energy_fault_detector.autoencoders.multilayer_autoencoder

Feedforward dense autoencoder.

class MultilayerAutoencoder(layers=None, code_size=10, learning_rate=0.001, batch_size=128, epochs=10, loss_name='mean_squared_error', metrics=None, kernel_initializer='he_normal', act='prelu', last_act='linear', early_stopping=False, decay_rate=None, decay_steps=None, patience=3, min_delta=0.0001, noise=0.0)

Bases: Autoencoder

Multilayer symmetric autoencoder with Dense layers.

Parameters:

  • layers (List[int]) – list of integers indicating the size (# units) of the layers in both the encoder and in the decoder (reversed order in this case). Default [200]

  • code_size (int) – number of units of the encoded layer (bottleneck layer). (number of features to compress the input features to). Default 10.

  • learning_rate (float) – learning rate of the adam optimizer. Default 0.001

  • batch_size (int) – number of samples per batch. Default 128

  • epochs (int) – number of epochs to run. Default 10

  • loss_name (str) – name of loss metric to use. Default mean_squared_error

  • metrics (List[str]) – list of additional metrics to track. Default [mean_absolute_error].

  • act (str) – activation function to use, prelu, relu, … Defaults to prelu.

  • last_act (str) – activation function for last layer, prelu, relu, sigmoid, linear… Defaults to linear.

  • kernel_initializer (str) – initializer to use in each layer. Default he_normal.

  • early_stopping (bool) – Whether to use EarlyStopping(monitor=’val_loss’, min_delta=1e-4, patience=5, restore_best_weights=True). Cannot be used if there is no validation data. In that case, add a callback directly via the fit method.

  • decay_rate (float) – learning rate decay. Optional. If not defined, a fixed learning rate is used.

  • decay_steps (int) – number of steps to decay learning rate over. Optional.

  • patience (int) – parameter for early stopping. If early stopping is used the training will end if more than patience epochs in a row have not shown an improved loss. (Default is 3)

  • min_delta (float) – parameter of the early stopping callback. If the losses of an epoch and the next epoch differ by less than min_delta, they are considered equal (i.e. no improvement).

  • noise (float) – float value that determines the influence of the noise term on the training input. High values mean highly noisy input. 0 means no noise at all. Default 0. If noise >0 is used validation metrics will not be affected by it. Thus training loss and validation loss can differ depending on the magnitude of noise.

model

keras Model object - the autoencoder network.

encoder

keras Model object - encoder network of the autoencoder.

history

dictionary with the losses and metrics for each epoch.

Configuration example:

train:
  autoencoder:
    name: MultilayerAutoencoder
    params:
      layers: [200]
      code_size: 40
      learning_rate: 0.001
      batch_size: 128,
      epochs: 15
      loss_name: mse
create_model(input_dimension, **kwargs)

Compile a symmetric autoencoder model in Keras.

Parameters:

input_dimension (int) – number of features in input data.

Return type:

Model

Returns:

A Keras model.