hist_loss.append(h.history['loss']) hist_acc = np.hstack(hist_acc) hist_loss = np.hstack(hist_loss) plt.figure() plt.plot(hist_acc) plt.legend(['train', 'val']) plt.xlabel('Epochs') plt.ylabel('Accuracy') plt.figure() plt.plot(hist_loss) plt.legend(['train', 'val']) plt.xlabel('Epochs') plt.ylabel('Loss') #%% Performance on training dataset y_hat_train = mdl.predict(X_train) y_pred_train = np.argmax(y_hat_train, axis=1) acc_train = accuracy_score(y_train, y_pred_train) cm_train = confusion_matrix(y_train, y_pred_train) names = ['berry', 'bird', 'dog', 'flower', 'other'] plt.figure() sns.heatmap(cm_train.astype(int), annot=True, fmt='d', cmap="YlGnBu", xticklabels=names, yticklabels=names) #%% Loading test data
batch_size=10, validation_data=({ 'model_input': x_test }, { 'd_output': x_test, 'p_output': y_test })) encoder = Model(main_input, encoded, name="encoder") decoded_input = Input(shape=(4, )) decoded_p = full_model.get_layer('d1')(decoded_input) decoded_p = full_model.get_layer('d2')(decoded_p) decoded_p = full_model.get_layer('d_output')(decoded_p) decoder = Model(decoded_input, decoded_p) regression = Model(main_input, regression) encoded_res = encoder.predict(x_test) decoded_res = decoder.predict(encoded_res) regression_res = regression.predict(x_test) decoder.save('decoder.h5') encoder.save('encoder.h5') regression.save('regression.h5') pd.DataFrame(np.round(x_test, 5)).to_csv("x_test.csv") pd.DataFrame(np.round(y_test, 5)).to_csv("y_test.csv") pd.DataFrame(np.round(x_train, 5)).to_csv("x_train.csv") pd.DataFrame(np.round(y_train, 5)).to_csv("y_train.csv") pd.DataFrame(np.round(encoded_res, 5)).to_csv("encoded_res.csv") pd.DataFrame(np.round(decoded_res, 5)).to_csv("decoded_res.csv") pd.DataFrame(np.round(regression_res, 5)).to_csv("regression_res.csv")