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- # 《TensorFlow实战》05 TensorFlow实现卷积神经网络
- # win10 Tensorflow1.0.1 python3.5.3
- # CUDA v8.0 cudnn-8.0-windows10-x64-v5.1
- # filename:sz05.01.py # 简单卷积网络
- from tensorflow.examples.tutorials.mnist import input_data
- import tensorflow as tf
- mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
- 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")
- x = tf.placeholder(tf.float32, [None, 784])
- y_ = tf.placeholder(tf.float32, [None, 10])
- x_image = tf.reshape(x, [-1, 28, 28, 1])
- W_conv1 = weight_variable([5, 5, 1, 32])
- b_conv1 = bias_variable([32])
- 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_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()
- 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}))
- '''
- step 0, training accuracy 0.04
- step 1000, training accuracy 0.96
- step 2000, training accuracy 0.92
- ...
- step 16000, training accuracy 0.98
- step 17000, training accuracy 1
- step 18000, training accuracy 1
- step 19000, training accuracy 1
- test accuracy 0.9918
- '''
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