加载imdb数据以及TensorBoard的使用方法

加载imdb数据

修改imdb.py

这里我把imdb数据下载下来,放在keras>datasets文件夹下面,并且需要修改一下imdb.py文件

# path = get_file(path,
# origin='https://s3.amazonaws.com/text-datasets/imdb.npz',
# file_hash='599dadb1135973df5b59232a0e9a887c')
    path = r'D:\pythonText\venv\Lib\site-packages\keras\datasets\imdb.npz'

这里把原来使用的path路径注释掉,使用我们用来放置imdb.npz的路径

加载imdb数据
from keras.datasets import imdb
from keras import preprocessing

max_feature = 10000
max_len = 100

(train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=max_feature)
train_data = preprocessing.sequence.pad_sequences
boston_housing module: Boston housing price regression dataset. cifar10 module: CIFAR10 small images classification dataset. cifar100 module: CIFAR100 small images classification dataset. fashion_mnist module: Fashion-MNIST dataset. imdb module: IMDB sentiment classification dataset. mnist module: MNIST handwritten digits dataset. reuters module: Reuters topic classification dataset. import tensorflow as tf from tensorflow import keras fashion_mnist = keras.datasets.fashion_mnist (x_train, y_train), (x_test, y_test) = fashion_mnist.load_data() mnist = keras.datasets.mnist (x_train, y_train), (x_test, y_test) = mnist.load_data() cifar100 = keras.datasets.cifar100 (x_train, y_train), (x_test, y_test) = cifar100.load_data() cifar10 = keras.datasets.cifar10 (x_train, y_train), (x_test, y_test) = cifar10.load_data() imdb = keras.datasets.imdb (x_train, y_train), (x_test, y_test) = imdb.load_data() # word_index is a dictionary mapping words to an integer index word_index = imdb.get_word_index() # We reverse it, mapping integer indices to words reverse_word_index = dict([(value, key) for (key, value) in word_index.items()]) # We decode the review; note that our indices were offset by 3 # because 0, 1 and 2 are reserved indices for "padding", "start of sequence", and "unknown". decoded_review = ' '.join([reverse_word_index.get(i - 3, '?') for i in x_train[0]]) print(decoded_review) boston_housing = keras.datasets.boston_housing (x_train, y_train), (x_test, y_test) = boston_housing.load_data() reuters= keras.datasets.reuters (x_train, y_train), (x_test, y_test) = reuters.load_data() tf.keras.datasets.reuters.get_word_index( path='reuters_word_index.json' )
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