Sequential搭建模型

构建一个最简单的神经网络模型，它只有3个全连接层组成：

from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activationmodel = Sequential() # 顺序模型

输入层

model.add(Dense(7, input_shape=(4,)))  # Dense就是常用的全连接层
model.add(Activation('sigmoid')) # 激活函数

隐层

model.add(Dense(13))  # Dense就是常用的全连接层
model.add(Activation('sigmoid')) # 激活函数

输出层

model.add(Dense(5))
model.add(Activation('softmax'))model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=["accuracy"])model.summary()

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_4 (Dense)              (None, 7)                 35        
_________________________________________________________________
activation_4 (Activation)    (None, 7)                 0         
_________________________________________________________________
dense_5 (Dense)              (None, 13)                104       
_________________________________________________________________
activation_5 (Activation)    (None, 13)                0         
_________________________________________________________________
dense_6 (Dense)              (None, 5)                 70        
_________________________________________________________________
activation_6 (Activation)    (None, 5)                 0         
=================================================================
Total params: 209
Trainable params: 209
Non-trainable params: 0
_________________________________________________________________

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