])) b = tf.Variable(tf.zeros([10]))
sess.run(tf.initialize_all_variables())
y = tf.nn.softmax(tf.matmul(x,W)+b) cross_entropy = -tf.reduce_sum(y_*tf.log(y))
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_2_2(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_2_2(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_2_2(h_conv2)
W_conv3 = weight_variable([7*7*64,1024]) b_conv3 = bias_variable([1024])
h_conv3_flat = tf.reshape(h_pool2,[-1,7*7*64])
h_conv3_fc1 = tf.nn.relu(tf.matmul(h_conv3_flat,W_conv3)+b_conv3)
keep_prob = tf.placeholder('float') h_conv3_drop = tf.nn.dropout(h_conv3_fc1,keep_prob)
W_output = weight_variable([1024,10]) b_output = bias_variable([10])
y_conv = tf.nn.softmax(tf.matmul(h_conv3_drop,W_output)+b_output)
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, "float"))
sess.run(tf.initialize_all_variables()) for i in range(20000): batch = mnist.train.next_batch(50) if i%1000 == 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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