Ich versuche, das Keras VAE-Beispiel durch Hinzufügen einer weiteren Schicht an ein tiefes Netzwerk anzupassen.Keras deep variational Autoencoder
Originalcode: Original VAE code
ÄNDERUNGEN:
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)
#####
Compile Ich habe mir diesen Fehler:
/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: []
Ich habe die anderen Beispiele in den Repo, und es scheint eine gültige Art und Weise es zu tun. Fehle ich etwas?
danke ich las den Code eine Million Mal und ich konnte es nicht sehen !!! x wurde ersetzt. Die Verlässlichkeit war ein Tippfehler von der Kopie hier – OHTO