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)[source]¶

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

Attributes:

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

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__(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)[source]¶

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

Attributes:

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.

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`(batch_size)	Returns the images and labels for the next batch to be computed.
`run_epoch`(name, store_loss_and_accuracy, …)	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(batch_size)[source]¶

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

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

Get predictions¶

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

class get_predictions.GetPrediction(data_set)[source]¶

Manages the inference of the identities of a set of images

Attributes:

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

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

get_predictions_softmax(batch_operation)[source]¶

Runs a batch_operation (typically predict())

Parameters:	batch_operation : func Function to be run in the epoch

next_batch(batch_size)[source]¶

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

Table of Contents

Identification network¶

idCNN¶

Network parameters¶

Stop training criteria (idCNN)¶

Store accurcay and loss¶

Get data¶

Epoch runner¶

Get predictions¶