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))