Tensorflow小技巧(一)-阿里云开发者社区

开发者社区> 开发与运维> 正文
登录阅读全文

Tensorflow小技巧(一)

简介: Tensorflow小技巧(一)

how-do-i-select-rows-from-a-dataframe-based-on-column-values

To select rows whose column value equals a scalar, some_value, use ==:

df.loc[df['column_name'] == some_value]

To select rows whose column value is in an iterable, some_values, use isin:

df.loc[df['column_name'].isin(some_values)]

how-do-i-sort-a-dictionary-by-value

x = {1: 2, 3: 4, 4: 3, 2: 1, 0: 0}
dict(sorted(x.items(), key=lambda item: item[1]))

how-can-i-count-the-occurrences-of-a-list-item

from collections import Counter

l = ["a","b","b"]
Counter(l)

pandas.DataFrame.drop_duplicates

df = pd.DataFrame({
...     'brand': ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie'],
...     'style': ['cup', 'cup', 'cup', 'pack', 'pack'],
...     'rating': [4, 4, 3.5, 15, 5]
... })

df.drop_duplicates(subset=['brand'])

tf.data.Dataset-----as_numpy_iterator()

Returns an iterator which converts all elements of the dataset to numpy.

dataset = tf.data.Dataset.from_tensor_slices([1, 2, 3])
for element in dataset.as_numpy_iterator():
  print(element)

tf.data.Dataset

The tf.data.Dataset API supports writing descriptive and efficient input pipelines. Dataset usage follows a common pattern:

  1. Create a source dataset from your input data.
  2. Apply dataset transformations to preprocess the data.
  3. Iterate over the dataset and process the elements.

Iteration happens in a streaming fashion, so the full dataset does not need to fit into memory.

The simplest way to create a dataset is to create it from a python list:

dataset = tf.data.Dataset.from_tensor_slices([1, 2, 3])
for element in dataset:
  print(element)

Once you have a dataset, you can apply transformations to prepare the data for your model:

dataset = tf.data.Dataset.from_tensor_slices([1, 2, 3])
dataset = dataset.map(lambda x: x*2)
list(dataset.as_numpy_iterator())

版权声明:本文内容由阿里云实名注册用户自发贡献,版权归原作者所有,阿里云开发者社区不拥有其著作权,亦不承担相应法律责任。具体规则请查看《阿里云开发者社区用户服务协议》和《阿里云开发者社区知识产权保护指引》。如果您发现本社区中有涉嫌抄袭的内容,填写侵权投诉表单进行举报,一经查实,本社区将立刻删除涉嫌侵权内容。

分享: