Tensorflow如何用于准备IMDB数据集以在Python中进行训练？
Tensorflow是Google提供的一种机器学习框架。它是一个开放源代码框架，与Python结合使用以实现算法，深度学习应用程序等等。它用于研究和生产目的。它具有优化技术，可帮助快速执行复杂的数学运算。
可以使用下面的代码行在Windows上安装'tensorflow'软件包-
pip install tensorflow
Tensor是TensorFlow中使用的数据结构。它有助于连接流程图中的边缘。该流程图称为“数据流程图”。张量不过是多维数组或列表。
“IMDB”数据集包含超过5万部电影的评论。该数据集通常与与自然语言处理相关的操作一起使用。
我们正在使用Google合作实验室来运行以下代码。GoogleColab或Colaboratory可以帮助通过浏览器运行Python代码，并且需要零配置和对GPU（图形处理单元）的免费访问。合作已建立在JupyterNotebook的基础上。
以下是IMDB数据集的代码片段-
示例
def vectorize_text(text, label):
text = tf.expand_dims(text, −1)
return vectorize_layer(text), label
text_batch, label_batch = next(iter(raw_train_ds))
first_review, first_label = text_batch[0], label_batch[0]
print("Review is ", first_review)
print("Label is ", raw_train_ds.class_names[first_label])
print("Vectorized review is ", vectorize_text(first_review, first_label))
print("1222 −−−> ",vectorize_layer.get_vocabulary()[1222])
print(" 451 −−−> ",vectorize_layer.get_vocabulary()[451])
print('Vocabulary size: {}'.format(len(vectorize_layer.get_vocabulary())))
train_ds = raw_train_ds.map(vectorize_text)
val_ds = raw_val_ds.map(vectorize_text)
test_ds = raw_test_ds.map(vectorize_text)代码信用-https://www.tensorflow.org/tutorials/keras/text_classification
输出结果
Review is tf.Tensor(b'Silent Night, Deadly Night 5 is the very last of the series, and like part 4, it\'s unrelated to the first three except by title and the fact that it\'s a Christmas-themed horror flick.<br /><br />Except to the oblivious, there\'s some obvious things going on here...Mickey Rooney plays a toymaker named Joe Petto and his creepy son\'s name is Pino. Ring a bell, anyone? Now, a little boy named Derek heard a knock at the door one evening, and opened it to find a present on the doorstep for him. Even though it said "don\'t open till Christmas", he begins to open it anyway but is stopped by his dad, who scolds him and sends him to bed, and opens the gift himself. Inside is a little red ball that sprouts Santa arms and a head, and proceeds to kill dad. Oops, maybe he should have left well-enough alone. Of course Derek is then traumatized by the incident since he watched it from the stairs, but he doesn\'t grow up to be some killer Santa, he just stops talking.<br /><br />There\'s a mysterious stranger lurking around, who seems very interested in the toys that Joe Petto makes. We even see him buying a bunch when Derek\'s mom takes him to the store to find a gift for him to bring him out of his trauma. And what exactly is this guy doing? Well, we\'re not sure but he does seem to be taking these toys apart to see what makes them tick. He does keep his landlord from evicting him by promising him to pay him in cash the next day and presents him with a "Larry the Larvae" toy for his kid, but of course "Larry" is not a good toy and gets out of the box in the car and of course, well, things aren\'t pretty.<br /><br />Anyway, eventually what\'s going on with Joe Petto and Pino is of course revealed, and as with the old story, Pino is not a "real boy". Pino is probably even more agitated and naughty because he suffers from "Kenitalia" (a smooth plastic crotch) so that could account for his evil ways. And the identity of the lurking stranger is revealed too, and there\'s even kind of a happy ending of sorts. Whee.<br /><br />A step up from part 4, but not much of one. Again, Brian Yuzna is involved, and Screaming Mad George, so some decent special effects, but not enough to make this great. A few leftovers from part 4 are hanging around too, like Clint Howard and Neith Hunter, but that doesn\'t really make any difference. Anyway, I now have seeing the whole series out of my system. Now if I could get some of it out of my brain. 4 out of 5.', shape=(), dtype=string) Label is neg Vectorized review is (<tf.Tensor: shape=(1, 250), dtype=int64, numpy= array([[1287, 313, 2380, 313, 661, 7, 2, 52, 229, 5, 2, 200, 3, 38, 170, 669, 29, 5492, 6, 2, 83, 297, 549, 32, 410, 3, 2, 186, 12, 29, 4, 1, 191, 510, 549, 6, 2, 8229, 212, 46, 576, 175, 168, 20, 1, 5361, 290, 4, 1, 761, 969, 1, 3, 24, 935, 2271, 393, 7, 1, 1675, 4, 3747, 250, 148, 4, 112, 436, 761, 3529, 548, 4, 3633, 31, 2, 1331, 28, 2096, 3, 2912, 9, 6, 163, 4, 1006, 20, 2, 1, 15, 85, 53, 147, 9, 292, 89, 959, 2314, 984, 27, 762, 6, 959, 9, 564, 18, 7, 2140, 32, 24, 1254, 36, 1, 85, 3, 3298, 85, 6, 1410, 3, 1936, 2, 3408, 301, 965, 7, 4, 112, 740, 1977, 12, 1, 2014, 2772, 3, 4, 428, 3, 5177, 6, 512, 1254, 1, 278, 27, 139, 25, 308, 1, 579, 5, 259, 3529, 7, 92, 8981, 32, 2, 3842, 230, 27, 289, 9, 35, 2, 5712, 18, 27, 144, 2166, 56, 6, 26, 46, 466, 2014, 27, 40, 2745, 657, 212, 4, 1376, 3002, 7080, 183, 36, 180, 52, 920, 8, 2, 4028, 12, 969, 1, 158, 71, 53, 67, 85, 2754, 4, 734, 51, 1, 1611, 294, 85, 6, 2, 1164, 6, 163, 4, 3408, 15, 85, 6, 717, 85, 44, 5, 24, 7158, 3, 48, 604, 7, 11, 225, 384, 73, 65, 21, 242, 18, 27, 120, 295, 6, 26, 667, 129, 4028, 948, 6, 67, 48, 158, 93, 1]])>, <tf.Tensor: shape=(), dtype=int32, numpy=0>) 1222 ---> stick 451 ---> already Vocabulary size: 10000
解释
定义了一个名为“vectorize_text”的函数，该函数基本上将给定的文本转换为数字，以便计算机可以理解。
IMDB数据集用于训练模型。
审查，标签和矢量化数据的样本显示在控制台上。
训练数据，测试数据和验证数据均被矢量化。