x_train /= 255 y_train = np_utils.to_categorical(y_train) x_test = x_test.reshape(x_test.shape[0], 1, 28 * 28) x_test = x_test.astype("float32") x_test /= 255 y_test = np_utils.to_categorical(y_test) # Model nn = Network() nn.add(Dense(28 * 28, 100)) nn.add(Activation(Tanh, dTanh)) nn.add(Dense(100, 50)) nn.add(Activation(Tanh, dTanh)) nn.add(Dense(50, 10)) nn.add(Activation(Tanh, dTanh)) # Training nn.useLoss(MSE, dMSE) nn.useOptimizer(RMSProp(),learning_rate=config.learning_rate, beta=config.beta) nn.fit(x_train[0:2000], y_train[0:2000], epochs=config.epochs) # Prediction out = nn.predict(x_test[0:2]) print("\nPredicted Values: ") print(out, end="\n") print("True Values: ") print(y_test[0:2])