小编Iya*_*pta的帖子

如何在 epoch 上绘制 MSE，我想在训练数据集中可视化收敛率

from sklearn.neural_network import MLPRegressor 
from sklearn.metrics import mean_absolute_error

dataset = open_dataset("forex.csv")
dataset_vector = [float(i[-1]) for i in dataset]
normalized_dataset_vector = normalize_vector(dataset_vector)
training_vector, validation_vector, testing_vector = split_dataset(training_size, validation_size, testing_size, normalized_dataset_vector)
training_features = get_features(training_vector)
training_fact = get_fact(training_vector)
validation_features = get_features(validation_vector)
validation_fact = get_fact(validation_vector)

model = MLPRegressor(activation=activation, alpha=alpha, hidden_layer_sizes=(neural_net_structure[1],), max_iter=number_of_iteration, random_state=seed)
model.fit(training_features, training_fact)

pred = model.predict(training_features)
err = mean_absolute_error(pred, validation_fact)

print(err)