ResNet1DInterface

class ketos.neural_networks.resnet.ResNet1DInterface(block_sets=[2, 2, 2], n_classes=2, initial_filters=2, initial_strides=1, initial_kernel=30, strides=2, kernel=300, optimizer=Adam ketos recipe, loss_function=CategoricalCrossentropy ketos recipe, metrics=[CategoricalAccuracy ketos recipe, Precision ketos recipe, Recall ketos recipe])

Methods

transform_batch(x, y[, n_classes])

Transforms a training batch into the format expected by the network.

Attributes

checkpoint_dir
early_stopping_monitor	Sets an early stopping monitor.
log_dir
test_generator
train_generator
val_generator
valid_losses
valid_metrics
valid_optimizers

classmethod transform_batch(x, y, n_classes=2)

Transforms a training batch into the format expected by the network.

When this interface is subclassed to make new neural_network classes, this method can be overwritten to accomodate any transformations required. Common operations are reshaping of input arrays and parsing or one hot encoding of the labels.

Args:

x:numpy.array

The batch of inputs with shape (batch_size, width, height)

y:numpy.array

The batch of labels. Each label must be represented as an integer, ranging from zero to n_classes The array is expected to have a field named ‘label’.

n_classes:int

The number of possible classes for one hot encoding.

Returns:

X:numpy.array

The transformed batch of inputs

Y:numpy.array

The transformed batch of labels

Examples:

>>> import numpy as np
>>> # Create a batch of 10 5x5 arrays
>>> inputs = np.random.rand(10,5,5)
>>> inputs.shape
(10, 5, 5)

>>> # Create a batch of 10 labels (0 or 1)
>>> labels = np.random.choice([0,1], size=10)
>>> labels.shape
(10,)

>>> transformed_inputs, transformed_labels = NNInterface.transform_batch(inputs, labels, n_classes=2)
>>> transformed_inputs.shape
(10, 5, 5, 1)

>>> transformed_labels.shape
(10, 2)