Identification network

idCNN

This module contains the main Tensorflow operations needed to train and the idCNN

Network parameters

Stop training criteria (idCNN)

class stop_training_criteria.Stop_Training(number_of_animals, epochs_before_checking_stopping_conditions=10, check_for_loss_plateau=True, first_accumulation_flag=False)

Stops the training of the network according to the conditions specified in __call__()

Attributes:

num_epochs : int

Number of epochs before starting the training

number_of_animals : int

Number of animals in the video

epochs_before_checking_stopping_conditions : int

Number of epochs before starting to check the stopping conditions

overfitting_counter : int

Counts the number of consecutive overfitting epochs during training

check_for_loss_plateau : bool

Flag to check for a plateu in the loss of the validation

first_accumulation_flag : bool

Flag to indicate that it is the first step of the accumulation

Methods

__call__(self, loss_accuracy_training, …)

Returns True when one of the conditions to stop the training is satisfied, otherwise it returns False

Store accurcay and loss

Get data

Epoch runner

Computes a given operation for an entire epoch divided in batches

class epoch_runner.EpochRunner(data_set, starting_epoch=0, print_flag=False, batch_size=None)

Runs an epoch divided in batches for a given operation and a given data set (DataSet).

Attributes:

epochs_completed : int

Number of epochs completed in the current step of the training

starting_epoch : int

Epoch at which the training step started

print_flag : bool

If True prints the values of the loss, the accucacy, and the individual accuracy at the end of the epoch

data_set : <DataSet object>

Object containing the images and labels to be passed through the network in the current epoch (see DataSet)

batch_size : int

Number of images to be passed through the network in each bach.

Methods

next_batch(self, batch_size)	Returns the images and labels for the next batch to be computed.
run_epoch(self, name, …)	Performs a given batch_operation for an entire epoch and stores the values of the loss and the accurcaies in a Store_Accuracy_and_Loss object for visualization

next_batch(self, batch_size)

Returns the images and labels for the next batch to be computed. Images and labels are extracted from a get_data.DataSet object

Parameters:

batch_size : int

Number of examples to be passed through the network in this batch

Returns:

images : ndarray

Array of shape [batch_size, height, width, channels] containing the images to be used in this batch

labels : ndarray

Array of shape [batch_size, number_of_classes] containing the labels corresponding to the images to be used in this batch

See also

get_data.DataSet

run_epoch(self, name, store_loss_and_accuracy, batch_operation)

Performs a given batch_operation for an entire epoch and stores the values of the loss and the accurcaies in a Store_Accuracy_and_Loss object for visualization

Parameters:

name : string

A string to be printed in the epoch information. Typically ‘Training’ or ‘Validation’.

store_loss_and_accuracy : <Store_Accuracy_and_Loss object>

Object collecting the values of the loss, accurcay and individual accuracies (see store_accuracy_and_loss.Store_Accuracy_and_Loss)

batch_operation : func

Function to be run in the epoch

Returns:

feed_dict : dict

Dictionary with the parameters and variables needed to run the batch_operation. It is used to save the Tensorflow summaries if needed

See also

get_data.DataSet

store_accuracy_and_loss.Store_Accuracy_and_Loss

next_batch()

Get predictions

Retrieves the predictions of the model idCNN (ConvNetwork) for a set of images

class get_predictions.GetPrediction(data_set)

Manages the inference of the identities of a set of images

Attributes:

data_set : <DataSet object>

Object containing the images whose labels has to be predicted (see DataSet)

softmax_probs : ndarray

Array of shape [number of images, number of animals] with the softmax vectors for every image in the data_set

predictions : ndarray

Array of shape [number of images, 1] with the predictions computed as the argmax of the softmax vector of every image (in dense format).

_fc_vectors : ndarray

Array of shape [number of images, number of neurons in second to last fully connected layer]

batch_size : int

Size of the batch to send the images through the network to get the predictions

Methods

get_predictions_softmax(self, batch_operation)	Runs a batch_operation (typically predict())
next_batch(self, batch_size)	Return the next batch_size examples from this data set.

get_predictions_softmax(self, batch_operation)

Runs a batch_operation (typically predict())

Parameters:

batch_operation : func

Function to be run in the epoch

next_batch(self, batch_size)

Return the next batch_size examples from this data set.

Parameters:

batch_size : int

Number of images to get for the next batch

Returns:

images : ndarray

Array of shape [batch_size, height, width, channels] with the images of the batch