# 1. 通过TensorFlow-Slim定义卷机神经网络 import numpy as np import tensorflow as tf import tensorflow.contrib.slim as slim from tensorflow.examples.tutorials.mnist import input_data # 通过TensorFlow-Slim来定义LeNet-5的网络结构。 def lenet5(inputs): inputs = tf.reshape(inputs, [-1, 28, 28, 1]) net = slim.conv2d(inputs, 32, [5, 5], padding=‘SAME‘, scope=‘layer1-conv‘) net = slim.max_pool2d(net, 2, stride=2, scope=‘layer2-max-pool‘) net = slim.conv2d(net, 64, [5, 5], padding=‘SAME‘, scope=‘layer3-conv‘) net = slim.max_pool2d(net, 2, stride=2, scope=‘layer4-max-pool‘) net = slim.flatten(net, scope=‘flatten‘) net = slim.fully_connected(net, 500, scope=‘layer5‘) net = slim.fully_connected(net, 10, scope=‘output‘) return net # 训练模型。 def train(mnist): x = tf.placeholder(tf.float32, [None, 784], name=‘x-input‘) y_ = tf.placeholder(tf.float32, [None, 10], name=‘y-input‘) y = lenet5(x) cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1)) loss = tf.reduce_mean(cross_entropy) train_op = tf.train.GradientDescentOptimizer(0.01).minimize(loss) with tf.Session() as sess: tf.global_variables_initializer().run() for i in range(3000): xs, ys = mnist.train.next_batch(100) _, loss_value = sess.run([train_op, loss], feed_dict={x: xs, y_: ys}) if i % 1000 == 0: print("After %d training step(s), loss on training batch is %g." % (i, loss_value))
# 主程序 def main(argv=None): mnist = input_data.read_data_sets("F:\\TensorFlowGoogle\\201806-github\\datasets\\MNIST_data\\", one_hot=True) train(mnist) if __name__ == ‘__main__‘: main()
原文地址:https://www.cnblogs.com/tszr/p/12069987.html
时间: 2024-10-07 21:31:52