Tensorflow实例：mnist手写数字

Tensorflow实例：mnist手写数字

本文将给出一个完整的Tensorflow程序来解决MNIST手写数字识别问题。代码中包含的功能有：带指数衰减的学习率设置、使用正则化来避免过拟合、使用滑动平均模型来使得最终模型更加健壮。具体代码如下：

import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_data#mnist = input_data.read_data_sets("E:\科研\TensorFlow教程\MNIST_data", one_hot=True)"""print("Training data size: %d" % mnist.train.num_examples)print("Validating data size: %d" % mnist.validation.num_examples)print("Testing data size: %d" % mnist.test.num_examples)batch_size = 100xs, ys = mnist.train.next_batch(batch_size)print("X shape: ", xs.shape)print("Y shape: ", ys.shape)"""INPUT_NODE = 784OUTPUT_NODE = 10LAYER1_NODE = 500BATCH_SIZE = 100LEARNING_RATE_BASE = 0.8LEARNING_RATE_DECAY = 0.99REGULARIZATION_RATE = 0.0001TRAINING_STEPS = 30000MOVING_AVERAGE_DECAY = 0.99def inference(input_tensor, avg_class, weights1, biases1,              weights2, biases2):    if avg_class == None:        layer1 = tf.nn.relu(tf.matmul(input_tensor, weights1) + biases1)        return tf.matmul(layer1, weights2) + biases2    else:        layer1 = tf.nn.relu(tf.matmul(input_tensor, avg_class.average(weights1)) + avg_class.average(biases1))        return tf.matmul(layer1, avg_class.average(weights2)) + avg_class.average(biases2)def train(mnist):    x = tf.placeholder(tf.float32, [None, INPUT_NODE], name="x-input")    y_ = tf.placeholder(tf.float32, [None, OUTPUT_NODE], name="y-input")    weights1 = tf.Variable(tf.truncated_normal([INPUT_NODE, LAYER1_NODE], stddev=0.1))    biases1 = tf.Variable(tf.constant(0.1, shape=[LAYER1_NODE]))    weights2 = tf.Variable(tf.truncated_normal([LAYER1_NODE, OUTPUT_NODE], stddev=0.1))    biases2 = tf.Variable(tf.constant(0.1, shape=[OUTPUT_NODE]))    y = inference(x, None, weights1=weights1, biases1=biases1, weights2=weights2, biases2=biases2)    global_step = tf.Variable(0, trainable=False)    variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step)    variable_averages_op = variable_averages.apply(tf.trainable_variables())    average_y = inference(x, variable_averages, weights1, biases1, weights2, biases2)    cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=tf.argmax(y_, 1), logits=y)    cross_entropy_mean = tf.reduce_mean(cross_entropy)    regularizer = tf.contrib.layers.l2_regularizer(REGULARIZATION_RATE)    regularization = regularizer(weights1) + regularizer(weights2)    loss = cross_entropy_mean + regularization    learning_rate = tf.train.exponential_decay(LEARNING_RATE_BASE, global_step, mnist.train.num_examples / BATCH_SIZE, LEARNING_RATE_DECAY)    train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step)    with tf.control_dependencies([train_step, variable_averages_op]):        train_op = tf.no_op(name="train")    correct_prediction = tf.equal(tf.argmax(average_y, 1), tf.argmax(y_, 1))    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))    with tf.Session() as sess:        tf.global_variables_initializer().run()        validate_feed = {x: mnist.validation.images,                         y_: mnist.validation.labels}        test_feed = {x: mnist.test.images, y_: mnist.test.labels}        for i in range(TRAINING_STEPS):            if i % 1000 == 0:                validate_acc = sess.run(accuracy, feed_dict=validate_feed)                print("After %d training steps, validation accuracy"                      "using average model is %g " % (i, validate_acc))            xs, ys = mnist.train.next_batch(BATCH_SIZE)            sess.run(train_op, feed_dict={x: xs, y_: ys})        test_acc = sess.run(accuracy, feed_dict=test_feed)        print("After %d training steps, test accuracy using average "              "model is %g" % (TRAINING_STEPS, test_acc))def main(argv=None):    mnist = input_data.read_data_sets("E:\科研\TensorFlow教程\MNIST_data", one_hot=True)    train(mnist)if __name__ == "__main__":    tf.app.run()