deephyp.autoencoder.mlp_1D_network¶
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class
deephyp.autoencoder.
mlp_1D_network
(configFile=None, inputSize=None, encoderSize=[50, 30, 10], activationFunc='sigmoid', tiedWeights=None, weightInitOpt='truncated_normal', weightStd=0.1, skipConnect=False, activationFuncFinal='linear')[source]¶ Class for setting up a 1-D multi-layer perceptron (mlp) autoencoder network. Layers are all fully-connected (i.e. dense).
Parameters: - configFile (str) – Optional way of setting up the network. All other inputs can be ignored (will be overwritten). Pass the address of the .json config file.
- inputSize (int) – Number of dimensions of input data (i.e. number of spectral bands). Value must be input if not using a config file.
- encoderSize (int list) – Number of nodes at each layer of the encoder. List length is number of encoder layers.
- activationFunc (str) – Activation function for all layers except the last one. Current options: [‘sigmoid’, ‘relu’, ‘linear’].
- tiedWeights (binary list or None) – Specifies whether or not to tie weights at each layer: - 1: tied weights of specific encoder layer to corresponding decoder weights - 0: do not tie weights of specific layer - None: sets all layers to 0
- weightInitOpt (string) – Method of weight initialisation. Current options: [‘gaussian’, ‘truncated_normal’, ‘xavier’, ‘xavier_improved’].
- weightStd (float) – Used by ‘gaussian’ and ‘truncated_normal’ weight initialisation methods.
- skipConnect (boolean) – Whether to use skip connections throughout the network.
- activationFuncFinal (str) – Activation function for final layer. Current options: [‘sigmoid’, ‘relu’, ‘linear’].
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inputSize
¶ Number of dimensions of input data (i.e. number of spectral bands).
Type: int
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activationFunc
¶ Activation function for all layers except the last one.
Type: str
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tiedWeights
¶ Whether (1) or not (0) the weights of an encoder layer are tied to a decoder layer.
Type: binary list
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skipConnect
¶ Whether the network uses skip connections between corresponding encoder and decoder layers.
Type: boolean
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weightInitOpt
¶ Method of weight initialisation.
Type: string
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weightStd
¶ Parameter for ‘gaussian’ and ‘truncated_normal’ weight initialisation methods.
Type: float
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activationFuncFinal
¶ Activation function for final layer.
Type: str
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encoderSize
¶ Number of inputs and number of nodes at each layer of the encoder.
Type: int list
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decoderSize
¶ Number of nodes at each layer of the decoder and number of outputs.
Type: int list
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z
¶ Latent representation of data. Accessible through the encoder class function, requiring a trained model.
Type: tensor
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y_recon
¶ Reconstructed output of network. Accessible through the decoder and encoder_decoder class functions, requiring a trained model.
Type: tensor
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train_ops
¶ Dictionary of names of train and loss ops (suffixed with _train and _loss) added to the network using the add_train_op class function. The name (without suffix) is passed to the train class function to train the network with the referenced train and loss op.
Type: dict
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modelsAddrs
¶ Dictionary of model names added to the network using the add_model class function. The names reference models which can be used by the encoder, decoder and encoder_decoder class functions.
Type: dict
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add_model
(addr, modelName)[source]¶ Loads a saved set of model parameters for the network.
Parameters: - addr (str) – Address of the directory containing the checkpoint files.
- modelName (str) – Name of the model (to refer to it later in-case of multiple models for a given network).
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add_train_op
(name, lossFunc='CSA', learning_rate=0.001, decay_steps=None, decay_rate=None, piecewise_bounds=None, piecewise_values=None, method='Adam', wd_lambda=0.0)[source]¶ Constructs a loss op and training op from a specific loss function and optimiser. User gives the ops a name, and the train op and loss opp are stored in a dictionary (train_ops) under that name.
Parameters: - name (str) – Name of the training op (to refer to it later in-case of multiple training ops).
- lossFunc (str) – Reconstruction loss function.
- learning_rate (float) – Controls the degree to which the weights are updated during training.
- decay_steps (int) – Epoch frequency at which to decay the learning rate.
- decay_rate (float) – Fraction at which to decay the learning rate.
- piecewise_bounds (int list) – Epoch step intervals for decaying the learning rate. Alternative to decay steps.
- piecewise_values (float list) – Rate at which to decay the learning rate at the piecewise_bounds.
- method (str) – Optimisation method.
- wd_lambda (float) – Scalar to control weighting of weight decay in loss.
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decoder
(modelName, dataZ)[source]¶ Extract the reconstruction of some dataSamples from their latent representation encoding using a trained model.
Parameters: - modelName (str) – Name of the model to use (previously added with add_model() ).
- dataZ (np.array) – Latent representation of data samples to reconstruct using the network. Shape [numSamples x arbitrary].
Returns: Reconstructed data (y_recon attribute). Shape [numSamples x arbitrary].
Return type: (np.array)
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encoder
(modelName, dataSamples)[source]¶ Extract the latent variable of some dataSamples using a trained model.
Parameters: - modelName (str) – Name of the model to use (previously added with add_model() ).
- dataSample (np.array) – Shape [numSamples x inputSize].
Returns: Latent representation z of dataSamples. Shape [numSamples x arbitrary].
Return type: (np.array)
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encoder_decoder
(modelName, dataSamples)[source]¶ Extract the reconstruction of some dataSamples using a trained model.
Parameters: - modelName (str) – Name of the model to use (previously added with add_model() ).
- dataSample (np.array) – Data samples to reconstruct using the network. Shape [numSamples x inputSize].
Returns: Reconstructed data (y_recon attribute). Shape [numSamples x arbitrary].
Return type: (np.array)
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train
(dataTrain, dataVal, train_op_name, n_epochs, save_addr, visualiseRateTrain=0, visualiseRateVal=0, save_epochs=[1000])[source]¶ Calls network_ops function to train a network.
Parameters: - dataTrain (obj) – Iterator object for training data.
- dataVal (obj) – Iterator object for validation data.
- train_op_name (str) – Name of training op created.
- n_epochs (int) – Number of loops through dataset to train for.
- save_addr (str) – Address of a directory to save checkpoints for desired epochs, or address of saved checkpoint. If address is for an epoch and contains a previously saved checkpoint, then the network will start training from there. Otherwise it will be trained from scratch.
- visualiseRateTrain (int) – Epoch rate at which to print training loss in console.
- visualiseRateVal (int) – Epoch rate at which to print validation loss in console.
- save_epochs (int list) – Epochs to save checkpoints at.