import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_dataINPUT_NODE = 784 # 輸入節點 OUTPUT_NODE = 10 # 輸出節點 BATCH_SIZE = 100 # 每次batch打包的樣本個數 # 模型相關的參數 LEARNING_RATE_BASE = 0.01 LEARNING_RATE_DECAY = 0.99 REGULARAZTION_RATE = 0.0001 TRAINING_STEPS = 5000 MOVING_AVERAGE_DECAY = 0.99 def inference(input_tensor, avg_class, weights1, biases1):# 不使用滑動平均類if avg_class == None:layer1 = tf.nn.relu(tf.matmul(input_tensor, weights1) + biases1)return layer1else:# 使用滑動平均類layer1 = tf.nn.relu(tf.matmul(input_tensor, avg_class.average(weights1)) + avg_class.average(biases1))return layer1def train(mnist):x = tf.placeholder(tf.float32, [None, INPUT_NODE], name='x-input')y_ = tf.placeholder(tf.float32, [None, OUTPUT_NODE], name='y-input')# 生成輸出層的參數。weights1 = tf.Variable(tf.truncated_normal([INPUT_NODE, OUTPUT_NODE], stddev=0.1))biases1 = tf.Variable(tf.constant(0.1, shape=[OUTPUT_NODE]))# 計算不含滑動平均類的前向傳播結果y = inference(x, None, weights1, biases1)# 定義訓練輪數及相關的滑動平均類 global_step = tf.Variable(0, trainable=False)variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step)variables_averages_op = variable_averages.apply(tf.trainable_variables())average_y = inference(x, variable_averages, weights1, biases1)# 計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)# 損失函數的計算regularizer = tf.contrib.layers.l2_regularizer(REGULARAZTION_RATE)regularaztion = regularizer(weights1)loss = cross_entropy_mean + regularaztion# 設置指數衰減的學習率。learning_rate = tf.train.exponential_decay(LEARNING_RATE_BASE,global_step,mnist.train.num_examples / BATCH_SIZE,LEARNING_RATE_DECAY,staircase=True)# 優化損失函數train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step)# 反向傳播更新參數和更新每一個參數的滑動平均值 with tf.control_dependencies([train_step, variables_averages_op]):train_op = tf.no_op(name='train')# 計算正確率correct_prediction = tf.equal(tf.argmax(average_y, 1), tf.argmax(y_, 1))accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))# 初始化會話，并開始訓練過程。 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} # 循環的訓練神經網絡。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()

?

轉載于:https://www.cnblogs.com/tszr/p/10875835.html

總結

以上是生活随笔為你收集整理的吴裕雄 python 神经网络——TensorFlow训练神经网络：不使用隐藏层的全部內容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網站內容還不錯，歡迎將生活随笔推薦給好友。