Tensorflow训练MNIST手写数字识别模型

这篇文章主要为大家详细介绍了Tensorflow训练MNIST手写数字识别模型，文中示例代码介绍的非常详细，具有一定的参考价值，感兴趣的小伙伴们可以参考一下

本文实例为大家分享了Tensorflow训练MNIST手写数字识别模型的具体代码，供大家参考，具体内容如下

import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data INPUT_NODE = 784 # 输入层节点=图片像素=28x28=784 OUTPUT_NODE = 10 # 输出层节点数=图片类别数目 LAYER1_NODE = 500 # 隐藏层节点数，只有一个隐藏层 BATCH_SIZE = 100 # 一个训练包中的数据个数，数字越小 # 越接近随机梯度下降，越大越接近梯度下降 LEARNING_RATE_BASE = 0.8 # 基础学习率 LEARNING_RATE_DECAY = 0.99 # 学习率衰减率 REGULARIZATION_RATE = 0.0001 # 正则化项系数 TRAINING_STEPS = 30000 # 训练轮数 MOVING_AVG_DECAY = 0.99 # 滑动平均衰减率 # 定义一个辅助函数，给定神经网络的输入和所有参数，计算神经网络的前向传播结果 def inference(input_tensor, avg_class, weights1, biases1, weights2, biases2): # 当没有提供滑动平均类时，直接使用参数当前取值 if avg_class == None: # 计算隐藏层前向传播结果 layer1 = tf.nn.relu(tf.matmul(input_tensor, weights1) + biases1) # 计算输出层前向传播结果 return tf.matmul(layer1, weights2) + biases2 else: # 首先计算变量的滑动平均值，然后计算前向传播结果 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) # 训练模型的过程 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])) # 计算前向传播结果，不使用参数滑动平均值 avg_class=None y = inference(x, None, weights1, biases1, weights2, biases2) # 定义训练轮数变量，指定为不可训练 global_step = tf.Variable(0, trainable=False) # 给定滑动平均衰减率和训练轮数的变量，初始化滑动平均类 variable_avgs = tf.train.ExponentialMovingAverage( MOVING_AVG_DECAY, global_step) # 在所有代表神经网络参数的可训练变量上使用滑动平均 variables_avgs_op = variable_avgs.apply(tf.trainable_variables()) # 计算使用滑动平均值后的前向传播结果 avg_y = inference(x, variable_avgs, weights1, biases1, weights2, biases2) # 计算交叉熵作为损失函数 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) # 计算L2正则化损失函数 regularizer = tf.contrib.layers.l2_regularizer(REGULARIZATION_RATE) regularization = regularizer(weights1) + regularizer(weights2) loss = cross_entropy_mean + regularization # 设置指数衰减的学习率 learning_rate = tf.train.exponential_decay( LEARNING_RATE_BASE, global_step, # 当前迭代轮数 mnist.train.num_examples / BATCH_SIZE, # 过完所有训练数据的迭代次数 LEARNING_RATE_DECAY) # 优化损失函数 train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize( loss, global_step=global_step) # 反向传播同时更新神经网络参数及其滑动平均值 with tf.control_dependencies([train_step, variables_avgs_op]): train_op = tf.no_op(name='train') # 检验使用了滑动平均模型的神经网络前向传播结果是否正确 correct_prediction = tf.equal(tf.argmax(avg_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 with tf.Session() as sess: tf.global_variables_initializer().run() # 准备验证数据，用于判断停止条件和训练效果 validate_Feed = {x: mnist.validation.images, y_: mnist.validation.labels} # 准备测试数据，用于模型优劣的最后评价标准 test_Feed = {x: mnist.test.images, y_: mnist.test.labels} # 迭代训练神经网络 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}) # 训练结束后在测试集上检测模型的最终正确率 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)) # 主程序入口 def main(argv=None): mnist = input_data.read_data_sets("/tmp/data", one_hot=True) train(mnist) # Tensorflow主程序入口 if __name__ == '__main__': tf.app.run()

输出结果如下：

Extracting /tmp/data/train-images-idx3-ubyte.gz Extracting /tmp/data/train-labels-idx1-ubyte.gz Extracting /tmp/data/t10k-images-idx3-ubyte.gz Extracting /tmp/data/t10k-labels-idx1-ubyte.gz After 0 training step(s), validation accuracy using average model is 0.0462 After 1000 training step(s), validation accuracy using average model is 0.9784 After 2000 training step(s), validation accuracy using average model is 0.9806 After 3000 training step(s), validation accuracy using average model is 0.9798 After 4000 training step(s), validation accuracy using average model is 0.9814 After 5000 training step(s), validation accuracy using average model is 0.9826 After 6000 training step(s), validation accuracy using average model is 0.9828 After 7000 training step(s), validation accuracy using average model is 0.9832 After 8000 training step(s), validation accuracy using average model is 0.9838 After 9000 training step(s), validation accuracy using average model is 0.983 After 10000 training step(s), validation accuracy using average model is 0.9836 After 11000 training step(s), validation accuracy using average model is 0.9822 After 12000 training step(s), validation accuracy using average model is 0.983 After 13000 training step(s), validation accuracy using average model is 0.983 After 14000 training step(s), validation accuracy using average model is 0.9844 After 15000 training step(s), validation accuracy using average model is 0.9832 After 16000 training step(s), validation accuracy using average model is 0.9844 After 17000 training step(s), validation accuracy using average model is 0.9842 After 18000 training step(s), validation accuracy using average model is 0.9842 After 19000 training step(s), validation accuracy using average model is 0.9838 After 20000 training step(s), validation accuracy using average model is 0.9834 After 21000 training step(s), validation accuracy using average model is 0.9828 After 22000 training step(s), validation accuracy using average model is 0.9834 After 23000 training step(s), validation accuracy using average model is 0.9844 After 24000 training step(s), validation accuracy using average model is 0.9838 After 25000 training step(s), validation accuracy using average model is 0.9834 After 26000 training step(s), validation accuracy using average model is 0.984 After 27000 training step(s), validation accuracy using average model is 0.984 After 28000 training step(s), validation accuracy using average model is 0.9836 After 29000 training step(s), validation accuracy using average model is 0.9842 After 30000 training steps, test accuracy using average model is 0.9839

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