动手学深度学习第五章，LeNet、AlexNet、VGG
LeNet
首先导包：
import tensorflow as tf
print(tf.__version__)
输出：
2.1.0
GPU内存设置：
gpus = tf.config.experimental.list_physical_devices('GPU')
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu,True)
模型定义：
model = tf.keras.models.Sequential() model.add(tf.keras.layers.Conv2D(filters=6,kernel_size=5,activation='relu',input_shape=(28,28,1))) model.add(tf.keras.layers.MaxPool2D(pool_size=2,strides=2)) model.add(tf.keras.layers.Conv2D(filters=16,kernel_size=5,activation='relu')) model.add(tf.keras.layers.MaxPool2D(pool_size=2,strides=2)) model.add(tf.keras.layers.Flatten()) model.add(tf.keras.layers.Dense(120,activation='relu')) model.add(tf.keras.layers.Dense(84,activation='relu')) model.add(tf.keras.layers.Dense(10,activation='softmax'))
我们输出每一层查看一下：
for layer in model.layers:
print(layer.name)
输出：
conv2d_6 max_pooling2d_6 conv2d_7 max_pooling2d_7 flatten_3 dense_9 dense_10 dense_11
读取数据集：
fashion_mnist = tf.keras.datasets.fashion_mnist (train_images,train_labels),(test_images,test_labels) = fashion_mnist.load_data()
将三维数据变成四维（图片数量，图片长，图片宽，图片channel数），其中由于fashion mnist channel数为1，所以手动加上一维
# 三维变4维 train_images = tf.reshape(train_images,(train_images.shape[0],train_images.shape[1],train_images.shape[2],1)) print(train_images.shape) test_images = tf.reshape(test_images,(test_images.shape[0],test_images.shape[1],test_images.shape[2],1)) print(test_images.shape)
输出：
(60000, 28, 28, 1) (10000, 28, 28, 1)
训练：
model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy']) model.fit(train_images,train_labels,epochs=5,validation_split=0.2)
输出：
Train on 48000 samples, validate on 12000 samples Epoch 1/5 48000/48000 [==============================] - 6s 132us/sample - loss: 0.7950 - accuracy: 0.7685 - val_loss: 0.5144 - val_accuracy: 0.8116 Epoch 2/5 48000/48000 [==============================] - 6s 130us/sample - loss: 0.4314 - accuracy: 0.8410 - val_loss: 0.4012 - val_accuracy: 0.8550 Epoch 3/5 48000/48000 [==============================] - 6s 129us/sample - loss: 0.3699 - accuracy: 0.8647 - val_loss: 0.3891 - val_accuracy: 0.8627 Epoch 4/5 48000/48000 [==============================] - 6s 126us/sample - loss: 0.3416 - accuracy: 0.8738 - val_loss: 0.3661 - val_accuracy: 0.8683 Epoch 5/5 48000/48000 [==============================] - 6s 124us/sample - loss: 0.3174 - accuracy: 0.8814 - val_loss: 0.3666 - val_accuracy: 0.8689
验证测试集：
model.evaluate(test_images, test_labels)
10000/10000 [==============================] - 1s 84us/sample - loss: 0.3856 - accuracy: 0.8667
AlexNet
首先导包：
import tensorflow as tf
print(tf.__version__)
输出：
2.1.0
GPU设置：
for gpu in tf.config.experimental.list_physical_devices('GPU'):
tf.config.experimental.set_memory_growth(gpu, True)
模型：
model = tf.keras.Sequential() model.add(tf.keras.layers.Conv2D(filters=96,kernel_size=11,strides=4,activation='relu')) model.add(tf.keras.layers.MaxPool2D(pool_size=3,strides=2)) model.add(tf.keras.layers.Conv2D(filters=256,kernel_size=5,padding='same',activation='relu')) model.add(tf.keras.layers.MaxPool2D(pool_size=3,strides=2)) model.add(tf.keras.layers.Conv2D(383,kernel_size=3,padding='same',activation='relu')) model.add(tf.keras.layers.Conv2D(384,kernel_size=3,padding='same',activation='relu')) model.add(tf.keras.layers.Conv2D(256,kernel_size=3,padding='same',activation='relu')) model.add(tf.keras.layers.MaxPool2D(pool_size=3,strides=2)) model.add(tf.keras.layers.Flatten()) model.add(tf.keras.layers.Dense(4096,activation='relu')) model.add(tf.keras.layers.Dropout(0.5)) model.add(tf.keras.layers.Dense(4096,activation='relu')) model.add(tf.keras.layers.Dropout(0.5)) model.add(tf.keras.layers.Dense(10,activation='sigmoid'))
输出每一层的名称：
for layer in model.layers:
print(layer.name)
输出如下：
conv2d max_pooling2d conv2d_1 max_pooling2d_1 conv2d_2 conv2d_3 conv2d_4 max_pooling2d_2 flatten dense dropout dense_1 dropout_1 dense_2
这里不做进一步演示了，都差不多
VGG（block）
这里，我们第一次接触block，即n个重复模块的基本单位。
首先，老生常谈，不做赘述。
import tensorflow as tf
print(tf.__version__)
2.1.0
for gpu in tf.config.experimental.list_physical_devices('GPU'):
tf.config.experimental.set_memory_growth(gpu, True)
然后我们定义block，也就是n个卷积之后连接一个池化层：
def vgg_block(num_convs, num_channels):
blk = tf.keras.models.Sequential()
for _ in range(num_convs):
blk.add(tf.keras.layers.Conv2D(num_channels,kernel_size=3,
padding='same',activation='relu'))
blk.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2))
return blk
然后我们定义网络，通过conv_arch来对整个网络进行定义：
conv_arch = ((1, 64), (1, 128), (2, 256), (2, 512), (2, 512))
# VGG-11
def vgg(conv_arch):
net = tf.keras.models.Sequential()
for (num_convs, num_channels) in conv_arch:
net.add(vgg_block(num_convs,num_channels))
net.add(tf.keras.models.Sequential([tf.keras.layers.Flatten(),
tf.keras.layers.Dense(4096,activation='relu'),
tf.keras.layers.Dropout(0.5),
tf.keras.layers.Dense(4096,activation='relu'),
tf.keras.layers.Dropout(0.5),
tf.keras.layers.Dense(10,activation='sigmoid')]))
return net
net = vgg(conv_arch)
for blk in net.layers:
print(blk.name)
输出，我们可以看到有5个人block和最后的一个全连接，一共6个部分：
sequential_1 sequential_2 sequential_3 sequential_4 sequential_5 sequential_6
剩下的不赘述，详细代码移步github。
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