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| #!/usr/bin/env python # encoding: utf-8
import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
def weight_variable(shape): initial = tf.truncated_normal(shape, stddev=0.1) return tf.Variable(initial, name="W")
def bias_variable(shape): initial = tf.constant(0.1, shape=shape) return tf.Variable(initial, name="bias")
def conv2d(x, W): return tf.nn.conv2d(x, W, strides=[1,1,1,1], padding="SAME", name="conv2d")
def max_pool(x): return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding="SAME", name="pooled")
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])
# conv_1 layer with tf.name_scope('conv-layer-1'): W_conv1 = weight_variable([5,5,1,32]) # outsize=32 : convolutions units b_conv1 = bias_variable([32]) h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) # 28 * 28 * 32 h_pooled_1 = max_pool(h_conv1) # 14*14*32
# conv_2 layer with tf.name_scope('conv-layer-2'): W_conv2 = weight_variable([5,5,32,64]) # outsize=64 b_conv2 = bias_variable([64]) h_conv2 = tf.nn.relu(conv2d(h_pooled_1, W_conv2) + b_conv2) # 14 * 14 *64 h_pooled_2 = max_pool(h_conv2) # 7 * 7 * 64
# func1 layer with tf.name_scope('nn-layer-1'): W_fun1 = weight_variable([7*7*64, 1024]) b_fun1 = bias_variable([1024]) h_pool2_flat = tf.reshape(h_pooled_2, [-1, 7*7*64]) h_fun2 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fun1) + b_fun1) h_fun2_drop = tf.nn.dropout(h_fun2, keep_prob)
# func2 layer with tf.name_scope('nn-layer-2'): W_fun2 = weight_variable([1024, 10]) b_fun2 = bias_variable([10]) prediction = tf.nn.softmax(tf.matmul(h_fun2_drop, W_fun2) + b_fun2)
cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction))) train_step = tf.train.AdamOptimizer(1e-04).minimize(cross_entropy)
## accuracy correct_prediction = tf.equal(tf.argmax(prediction, 1), tf.argmax(ys, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
import time n_epochs = 15 batch_size = 100
with tf.Session() as sess: sess.run(tf.global_variables_initializer()) st = time.time() for epoch in range(n_epochs): n_batch = mnist.train.num_examples / batch_size for i in range(n_batch): batch_xs, batch_ys = mnist.train.next_batch(batch_size) sess.run(train_step, feed_dict={xs: batch_xs, ys: batch_ys, keep_prob:0.6})
print 'epoch', 1+epoch, 'accuracy:', sess.run(accuracy, feed_dict={keep_prob:1.0, xs: mnist.test.images, ys: mnist.test.labels}) end = time.time()
print '*' * 30 print 'training finish.\ncost time:', int(end-st) , 'seconds;\naccuracy:', sess.run(accuracy, feed_dict={keep_prob:1.0, xs: mnist.test.images, ys: mnist.test.labels})
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