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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
首先附上百度百科不要钱的网络结构图:
再附上 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:
AlexNet网络
import tensorflow as tf
import tensorflow.examples.tutorials.mnist.input_data as input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) # 导入数据集
'''训练图像和数据集时表现出色,比LeNet出色的另一个地方Relu, 原文是用来训练三通道彩图用的.'''
class AlexNet:
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, 8, 8, 96) # (原文:filters=96, kernel_size=11, strides=(4, 4))
self.conv1 = tf.layers.Conv2D(filters=96, kernel_size=7, strides=(3, 3),
kernel_initializer=tf.truncated_normal_initializer(stddev=tf.sqrt(1 / 48)))
# 池化层 (batch, 8, 8, 96) -> (batch, 4, 4, 96) # (原文:pool_size=(3, 3), strides=(2, 2))
self.pool1 = tf.layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2))
# 卷积层 (batch, 4, 4, 96) -> (batch, 4, 4, 256) # (原文:filters=256, kernel_size=5)
self.conv2 = tf.layers.Conv2D(filters=256, kernel_size=3, padding="SAME",
kernel_initializer=tf.truncated_normal_initializer(stddev=tf.sqrt(1 / 128)))
# 池化层 (batch, 4, 4, 256) -> (batch, 2, 2, 256) # (原文:pool_size=(3, 3), strides=(2, 2))
self.pool2 = tf.layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2))
# 卷积层 (batch, 2, 2, 256) -> (batch, 2, 2, 384) # (原文:filters=384, kernel_size=3)
self.conv3 = tf.layers.Conv2D(filters=384, kernel_size=1, padding="SAME",
kernel_initializer=tf.truncated_normal_initializer(stddev=tf.sqrt(1 / 192)))
# 卷积层 (batch, 2, 2, 384) -> (batch, 2, 2, 384) # (原文:filters=384, kernel_size=3)
self.conv4 = tf.layers.Conv2D(filters=384, kernel_size=1, padding="SAME",
kernel_initializer=tf.truncated_normal_initializer(stddev=tf.sqrt(1 / 192)))
# 卷积层 (batch, 2, 2, 384) -> (batch, 2, 2, 256) # (原文:filters=256, kernel_size=3)
self.conv5 = tf.layers.Conv2D(filters=256, kernel_size=1, padding="SAME",
kernel_initializer=tf.truncated_normal_initializer(stddev=tf.sqrt(1 / 128)))
# 池化层 (batch, 2, 2, 256) -> (batch, 1, 1, 256) # (原文:pool_size=(3, 3), strides=(2, 2))
self.pool3 = tf.layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2))
# reshape(-1, 256) -> (batch, 256) # (原文:units=9216)
self.fc1 = tf.layers.Dense(units=256,
kernel_initializer=tf.truncated_normal_initializer(stddev=tf.sqrt(1 / 128)))
# (batch, 256) -> (batch, 128) # (原文:units=4096)
self.fc2 = tf.layers.Dense(units=128,
kernel_initializer=tf.truncated_normal_initializer(stddev=tf.sqrt(1 / 64)))
# (batch, 128) -> (batch, 128) # (原文:units=4096)
self.fc3 = tf.layers.Dense(units=128,
kernel_initializer=tf.truncated_normal_initializer(stddev=tf.sqrt(1 / 64)))
# (batch, 128) -> (batch, 10) # (原文:根据自己分类需要)
self.fc4 = tf.layers.Dense(units=10, kernel_initializer=tf.truncated_normal_initializer(stddev=tf.sqrt(1 / 5)))
def forward(self): # 因为方便训练集 所以改了部分AlexNet网络参数 和训练的训练集 改的地方已注明
self.conv1_out = tf.nn.relu(self.conv1(self.in_x))
self.poo1_out = self.pool1(self.conv1_out)
self.conv2_out = tf.nn.relu(self.conv2(self.poo1_out))
self.poo2_out = self.pool2(self.conv2_out)
self.conv3_out = tf.nn.relu(self.conv3(self.poo2_out))
self.conv4_out = tf.nn.relu(self.conv4(self.conv3_out))
self.conv5_out = tf.nn.relu(self.conv5(self.conv4_out))
self.pool3 = self.pool3(self.conv5_out)
self.flat = tf.reshape(self.pool3, shape=[-1, 256])
self.fc1_out = tf.nn.relu(self.fc1(self.flat))
self.fc2_out = tf.nn.relu(self.fc2(self.fc1_out))
self.fc3_out = tf.nn.relu(self.fc3(self.fc2_out))
self.fc4_out = self.fc4(self.fc3_out)
def backward(self): # 后向计算
self.loss = tf.reduce_mean((self.fc4_out - self.in_y) ** 2)
self.opt = tf.train.AdamOptimizer().minimize(self.loss)
def acc(self): # 精度计算(可不写, 不影响网络使用)
self.acc1 = tf.equal(tf.argmax(self.fc4_out, 1), tf.argmax(self.in_y, 1))
self.accaracy = tf.reduce_mean(tf.cast(self.acc1, dtype=tf.float32))
if __name__ == '__main__':
net = AlexNet() # 创建AlexNet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:
