天天向上

import keras

from keras import layers

from keras import models

print('keras version: {}'.format(keras.__version__))

model = models.Sequential()

model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))

model.summary()

keras version: 2.4.3
Model: "sequential_5"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_5 (Conv2D) (None, 26, 26, 32) 320
=================================================================
Total params: 320
Trainable params: 320
Non-trainable params: 0
_________________________________________________________________

number of parameters: [(3*3)*32]+32=320

model = models.Sequential()

model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))

model.add(layers.MaxPooling2D((2, 2)))

model.summary()

Layer (type) Output Shape Param #
=================================================================
conv2d_9 (Conv2D) (None, 26, 26, 32) 320
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 13, 13, 32) 0
=================================================================
Total params: 320
Trainable params: 320
Non-trainable params: 0
_________________________________________________________________

model = models.Sequential()

model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))

model.add(layers.MaxPooling2D((2, 2)))

model.add(layers.Conv2D(64, (3, 3), activation='relu'))

model.summary()

_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_10 (Conv2D) (None, 26, 26, 32) 320
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 13, 13, 32) 0
_________________________________________________________________
conv2d_11 (Conv2D) (None, 11, 11, 64) 18496
=================================================================
Total params: 18,816
Trainable params: 18,816
Non-trainable params: 0
_________________________________________________________________

number of parameters: {[(3*3)*64]*32}+64=18,496

320+18,496=18,816

from keras import layers

from keras import models

from keras.datasets import mnist

from keras.utils import to_categorical

model = models.Sequential()

model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))

model.add(layers.MaxPooling2D((2, 2)))

model.add(layers.Conv2D(64, (3, 3), activation='relu'))

model.add(layers.MaxPooling2D((2, 2)))

model.summary()

Layer (type) Output Shape Param #
=================================================================
conv2d_12 (Conv2D) (None, 26, 26, 32) 320
_________________________________________________________________
max_pooling2d_5 (MaxPooling2 (None, 13, 13, 32) 0
_________________________________________________________________
conv2d_13 (Conv2D) (None, 11, 11, 64) 18496
_________________________________________________________________
max_pooling2d_6 (MaxPooling2 (None, 5, 5, 64) 0
=================================================================
Total params: 18,816
Trainable params: 18,816
Non-trainable params: 0
_________________________________________________________________