35 lines
1.3 KiB
Python
35 lines
1.3 KiB
Python
import tensorflow as tf
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from tensorflow import keras
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import matplotlib.pyplot as plt
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fashion_mnist = keras.datasets.fashion_mnist
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(train_image, train_label), (test_image, test_label) = fashion_mnist.load_data()
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train_image = train_image / 255.0
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test_image = test_image / 255.0
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'''input=keras.Input(shape=(28,28))
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x=keras.layers.Flatten()(input)
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x=keras.layers.Dense(32,activation='relu')(x)
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x=keras.layers.Dropout(0.5)(x)
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x=keras.layers.Dense(64,activation='relu')(x)
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output=keras.layers.Dense(10,activation='softmax')(x)
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model=keras.Model(inputs=input,outputs=output)
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model.summary()
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model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['acc'],)
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history=model.fit(train_image,train_label,epochs=10,validation_data=(test_image,test_label),batch_size=5)
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plt.plot(history.epoch,history.history.get('acc'),label='acc')
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plt.plot(history.epoch,history.history.get('val_acc'),label='val_acc')
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plt.legend()
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plt.show()
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model.evaluate(test_image,test_label,batch_size=5)'''
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input1 = keras.Input(shape=(28, 28))
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input2 = keras.Input(shape=(28, 28))
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x1 = keras.layers.Flatten()(input1)
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x2 = keras.layers.Flatten()(input2)
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x = keras.layers.concatenate([x1, x2])
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x = keras.layers.Dense(32, activation='relu')(x)
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output = keras.layers.Dense(1, activation='sigmoid')(x)
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model = keras.Model(inputs=[input1, input2], outputs=output)
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model.summary()
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