利用嵌套循环打印九九乘法表(nest_for.py)(三种方法)
时间: 2023-05-24 10:01:23 浏览: 60
以下是三种利用嵌套循环打印九九乘法表的 Python 代码:
方法一:
for i in range(1, 10):
for j in range(1, i + 1):
print('{}*{}={}\t'.format(i, j, i*j), end='')
print()
方法二:
i = 1
while i <= 9:
j = 1
while j <= i:
print('{}*{}={}\t'.format(i, j, i*j), end='')
j += 1
print()
i += 1
方法三:
i = 1
while i <= 9:
for j in range(i, 10):
print('{}*{}={}\t'.format(i, j, i*j), end='')
print()
i += 1
请注意,以上代码中的任何一个都可以实现相同的功能,只是实现方式略有不同。
相关问题
import nest_asyncio nest_asyncio.apply() import collections import numpy as np import tensorflow as tf import tensorflow_federated as tff np.random.seed(0) tff.federated_computation(lambda: 'Hello, World!')() emnist_train, emnist_test = tff.simulation.datasets.emnist.load_data(cache_dir = '/home/cqx/PycharmProjects/cache/fed_emnist_digitsonly') example_dataset = emnist_train.create_tf_dataset_for_client( emnist_train.client_ids[0]) example_element = next(iter(example_dataset)) example_element['label'].numpy()当把数据集换成本地fashion_mnist时,代码该如何修改
如果要将数据集从emnist_train更改为fashion_mnist,则需要对代码进行以下更改:
1. 导入fashion_mnist数据集
```
from tensorflow.keras.datasets import fashion_mnist
```
2. 加载fashion_mnist数据集
```
(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()
```
3. 将数据集转换为TFF格式
```
def preprocess_fn(images, labels):
images = tf.expand_dims(images, axis=-1)
images = tf.cast(images, tf.float32) / 255.0
labels = tf.one_hot(labels, 10)
return collections.OrderedDict(x=images, y=labels)
def make_federated_data(client_data, client_ids):
return [client_data.create_tf_dataset_for_client(client_id)
.map(preprocess_fn)
.shuffle(500)
.repeat(NUM_EPOCHS)
.batch(BATCH_SIZE)
.prefetch(PREFETCH_BUFFER)
for client_id in client_ids]
train_data = make_federated_data(fashion_mnist, client_ids)
```
在替换原始数据集之后,这些更改将确保新数据集与代码一起工作。
Traceback (most recent call last): File "E:\Duzhuan\anaconda\lib\site-packages\IPython\core\interactiveshell.py", line 3460, in run_code exec(code_obj, self.user_global_ns, self.user_ns) File "<ipython-input-2-c833f4fdcae2>", line 1, in <module> runfile('C:\\Users\\zhangxiao\\Desktop\\Algorithm Integration\\apc_1.py', wdir='C:\\Users\\zhangxiao\\Desktop\\Algorithm Integration') File "E:\Duzhuan\JetBrainsPyCharm 2023.1\PyCharm 2023.1\plugins\python\helpers\pydev\_pydev_bundle\pydev_umd.py", line 198, in runfile pydev_imports.execfile(filename, global_vars, local_vars) # execute the script File "E:\Duzhuan\JetBrainsPyCharm 2023.1\PyCharm 2023.1\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile exec(compile(contents+"\n", file, 'exec'), glob, loc) File "C:\Users\zhangxiao\Desktop\Algorithm Integration\apc_1.py", line 184, in <module> integrated_optimization() File "C:\Users\zhangxiao\Desktop\Algorithm Integration\apc_1.py", line 160, in integrated_optimization global_best_nest = cuckoo_search(objective_function, num_nests, num_generations) NameError: name 'objective_function' is not defined
这个错误是因为在调用`cuckoo_search`函数时,`objective_function`未被定义。请确保您已经定义了目标函数`objective_function`,并将其作为参数传递给`cuckoo_search`函数。以下是一个示例的目标函数,您可以根据实际情况修改或替换它:
```python
# 示例目标函数,这里使用一个简单的二维函数作为示例
def objective_function(x):
return np.sum(np.square(x))
```
将此目标函数定义添加到代码中,并确保它在调用`cuckoo_search`函数之前可用。然后再次运行代码,看看是否解决了问题。如果还有其他问题,请随时提问。