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- def tempsigmoid(x, temp=1.0):
- return K.sigmoid(x/temp)
- def baseline_model():
- # create model
- model = Sequential()
- model.add(Conv2D(101, (5, 5), input_shape=(1, 28, 28), activation='relu'))
- model.add(MaxPooling2D(pool_size=(2, 2)))
- model.add(Dropout(0.2))
- model.add(Flatten())
- model.add(Dense(128, activation='relu'))
- model.add(Dense(num_classes, activation='tempsigmoid'))
- # Compile model
- model.compile(loss='mse', optimizer='adam', metrics=['accuracy'])
- return model
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