在 Sklearn 中再次绘制测试、验证和训练 Acc Epochs

Posted 2023-03-12

【中文标题】在 Sklearn 中再次绘制测试、验证和训练 Acc Epochs

【英文标题】：Plotting Test, Valid and Train Acc again Epochs in Sklearn

【发布时间】：2019-02-20 06:46:10

【问题描述】：

是否有任何内置方法可以在 Sklearn 中为 MLP 分类器在每个时期绘制 Train、Valid、Test 图？

【问题讨论】：

我不太确定你在问什么。 scikit-learn verbose parameter 是您要找的吗？

这仅显示训练进度。我需要有关每次迭代或时期的训练、验证和测试数据的准确性的信息。或者一种获得这三个精度对迭代或时期的图的方法。感谢您的反馈。

是的。我正在寻找这个解决方案。谢谢兄弟....

【参考方案1】：

这个解决方案（代码取自here）应该可以帮助你：

import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import fetch_mldata
from sklearn.neural_network import MLPClassifier
np.random.seed(1)

""" Example based on sklearn's docs """
mnist = fetch_mldata("MNIST original")
# rescale the data, use the traditional train/test split
X, y = mnist.data / 255., mnist.target
X_train, X_test = X[:60000], X[60000:]
y_train, y_test = y[:60000], y[60000:]

mlp = MLPClassifier(hidden_layer_sizes=(50,), max_iter=10, alpha=1e-4,
                    solver='adam', verbose=0, tol=1e-8, random_state=1,
                    learning_rate_init=.01)

""" Home-made mini-batch learning
    -> not to be used in out-of-core setting!
"""
N_TRAIN_SAMPLES = X_train.shape[0]
N_EPOCHS = 25
N_BATCH = 128
N_CLASSES = np.unique(y_train)

scores_train = []
scores_test = []

# EPOCH
epoch = 0
while epoch < N_EPOCHS:
    print('epoch: ', epoch)
    # SHUFFLING
    random_perm = np.random.permutation(X_train.shape[0])
    mini_batch_index = 0
    while True:
        # MINI-BATCH
        indices = random_perm[mini_batch_index:mini_batch_index + N_BATCH]
        mlp.partial_fit(X_train[indices], y_train[indices], classes=N_CLASSES)
        mini_batch_index += N_BATCH

        if mini_batch_index >= N_TRAIN_SAMPLES:
            break

    # SCORE TRAIN
    scores_train.append(mlp.score(X_train, y_train))

    # SCORE TEST
    scores_test.append(mlp.score(X_test, y_test))

    epoch += 1

""" Plot """
fig, ax = plt.subplots(2, sharex=True, sharey=True)
ax[0].plot(scores_train)
ax[0].set_title('Train')
ax[1].plot(scores_test)
ax[1].set_title('Test')
fig.suptitle("Accuracy over epochs", fontsize=14)
plt.show()