load_and_train module

class load_and_train.ModelGenerator(labels, path)

Bases: object

BuildModel()

Add layers to the model

CheckGPU()

Check that there is a GPU device for Tensorflow to use

EvaluateModel()

Get loss value & metrics values for the model in test mode.

Returns

_description_

Return type

_type_

ImageToArray(file)

Converts an image from RGB to numpy array so that it can be processed.

Parameters

file (str) – Path to the file to be converted to an array

Returns

Numpy array of the image

Return type

numpy arr

ProcessArrays()

Process the training arrays to be compatible with the model

ProcessImages()

Generate the array of all the images in the dataset and t array with the corresponding labels

SaveModel()

Save the model so that it can be accessed later without having to retrain each time

SplitDataset()

Splits the dataset into training and testing sets.

TrainModel()

Trains the model using the dataset provided: epochs=20, batch size=32, validation split=0.2 :returns: Model history :rtype: _type_