MLP实现fashion_mnist数据集分类(2)-函数式API构建模型(tensorflow)

使用函数式API构建模型，使得模型可以处理多输入多输出。

1、查看tensorflow版本

import tensorflow as tfprint('Tensorflow Version:{}'.format(tf.__version__))
print(tf.config.list_physical_devices())

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2、fashion_mnist数据集分类模型

2.1 使用Sequential构建模型

from keras import Sequential
from keras.layers import Flatten,Dense,Dropout
from keras import Inputmodel = Sequential()
model.add(Input(shape=(28,28)))
model.add(Flatten())
model.add(Dense(units=256,kernel_initializer='normal',activation='relu'))
model.add(Dropout(rate=0.1))
model.add(Dense(units=64,kernel_initializer='normal',activation='relu'))
model.add(Dropout(rate=0.1))
model.add(Dense(units=10,kernel_initializer='normal',activation='softmax'))
model.summary()

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2.2 使用函数式API构建模型

from keras.layers import Flatten,Dense,Dropout
from keras import Input,Modelinput = Input(shape=(28,28))
x = Flatten()(input)
x = Dense(units=256,kernel_initializer='normal',activation='relu')(x)
x = Dropout(rate=0.1)(x)
x = Dense(units=64,kernel_initializer='normal',activation='relu')(x)
x = Dropout(rate=0.1)(x)
output = Dense(units=10,kernel_initializer='normal',activation='softmax')(x)
model = Model(inputs=input, outputs=output)
model.summary()

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可以看到两个模型的结构是一样的，编译和训练也是一样的。

3、使用函数式API搭建多输入多输出模型

两个输入一个输出，对比两个图片是否一样。

from keras.layers import Flatten,Dense,Dropout
from keras import Input,Model
import kerasinput1 = Input(shape=(28,28))
input2 = Input(shape=(28,28))
x1 = Flatten()(input1)
x2 = Flatten()(input2)
x = keras.layers.concatenate([x1,x2])
x = Dense(units=256,kernel_initializer='normal',activation='relu')(x)
x = Dropout(rate=0.1)(x)
x = Dense(units=64,kernel_initializer='normal',activation='relu')(x)
x = Dropout(rate=0.1)(x)
output = Dense(units=1,kernel_initializer='normal',activation='sigmoid')(x)
model = Model(inputs=[input1,input2], outputs=output) # 两个输入，一个输出
model.summary()