def train(): neural_network = NeuralNet(row_size, row_size) neural_network.build_layers() (training_data, training_labels, label_mapper) = load_from_dataset('DS', row_size, row_size) #(training_data , training_labels ) = prepare_data('Datasets/DatasetSample0/Images',labels_dictionary,row_size,row_size) neural_network.fit_data(training_data, training_labels, 10, 0.998) neural_network.save_model('StoredModel') return "Finished Fitting/Saving Model"
import numpy as np import sys from neural import NeuralNet from dataset import get_all_categories_shuffled, numpy_array_from_file, prepare_data labels_dictionary = {'Circle': 0, 'L': 1, 'RightArrow': 2} labels_map = ['Circle', 'L', 'RightArrow'] #Replace with your root folder that holds the dataset of images all_bytes, all_labels = prepare_data("Datasets/DatasetSample0/Images", labels_dictionary, 56, 56) nn = NeuralNet(56, 56) nn.build_layers() nn.fit_data(training_data=all_bytes, training_labels=all_labels, epochs=10, accuracy=0.999) nn.save_model('easter_egg_ahlabikyafraise_') #nn.load_model('easter_egg_ahlabikyafraise_') image_path = sys.argv[1] test_image = numpy_array_from_file(image_path) test_image = test_image / 255 arg_max, prediction_level = nn.predict_element(test_image) print('arg_max : {} which is {} with prediction : {}'.format( arg_max, labels_map[arg_max], prediction_level))