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本章所需知识:
- 没有基础的请观看深度学习系列视频
- tensorflow
恩达老师的可视化极强的网络结构图:
接着加上我自己使用Tensorflow实现的代码:
LeNet_5网络
import tensorflow as tfimport tensorflow.examples.tutorials.mnist.input_data as input_datamnist = input_data.read_data_sets("MNIST_data/", one_hot=True) ?# 导入数据集‘‘‘LeNet_5, 可以识别图片中的手写数字, 针对灰度图像训练的‘‘‘class LeNet_5: ???def __init__(self): ???????self.in_x = tf.placeholder(dtype=tf.float32, shape=[None, 28, 28, 1], name="in_x") ???????self.in_y = tf.placeholder(dtype=tf.float32, shape=[None, 10], name="in_y") ???????# 卷积层 (batch, 28, 28, 1) -> (batch, 24, 24, 6) ???????self.conv1 = tf.layers.Conv2D(filters=6, kernel_size=5, strides=(1, 1), ?????????????????????????????????????kernel_initializer=tf.truncated_normal_initializer(stddev=tf.sqrt(1 / 6))) ???????# 池化层 (batch, 24, 24, 6) -> (batch, 12, 12, 6) ???????self.pool1 = tf.layers.AveragePooling2D(pool_size=(2, 2), strides=(2, 2)) ???????# 卷积层 (batch, 12, 12, 6) -> (batch, 8, 8, 16) ???????self.conv2 = tf.layers.Conv2D(filters=16, kernel_size=5, strides=(1, 1), ?????????????????????????????????????kernel_initializer=tf.truncated_normal_initializer(stddev=tf.sqrt(1 / 16))) ???????# 池化层 (batch, 8, 8, 16) -> (batch, 4, 4, 16) ???????self.pool2 = tf.layers.AveragePooling2D(pool_size=(2, 2), strides=(2, 2)) ???????# 全链接层 (batch, 4*4*16(256)) -> (batch, 120) ???????self.fc1 = tf.layers.Dense(120, kernel_initializer=tf.truncated_normal_initializer(stddev=tf.sqrt(1 / 120))) ???????# 全链接层 (batch, 120) -> (batch, 10) ???????self.fc2 = tf.layers.Dense(10, kernel_initializer=tf.truncated_normal_initializer(stddev=tf.sqrt(1 / 10))) ???def forward(self): ?# 因为是还原LeNet5 所以使用sigmoid ???????self.conv1_out = tf.nn.sigmoid(self.conv1(self.in_x)) ?# 将图片传入 conv1 ???????self.pool1_out = self.pool1(self.conv1_out) ?# 将 conv1 的输出传入 pool1 ???????self.conv2_out = tf.nn.sigmoid(self.conv2(self.pool1_out)) ?# 将 pool1 的输出传入 conv2 ???????self.pool2_out = self.pool2(self.conv2_out) ?# 将 conv2 的输出传入 pool2 ???????self.flat = tf.reshape(self.pool2_out, shape=[-1, 256]) ?# 将 pool2 的输出reshape成 (batch, -1(-1指这里的256,具体看计算出的图大小)) ???????self.fc1_out = tf.nn.sigmoid(self.fc1(self.flat)) ?# 将 reshape 后的图传入 fc1 ???????self.fc2_out = tf.nn.softmax(self.fc2(self.fc1_out)) ?# 将 fc1 的输出传入 fc2 ???def backward(self): ?# 后向计算 ???????self.loss = tf.reduce_mean((self.fc2_out - self.in_y) ** 2) ?# 均方差计算损失 ???????self.opt = tf.train.AdamOptimizer().minimize(self.loss) ?# 使用Adam优化器优化损失 ???def acc(self): ?# 精度计算(可不写, 不影响网络使用) ???????self.acc1 = tf.equal(tf.argmax(self.fc2_out, 1), tf.argmax(self.in_y, 1)) ???????self.accaracy = tf.reduce_mean(tf.cast(self.acc1, dtype=tf.float32))if __name__ == ‘__main__‘: ???net = LeNet_5() ?# 创建LeNet_5的对象 ???net.forward() ?# 执行前向计算 ???net.backward() ?# 执行后向计算 ???net.acc() ?# 执行精度计算 ???init = tf.global_variables_initializer() ?# 初始化所有tensorflow变量 ???with tf.Session() as sess: ???????sess.run(init) ???????for i in range(10000): ???????????train_x, train_y = mnist.train.next_batch(100) ?# 取出mnist训练集的 100 批数据和标签 ???????????train_x_flat = train_x.reshape([-1, 28, 28, 1]) ?# 将数据整型 ???????????# 将数据传入网络,并得到计算后的精度和损失 ???????????acc, loss, _ = sess.run(fetches=[net.accaracy, net.loss, net.opt], ???????????????????????????????????feed_dict={net.in_x: train_x_flat, net.in_y: train_y}) ???????????if i % 100 == 0: ?# 每训练100次打印一次训练集精度和损失 ???????????????print("训练集精度:|", acc) ???????????????print("训练集损失:|", loss) ???????????????test_x, test_y = mnist.test.next_batch(100) ?# 取出100批测试集数据进行测试 ???????????????test_x_flat = test_x.reshape([-1, 28, 28, 1]) ?# 同上 ???????????????# 同上 ???????????????test_acc, test_loss = sess.run(fetches=[net.accaracy, net.loss], ??????????????????????????????????????????????feed_dict={net.in_x: test_x_flat, net.in_y: test_y}) ???????????????print(‘----------‘) ???????????????print("验证集精度:|", test_acc) ?# 打印验证集精度 ???????????????print("验证集损失:|", test_loss) ?# 打印验证集损失 ???????????????print(‘--------------------‘)最后附上训练截图:
LeNet_5网络实现
原文地址：https://www.cnblogs.com/Mrzhang3389/p/9977632.html