keras 模型参数详细

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实验平台:
win10 pro, python3.5.2,
直接上代码

from tensorflow import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Flatten
from tensorflow.keras.layers import Conv2D, MaxPool2D

# 下面代码在判断有没 mnist.npz, 如果没有, 在网上下载
# (train_data, train_labels), (test_data, test_labels) = keras.datasets.mnist.load_data()

# 
(train_data, train_labels), (test_data, test_labels) = keras.datasets.mnist.load_data(path='mnist.npz')
train_data = train_data.reshape(-1, 28, 28, 1)
print("train data type:{}, shape:{}, dim:{}".format(type(train_data), train_data.shape, train_data.ndim))

# 第一组
model = Sequential()
model.add(Conv2D(filters=32, kernel_size=(3, 3), strides=(1, 1), padding='valid', activation='relu', input_shape=(28, 28, 1)))
model.add(Conv2D(filters=32, kernel_size=(3, 3), strides=(1, 1), padding='valid', activation='relu'))
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

# 第二组
model.add(Conv2D(filters=64, kernel_size=(3, 3), strides=(1, 1), padding='valid', activation='relu'))
model.add(Conv2D(filters=64, kernel_size=(3, 3), strides=(1, 1), padding='valid', activation='relu'))
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

# 第三组
model.add(Flatten())
model.add(Dense(units=256, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(units=10, activation='softmax'))


# 查看模型
model.summary()

输出:

Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz
11493376/11490434 [==============================] - 3s 0us/step
train data type:<class 'numpy.ndarray'>, shape:(60000, 28, 28, 1), dim:4
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
conv2d (Conv2D)              (None, 26, 26, 32)        320
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 24, 24, 32)        9248
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 12, 12, 32)        0
_________________________________________________________________
dropout (Dropout)            (None, 12, 12, 32)        0
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 10, 10, 64)        18496
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 8, 8, 64)          36928
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 4, 4, 64)          0
_________________________________________________________________
dropout_1 (Dropout)          (None, 4, 4, 64)          0
_________________________________________________________________
flatten (Flatten)            (None, 1024)              0
_________________________________________________________________
dense (Dense)                (None, 256)               262400
_________________________________________________________________
dropout_2 (Dropout)          (None, 256)               0
_________________________________________________________________
dense_1 (Dense)              (None, 10)                2570
=================================================================
Total params: 329,962
Trainable params: 329,962
Non-trainable params: 0
_________________________________________________________________

遇到的错误:

TimeoutError: [WinError 10060] 由于连接方在一段时间后没有正确答复或连接的主机没有反应，连接尝试失败。

解决办法:
用浏览器下载mnist.npz

https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz

放在程序中所在目录下, 解决之