Keras/Tensorflow：单个 output 的组合损失 function

Question

I have only one output for my model, but I would like to combine two different loss functions:

def get_model():
    # create the model here
    model = Model(inputs=image, outputs=output)

    alpha = 0.2
    model.compile(loss=[mse, gse],
                      loss_weights=[1-alpha, alpha]
                      , ...)

but it complains that I need to have two outputs because I defined two losses:

ValueError: When passing a list as loss, it should have one entry per model outputs. 
The model has 1 outputs, but you passed loss=[<function mse at 0x0000024D7E1FB378>, <function gse at 0x0000024D7E1FB510>]

Can I possibly write my final loss function without having to create another loss function (because that would restrict me from changing the alpha outside the loss function)?

How do I do something like (1-alpha)*mse + alpha*gse ?

---

Update:

Both my loss functions are equivalent to the function signature of any builtin keras loss function, takes in y_true and y_pred and gives a tensor back for loss (which can be reduced to a scalar using K.mean() ), but I believe, how these loss functions are defined shouldn't affect the answer as long as they return valid losses.

def gse(y_true, y_pred):
    # some tensor operation on y_pred and y_true
    return K.mean(K.square(y_pred - y_true), axis=-1)

Answer 1

Specify a custom function for the loss:

model = Model(inputs=image, outputs=output)

alpha = 0.2
model.compile(
    loss=lambda y_true, y_pred: (1 - alpha) * mse(y_true, y_pred) + alpha * gse(y_true, y_pred),
    ...)

Or if you don't want an ugly lambda make it into an actual function:

def my_loss(y_true, y_pred):
    return (1 - alpha) * mse(y_true, y_pred) + alpha * gse(y_true, y_pred)

model = Model(inputs=image, outputs=output)

alpha = 0.2
model.compile(loss=my_loss, ...)

EDIT:

If your alpha is not some global constant, you can have a "loss function factory":

def make_my_loss(alpha):
    def my_loss(y_true, y_pred):
        return (1 - alpha) * mse(y_true, y_pred) + alpha * gse(y_true, y_pred)
    return my_loss

model = Model(inputs=image, outputs=output)

alpha = 0.2
my_loss = make_my_loss(alpha)
model.compile(loss=my_loss, ...)

Answer 2

loss function should be one function.You are giving your model a list of two functions

try:

def mse(y_true, y_pred):
    return K.mean(K.square(y_pred - y_true), axis=-1)

model.compile(loss= (mse(y_true, y_pred)*(1-alpha) + gse(y_true, y_pred)*alpha),
              , ...)

Answer 3

Yes, define your own custom loss function and pass that to the loss argument upon compiling:

def custom_loss(y_true, y_pred):
    return (1-alpha) * K.mean(K.square(y_true-y_pred)) + alpha * gse

(Not sure what you mean with gse ). It can be helpful to have a look at how the vanilla losses are implemented in Keras:https://github.com/keras-team/keras/blob/master/keras/losses.py

Answer 4

Not that this answer particularly addresses the original question, I thought of writing it because the same error occurs when trying to load a keras model that has a custom loss using keras.models.load_model , and it's not been properly answered anywhere. Specifically, following the VAE example code in keras github repository , this error occurs when loading the VAE model after been saved with model.save .

The solution is to save only the weights using vae.save_weights('file.h5') instead of saving the full model. However, you would have to build and compile the model again before loading the weights using vae.load_weights('file.h5') .

Following is an example implementation.

class VAE():
    def build_model(self): # latent_dim and intermediate_dim can be passed as arguments
        def sampling(args):
            """Reparameterization trick by sampling from an isotropic unit Gaussian.
            # Arguments
                args (tensor): mean and log of variance of Q(z|X)
            # Returns
                z (tensor): sampled latent vector
            """

            z_mean, z_log_var = args
            batch = K.shape(z_mean)[0]
            dim = K.int_shape(z_mean)[1]
            # by default, random_normal has mean = 0 and std = 1.0
            epsilon = K.random_normal(shape=(batch, dim))
            return z_mean + K.exp(0.5 * z_log_var) * epsilon

        # original_dim = self.no_features
        # intermediate_dim = 256
        latent_dim = 8
        inputs = Input(shape=(self.no_features,))
        x = Dense(256, activation='relu')(inputs)
        x = Dense(128, activation='relu')(x)
        x = Dense(64, activation='relu')(x)
        z_mean = Dense(latent_dim, name='z_mean')(x)
        z_log_var = Dense(latent_dim, name='z_log_var')(x)
        # use reparameterization trick to push the sampling out as input
        # note that "output_shape" isn't necessary with the TensorFlow backend
        z = Lambda(sampling, output_shape=(latent_dim,), name='z')([z_mean, z_log_var])
        # instantiate encoder model
        encoder = Model(inputs, [z_mean, z_log_var, z], name='encoder')


        # build decoder model
        latent_inputs = Input(shape=(latent_dim,), name='z_sampling')
        x = Dense(32, activation='relu')(latent_inputs)
        x = Dense(48, activation='relu')(x)
        x = Dense(64, activation='relu')(x)
        outputs = Dense(self.no_features, activation='linear')(x)

        # instantiate decoder model
        decoder = Model(latent_inputs, outputs, name='decoder')

        # instantiate VAE model
        outputs = decoder(encoder(inputs)[2])
        VAE = Model(inputs, outputs, name='vae_mlp')
        reconstruction_loss = mse(inputs, outputs)
        reconstruction_loss *= self.no_features
        kl_loss = 1 + z_log_var - K.square(z_mean) - K.exp(z_log_var)
        kl_loss = K.sum(kl_loss, axis=-1)
        kl_loss *= -0.5
        vae_loss = K.mean(reconstruction_loss + kl_loss)
        VAE.add_loss(vae_loss)
        VAE.compile(optimizer='adam')
        return VAE

Now,

vae_cls = VAE()
vae = vae_cls.build_model()
# vae.fit()
vae.save_weights('file.h5')

Load model and predict (if in a different script, you need to import the VAE class),

vae_cls = VAE()
vae = vae_cls.build_model()
vae.load_weights('file.h5')
# vae.predict()

Finally, The Difference : [ ref ]

Keras model.save saves,

1. Model weights
2. Model architecture
3. Model compilation details (loss function(s) and metrics)
4. Model optimizer and regularizer states

Keras model.save_weights saves only the model weights. Keras model.to_json() saves the model architecture.

Hope this helps someone experimenting with variational autoencoders.

Answer 5

Combine MAE and RMSE together:

import tensorflow as tf
from tensorflow import keras

def loss_fn_mae_rmse(y_true, y_pred, alpha=0.8):
    mae = keras.losses.MeanAbsoluteError()
    mse = keras.losses.MeanSquaredError()
    return alpha * mae(y_true, y_pred) + (1 - alpha) * tf.sqrt(mse(y_true, y_pred))

model = keras.Model(inputs=..., outputs=...)
opt = keras.optimizers.Adam(learning_rate=1e-4)
model.compile(optimizer=opt, loss=loss_fn_mae_rmse, metrics=['mae'])

At the same time, if you want to load this model after training and saved to disk:

model = keras.models.load_model('path/to/model.h5', custom_objects={'loss_fn_mae_rmse': loss_fn_mae_rmse})