def get_model():
inputs = Input(shape=(64, 64, 3))
conv_1 = Conv2D(1, (3, 3), strides=(1, 1), padding='same')(inputs)
act_1 = Activation('relu')(conv_1)
conv_2 = Conv2D(64, (3, 3), strides=(1, 1), padding='same')(act_1)
act_2 = Activation('relu')(conv_2)
deconv_1 = Conv2DTranspose(64, (3, 3), strides=(1, 1), padding='same')(act_2)
act_3 = Activation('relu')(deconv_1)
merge_1 = concatenate([act_3, act_1], axis=3)
deconv_2 = Conv2DTranspose(1, (3, 3), strides=(1, 1), padding='same')(merge_1)
act_4 = Activation('relu')(deconv_2)
model = Model(inputs=[inputs], outputs=[act_4])
model.compile(optimizer='adadelta', loss=dice_coef_loss, metrics=[dice_coef])
return model
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