x = range(10000)
时间: 2023-11-24 21:05:46 浏览: 18
As an AI language model, I can create the range object with the specified range of numbers, but I cannot display the object itself. Here's how you can create a range object with a range of numbers from 0 to 9999:
x = range(10000)
This will create a range object that can be used to iterate over the numbers from 0 to 9999. You can use this range object in a for loop or any other place where you need to iterate over a sequence of numbers.
相关问题
python求函数值,x=1时 f(x)=10;x>1时 f(x)=f(x-1)+2,输入整型变量x,其中x>=1 且 x <=10000,输出f(x)
可以使用递归函数来实现:
```python
def f(x):
if x == 1:
return 10
else:
return f(x-1) + 2
x = int(input("请输入x:"))
print("f(x)=", f(x))
```
或者使用循环实现:
```python
x = int(input("请输入x:"))
fx = 10
for i in range(2, x+1):
fx += 2
print("f(x)=", fx)
```
两种方式都可以得到正确的结果。
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。