- import tensorflow as tf
- from tensorflow.examples.tutorials.mnist import input_data
- INPUT_NODE = 784 # 输入节点
- OUTPUT_NODE = 10 # 输出节点
- LAYER1_NODE = 500 # 隐藏层数
- BATCH_SIZE = 100 # 每次 batch 打包的样本个数
- # 模型相关的参数
- LEARNING_RATE_BASE = 0.8
- LEARNING_RATE_DECAY = 0.99
- REGULARAZTION_RATE = 0.0001
- TRAINING_STEPS = 5000
- def inference(input_tensor, avg_class, weights1, biases1, weights2, biases2):
- # 不使用滑动平均类
- if avg_class == None:
- layer1 = tf.nn.relu(tf.matmul(input_tensor, weights1) + biases1)
- return tf.matmul(layer1, weights2) + biases2
- else:
- # 使用滑动平均类
- 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')
- # 生成隐藏层的参数.
- weights1 = tf.Variable(tf.truncated_normal([INPUT_NODE, LAYER1_NODE], stddev=0.1))
- biases1 = tf.Variable(tf.constant(0.1, shape=[LAYER1_NODE]))
- # 生成输出层的参数.
- weights2 = tf.Variable(tf.truncated_normal([LAYER1_NODE, OUTPUT_NODE], stddev=0.1))
- biases2 = tf.Variable(tf.constant(0.1, shape=[OUTPUT_NODE]))
- # 计算不含滑动平均类的前向传播结果
- y = inference(x, None, weights1, biases1, weights2, biases2)
- # 定义训练轮数及相关的滑动平均类
- global_step = tf.Variable(0, trainable=False)
- # 计算交叉熵及其平均值
- cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1))
- cross_entropy_mean = tf.reduce_mean(cross_entropy)
- # 损失函数的计算
- regularizer = tf.contrib.layers.l2_regularizer(REGULARAZTION_RATE)
- regularaztion = regularizer(weights1) + regularizer(weights2)
- loss = cross_entropy_mean + regularaztion
- # 设置指数衰减的学习率.
- learning_rate = tf.train.exponential_decay(
- LEARNING_RATE_BASE,
- global_step,
- mnist.train.num_examples / BATCH_SIZE,
- LEARNING_RATE_DECAY,
- staircase=True)
- # 优化损失函数
- train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step)
- # 反向传播更新参数
- with tf.control_dependencies([train_step]):
- train_op = tf.no_op(name='train')
- # 计算正确率
- correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
- accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
- # 初始化会话, 并开始训练过程.
- 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}
- # 循环的训练神经网络.
- for i in range(TRAINING_STEPS):
- if i % 1000 == 0:
- validate_acc = sess.run(accuracy, feed_dict=validate_feed)
- print("After %d training step(s), 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 step(s), test accuracy using average model is %g" %(TRAINING_STEPS, test_acc)))
- def main(argv=None):
- mnist = input_data.read_data_sets("E:\\MNIST_data\\", one_hot=True)
- train(mnist)
- if __name__=='__main__':
- main()
来源: http://www.bubuko.com/infodetail-3061051.html