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法學研究如何在Python中使用TensorFlow下載和探索IMDB資料集？
TensorFlow是Google提供的機器學習框架。它是一個開源框架，與Python結合使用，可以實現演算法、深度學習應用程式等等。它用於研究和生產目的。
這是因為它使用NumPy和多維陣列。這些多維陣列也稱為“張量”。該框架支援使用深度神經網路。它具有高度的可擴充套件性，並附帶許多流行的資料集。它使用GPU計算並自動管理資源。它附帶大量的機器學習庫，並且得到良好的支援和文件記錄。該框架能夠執行深度神經網路模型，訓練它們，並建立預測相應資料集相關特徵的應用程式。
可以使用以下程式碼行在Windows上安裝“tensorflow”包：
pip install tensorflow
張量是TensorFlow中使用的資料結構。它有助於連線資料流圖中的邊。這個資料流圖被稱為“資料流圖”。張量只不過是多維陣列或列表。它們可以使用三個主要屬性來識別：
“IMDB”資料集包含超過5萬部電影的評論。此資料集通常與自然語言處理相關的操作一起使用。
我們使用Google Colaboratory執行以下程式碼。Google Colab或Colaboratory幫助在瀏覽器上執行Python程式碼，無需任何配置，並且可以免費訪問GPU（圖形處理單元）。Colaboratory構建在Jupyter Notebook之上。
以下是程式碼：
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)程式碼來源 − https://www.tensorflow.org/tutorials/keras/text_classification
輸出
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.
解釋
匯入併為所需的包指定別名。
載入IMDB資料並將其儲存在Colab可以訪問的位置。
在控制檯上顯示原始資料的樣本。
將原始資料拆分為訓練資料集和測試資料集。
使用訓練資料構建模型。
嘗試將給定資料分類為負面評論或正面評價。
更新於：2021年1月19日
272 次瀏覽
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