在建立编码器/解码器模型之前对自动编码器进行训练是否有效？

Question

我正在遵循教程https://blog.keras.io/building-autoencoders-in-keras.html来构建我的自动编码器。为此，我有两种策略：

A)步骤1:构建自动编码器；步骤2:生成编码器；步骤3:生成解码器；步骤4:编译自动编码器；步骤5:训练自动编码器。

B)步骤1:构建自动编码器；步骤2:编译自动编码器；步骤3:训练自动编码器；步骤4:生成编码器；步骤5:生成解码器。

对于这两种情况，模型收敛到损失0.100。然而，就本教程中所述的战略A而言，重建工作非常糟糕。在策略B的情况下，重建效果更好。

在我看来，这是有意义的，因为在策略A中，编码器和解码器模型的权重是在未经训练的层上建立的，并且结果是随机的。而在策略B中，训练后的权重定义得更好，因此重建效果更好。

我的问题是，B策略是有效的还是我在重建中作弊？在策略A中，Keras是否应该自动更新编解码模型的权重，因为它们的模型是建立在自动编码器层的基础上的？

###### Code for Strategy A

# Step 1
features = Input(shape=(x_train.shape[1],))

encoded = Dense(1426, activation='relu')(features)
encoded = Dense(732, activation='relu')(encoded)
encoded = Dense(328, activation='relu')(encoded)

encoded = Dense(encoding_dim, activation='relu')(encoded)

decoded = Dense(328, activation='relu')(encoded)
decoded = Dense(732, activation='relu')(decoded)
decoded = Dense(1426, activation='relu')(decoded)
decoded = Dense(x_train.shape[1], activation='relu')(decoded)

autoencoder = Model(inputs=features, outputs=decoded)

# Step 2
encoder = Model(features, encoded)

# Step 3
encoded_input = Input(shape=(encoding_dim,))
decoder_layer = autoencoder.layers[-4](encoded_input)
decoder_layer = autoencoder.layers[-3](decoder_layer)
decoder_layer = autoencoder.layers[-2](decoder_layer)
decoder_layer = autoencoder.layers[-1](decoder_layer)

decoder = Model(encoded_input, decoder_layer)

# Step 4
autoencoder.compile(optimizer='adam', loss='mse')

# Step 5
history = autoencoder.fit(x_train, 
                         x_train,
                         epochs=150,
                         batch_size=256,
                         shuffle=True,
                         verbose=1,
                         validation_split=0.2)

# Testing encoding
encoded_fts = encoder.predict(x_test)
decoded_fts = decoder.predict(encoded_fts)

###### Code for Strategy B

# Step 1
features = Input(shape=(x_train.shape[1],))

encoded = Dense(1426, activation='relu')(features)
encoded = Dense(732, activation='relu')(encoded)
encoded = Dense(328, activation='relu')(encoded)

encoded = Dense(encoding_dim, activation='relu')(encoded)

decoded = Dense(328, activation='relu')(encoded)
decoded = Dense(732, activation='relu')(decoded)
decoded = Dense(1426, activation='relu')(decoded)
decoded = Dense(x_train.shape[1], activation='relu')(decoded)

autoencoder = Model(inputs=features, outputs=decoded)

# Step 2
autoencoder.compile(optimizer='adam', loss='mse')

# Step 3
history = autoencoder.fit(x_train, 
                         x_train,
                         epochs=150,
                         batch_size=256,
                         shuffle=True,
                         verbose=1,
                         validation_split=0.2)
# Step 4
encoder = Model(features, encoded)

# Step 5
encoded_input = Input(shape=(encoding_dim,))
decoder_layer = autoencoder.layers[-4](encoded_input)
decoder_layer = autoencoder.layers[-3](decoder_layer)
decoder_layer = autoencoder.layers[-2](decoder_layer)
decoder_layer = autoencoder.layers[-1](decoder_layer)

decoder = Model(encoded_input, decoder_layer)

# Testing encoding
encoded_fts = encoder.predict(x_test)
decoded_fts = decoder.predict(encoded_fts)

Answer 1

我的问题是，B策略是有效的还是我在重建中作弊？

A和B是等价的；不，你没有作弊。

在策略A中，Keras是否应该自动更新编解码模型的权重，因为它们的模型是建立在自动编码器层的基础上的？

译码器模型只使用自动编码器层。万一A

decoder.layers
Out:
[<keras.engine.input_layer.InputLayer at 0x7f8a44d805c0>,
 <keras.layers.core.Dense at 0x7f8a44e58400>,
 <keras.layers.core.Dense at 0x7f8a44e746d8>,
 <keras.layers.core.Dense at 0x7f8a44e14940>,
 <keras.layers.core.Dense at 0x7f8a44e2dba8>]

autoencoder.layers
Out:[<keras.engine.input_layer.InputLayer at 0x7f8a44e91c18>,
 <keras.layers.core.Dense at 0x7f8a44e91c50>,
 <keras.layers.core.Dense at 0x7f8a44e91ef0>,
 <keras.layers.core.Dense at 0x7f8a44e89080>,
 <keras.layers.core.Dense at 0x7f8a44e89da0>,
 <keras.layers.core.Dense at 0x7f8a44e58400>,
 <keras.layers.core.Dense at 0x7f8a44e746d8>,
 <keras.layers.core.Dense at 0x7f8a44e14940>,
 <keras.layers.core.Dense at 0x7f8a44e2dba8>]

每个列表的最后4行的十六进制数(对象id)都是相同的--因为它是相同的对象。当然，他们也分享自己的重量。

万一B：

decoder.layers
Out:
[<keras.engine.input_layer.InputLayer at 0x7f8a41de05f8>,
 <keras.layers.core.Dense at 0x7f8a41ee4828>,
 <keras.layers.core.Dense at 0x7f8a41eaceb8>,
 <keras.layers.core.Dense at 0x7f8a41e50ac8>,
 <keras.layers.core.Dense at 0x7f8a41e5d780>]

autoencoder.layers
Out:
[<keras.engine.input_layer.InputLayer at 0x7f8a41da3940>,
 <keras.layers.core.Dense at 0x7f8a41da3978>,
 <keras.layers.core.Dense at 0x7f8a41da3a90>,
 <keras.layers.core.Dense at 0x7f8a41da3b70>,
 <keras.layers.core.Dense at 0x7f8a44720cf8>,
 <keras.layers.core.Dense at 0x7f8a41ee4828>,
 <keras.layers.core.Dense at 0x7f8a41eaceb8>,
 <keras.layers.core.Dense at 0x7f8a41e50ac8>,
 <keras.layers.core.Dense at 0x7f8a41e5d780>]

• 图层是一样的。

因此，A和B的训练顺序是相等的。更一般的情况是，如果您共享层(以及相应的权重)，那么在大多数情况下，构建、编译和培训的顺序并不重要，因为它们在相同的tensorflow图中。

我在mnist数据集上运行了这个示例，它们显示了相同的性能并很好地重建了图像。我想，如果您在case A上遇到了麻烦，那么您就错过了其他的事情(看看如何，因为我复制了粘贴您的代码，一切都很好)。

如果您使用jupyter，有时重新启动并运行自上而下的帮助。