y_test) = dataset4.load_data(train_size=train_size, show=True) model = Sequential() model.add(Dense(neurons_number[0], input_dim=2, activation='relu')) model.add(Dense(neurons_number[1], activation='linear')) model.add(Dense(neurons_number[2], activation='linear')) model.add(Dense(neurons_number[3], activation='sigmoid')) # 3 setting stopper callbacks = [ EarlyStoppingByLossVal(monitor='val_loss', value=goal_loss, verbose=1) ] # 4 model fitting model.compile(optimizer=optimizer, loss='mse', metrics=['mse']) history = model.fit(x=x_train, y=y_train, batch_size=batch_size, epochs=epochs, verbose=verbose, callbacks=callbacks, validation_data=(x_test, y_test)) gr.plot_graphic(x=history.epoch, y=np.array(history.history["val_loss"]),
# 2 model and data initializing--------------------------------------------------------- (x_train, y_train), (x_test, y_test) = dataset5.load_data(train_size=train_size, show=False) x_train = np.transpose(np.append(x_train, np.ones(x_train.size)).reshape(2, x_train.size)) x_test = np.transpose(np.append(x_test, np.ones(x_test.size)).reshape(2, x_test.size)) model = Sequential() model.add( Dense(neurons_number[0], input_dim=2, activation='sigmoid')) model.add(Dense(1, activation='linear')) # 3 setting stopper--------------------------------------------------------- callbacks = [EarlyStoppingByLossVal(monitor='val_loss', value=goal_loss, verbose=0)] model.compile(optimizer=optimizer, loss='mse') # 4 model fitting--------------------------------------------------------- dir_name = None compare_title = 'approximation comparison\nlr = %.3f\n neurons = %.d' % \ (lr, neurons_number[0]) history = model.fit(x=x_train, y=y_train, batch_size=batch_size, epochs=epochs, verbose=verbose, callbacks=callbacks, validation_data=(x_test, y_test), ) plt.plot(np.transpose(x_test)[0], y_test, '.') plt.plot(np.transpose(x_test)[0], model.predict(x_test), '.')