def recognize(sett):
'''Обучение и тестирование нейронной сети'''
# Инициализация экземпляра нейроннйо сети
nn = NN(sett.input_nodes, sett.hidden_nodes, sett.output_nodes, sett.rate)
# Загрузка тренировочных данных
training_data = open("dataset/mnist_train_100.csv", 'r')
training_list = training_data.readlines()
training_data.close()
# Обучение
for _ in range(5):
for record in training_list:
all_values = record.split(',')
inputs = (np.asfarray(all_values[1:]) / 255.0 * 0.99) + 0.01
targets = np.zeros(sett.output_nodes) + 0.01
targets[int(all_values[0])] = 0.99
nn.train(inputs, targets)
# Загрзука тестовых данных
test_data = open("dataset/mnist_test_10.csv", 'r')
test_list = test_data.readlines()
test_data.close()
score_card = []
# Тестирование сети
for record in test_list:
all_values = record.split(',')
correct_val = int(all_values[0])
inputs = (np.asfarray(all_values[1:]) / 255.0 * 0.99) + 0.01
outputs = nn.query(inputs)
network_val = np.argmax(outputs)
score_card.append(1 if correct_val == network_val else 0)
# Загрузка входного изображения и преобразование его в массив
img_array = smc.imread("numbs/new.png", flatten=True)
img_data = 255.0 - img_array.reshape(784)
img_data = (img_data / 255.0 * 0.99) + 0.01
# Выходные сигналы
outputs_new = nn.query(img_data)
# Значение цифры, которое имеет наибольший процент сходства с изображением
network_val = np.argmax(outputs_new)
return network_val
#define neural network model-1 parameters
hidden_layers = [
5, 5
] # hidden layer size is passed as a list ith position value determines number of neuron in ith layer
l_rate = 0.6 # learning rate
epochs = 1000 # number of training epochs
#check the timing of model-1
t0 = time.time() #start time
#define model-1
model1 = NN(input_layer_size=m,
output_layer_size=n_class,
hidden_layer_size=hidden_layers)
model1.train(X_train, Y_train, l_rate=l_rate, n_epochs=epochs)
#end time
t1 = time.time()
total_time = t1 - t0
#prediction of our model on test and training data
Y_train_predict = model1.predict(X_train)
Y_test_predict = model1.predict(X_test)
accuracy_train = 100 * np.sum(Y_train == Y_train_predict) / len(Y_train)
accuracy_test = 100 * np.sum(Y_test == Y_test_predict) / len(Y_test)
print("total time for training model-1:", total_time)
print("accuracy of model-1 on training data", accuracy_train)
print("accuracy of model-1 on test data", accuracy_test)
#define neural network model-2 parameters
hidden_layers = [