Question

我使用keras代码。但结果损失值不可接受因为它是负值。怎么了？

def nll(y_true, y_pred):
    """ Negative log likelihood (Bernoulli). """

    # keras.losses.binary_crossentropy gives the mean
    # over the last axis. we require the sum
    return K.sum(K.binary_crossentropy(y_true, y_pred), axis=-1)

我认为'KLDivergenceLayer'类是计算vae损失值的区域。但是我找不到。

class KLDivergenceLayer(Layer):


    def __init__(self, *args, **kwargs):
        self.is_placeholder = True
        super(KLDivergenceLayer, self).__init__(*args, **kwargs)

    def call(self, inputs):

        mu, log_var = inputs

        kl_batch = - .5 * K.sum(1 + log_var -
                                K.square(mu) -
                                K.exp(log_var), axis=-1)

        self.add_loss(K.mean(kl_batch), inputs=inputs)

        return inputs





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


decoder = Sequential([
    Dense(intermediate_dim, input_dim=latent_dim, activation='relu'),
    Dense(original_dim, activation='sigmoid')
])

x = Input(shape=(original_dim,))
h = Dense(intermediate_dim, activation='relu')(x)

z_mu = Dense(latent_dim)(h)
z_log_var = Dense(latent_dim)(h)

z_mu, z_log_var = KLDivergenceLayer()([z_mu, z_log_var])
z_sigma = Lambda(lambda t: K.exp(.5*t))(z_log_var)


eps = Input(tensor=K.random_normal(stddev=epsilon_std,
                                   shape=(K.shape(x)[0], latent_dim)))
z_eps = Multiply()([z_sigma, eps])
z = Add()([z_mu, z_eps])

x_pred = decoder(z)

vae = Model(inputs=[x, eps], outputs=x_pred)
vae.compile(optimizer='rmsprop', loss=nll)

这是模型拟合区域＃-----------------------------

hist = vae.fit(x_train,
        x_train,
        shuffle=True,
        epochs=epochs,
        batch_size=batch_size,
        validation_data=(x_train,x_train))

encoder = Model(x, z_mu)

这个区域是我能够看到的损耗值
＃------------------------

%matplotlib inline
import matplotlib.pyplot as plt

plt.plot(hist.history['loss'])
plt.ylim(-563, -300)
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train'], loc='upper left')
plt.show()

Keras VAE损失值似乎很奇怪

0 个答案: