def add_labels(train_test): X = [] Y = [] label = 0 for i in filenames: x = train_test[i] X += x lenx = len(x) Y += [label] * lenx label += 1 return X, Y
时间: 2023-02-14 14:48:30 浏览: 48
这个函数的作用是将给定的文件名列表中的文件内容读取出来,并将每个文件的内容存储到X列表中,将每个文件对应的标签存储到Y列表中。其中label变量用于标识每个文件的标签,初始值为0,每次读取一个文件后会将label的值更新为1。最后返回X和Y列表。
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import pickle import numpy as np import os # from scipy.misc import imread def load_CIFAR_batch(filename): with open(filename, 'rb') as f: datadict = pickle.load(f, encoding='bytes') X = datadict[b'data'] Y = datadict[b'labels'] X = X.reshape(10000, 3, 32, 32).transpose(0, 2, 3, 1).astype("float") Y = np.array(Y) return X, Y def load_CIFAR10(ROOT): xs = [] ys = [] for b in range(1, 2): f = os.path.join(ROOT, 'data_batch_%d' % (b,)) X, Y = load_CIFAR_batch(f) xs.append(X) ys.append(Y) Xtr = np.concatenate(xs) Ytr = np.concatenate(ys) del X, Y Xte, Yte = load_CIFAR_batch(os.path.join(ROOT, 'test_batch')) return Xtr, Ytr, Xte, Yte 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, } def load_models(models_dir): models = {} for model_file in os.listdir(models_dir): with open(os.path.join(models_dir, model_file), 'rb') as f: try: models[model_file] = pickle.load(f)['model'] except pickle.UnpicklingError: continue return models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,不使用TensorFlow
可以使用以下代码加载MNIST数据集:
```
import gzip
import os
import pickle
import numpy as np
def load_mnist(path, kind='train'):
labels_path = os.path.join(path, '%s-labels-idx1-ubyte.gz' % kind)
images_path = os.path.join(path, '%s-images-idx3-ubyte.gz' % kind)
with gzip.open(labels_path, 'rb') as lbpath:
labels = np.frombuffer(lbpath.read(), dtype=np.uint8, offset=8)
with gzip.open(images_path, 'rb') as imgpath:
images = np.frombuffer(imgpath.read(), dtype=np.uint8, offset=16).reshape(len(labels), 784)
return images, labels
def get_mnist_data(num_training=5000, num_validation=500, num_test=500):
mnist_dir = r'D:\daima\mnist' # 修改为mnist数据集所在的目录
X_train, y_train = load_mnist(mnist_dir, kind='train')
X_test, y_test = load_mnist(mnist_dir, kind='t10k')
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]
X_train = X_train.astype('float32') / 255
X_val = X_val.astype('float32') / 255
X_test = X_test.astype('float32') / 255
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,
}
```
这个函数将会返回训练集、验证集和测试集的图像和标签。其中,图像是一个形如`(num_samples, 784)`的数组,标签是一个形如`(num_samples,)`的数组。
def predict(self, X_test): y_pred = [] for test_sample in X_test: distances = [self.euclidean_distance(test_sample, x) for x in self.X] nearest_indices = np.argsort(distances)[:self.n_neighbors] nearest_labels = self.y[nearest_indices] unique_labels, counts = np.unique(nearest_labels, return_counts=True) predicted_label = unique_labels[np.argmax(counts)] y_pred.append(predicted_label) return np.array(y_pred)
这段代码定义了KNN算法中的`predict`方法,用于对测试样本进行预测。
在这个方法中,首先创建一个空列表`y_pred`,用于存储预测结果。
然后,对于测试样本集`X_test`中的每一个样本`test_sample`,计算它与训练样本集`self.X`中每个样本的欧几里德距离,并将距离存储在列表`distances`中。
接下来,根据距离从小到大对索引进行排序,取前`self.n_neighbors`个最近邻居的索引,并将其存储在`nearest_indices`中。
然后,根据最近邻居的索引获取对应的标签,并将其存储在`nearest_labels`中。
接着,使用`np.unique()`函数获取最近邻居标签数组中的唯一值和对应的计数值,并分别存储在`unique_labels`和`counts`中。
最后,根据计数值最大的标签作为预测结果,并将其添加到`y_pred`列表中。
循环结束后,将`y_pred`转换为NumPy数组并返回作为最终的预测结果。
这个方法实现了KNN算法中的预测过程,根据最近邻居的标签进行投票,并选择出现次数最多的标签作为预测结果。
希望这能解答你的问题!