(x_train, y_train), (x_test, 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='sigmoid'))
# plot_model(model, to_file="C_Model.png", show_shapes=True, show_layer_names=True)
# 3 setting stopper
# callbacks.EarlyStopping(monitor='acc', min_delta=0, patience=5, mode='max')
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))
# Save information about learning and save NN
dir_name = "E_" + opt_name + "_" + str(history.epoch.__len__()) + "_" + str(lr) + str()
# os.mkdir(dir_name)
gr.plot_graphic(x=history.epoch, y=np.array(history.history["val_loss"]), x_label='epochs', y_label='val_loss',
title="val_loss" + ' history', save_path=dir_name + "/" + "val_loss.png", save=False, show=True)
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(neurons_number[1], activation='sigmoid'))
model.add(Dense(neurons_number[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='mae')
# 4 model fitting---------------------------------------------------------
dir_name = None
compare_title = 'aproximation comparison\nlr = %.3f\n neurons = %.d %.d %.d' % \
(lr, neurons_number[0], neurons_number[1], neurons_number[2])
model = custom_fit(model=model,
callbacks=callbacks,
x_train=x_train,
y_train=y_train,