from numpy.lib.npyio import load
from sklearn.model_selection import validation_curve
from sklearn.datasets import load_digits
from sklearn.svm import SVC
import numpy as np
import matplotlib.pyplot as plt
digits=load_digits()
X=digits.data
y=digits.target
param_range=np.logspace(-6,-2.3,5)
train_loss, test_loss = validation_curve(
        SVC(), X, y, param_name='gamma', param_range=param_range, cv=10,
        scoring='neg_mean_squared_error')
train_loss_mean = -np.mean(train_loss, axis=1)
test_loss_mean = -np.mean(test_loss, axis=1)

plt.plot(param_range, train_loss_mean, 'o-', color="r",
             label="Training")
plt.plot(param_range, test_loss_mean, 'o-', color="g",
             label="Cross-validation")

plt.xlabel("gamma")
plt.ylabel("Loss")
plt.legend(loc="best")
plt.show()