diktya.callbacks

class OnEpochEnd(func, every_nth_epoch=10)

Bases: keras.callbacks.Callback

on_epoch_end(epoch, logs={})

class SampleGAN(sample_func, discriminator_func, z, real_data, callbacks, should_sample_func=None)

Bases: keras.callbacks.Callback

Keras callback that provides samples on_epoch_end to other callbacks.

Parameters:

• sample_func – is called with z and should return fake samples.
• discriminator_func – Should return the discriminator score.
• z – Batch of random vectors
• real_data – Batch of real data
• callbacks – List of callbacks, called with the generated samples.
• should_sample_func (optional) – Gets the current epoch and returns a bool if we should sample at the given epoch.

sample()

on_train_begin(logs=None)

on_epoch_end(epoch, logs=None)

class VisualiseGAN(nb_samples, output_dir=None, show=False, preprocess=None)

Bases: keras.callbacks.Callback

Visualise nb_samples fake images from the generator.

Warning

Cannot be used as normal keras callback. Can only be used as callback for the SampleGAN callback.

Parameters:

• nb_samples – number of samples
• output_dir (optional) – Save image to this directory. Format is {epoch:05d}.
• (default (show) – False): Show images as matplotlib plot
• preprocess (optional) – Apply this preprocessing function to the generated images.

on_train_begin(logs={})

call(samples)

on_epoch_end(epoch, logs={})

class SaveModels(models, output_dir=None, every_epoch=50, overwrite=True, hdf5_attrs=None)

Bases: keras.callbacks.Callback

on_epoch_end(epoch, log={})

class DotProgressBar

Bases: diktya.callbacks.OnEpochEnd

class LearningRateScheduler(optimizer, schedule)

Bases: keras.callbacks.Callback

Learning rate scheduler

Parameters:

• optimizer (keras Optimizer) – schedule the learning rate of this optimizer
• schedule (dict) – Dictionary of epoch -> lr_value

on_epoch_end(epoch, logs={})

class AutomaticLearningRateScheduler(optimizer, metric='loss', min_improvement=0.001, epoch_patience=3, factor=0.25)

Bases: keras.callbacks.Callback

This callback automatically reduces the learning rate of the optimizer. If the metric did not improve by at least the min_improvement amount in the last epoch_patience epochs, the learning rate of optimizer will be decreased by factor.

Parameters:

• optimizer (keras Optimizer) – Decrease learning rate of this optimizer
• metric (str) – Name of the metric
• min_improvement (float) – minimum-improvement
• epoch_patience (int) – Number of epochs to wait until the metric decreases
• factor (float) – Reduce learning rate by this factor

on_train_begin(logs={})

on_epoch_begin(epoch, logs={})

on_batch_end(batch, logs={})

on_epoch_end(epoch, logs={})

class HistoryPerBatch(output_dir=None, extra_metrics=None)

Bases: keras.callbacks.Callback

Saves the metrics of every batch.

Parameters:

• output_dir (optional str) – Save history and plot to this directory.
• extra_metrics (optional list) – Also montior this metrics.

batch_history

history of every batch. Use batch_history[metric_name][epoch_idx][batch_idx] to index.

epoch_history

history of every epoch. Use epoch_history[metric_name][epoch_idx] to index.

static from_config(batch_history, epoch_history)

history

metrics

List of metrics to montior.

on_epoch_begin(epoch, logs=None)

on_batch_end(batch, logs={})

on_epoch_end(epoch, logs={})

plot_callback(fname=None, every_nth_epoch=1, **kwargs)

Returns a keras callback that plots this figure on_epoch_end.

Parameters:

• fname (optional str) – filename where to save the plot. Default is {self.output}/history.png
• every_nth_epoch – Plot frequency
• **kwargs – Passed to self.plot(**kwargs)

save(fname=None)

on_train_end(logs={})

plot(metrics=None, fig=None, ax=None, skip_first_epoch=False, use_every_nth_batch=1, save_as=None, batch_window_size=128, percentile=(1, 99), end=None, kwargs=None)

Plots the losses and variance for every epoch.

Parameters:

• metrics (list) – this metric names will be plotted
• skip_first_epoch (bool) – skip the first epoch. Use full if the first batch has a high loss and brakes the scaling of the loss axis.
• fig – matplotlib figure
• ax – matplotlib axes
• save_as (str) – Save figure under this path. If save_as is a relative path and self.output_dir is set, it is appended to self.output_dir.

Returns:

A tuple of fig, axes

class SaveModelAndWeightsCheckpoint(filepath, monitor='val_loss', verbose=0, save_best_only=False, mode='auto', hdf5_attrs=None)

Bases: keras.callbacks.Callback

Similiar to keras ModelCheckpoint, but uses save_model() to save the model and weights in one file.

filepath can contain named formatting options, which will be filled the value of epoch and keys in logs (passed in on_epoch_end).

For example: if filepath is weights.{epoch:02d}-{val_loss:.2f}.hdf5, then multiple files will be save with the epoch number and the validation loss.

# Arguments

filepath: string, path to save the model file. monitor: quantity to monitor. verbose: verbosity mode, 0 or 1. save_best_only: if save_best_only=True,

the latest best model according to the validation loss will not be overwritten.

mode: one of {auto, min, max}.

If save_best_only=True, the decision to overwrite the current save file is made based on either the maximization or the minization of the monitored. For val_acc, this should be max, for val_loss this should be min, etc. In auto mode, the direction is automatically inferred from the name of the monitored quantity.

hdf5_attrs: Dict of attributes for the hdf5 file.

save_model(fname, overwrite=False, attrs={})

on_epoch_end(epoch, logs={})