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' )
某某自来水业务系统,是一套适合各种规模自来水公司的网络版自来水多种类业务管理软件。根据各大自来水公司存在的问题和需求自主...
某某自来水业务系统,是一套适合各种规模自来水公司的网络版自来水多种类业务管理软件。根据各大自来水公司存在的问题和需求自主...
某某自来水业务系统,是一套适合各种规模自来水公司的网络版自来水多种类业务管理软件。根据各大自来水公司存在的问题和需求自主...
某某自来水业务系统,是一套适合各种规模自来水公司的网络版自来水多种类业务管理软件。根据各大自来水公司存在的问题和需求自主...