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About Keras models

keras中有两个主要的模型类型 the Sequential model 和 the Model class used with the functional API

模型中有一系列的方法和属性

from keras.layers import Dense, Input
from keras.models import Model, Sequential
import keras
import numpy as np

# 举例模型
data_input = Input(shape=(100, ))
x = Dense(64, activation=relu)(data_input)
x = Dense(64, activation=relu)(x)
y = Dense(1, activation=sigmoid)(x)

model = Model(data_input, y)

model.compile(optimizer=keras.optimizers.Adam(),
              loss=keras.losses.binary_crossentropy,
              metrics=[accuracy])

# 创建numpy数据
x_train = np.random.random((100, 100))
y_train = np.random.randint(2, size=(100, 1))

# 训练模型
model.fit(x_train, y_train, epochs=10, batch_size=16)

· model.layers 是一个包含组成该模型的层的扁平列表

print(model.layers)
#[<keras.engine.input_layer.InputLayer object at 0x126997c88>, <keras.layers.core.Dense object at 0x126997cf8>, 
#<keras.layers.core.Dense object at 0x126997b38>, <keras.layers.core.Dense object at 0x126997668>]

· model.inputs 是一个包含输入张量的列表

print(model.inputs)
#[<tf.Tensor input_1:0 shape=(?, 100) dtype=float32>]

· model.outputs 是一个包含输出张量的列表

print(model.outputs)
#[<tf.Tensor dense_3/Sigmoid:0 shape=(?, 1) dtype=float32>]

· model.summary() 可以打印出模型结构信息

model.summary()

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         (None, 100)               0         
_________________________________________________________________
dense_1 (Dense)              (None, 64)                6464      
_________________________________________________________________
dense_2 (Dense)              (None, 64)                4160      
_________________________________________________________________
dense_3 (Dense)              (None, 1)                 65        
=================================================================
Total params: 10,689
Trainable params: 10,689
Non-trainable params: 0
_________________________________________________________________

· model.get_config() 返回一个包含模型配置的字典。模型可以通过以下方式从配置中恢复:

config = model.get_config()
reconfig_model = Model.from_config(config)
# 或者，对于Sequential模型
reconfig_model = Sequential.from_config(config)
reconfig_model.summary()

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_1 (Dense)              (None, 64)                6464      
_________________________________________________________________
dense_2 (Dense)              (None, 64)                4160      
_________________________________________________________________
dense_3 (Dense)              (None, 1)                 65        
=================================================================
Total params: 10,689
Trainable params: 10,689
Non-trainable params: 0

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