Kerasディープオートメーションオートエンコーダー

Question

もう1つのレイヤーを追加して、深いネットワークにKeras VAEの例を適用しようとしています。

オリジナルコード：Original VAE code

CHANGES：私はレポでの他の例を持っており、それが有効な方法だ

/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: [] 

：

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) 
##### 

コンパイル私は私にこのエラーが発生しましたそれをするために。 何か不足していますか？

Answer 1

新しい隠れたレイヤーを追加するときに、x変数をオーバーライドすると、入力レイヤーがなくなります。また、有効な有効化オプションに「頼っていますか？