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- optimizer = RMSprop(lr=1e-4)
- objective = 'binary_crossentropy'
- def malefemale():
- model = Sequential()
- model.add(Convolution2D(32, 3, 3, border_mode='same', input_shape=(3, ROWS, COLS), activation='relu'))
- model.add(Convolution2D(32, 3, 3, border_mode='same', activation='relu'))
- model.add(MaxPooling2D(pool_size=(2, 2), dim_ordering="th"))
- model.add(Convolution2D(64, 3, 3, border_mode='same', activation='relu'))
- model.add(Convolution2D(64, 3, 3, border_mode='same', activation='relu'))
- model.add(MaxPooling2D(pool_size=(2, 2), dim_ordering="th"))
- model.add(Convolution2D(128, 3, 3, border_mode='same', activation='relu'))
- model.add(Convolution2D(128, 3, 3, border_mode='same', activation='relu'))
- model.add(MaxPooling2D(pool_size=(2, 2), dim_ordering="th"))
- model.add(Convolution2D(256, 3, 3, border_mode='same', activation='relu'))
- model.add(Convolution2D(256, 3, 3, border_mode='same', activation='relu'))
- # model.add(Convolution2D(256, 3, 3, border_mode='same', activation='relu'))
- model.add(MaxPooling2D(pool_size=(2, 2), dim_ordering="th"))
- model.add(Flatten())
- model.add(Dense(256, activation='relu'))
- model.add(Dropout(0.5))
- model.add(Dense(256, activation='relu'))
- model.add(Dropout(0.5))
- model.add(Dense(1))
- model.add(Activation('sigmoid'))
- model.compile(loss=objective, optimizer=optimizer, metrics=['accuracy'])
- return model
- model = malefemale()
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