【小白学习keras教程】三、Kears中常见模型层Padding、Conv2D、MaxPooling2D、Flatten

from matplotlib import pyplot as plt
from tensorflow.keras.models import Sequential
from tensorflow.keras import optimizers
from tensorflow.keras.layers import Dense, Activation, Flatten, Conv2D, MaxPooling2D, AveragePooling2D, GlobalMaxPooling2D, ZeroPadding2D, Input
from tensorflow.keras.models import Model
from tensorflow.keras.datasets import cifar10, mnist
from tensorflow.keras.preprocessing import image

(x_train, y_train), _ = cifar10.load_data()
print(x_train[0].shape)
# showing figures
fig = plt.figure(figsize = (10, 10))
for i in range(9):
  fig.add_subplot(3, 3, i+1)
  plt.imshow(x_train[i])

plt.show()

(x_train, y_train), _ = mnist.load_data()
print(x_train[0].shape)   # Images in mnist are 2-D since they don't have color channel
# showing figures
fig = plt.figure(figsize = (10, 10))
for i in range(9):
  fig.add_subplot(3, 3, i+1)
  plt.imshow(x_train[i])

plt.show()

# when padding = 'valid'
model = Sequential()
model.add(Conv2D(input_shape = (10, 10, 3), filters = 10, kernel_size = (3,3), strides = (1,1), padding = 'valid'))
print(model.output_shape)

# when padding = 'same'
model = Sequential()
model.add(Conv2D(input_shape = (10, 10, 3), filters = 10, kernel_size = (3,3), strides = (1,1), padding = 'same'))
print(model.output_shape)

# user-customized padding
input_layer = Input(shape = (10, 10, 3))
padding_layer = ZeroPadding2D(padding = (1,1))(input_layer)

model = Model(inputs = input_layer, outputs = padding_layer)
print(model.output_shape)

# when filter size = 10
model = Sequential()
model.add(Conv2D(input_shape = (10, 10, 3), filters = 10, kernel_size = (3,3), strides = (1,1), padding = 'same'))
# you could see that the depth of output = 10
print(model.output_shape)

# when filter size = 20
model = Sequential()
model.add(Conv2D(input_shape = (10, 10, 3), filters = 20, kernel_size = (3,3), strides = (1,1), padding = 'same'))
#你可以看到输出的深度=20
print(model.output_shape)

model = Sequential()
model.add(Conv2D(input_shape = (10, 10, 3), filters = 10, kernel_size = (3,3), strides = (1,1), padding = 'same'))
print(model.output_shape)

# 如果未定义“步长”参数，步长等于“池大小”
model.add(MaxPooling2D(pool_size = (2,2), padding = 'valid'))
print(model.output_shape)

model = Sequential()
model.add(Conv2D(input_shape = (10, 10, 3), filters = 10, kernel_size = (3,3), strides = (1,1), padding = 'same'))
model.add(MaxPooling2D(pool_size = (2,2), strides = (1,1), padding = 'valid'))
print(model.output_shape)

model = Sequential()
model.add(Conv2D(input_shape = (10, 10, 3), filters = 10, kernel_size = (3,3), strides = (1,1), padding = 'same'))
model.add(AveragePooling2D(pool_size = (2,2), padding = 'valid'))
print(model.output_shape)

# globalMapPooling在深度为1的整个通道上执行最大池model = Sequential()
model.add(Conv2D(input_shape = (10, 10, 3), filters = 10, kernel_size = (3,3), strides = (1,1), padding = 'same'))
model.add(GlobalMaxPooling2D())
# 当筛选器数=10时，将返回10个值作为globalMapPooling2D的结果
print(model.output_shape)

model = Sequential()
model.add(Conv2D(input_shape = (10, 10, 3), filters = 10, kernel_size = (3,3), strides = (1,1), padding = 'same'))
print(model.output_shape)

model.add(Flatten())
print(model.output_shape)

model = Sequential()
model.add(Conv2D(input_shape = (10, 10, 3), filters = 10, kernel_size = (3,3), strides = (1,1), padding = 'same'))
model.add(Flatten())
model.add(Dense(50))
print(model.output_shape)