def clear_history(): Layer.clear_history()
X_train, y_train = loader.load_train() X_test, y_test = loader.load_test() learning_rate = 0.022 model = Network() optimizer = Optimizer() for _ in range(epochs): for ex in range(len(X_train)): model.forward(X_train[ex].T / 100) optimizer.get_gradients(y_train[ex]) optimizer.apply_gradients(learning_rate) Layer.clear_history() print('trained') correct = 0 total = 0 for ex in range(len(X_test)): # len(X_test) model.forward(X_test[ex].T) real = y_test[ex] pred = Layer.operation_history[6]['result'].T real = real.argmax(axis=1) pred = pred.argmax(axis=1) for i in range(len(real)): total += 1 if real[i] == pred[i]: correct += 1