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法学研究如何在Python中使用TensorFlow下载和探索IMDB数据集？
TensorFlow是Google提供的机器学习框架。它是一个开源框架，与Python结合使用，可以实现算法、深度学习应用程序等等。它用于研究和生产目的。
这是因为它使用NumPy和多维数组。这些多维数组也称为“张量”。该框架支持使用深度神经网络。它具有高度的可扩展性，并附带许多流行的数据集。它使用GPU计算并自动管理资源。它附带大量的机器学习库，并且得到良好的支持和文档记录。该框架能够运行深度神经网络模型，训练它们，并创建预测相应数据集相关特征的应用程序。
可以使用以下代码行在Windows上安装“tensorflow”包：
pip install tensorflow
张量是TensorFlow中使用的数据结构。它有助于连接数据流图中的边。这个数据流图被称为“数据流图”。张量只不过是多维数组或列表。它们可以使用三个主要属性来识别：
“IMDB”数据集包含超过5万部电影的评论。此数据集通常与自然语言处理相关的操作一起使用。
我们使用Google Colaboratory运行以下代码。Google Colab或Colaboratory帮助在浏览器上运行Python代码，无需任何配置，并且可以免费访问GPU（图形处理单元）。Colaboratory构建在Jupyter Notebook之上。
以下是代码：
示例
import matplotlib.pyplot as plt
import os
import re
import shutil
import string
import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras import losses
from tensorflow.keras import preprocessing
from tensorflow.keras.layers.experimental.preprocessing import TextVectorization
print("The tensorflow version is ")
print(tf.__version__)
url = "https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz"
dataset = tf.keras.utils.get_file("aclImdb_v1.tar.gz", url,
untar=True, cache_dir='.',
cache_subdir='')
print("The dataset is being downloaded")
dataset_dir = os.path.join(os.path.dirname(dataset), 'aclImdb')
print("The directories in the downloaded folder are ")
os.listdir(dataset_dir)
train_dir = os.path.join(dataset_dir, 'train')
os.listdir(train_dir)
print("The sample of data : ")
sample_file = os.path.join(train_dir, 'pos/1181_9.txt')
with open(sample_file) as f:
print(f.read())
remove_dir = os.path.join(train_dir, 'unsup')
shutil.rmtree(remove_dir)
batch_size = 32
seed = 42
print("The batch size is")
print(batch_size)
raw_train_ds = tf.keras.preprocessing.text_dataset_from_directory(
'aclImdb/train',
batch_size=batch_size,
validation_split=0.2,
subset='training',
seed=seed)
for text_batch, label_batch in raw_train_ds.take(1):
for i in range(3):
print("Review", text_batch.numpy()[i])
print("Label", label_batch.numpy()[i])
print("Label 0 corresponds to", raw_train_ds.class_names[0])
print("Label 1 corresponds to", raw_train_ds.class_names[1])
raw_val_ds = tf.keras.preprocessing.text_dataset_from_directory(
'aclImdb/train',
batch_size=batch_size,
validation_split=0.2,
subset='validation',
seed=seed)
raw_test_ds = tf.keras.preprocessing.text_dataset_from_directory(
'aclImdb/test',
batch_size=batch_size)代码来源 − https://tensorflowcn.cn/tutorials/keras/text_classification
输出
The tensorflow version is 2.4.0 The dataset is being downloaded The directories in the downloaded folder are The sample of data : Rachel Griffiths writes and directs this award winning short film. A heartwarming story about coping with grief and cherishing the memory of those we've loved and lost. Although, only 15 minutes long, Griffiths manages to capture so much emotion and truth onto film in the short space of time. Bud Tingwell gives a touching performance as Will, a widower struggling to cope with his wife's death. Will is confronted by the harsh reality of loneliness and helplessness as he proceeds to take care of Ruth's pet cow, Tulip. The film displays the grief and responsibility one feels for those they have loved and lost. Good cinematography, great direction, and superbly acted. It will bring tears to all those who have lost a loved one, and survived. The batch size is 32 Found 25000 files belonging to 2 classes. Using 20000 files for training. Review b'"Pandemonium" is a horror movie spoof that comes off more stupid than funny. Believe me when I tell you, I love comedies. Especially comedy spoofs. "Airplane", "The Naked Gun" trilogy, "Blazing Saddles", "High Anxiety", and "Spaceballs" are some of my favorite comedies that spoof a particular genre. "Pandemonium" is not up there with those films. Most of the scenes in this movie had me sitting there in stunned silence because the movie wasn\'t all that funny. There are a few laughs in the film, but when you watch a comedy, you expect to laugh a lot more than a few times and that\'s all this film has going for it. Geez, "Scream" had more laughs than this film and that was more of a horror film. How bizarre is that? *1/2 (out of four)' Label 0 Review b"David Mamet is a very interesting and a very un-equal director. His first movie 'House of Games' was the one I liked best, and it set a series of films with characters whose perspective of life changes as they get into complicated situations, and so does the perspective of the viewer. So is 'Homicide' which from the title tries to set the mind of the viewer to the usual crime drama. The principal characters are two cops, one Jewish and one Irish who deal with a racially charged area. The murder of an old Jewish shop owner who proves to be an ancient veteran of the Israeli Independence war triggers the Jewish identity in the mind and heart of the Jewish detective. This is were the flaws of the film are the more obvious. The process of awakening is theatrical and hard to believe, the group of Jewish militants is operatic, and the way the detective eventually walks to the final violent confrontation is pathetic. The end of the film itself is Mamet-like smart, but disappoints from a human emotional perspective. Joe Mantegna and William Macy give strong performances, but the flaws of the story are too evident to be easily compensated." Label 0 Review b'Great documentary about the lives of NY firefighters during the worst terrorist attack of all time.. That reason alone is why this should be a must see collectors item.. What shocked me was not only the attacks, but the"High Fat Diet" and physical appearance of some of these firefighters. I think a lot of Doctors would agree with me that,in the physical shape they were in, some of these firefighters would NOT of made it to the 79th floor carrying over 60 lbs of gear. Having said that i now have a greater respect for firefighters and i realize becoming a firefighter is a life altering job. The French have a history of making great documentary\'s and that is what this is, a Great Documentary.....' Label 1 Label 0 corresponds to neg Label 1 corresponds to pos Found 25000 files belonging to 2 classes. Using 5000 files for validation. Found 25000 files belonging to 2 classes.
解释
导入并为所需的包指定别名。
加载IMDB数据并将其存储在Colab可以访问的位置。
在控制台上显示原始数据的样本。
将原始数据拆分为训练数据集和测试数据集。
使用训练数据构建模型。
尝试将给定数据分类为负面评论或正面评价。
更新于：2021年1月19日
272 次浏览
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