如何为解码器加载经过训练的自动编码器权重？

我有一个 CNN 1d 自动编码器，它有一个密集的中央层。我想训练这个自动编码器并保存它的模型。我还想保存解码器部分，目标是：将一些中心特征（独立计算）提供给经过训练和加载的解码器，通过解码器查看这些独立计算的特征的图像是什么。

## ENCODER
encoder_input = Input(batch_shape=(None,501,1))
x  = Conv1D(256,3, activation='tanh', padding='valid')(encoder_input)
x  = MaxPooling1D(2)(x)
x  = Conv1D(32,3, activation='tanh', padding='valid')(x)
x  = MaxPooling1D(2)(x)
_x = Flatten()(x)
encoded = Dense(32,activation = 'tanh')(_x)

## DECODER (autoencoder)
y = Conv1D(32, 3, activation='tanh', padding='valid')(x)
y = UpSampling1D(2)(y)
y = Conv1D(256, 3, activation='tanh', padding='valid')(y)
y = UpSampling1D(2)(y)
y = Flatten()(y)
y = Dense(501)(y)
decoded = Reshape((501,1))(y)

autoencoder = Model(encoder_input, decoded)
autoencoder.save('autoencoder.hdf5')

## DECODER (independent)
decoder_input = Input(batch_shape=K.int_shape(x))  # import keras.backend as K
y = Conv1D(32, 3, activation='tanh', padding='valid')(decoder_input)
y = UpSampling1D(2)(y)
y = Conv1D(256, 3, activation='tanh', padding='valid')(y)
y = UpSampling1D(2)(y)
y = Flatten()(y)
y = Dense(501)(y)
decoded = Reshape((501,1))(y)

decoder = Model(decoder_input, decoded)
decoder.save('decoder.hdf5')

EDIT:

为了确保清楚，我首先需要加入encoded和第一个y， 在某种意义上说y必须采取encoded作为输入。完成此操作后，我需要一种方法来加载经过训练的解码器并替换encoded具有一些新的核心功能，我将向我的解码器提供这些功能。

编辑以下答案：

我实施了建议，请参阅下面的代码

## ENCODER
encoder_input = Input(batch_shape=(None,501,1))
x  = Conv1D(256,3, activation='tanh', padding='valid')(encoder_input)
x  = MaxPooling1D(2)(x)
x  = Conv1D(32,3, activation='tanh', padding='valid')(x)
x  = MaxPooling1D(2)(x)
_x = Flatten()(x)
encoded = Dense(32,activation = 'tanh')(_x)

## DECODER (autoencoder)
encoded = Reshape((32,1))(encoded)
y = Conv1D(32, 3, activation='tanh', padding='valid')(encoded)
y = UpSampling1D(2)(y)
y = Conv1D(256, 3, activation='tanh', padding='valid')(y)
y = UpSampling1D(2)(y)
y = Flatten()(y)
y = Dense(501)(y)
decoded = Reshape((501,1))(y)

autoencoder = Model(encoder_input, decoded)
autoencoder.compile(optimizer='adam', loss='mse')
epochs = 10
batch_size = 100
validation_split = 0.2
# train the model
history = autoencoder.fit(x = training, y = training,
                    epochs=epochs,
                    batch_size=batch_size,
                    validation_split=validation_split)
autoencoder.save_weights('autoencoder_weights.h5')


## DECODER (independent)
decoder_input = Input(batch_shape=K.int_shape(encoded))  # import keras.backend as K
y = Conv1D(32, 3, activation='tanh', padding='valid', name='decod_conv1d_1')(decoder_input)
y = UpSampling1D(2, name='decod_upsampling1d_1')(y)
y = Conv1D(256, 3, activation='tanh', padding='valid', name='decod_conv1d_2')(y)
y = UpSampling1D(2, name='decod_upsampling1d_2')(y)
y = Flatten(name='decod_flatten')(y)
y = Dense(501, name='decod_dense1')(y)
decoded = Reshape((501,1), name='decod_reshape')(y)

decoder = Model(decoder_input, decoded)
decoder.save_weights('decoder_weights.h5')


encoder = Model(inputs=encoder_input, outputs=encoded, name='encoder')
features = encoder.predict(training) # features
np.savetxt('features.txt', np.squeeze(features))

predictions = autoencoder.predict(training)
predictions = np.squeeze(predictions)
np.savetxt('predictions.txt', predictions)

然后我打开另一个文件

import h5py
import keras.backend as K

def load_weights(model, filepath):
    with h5py.File(filepath, mode='r') as f:
        file_layer_names = [n.decode('utf8') for n in f.attrs['layer_names']]
        model_layer_names = [layer.name for layer in model.layers]

        weight_values_to_load = []
        for name in file_layer_names:
            if name not in model_layer_names:
                print(name, "is ignored; skipping")
                continue
            g = f[name]
            weight_names = [n.decode('utf8') for n in g.attrs['weight_names']]

            weight_values = []
            if len(weight_names) != 0:
                weight_values = [g[weight_name] for weight_name in weight_names]
            try:
                layer = model.get_layer(name=name)
            except:
                layer = None
            if layer is not None:
                symbolic_weights = (layer.trainable_weights + 
                                    layer.non_trainable_weights)
                if len(symbolic_weights) != len(weight_values):
                    print('Model & file weights shapes mismatch')
                else:
                    weight_values_to_load += zip(symbolic_weights, weight_values)

        K.batch_set_value(weight_values_to_load)

## DECODER (independent)
decoder_input = Input(batch_shape=(None,32,1))
y = Conv1D(32, 3, activation='tanh',padding='valid',name='decod_conv1d_1')(decoder_input)
y = UpSampling1D(2, name='decod_upsampling1d_1')(y)
y = Conv1D(256, 3, activation='tanh', padding='valid', name='decod_conv1d_2')(y)
y = UpSampling1D(2, name='decod_upsampling1d_2')(y)
y = Flatten(name='decod_flatten')(y)
y = Dense(501, name='decod_dense1')(y)
decoded = Reshape((501,1), name='decod_reshape')(y)

decoder = Model(decoder_input, decoded)
#decoder.save_weights('decoder_weights.h5')

load_weights(decoder, 'autoencoder_weights.h5')

# Read autoencoder
decoder.summary()

# read encoded features
features = np.loadtxt('features.txt'.format(batch_size, epochs))
features = np.reshape(features, [1500,32,1])

# evaluate loaded model on features
prediction = decoder.predict(features)



autoencoderpredictions = np.loadtxt('predictions.txt'.format(batch_size, epochs))

fig, ax = plt.subplots(5, figsize=(10,20))
for i in range(5):
        ax[i].plot(prediction[100*i], color='blue', label='Decoder')
        ax[i].plot(autoencoderpredictions[100*i], color='red', label='AE')
        ax[i].set_xlabel('Time components', fontsize='x-large')
        ax[i].set_ylabel('Amplitude', fontsize='x-large')
        ax[i].set_title('Seismogram n. {:}'.format(1500+100*i+1), fontsize='x-large')
        ax[i].legend(fontsize='x-large')
plt.subplots_adjust(hspace=1)
plt.close()

prediction and autoencoderpredictions不同意。看起来好像prediction只是很小的噪音，而autoencoder predictions具有合理的价值。

你需要：（1）保存AE（自动编码器）的权重； (2)负载权重文件； (3) 反序列化文件并仅分配那些与新模型（解码器）兼容的权重。

• (1): .save确实包括权重，但有一个额外的反序列化步骤，可以通过使用来避免.save_weights反而。还，.save保存优化器状态和模型架构，后者与您的新解码器无关
• (2): load_weights默认情况下尝试分配all节省了重量，但这是行不通的

下面的代码完成 (3)（以及补救措施 (2)）如下：

1. 加载所有重量
2. 检索加载的重量名称并将其存储在file_layer_names (list)
3. 取回当前型号权重名称并将它们存储在model_layer_names (list)
4. 迭代一遍file_layer_names as name; if name is in model_layer_names，将带有该名称的加载重量附加到weight_values_to_load
5. 分配权重weight_values_to_load建模使用K.batch_set_value

请注意，这需要您nameAE 和解码器模型中的每一层并使它们匹配。可以重写此代码以在 a 中按顺序进行暴力分配try-except循环，但这既低效又容易出错。

Usage:

## omitted; use code as in question but name all ## DECODER layers as below
autoencoder.save_weights('autoencoder_weights.h5')

## DECODER (independent)
decoder_input = Input(batch_shape=K.int_shape(x))
y = Conv1D(32, 3, activation='tanh',padding='valid',name='decod_conv1d_1')(decoder_input)
y = UpSampling1D(2, name='decod_upsampling1d_1')(y)
y = Conv1D(256, 3, activation='tanh', padding='valid', name='decod_conv1d_2')(y)
y = UpSampling1D(2, name='decod_upsampling1d_2')(y)
y = Flatten(name='decod_flatten')(y)
y = Dense(501, name='decod_dense1')(y)
decoded = Reshape((501,1), name='decod_reshape')(y)

decoder = Model(decoder_input, decoded)
decoder.save_weights('decoder_weights.h5')

load_weights(decoder, 'autoencoder_weights.h5')

功能：

import h5py
import keras.backend as K

def load_weights(model, filepath):
    with h5py.File(filepath, mode='r') as f:
        file_layer_names = [n.decode('utf8') for n in f.attrs['layer_names']]
        model_layer_names = [layer.name for layer in model.layers]

        weight_values_to_load = []
        for name in file_layer_names:
            if name not in model_layer_names:
                print(name, "is ignored; skipping")
                continue
            g = f[name]
            weight_names = [n.decode('utf8') for n in g.attrs['weight_names']]

            weight_values = []
            if len(weight_names) != 0:
                weight_values = [g[weight_name] for weight_name in weight_names]
            try:
                layer = model.get_layer(name=name)
            except:
                layer = None
            if layer is not None:
                symbolic_weights = (layer.trainable_weights + 
                                    layer.non_trainable_weights)
                if len(symbolic_weights) != len(weight_values):
                    print('Model & file weights shapes mismatch')
                else:
                    weight_values_to_load += zip(symbolic_weights, weight_values)

        K.batch_set_value(weight_values_to_load)