as follows,  CIFAR10 small image classification  CIFAR100 small image classification  IMDB Movie reviews sentiment classification  Reuters newswire topics classification  MNIST database processing to find the sentiment analysis of the given text. Let us create a LSTM model to analyze the IMDB movie reviews and find its positive/negative sentiment. The model for the sequence analysis can from keras.datasets import imdb Step 2: Load data Let us import the imdb dataset. (x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=2000) Here,  imdb is a dataset provided by Keras

sample from the IMDB reviews5 dataset with a larger validation set to measure the performance of text augmentations. Let’s dive in! Project: IMDB Reviews Sentiment Classification The imdb reviews dataset from time import time %%capture (train500_ds, train1000_ds, val_ds), ds_info = tfds.load( name='imdb_reviews', split=['train[:500]', 'train[:1000]', 'train[60%:]'], as_supervised=True, with_info=True

个等长(单词数量为 5)的句子序列可以表示为 shape 为[2,5,3] 的 3 维张量，其中 2 表示句子个数，5 表示单词数量，3 表示单词向量的长度。我们通过 IMDB 数据集来演示如何表示句子，代码如下： In [46]: # 自动加载 IMDB 电影评价数据集 from torchtext import data, datasets # 需要先安装 torchtext 库 # fix_length=80) LABEL = data.LabelField(dtype=torch.float) # 自动下载、加载、切割 IMDB 数据集 train_data, test_data = datasets.IMDB.splits(TEXT, LABEL) print('len of train data:', len(train_data)) # 打印训练集句子数量 过规定长度的部分单词。以 IMDB 数据集的加载为例，我们来演示如何将不等长的句子变 换为等长结构，代码如下： 待替换！！！ In [30]: total_words = 10000 # 设定词汇量大小 max_review_len = 80 # 最大句子长度 embedding_len = 100 # 词向量长度 # 加载 IMDB 数据集 (x_train, y_train)

graph gives each actor a“Bacon number” *，Unfortunately the co-star graph doesn't actually exist *1IMDB: *。Movies table 。Actors table *。 Movies-Actor table Anti-Pattern atd: :vectorcstd: :vectorcint>> | Library provided Index Adjacency List 悦.5 忆 刁.Ji 了 Six Degrees of Kevin Bacon (IMDB version) include "imdb-grap有.PP auto&& [G，辕 = make_plain_bipartite_graphs<>(movies，actors，movies_actors和 auto工

154 12.2 CIFAR100 小图像分类数据集 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154 12.3 IMDB 电影评论情感分类数据集 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154 12.4 路透社新闻主题分类 . . . . 这里有几个可以帮助你开始的例子！ 在 examples 目录 中，你可以找到真实数据集的示例模型： • CIFAR10 小图片分类：具有实时数据增强的卷积神经网络 (CNN) 快速开始 11 • IMDB 电影评论情感分类：基于词序列的 LSTM • Reuters 新闻主题分类：多层感知器 (MLP) • MNIST 手写数字分类：MLP 和 CNN • 基于 LSTM 的字符级文本生成 数组表示的类别标签（范围在 0-9 之间的整数） ，尺寸为 (num_samples,)。 • 参数： • label_mode: ”fine” 或者”coarse” 12.3 IMDB 电影评论情感分类数据集 数据集来自 IMDB 的 25,000 条电影评论，以情绪（正面/负面）标记。每一条评论已经过预 处理，并编码为词索引（整数）的序列表示。为了方便起见，将词按数据集中出现的频率进行索 引，例如整数

class Imdb(me.EmbeddedDocument): imdb_id = me.StringField() rating = me.DecimalField() votes = me.IntField() class Movie(me.Document): ... imdb = me.EmbeddedDocumentField(Imdb) Creating The Future", year=1985) bttf.actors = [ "Michael J. Fox", "Christopher Lloyd" ] bttf.imdb = Imdb(imdb_id="tt0088763", rating=8.5) bttf.save() Queries Use the class objects attribute to make queries

document. class Imdb(me.EmbeddedDocument): imdb_id = me.StringField() rating = me.DecimalField() votes = me.IntField() class Movie(me.Document): ... imdb = me.EmbeddedDocumentField(Imdb) Creating Data User’s Guide Flask Documentation (1.1.x), Release 1.1.4 (continued from previous page) ] bttf.imdb = Imdb(imdb_id="tt0088763", rating=8.5) bttf.save() Queries Use the class objects attribute to make queries

authors also found that fine-tuning such a pre-trained model for a binary classification problem (IMDb dataset) required only 100 labeled examples ( less labeled examples otherwise). If we add a middle-step Figure 6-6: Validation error w.r.t. number of training examples for different training methods on IMDb (from scratch, ULMFiT supervised: pre-training with WikiText-103 and fine-tuning using labeled data