Python keras.datasets.reuters.load_data() Examples

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Example #1
Source Project: CAPTCHA-breaking   Author: lllcho   File: test_datasets.py    License: MIT License 6 votes vote down vote up
def test_cifar(self):
        print('cifar10')
        (X_train, y_train), (X_test, y_test) = cifar10.load_data()
        print(X_train.shape)
        print(X_test.shape)
        print(y_train.shape)
        print(y_test.shape)

        print('cifar100 fine')
        (X_train, y_train), (X_test, y_test) = cifar100.load_data('fine')
        print(X_train.shape)
        print(X_test.shape)
        print(y_train.shape)
        print(y_test.shape)

        print('cifar100 coarse')
        (X_train, y_train), (X_test, y_test) = cifar100.load_data('coarse')
        print(X_train.shape)
        print(X_test.shape)
        print(y_train.shape)
        print(y_test.shape) 
Example #2
Source Project: CAPTCHA-breaking   Author: lllcho   File: test_datasets.py    License: MIT License 6 votes vote down vote up
def test_imdb(self):
        print('imdb')
        (X_train, y_train), (X_test, y_test) = imdb.load_data() 
Example #3
Source Project: DEC-keras   Author: XifengGuo   File: datasets.py    License: MIT License 6 votes vote down vote up
def load_retures_keras():
    from keras.preprocessing.text import Tokenizer
    from keras.datasets import reuters
    max_words = 1000

    print('Loading data...')
    (x, y), (_, _) = reuters.load_data(num_words=max_words, test_split=0.)
    print(len(x), 'train sequences')

    num_classes = np.max(y) + 1
    print(num_classes, 'classes')

    print('Vectorizing sequence data...')
    tokenizer = Tokenizer(num_words=max_words)
    x = tokenizer.sequences_to_matrix(x, mode='binary')
    print('x_train shape:', x.shape)

    return x.astype(float), y 
Example #4
Source Project: DEC-keras   Author: XifengGuo   File: datasets.py    License: MIT License 6 votes vote down vote up
def load_imdb():
    from keras.preprocessing.text import Tokenizer
    from keras.datasets import imdb
    max_words = 1000

    print('Loading data...')
    (x1, y1), (x2, y2) = imdb.load_data(num_words=max_words)
    x = np.concatenate((x1, x2))
    y = np.concatenate((y1, y2))
    print(len(x), 'train sequences')

    num_classes = np.max(y) + 1
    print(num_classes, 'classes')

    print('Vectorizing sequence data...')
    tokenizer = Tokenizer(num_words=max_words)
    x = tokenizer.sequences_to_matrix(x, mode='binary')
    print('x_train shape:', x.shape)

    return x.astype(float), y 
Example #5
Source Project: CAPTCHA-breaking   Author: lllcho   File: test_datasets.py    License: MIT License 5 votes vote down vote up
def test_reuters(self):
        print('reuters')
        (X_train, y_train), (X_test, y_test) = reuters.load_data() 
Example #6
Source Project: CAPTCHA-breaking   Author: lllcho   File: test_datasets.py    License: MIT License 5 votes vote down vote up
def test_mnist(self):
        print('mnist')
        (X_train, y_train), (X_test, y_test) = mnist.load_data()
        print(X_train.shape)
        print(X_test.shape)
        print(y_train.shape)
        print(y_test.shape) 
Example #7
Source Project: DeepLearning_Wavelet-LSTM   Author: hello-sea   File: test_datasets.py    License: MIT License 5 votes vote down vote up
def test_cifar():
    # only run data download tests 20% of the time
    # to speed up frequent testing
    random.seed(time.time())
    if random.random() > 0.8:
        (x_train, y_train), (x_test, y_test) = cifar10.load_data()
        assert len(x_train) == len(y_train) == 50000
        assert len(x_test) == len(y_test) == 10000
        (x_train, y_train), (x_test, y_test) = cifar100.load_data('fine')
        assert len(x_train) == len(y_train) == 50000
        assert len(x_test) == len(y_test) == 10000
        (x_train, y_train), (x_test, y_test) = cifar100.load_data('coarse')
        assert len(x_train) == len(y_train) == 50000
        assert len(x_test) == len(y_test) == 10000 
Example #8
Source Project: DeepLearning_Wavelet-LSTM   Author: hello-sea   File: test_datasets.py    License: MIT License 5 votes vote down vote up
def test_reuters():
    # only run data download tests 20% of the time
    # to speed up frequent testing
    random.seed(time.time())
    if random.random() > 0.8:
        (x_train, y_train), (x_test, y_test) = reuters.load_data()
        assert len(x_train) == len(y_train)
        assert len(x_test) == len(y_test)
        assert len(x_train) + len(x_test) == 11228
        (x_train, y_train), (x_test, y_test) = reuters.load_data(maxlen=10)
        assert len(x_train) == len(y_train)
        assert len(x_test) == len(y_test)
        word_index = reuters.get_word_index()
        assert isinstance(word_index, dict) 
Example #9
Source Project: DeepLearning_Wavelet-LSTM   Author: hello-sea   File: test_datasets.py    License: MIT License 5 votes vote down vote up
def test_mnist():
    # only run data download tests 20% of the time
    # to speed up frequent testing
    random.seed(time.time())
    if random.random() > 0.8:
        (x_train, y_train), (x_test, y_test) = mnist.load_data()
        assert len(x_train) == len(y_train) == 60000
        assert len(x_test) == len(y_test) == 10000 
Example #10
Source Project: DeepLearning_Wavelet-LSTM   Author: hello-sea   File: test_datasets.py    License: MIT License 5 votes vote down vote up
def test_imdb():
    # only run data download tests 20% of the time
    # to speed up frequent testing
    random.seed(time.time())
    if random.random() > 0.8:
        (x_train, y_train), (x_test, y_test) = imdb.load_data()
        (x_train, y_train), (x_test, y_test) = imdb.load_data(maxlen=40)
        assert len(x_train) == len(y_train)
        assert len(x_test) == len(y_test)
        word_index = imdb.get_word_index()
        assert isinstance(word_index, dict) 
Example #11
Source Project: DeepLearning_Wavelet-LSTM   Author: hello-sea   File: test_datasets.py    License: MIT License 5 votes vote down vote up
def test_boston_housing():
    # only run data download tests 20% of the time
    # to speed up frequent testing
    random.seed(time.time())
    if random.random() > 0.8:
        (x_train, y_train), (x_test, y_test) = boston_housing.load_data()
        assert len(x_train) == len(y_train)
        assert len(x_test) == len(y_test) 
Example #12
Source Project: DeepLearning_Wavelet-LSTM   Author: hello-sea   File: test_datasets.py    License: MIT License 5 votes vote down vote up
def test_cifar():
    # only run data download tests 20% of the time
    # to speed up frequent testing
    random.seed(time.time())
    if random.random() > 0.8:
        (x_train, y_train), (x_test, y_test) = cifar10.load_data()
        assert len(x_train) == len(y_train) == 50000
        assert len(x_test) == len(y_test) == 10000
        (x_train, y_train), (x_test, y_test) = cifar100.load_data('fine')
        assert len(x_train) == len(y_train) == 50000
        assert len(x_test) == len(y_test) == 10000
        (x_train, y_train), (x_test, y_test) = cifar100.load_data('coarse')
        assert len(x_train) == len(y_train) == 50000
        assert len(x_test) == len(y_test) == 10000 
Example #13
Source Project: DeepLearning_Wavelet-LSTM   Author: hello-sea   File: test_datasets.py    License: MIT License 5 votes vote down vote up
def test_reuters():
    # only run data download tests 20% of the time
    # to speed up frequent testing
    random.seed(time.time())
    if random.random() > 0.8:
        (x_train, y_train), (x_test, y_test) = reuters.load_data()
        assert len(x_train) == len(y_train)
        assert len(x_test) == len(y_test)
        assert len(x_train) + len(x_test) == 11228
        (x_train, y_train), (x_test, y_test) = reuters.load_data(maxlen=10)
        assert len(x_train) == len(y_train)
        assert len(x_test) == len(y_test)
        word_index = reuters.get_word_index()
        assert isinstance(word_index, dict) 
Example #14
Source Project: DeepLearning_Wavelet-LSTM   Author: hello-sea   File: test_datasets.py    License: MIT License 5 votes vote down vote up
def test_mnist():
    # only run data download tests 20% of the time
    # to speed up frequent testing
    random.seed(time.time())
    if random.random() > 0.8:
        (x_train, y_train), (x_test, y_test) = mnist.load_data()
        assert len(x_train) == len(y_train) == 60000
        assert len(x_test) == len(y_test) == 10000 
Example #15
Source Project: DeepLearning_Wavelet-LSTM   Author: hello-sea   File: test_datasets.py    License: MIT License 5 votes vote down vote up
def test_imdb():
    # only run data download tests 20% of the time
    # to speed up frequent testing
    random.seed(time.time())
    if random.random() > 0.8:
        (x_train, y_train), (x_test, y_test) = imdb.load_data()
        (x_train, y_train), (x_test, y_test) = imdb.load_data(maxlen=40)
        assert len(x_train) == len(y_train)
        assert len(x_test) == len(y_test)
        word_index = imdb.get_word_index()
        assert isinstance(word_index, dict) 
Example #16
Source Project: DeepLearning_Wavelet-LSTM   Author: hello-sea   File: test_datasets.py    License: MIT License 5 votes vote down vote up
def test_boston_housing():
    # only run data download tests 20% of the time
    # to speed up frequent testing
    random.seed(time.time())
    if random.random() > 0.8:
        (x_train, y_train), (x_test, y_test) = boston_housing.load_data()
        assert len(x_train) == len(y_train)
        assert len(x_test) == len(y_test) 
Example #17
Source Project: DeepLearning_Wavelet-LSTM   Author: hello-sea   File: test_datasets.py    License: MIT License 5 votes vote down vote up
def test_cifar():
    # only run data download tests 20% of the time
    # to speed up frequent testing
    random.seed(time.time())
    if random.random() > 0.8:
        (x_train, y_train), (x_test, y_test) = cifar10.load_data()
        assert len(x_train) == len(y_train) == 50000
        assert len(x_test) == len(y_test) == 10000
        (x_train, y_train), (x_test, y_test) = cifar100.load_data('fine')
        assert len(x_train) == len(y_train) == 50000
        assert len(x_test) == len(y_test) == 10000
        (x_train, y_train), (x_test, y_test) = cifar100.load_data('coarse')
        assert len(x_train) == len(y_train) == 50000
        assert len(x_test) == len(y_test) == 10000 
Example #18
Source Project: DeepLearning_Wavelet-LSTM   Author: hello-sea   File: test_datasets.py    License: MIT License 5 votes vote down vote up
def test_reuters():
    # only run data download tests 20% of the time
    # to speed up frequent testing
    random.seed(time.time())
    if random.random() > 0.8:
        (x_train, y_train), (x_test, y_test) = reuters.load_data()
        assert len(x_train) == len(y_train)
        assert len(x_test) == len(y_test)
        assert len(x_train) + len(x_test) == 11228
        (x_train, y_train), (x_test, y_test) = reuters.load_data(maxlen=10)
        assert len(x_train) == len(y_train)
        assert len(x_test) == len(y_test)
        word_index = reuters.get_word_index()
        assert isinstance(word_index, dict) 
Example #19
Source Project: DeepLearning_Wavelet-LSTM   Author: hello-sea   File: test_datasets.py    License: MIT License 5 votes vote down vote up
def test_mnist():
    # only run data download tests 20% of the time
    # to speed up frequent testing
    random.seed(time.time())
    if random.random() > 0.8:
        (x_train, y_train), (x_test, y_test) = mnist.load_data()
        assert len(x_train) == len(y_train) == 60000
        assert len(x_test) == len(y_test) == 10000 
Example #20
Source Project: DeepLearning_Wavelet-LSTM   Author: hello-sea   File: test_datasets.py    License: MIT License 5 votes vote down vote up
def test_imdb():
    # only run data download tests 20% of the time
    # to speed up frequent testing
    random.seed(time.time())
    if random.random() > 0.8:
        (x_train, y_train), (x_test, y_test) = imdb.load_data()
        (x_train, y_train), (x_test, y_test) = imdb.load_data(maxlen=40)
        assert len(x_train) == len(y_train)
        assert len(x_test) == len(y_test)
        word_index = imdb.get_word_index()
        assert isinstance(word_index, dict) 
Example #21
Source Project: DeepLearning_Wavelet-LSTM   Author: hello-sea   File: test_datasets.py    License: MIT License 5 votes vote down vote up
def test_boston_housing():
    # only run data download tests 20% of the time
    # to speed up frequent testing
    random.seed(time.time())
    if random.random() > 0.8:
        (x_train, y_train), (x_test, y_test) = boston_housing.load_data()
        assert len(x_train) == len(y_train)
        assert len(x_test) == len(y_test) 
Example #22
Source Project: DeepLearning_Wavelet-LSTM   Author: hello-sea   File: test_datasets.py    License: MIT License 5 votes vote down vote up
def test_cifar():
    # only run data download tests 20% of the time
    # to speed up frequent testing
    random.seed(time.time())
    if random.random() > 0.8:
        (x_train, y_train), (x_test, y_test) = cifar10.load_data()
        assert len(x_train) == len(y_train) == 50000
        assert len(x_test) == len(y_test) == 10000
        (x_train, y_train), (x_test, y_test) = cifar100.load_data('fine')
        assert len(x_train) == len(y_train) == 50000
        assert len(x_test) == len(y_test) == 10000
        (x_train, y_train), (x_test, y_test) = cifar100.load_data('coarse')
        assert len(x_train) == len(y_train) == 50000
        assert len(x_test) == len(y_test) == 10000 
Example #23
Source Project: DeepLearning_Wavelet-LSTM   Author: hello-sea   File: test_datasets.py    License: MIT License 5 votes vote down vote up
def test_reuters():
    # only run data download tests 20% of the time
    # to speed up frequent testing
    random.seed(time.time())
    if random.random() > 0.8:
        (x_train, y_train), (x_test, y_test) = reuters.load_data()
        assert len(x_train) == len(y_train)
        assert len(x_test) == len(y_test)
        assert len(x_train) + len(x_test) == 11228
        (x_train, y_train), (x_test, y_test) = reuters.load_data(maxlen=10)
        assert len(x_train) == len(y_train)
        assert len(x_test) == len(y_test)
        word_index = reuters.get_word_index()
        assert isinstance(word_index, dict) 
Example #24
Source Project: DeepLearning_Wavelet-LSTM   Author: hello-sea   File: test_datasets.py    License: MIT License 5 votes vote down vote up
def test_mnist():
    # only run data download tests 20% of the time
    # to speed up frequent testing
    random.seed(time.time())
    if random.random() > 0.8:
        (x_train, y_train), (x_test, y_test) = mnist.load_data()
        assert len(x_train) == len(y_train) == 60000
        assert len(x_test) == len(y_test) == 10000 
Example #25
Source Project: DeepLearning_Wavelet-LSTM   Author: hello-sea   File: test_datasets.py    License: MIT License 5 votes vote down vote up
def test_imdb():
    # only run data download tests 20% of the time
    # to speed up frequent testing
    random.seed(time.time())
    if random.random() > 0.8:
        (x_train, y_train), (x_test, y_test) = imdb.load_data()
        (x_train, y_train), (x_test, y_test) = imdb.load_data(maxlen=40)
        assert len(x_train) == len(y_train)
        assert len(x_test) == len(y_test)
        word_index = imdb.get_word_index()
        assert isinstance(word_index, dict) 
Example #26
Source Project: DeepLearning_Wavelet-LSTM   Author: hello-sea   File: test_datasets.py    License: MIT License 5 votes vote down vote up
def test_boston_housing():
    # only run data download tests 20% of the time
    # to speed up frequent testing
    random.seed(time.time())
    if random.random() > 0.8:
        (x_train, y_train), (x_test, y_test) = boston_housing.load_data()
        assert len(x_train) == len(y_train)
        assert len(x_test) == len(y_test) 
Example #27
Source Project: DeepLearning_Wavelet-LSTM   Author: hello-sea   File: test_datasets.py    License: MIT License 5 votes vote down vote up
def test_cifar():
    # only run data download tests 20% of the time
    # to speed up frequent testing
    random.seed(time.time())
    if random.random() > 0.8:
        (x_train, y_train), (x_test, y_test) = cifar10.load_data()
        assert len(x_train) == len(y_train) == 50000
        assert len(x_test) == len(y_test) == 10000
        (x_train, y_train), (x_test, y_test) = cifar100.load_data('fine')
        assert len(x_train) == len(y_train) == 50000
        assert len(x_test) == len(y_test) == 10000
        (x_train, y_train), (x_test, y_test) = cifar100.load_data('coarse')
        assert len(x_train) == len(y_train) == 50000
        assert len(x_test) == len(y_test) == 10000 
Example #28
Source Project: DeepLearning_Wavelet-LSTM   Author: hello-sea   File: test_datasets.py    License: MIT License 5 votes vote down vote up
def test_reuters():
    # only run data download tests 20% of the time
    # to speed up frequent testing
    random.seed(time.time())
    if random.random() > 0.8:
        (x_train, y_train), (x_test, y_test) = reuters.load_data()
        assert len(x_train) == len(y_train)
        assert len(x_test) == len(y_test)
        assert len(x_train) + len(x_test) == 11228
        (x_train, y_train), (x_test, y_test) = reuters.load_data(maxlen=10)
        assert len(x_train) == len(y_train)
        assert len(x_test) == len(y_test)
        word_index = reuters.get_word_index()
        assert isinstance(word_index, dict) 
Example #29
Source Project: DeepLearning_Wavelet-LSTM   Author: hello-sea   File: test_datasets.py    License: MIT License 5 votes vote down vote up
def test_mnist():
    # only run data download tests 20% of the time
    # to speed up frequent testing
    random.seed(time.time())
    if random.random() > 0.8:
        (x_train, y_train), (x_test, y_test) = mnist.load_data()
        assert len(x_train) == len(y_train) == 60000
        assert len(x_test) == len(y_test) == 10000 
Example #30
Source Project: DeepLearning_Wavelet-LSTM   Author: hello-sea   File: test_datasets.py    License: MIT License 5 votes vote down vote up
def test_imdb():
    # only run data download tests 20% of the time
    # to speed up frequent testing
    random.seed(time.time())
    if random.random() > 0.8:
        (x_train, y_train), (x_test, y_test) = imdb.load_data()
        (x_train, y_train), (x_test, y_test) = imdb.load_data(maxlen=40)
        assert len(x_train) == len(y_train)
        assert len(x_test) == len(y_test)
        word_index = imdb.get_word_index()
        assert isinstance(word_index, dict)