python - Keras 深度变分自动编码器

Question

我正在尝试通过添加一层来使 Keras VAE 示例适应深度网络。

原码:Original VAE code

变化:

batch_size = 200
original_dim = 784
latent_dim = 2
intermediate_dim_deep = 384 # <<<<<<<
intermediate_dim = 256
nb_epoch = 20
#
x = Input(batch_shape=(batch_size, original_dim))
x = Dense(intermediate_dim_deep, activation='relu')(x) # NEW LAYER <<<<<<
h = Dense(intermediate_dim, activation='relu')(x)
z_mean = Dense(latent_dim)(h)
z_log_var = Dense(latent_dim)(h)
#
def sampling(args):
    z_mean, z_log_var = args
    epsilon = K.random_normal(shape=(batch_size, latent_dim), mean=0.)
    return z_mean + K.exp(z_log_var / 2) * epsilon

# note that "output_shape" isn't necessary with the TensorFlow backend
z = Lambda(sampling, output_shape=(latent_dim,))([z_mean, z_log_var])
#
# we instantiate these layers separately so as to reuse them later
decoder_h = Dense(intermediate_dim, activation='relu')
decoder_d = Dense(intermediate_dim_deep, activation='rely') # NEW LAYER <<<<<<
decoder_mean = Dense(original_dim, activation='sigmoid')
h_decoded = decoder_h(z)
d_decoded = decoder_d(h_decoded) # ADDED ONE MORE STEP HERE <<<<<<<
x_decoded_mean = decoder_mean(d_decoded)
#
def vae_loss(x, x_decoded_mean):
    xent_loss = original_dim * objectives.binary_crossentropy(x, x_decoded_mean)
    kl_loss = - 0.5 * K.sum(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1)
    return xent_loss + kl_loss
#
vae = Model(x, x_decoded_mean)
vae.compile(optimizer='rmsprop', loss=vae_loss)
#####

编译我遇到了这个错误:

/usr/local/lib/python2.7/dist-packages/keras/engine/topology.py:1615: UserWarning: Model inputs must come from a Keras Input layer, they cannot be the output of a previous non-Input layer. Here, a tensor specified as input to "model_1" was not an Input tensor, it was generated by layer dense_1.
Note that input tensors are instantiated via `tensor = Input(shape)`.
The tensor that caused the issue was: None
  str(x.name))
---------------------------------------------------------------------------
Exception                                 Traceback (most recent call last)
<ipython-input-8-c9010948cdee> in <module>()
----> 1 vae = Model(x, x_decoded_mean)
      2 vae.compile(optimizer='rmsprop', loss=vae_loss)

/usr/local/lib/python2.7/dist-packages/keras/engine/topology.pyc in __init__(self, input, output, name)
   1788                                 'The following previous layers '
   1789                                 'were accessed without issue: ' +
-> 1790                                 str(layers_with_complete_input))
   1791                     for x in node.output_tensors:
   1792                         computable_tensors.append(x)

Exception: Graph disconnected: cannot obtain value for tensor input_1 at layer "input_1". The following previous layers were accessed without issue: []

我在存储库中有其他示例，这似乎是一种有效的方法。我错过了什么吗？

Answer 1

添加新的隐藏层时，您将覆盖 x 变量，因此您将没有输入层。另外，“依赖”是有效的激活选项吗？