deephyp.classifier.cnn_1D_network

class deephyp.classifier.cnn_1D_network(configFile=None, inputSize=None, numClasses=None, convFilterSize=[20, 10, 10], convNumFilters=[10, 10, 10], convStride=[1, 1, 1], fcSize=[20, 20], activationFunc='relu', weightInitOpt='truncated_normal', weightStd=0.1, padding='VALID')[source]

Class for setting up a 1-D convolutional neural network (cnn) for classification. Contains several convolutional layers followed by several fully-connected layers. The network outputs scores for each class, for a given set of input data samples.

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.
  • numClasses (int) – Number of labelled classes in the dataset (not including the zero class).
  • convFilterSize (int list) – Size of filter at each convolutional layer. List length is number of convolutional layers.
  • convNumFilters (int list) – Number of filters at each convolutional layer of the network. List length is number of convolutional layers.
  • convStride (int list) – Stride at each convolutional layer. List length is number of convolutional layers. fcSize (int list): Number of nodes at each fully-connected (i.e. dense) layer of the encoder. List length is number of fully-connected layers.
  • activationFunc (str) – Activation function for all layers except the last one. Current options: [‘sigmoid’, ‘relu’, ‘linear’].
  • 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.
  • padding (str) – Type of padding used. Current options: [‘VALID’, ‘SAME’].
inputSize

Number of dimensions of input data (i.e. number of spectral bands).

Type:int
activationFunc

Activation function for all layers except the last one.

Type:str
weightInitOpt

Method of weight initialisation.

Type:string
weightStd

Parameter for ‘gaussian’ and ‘truncated_normal’ weight initialisation methods.

Type:float
convFilterSize

Size of filter at each convolutional layer. List length is number of convolutional layers.

Type:int list
convNumFilters

Number of filters at each convolutional layer of the network. List length is number of convolutional layers.

Type:int list
convStride

Stride at each convolutional layer. List length is number of convolutional layers. padding (str): Type of padding used. Current options: [‘VALID’, ‘SAME’].

Type:int list
fcSize

Number of nodes at each fully-connected (i.e. dense) layer of the encoder. List length is number of fully-connected layers.

Type:int list
numLayers

Total number of layers (convolutional and fully-connected).

Type:int
y_pred

Output of network - class scores with shape [numSamples x numClasses]. Accessible through the predict_scores class functions, requiring a trained model.

Type:tensor
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
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 predict_scores, predict_labels and predict_features class functions.

Type:dict
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).
add_train_op(name, balance_classes=True, 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).
  • balance_classes (boolean) – Weight the samples during training so that the contribtion to the loss of each class is balanced by the number of samples the class has (in a given batch).
  • 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.
predict_features(modelName, dataSamples, layer)[source]

Extract the predicted feature values at a particular layer of the network.

Parameters:
  • modelName (str) – Name of the model to use (previously added with add_model() )
  • dataSamples (np.array) – Shape [numSamples x inputSize]
  • layer (int) – Layer at which to extract features. Must be between 1 and numLayers inclusive.
Returns:

Values of neurons at layer. Shape [numSamples x numNeurons] if fully-connected layer and [numSamples x convDim1 x convDim2] if convolutional layer.
Return type:

(np.array)
predict_labels(modelName, dataSamples)[source]

Extract the predicted classification labels of some dataSamples using a trained model.

Parameters:
  • modelName (str) – Name of the model to use (previously added with add_model() )
  • dataSamples (array) – Shape [numSamples x inputSize]
Returns:

Predicted classification labels of dataSamples. Shape [numSamples].
Return type:

(np.array)
predict_scores(modelName, dataSamples, useSoftmax=True)[source]

Extract the predicted classification scores of some dataSamples using a trained model.

Parameters:
  • modelName (str) – Name of the model to use (previously added with add_model() ).
  • dataSamples (np.array) – Shape [numSamples x inputSize].
  • useSoftmax (boolean) – Pass predicted scores output by network through a softmax function.
Returns:

Predicted classification scores of dataSamples. Shape [numSamples x numClasses].
Return type:

(np.array)
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.