How to write masked MSE loss in Keras?











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I was trying to write masked MSE loss:



def mae_loss_masked(mask):

    def loss_fn(y_true, y_pred):

        abs_vec = tf.multiply(tf.abs(y_pred-y_true), mask)

        loss = tf.reduce_mean(abs_vec)

        return loss

    return loss_fn


My model:



def MobileNet_v1():

    # MobileNet with dense layer on top



    # Keras 2.1.6

    mobilenet = MobileNet(input_shape=(config.IMAGE_H, config.IMAGE_W, config.N_CHANNELS),

                          alpha=1.0,

                          depth_multiplier=1,

                          include_top=False,

                          weights='imagenet'

                          )



    x = Flatten()(mobilenet.output)

    x = Dropout(0.5)(x)

    x = Dense(config.N_LANDMARKS * 2, activation='linear')(x)



    # -------------------------------------------------------



    model = Model(inputs=mobilenet.input, outputs=x)

    optimizer = Adadelta()

    model.compile(optimizer=optimizer, loss=mae_loss_masked)



    model.summary()

    import sys

    sys.exit()



    return model


But it give an error:
TypeError: mae_loss_masked() takes 1 positional argument but 2 were given



Also a question how batch generator output should look like in this case.






python tensorflow keras deep-learning mse







share|improve this question
























  • Your loss function takes 1 argument, while you are actually giving it 2. using mae_loss_masked(some_mask) will get you the actual loss function you need: stackoverflow.com/questions/46858016/… , batch should still be (x,y) , or optionally (x,y, weights)
    – Or Dinari
    yesterday















up vote
1
down vote

favorite












I was trying to write masked MSE loss:



def mae_loss_masked(mask):

    def loss_fn(y_true, y_pred):

        abs_vec = tf.multiply(tf.abs(y_pred-y_true), mask)

        loss = tf.reduce_mean(abs_vec)

        return loss

    return loss_fn


My model:



def MobileNet_v1():

    # MobileNet with dense layer on top



    # Keras 2.1.6

    mobilenet = MobileNet(input_shape=(config.IMAGE_H, config.IMAGE_W, config.N_CHANNELS),

                          alpha=1.0,

                          depth_multiplier=1,

                          include_top=False,

                          weights='imagenet'

                          )



    x = Flatten()(mobilenet.output)

    x = Dropout(0.5)(x)

    x = Dense(config.N_LANDMARKS * 2, activation='linear')(x)



    # -------------------------------------------------------



    model = Model(inputs=mobilenet.input, outputs=x)

    optimizer = Adadelta()

    model.compile(optimizer=optimizer, loss=mae_loss_masked)



    model.summary()

    import sys

    sys.exit()



    return model


But it give an error:
TypeError: mae_loss_masked() takes 1 positional argument but 2 were given



Also a question how batch generator output should look like in this case.






python tensorflow keras deep-learning mse







share|improve this question
























  • Your loss function takes 1 argument, while you are actually giving it 2. using mae_loss_masked(some_mask) will get you the actual loss function you need: stackoverflow.com/questions/46858016/… , batch should still be (x,y) , or optionally (x,y, weights)
    – Or Dinari
    yesterday













up vote
1
down vote

favorite









up vote
1
down vote

favorite











I was trying to write masked MSE loss:



def mae_loss_masked(mask):

    def loss_fn(y_true, y_pred):

        abs_vec = tf.multiply(tf.abs(y_pred-y_true), mask)

        loss = tf.reduce_mean(abs_vec)

        return loss

    return loss_fn


My model:



def MobileNet_v1():

    # MobileNet with dense layer on top



    # Keras 2.1.6

    mobilenet = MobileNet(input_shape=(config.IMAGE_H, config.IMAGE_W, config.N_CHANNELS),

                          alpha=1.0,

                          depth_multiplier=1,

                          include_top=False,

                          weights='imagenet'

                          )



    x = Flatten()(mobilenet.output)

    x = Dropout(0.5)(x)

    x = Dense(config.N_LANDMARKS * 2, activation='linear')(x)



    # -------------------------------------------------------



    model = Model(inputs=mobilenet.input, outputs=x)

    optimizer = Adadelta()

    model.compile(optimizer=optimizer, loss=mae_loss_masked)



    model.summary()

    import sys

    sys.exit()



    return model


But it give an error:
TypeError: mae_loss_masked() takes 1 positional argument but 2 were given



Also a question how batch generator output should look like in this case.






python tensorflow keras deep-learning mse







share|improve this question















I was trying to write masked MSE loss:



def mae_loss_masked(mask):

    def loss_fn(y_true, y_pred):

        abs_vec = tf.multiply(tf.abs(y_pred-y_true), mask)

        loss = tf.reduce_mean(abs_vec)

        return loss

    return loss_fn


My model:



def MobileNet_v1():

    # MobileNet with dense layer on top



    # Keras 2.1.6

    mobilenet = MobileNet(input_shape=(config.IMAGE_H, config.IMAGE_W, config.N_CHANNELS),

                          alpha=1.0,

                          depth_multiplier=1,

                          include_top=False,

                          weights='imagenet'

                          )



    x = Flatten()(mobilenet.output)

    x = Dropout(0.5)(x)

    x = Dense(config.N_LANDMARKS * 2, activation='linear')(x)



    # -------------------------------------------------------



    model = Model(inputs=mobilenet.input, outputs=x)

    optimizer = Adadelta()

    model.compile(optimizer=optimizer, loss=mae_loss_masked)



    model.summary()

    import sys

    sys.exit()



    return model


But it give an error:
TypeError: mae_loss_masked() takes 1 positional argument but 2 were given



Also a question how batch generator output should look like in this case.






python tensorflow keras deep-learning mse




python tensorflow keras deep-learning mse






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited yesterday









Milo Lu

1,48011326




1,48011326










asked yesterday









mrgloom

4,881954124




4,881954124












  • Your loss function takes 1 argument, while you are actually giving it 2. using mae_loss_masked(some_mask) will get you the actual loss function you need: stackoverflow.com/questions/46858016/… , batch should still be (x,y) , or optionally (x,y, weights)
    – Or Dinari
    yesterday


















  • Your loss function takes 1 argument, while you are actually giving it 2. using mae_loss_masked(some_mask) will get you the actual loss function you need: stackoverflow.com/questions/46858016/… , batch should still be (x,y) , or optionally (x,y, weights)
    – Or Dinari
    yesterday
















Your loss function takes 1 argument, while you are actually giving it 2. using mae_loss_masked(some_mask) will get you the actual loss function you need: stackoverflow.com/questions/46858016/… , batch should still be (x,y) , or optionally (x,y, weights)
– Or Dinari
yesterday




Your loss function takes 1 argument, while you are actually giving it 2. using mae_loss_masked(some_mask) will get you the actual loss function you need: stackoverflow.com/questions/46858016/… , batch should still be (x,y) , or optionally (x,y, weights)
– Or Dinari
yesterday

















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