importosimportshutilimportkerasimportnumpyasnpimporttensorflowastfimportautokerasasak Load Images from Disk If the data is too large to put in memory all at once, we can load it batch by batch into memory from disk with tf.data.Dataset. Thisfunctioncan help you build such a tf.data.Dataset...
import os import pathlib #load the IMAGES dataDirectory = ‘/p/home/username/tensorflow/newBirds’ dataDirectory = pathlib.Path(dataDirectory) imageCount = len(list(dataDirectory.glob(’/.jpg’))) print(‘Image count: {0}\n’.format(imageCount)) ...
import tensorflow as tf from tensorflow import keras def load_dataset(): # Step0 准备数据集, 可以是自己动手丰衣足食, 也可以从 tf.keras.datasets 加载需要的数据集(获取到的是numpy数据) # 这里以 mnist 为例 (x, y), (x_test, y_test) = keras.datasets.mnist.load_data() # Step1 使用 ...
importpandasaspd# Load a dataset from a CSV filedf=pd.read_csv('data.csv')# Display the first few rows of the datasetprint(df.head()) Copy Output: ID Name Age Gender Salary Target 0 1 Sara 25.0 Female 50000.0 0 1 2 Ophrah 30.0 Male 60000.0 1 ...
import tensorflow as tf from tensorflow import keras def load_dataset(): # Step0 准备数据集, 可以是自己动手丰衣足食, 也可以从 tf.keras.datasets 加载需要的数据集(获取到的是numpy数据) # 这里以 mnist 为例 (x, y), (x_test, y_test) = keras.datasets.mnist.load_data() ...
常见数据集格式:.mat. npz, .data train_test_split 文件读写 一、文件打开 传统方法的弊端 Ref:python 常用文件读写及with的用法 如果我们open一个文件之后,如果读写发生了异常,是不会调用close()的,那么这会造成文件描述符的资源浪费,久而久之,会造成系统的崩溃。
在上述代码中,我们首先使用mnist.load_data()函数加载MNIST数据集。然后,将数据进行预处理,将像素值进行归一化处理。接下来,我们通过tf.data.Dataset.from_tensor_slices()函数,将训练集和测试集分别转换为tf.data.Dataset对象。 为了增加模型训练的随机性,我们使用...
# This script needs these libraries to be installed: # numpy, transformers, datasets import wandb import os import numpy as np from datasets import load_dataset from transformers import TrainingArguments, Trainer from transformers import AutoTokenizer, AutoModelForSequenceClassification def tokenize_functio...
from datasets import load_dataset dataset = load_dataset("squad", split="train") dataset.features {'answers': Sequence(feature={'text': Value(dtype='string', id=None), 'answer_start': Value(dtype='int32', id=None)}, length=-1, id=None), 'context': Value(dtype='string', id=None...
from datasets import load_datasetsquad_it_dataset= load_dataset("json", data_files="./data/SQuAD_it-train.json", field="data") #也可以加载文本文件 dataset = load_dataset('text', data_files={'train': ['my_text_1.txt', 'my_text_2.txt'], 'test': 'my_test_file.txt'}) 1.2 加...