X_mean = X_train.mean(axis=0)

This line of code calculates the mean of the columns in the X_train dataset.

The "mean" function in numpy calculates the average of the specified axis. In this case, "axis=0" means that the function will calculate the mean of each column in the X_train dataset.

The resulting X_mean variable will be a numpy array containing the mean values for each column in X_train.

相关问题

def get_CIFAR10_data(num_training=5000, num_validation=500, num_test=500): cifar10_dir = r'D:\daima\cifar-10-python\cifar-10-batches-py' X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir) print(X_train.shape) mask = range(num_training, num_training + num_validation) X_val = X_train[mask] y_val = y_train[mask] mask = range(num_training) X_train = X_train[mask] y_train = y_train[mask] mask = range(num_test) X_test = X_test[mask] y_test = y_test[mask] mean_image = np.mean(X_train, axis=0) X_train -= mean_image X_val -= mean_image X_test -= mean_image X_train = X_train.transpose(0, 3, 1, 2).copy() X_val = X_val.transpose(0, 3, 1, 2).copy() X_test = X_test.transpose(0, 3, 1, 2).copy() return { 'X_train': X_train, 'y_train': y_train, 'X_val': X_val, 'y_val': y_val, 'X_test': X_test, 'y_test': y_test, }这是一个加载cifar10数据集的函数，如何修改使其能加载mnist数据集

要加载MNIST数据集，可以使用以下代码修改上述函数：

import tensorflow as tf
from tensorflow.keras.datasets import mnist
import numpy as np

def get_MNIST_data(num_training=50000, num_validation=10000, num_test=10000):
    (X_train, y_train), (X_test, y_test) = mnist.load_data()
    X_train = np.expand_dims(X_train, axis=-1)
    X_test = np.expand_dims(X_test, axis=-1)
    X_train = X_train.astype('float32') / 255
    X_test = X_test.astype('float32') / 255
    mean_image = np.mean(X_train, axis=0)
    X_train -= mean_image
    X_test -= mean_image
    mask = range(num_training, num_training + num_validation)
    X_val = X_train[mask]
    y_val = y_train[mask]
    mask = range(num_training)
    X_train = X_train[mask]
    y_train = y_train[mask]
    mask = range(num_test)
    X_test = X_test[mask]
    y_test = y_test[mask]
    return {
        'X_train': X_train, 'y_train': y_train,
        'X_val': X_val, 'y_val': y_val,
        'X_test': X_test, 'y_test': y_test,
    }

此代码使用 TensorFlow 的 MNIST 数据集加载函数，将数据集的维度转换为 (num_samples, height, width, depth) 的形式，并对图像像素值进行归一化。

rf = RandomForestClassifier(n_estimators=90, max_depth=) train_sizes, train_scores, cv_scores = learning_curve(rf,x_train,y_train,cv=5,train_sizes=np.linspace(0.01,1,100)) train_scores_mean = np.mean(train_scores, axis=1) train_scores_std = np.std(train_scores, axis=1) cv_scores_mean = np.mean(cv_scores, axis=1) cv_scores_std = np.std(cv_scores, axis=1) fig = plt.figure(figsize=(8,6), dpi=200) ax = fig.add_axes([0.1, 0.1, 0.8, 0.8]) ax.plot(train_sizes, train_scores_mean, color='dodgerblue', alpha=0.8) ax.plot(train_sizes, cv_scores_mean, color='g', alpha=0.8) ax.fill_between(train_sizes, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, alpha=0.1, color="dodgerblue") ax.fill_between(train_sizes, cv_scores_mean - cv_scores_std, cv_scores_mean + cv_scores_std, alpha=0.1, color="g") ax.legend(labels=['train_set_scores', 'cross_val_scores'], loc='best') ax.set_title('Learning curve of the random forests') ax.grid(True) ax.set_xlabel('The number of training samples') ax.set_ylabel('Model score') plt.savefig('Learning curve of the random forests.jpg') plt.show()

这段代码使用了随机森林分类器（RandomForestClassifier）进行训练，并使用学习曲线（learning_curve）来评估模型的性能。具体来说，它通过在训练集上逐渐增加样本数量，并在交叉验证集上计算模型得分，来绘制训练集和交叉验证集得分随训练集样本数量变化的曲线。这可以帮助我们判断模型是否存在过拟合或欠拟合的问题。

其中，n_estimators是指随机森林中树的个数，max_depth是指每棵树的最大深度。learning_curve函数中的train_sizes参数指定了训练集的大小范围，np.linspace(0.01,1,100)表示从0.01到1均匀取100个数，即训练集大小从1%到100%。cv参数指定了交叉验证的折数，这里设置为5。最后，绘制了训练集得分和交叉验证集得分随训练集样本数量变化的曲线，并保存了图片。