تصنيف MNIST باستخدام إطار العمل الخاص بنا

import numpy as np
from sklearn.datasets import load_digits
from types import SimpleNamespace

_course_digits = load_digits()
_course_images = np.kron(_course_digits.images.astype(np.float32) / 16.0, np.ones((3, 3), dtype=np.float32))
_course_images = np.pad(_course_images, ((0, 0), (2, 2), (2, 2)))
mnist = SimpleNamespace(
    data=(_course_images.reshape(len(_course_images), -1) * 255.0).astype(np.float32),
    target=np.asarray(_course_digits.target, dtype=np.int64),
)

all_features = np.asarray(mnist.data, dtype=np.float32)
all_labels = np.asarray(mnist.target, dtype=np.int64)
train_size = int(0.8 * len(all_features))
MNIST = {
    "Train": {
        "Features": all_features[:train_size],
        "Labels": all_labels[:train_size],
    },
    "Test": {
        "Features": all_features[train_size:],
        "Labels": all_labels[train_size:],
    },
}

labels = MNIST['Train']['Labels']
data = MNIST['Train']['Features']

data.shape

from sklearn.model_selection import train_test_split

features_train, features_test, labels_train, labels_test = train_test_split(data,labels,test_size=0.2)

print(f"Train samples: {len(features_train)}, test samples: {len(features_test)}")