Python keras.datasets.cifar10.load_data() Examples

The following are 30 code examples of keras.datasets.cifar10.load_data(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module keras.datasets.cifar10 , or try the search function .
Example #1
Source File: cnn.py    From DeepFashion with Apache License 2.0 7 votes vote down vote up 
def load_and_preprocess_data_3():
    # The data, shuffled and split between train and test sets:
    (X_train, y_train), (x_test, y_test) = cifar10.load_data()
    logging.debug('X_train shape: {}'.format(X_train.shape))
    logging.debug('train samples: {}'.format(X_train.shape[0]))
    logging.debug('test samples: {}'.format(x_test.shape[0]))

    # Convert class vectors to binary class matrices.
    y_train = keras.utils.to_categorical(y_train, num_classes)
    y_test = keras.utils.to_categorical(y_test, num_classes)

    X_train = X_train.astype('float32')
    x_test = x_test.astype('float32')
    X_train /= 255
    x_test /= 255

    input_shape = X_train[0].shape
    logging.debug('input_shape {}'.format(input_shape))
    input_shape = X_train.shape[1:]
    logging.debug('input_shape {}'.format(input_shape))

    return X_train, x_test, y_train, y_test, input_shape
Example #2
Source File: datasets.py    From super-simple-distributed-keras with MIT License 7 votes vote down vote up 
def get_mnist():
    """Retrieve the MNIST dataset and process the data."""
    # Set defaults.
    nb_classes = 10
    batch_size = 128
    input_shape = (784,)

    # Get the data.
    (x_train, y_train), (x_test, y_test) = mnist.load_data()
    x_train = x_train.reshape(60000, 784)
    x_test = x_test.reshape(10000, 784)
    x_train = x_train.astype('float32')
    x_test = x_test.astype('float32')
    x_train /= 255
    x_test /= 255

    # convert class vectors to binary class matrices
    y_train = to_categorical(y_train, nb_classes)
    y_test = to_categorical(y_test, nb_classes)

    return (nb_classes, batch_size, input_shape, x_train, x_test, y_train, y_test)
Example #3
Source File: utils.py    From RelativisticGAN-Tensorflow with MIT License 6 votes vote down vote up 
def load_cifar10(size=64) :
    (train_data, train_labels), (test_data, test_labels) = cifar10.load_data()
    train_data = normalize(train_data)
    test_data = normalize(test_data)

    x = np.concatenate((train_data, test_data), axis=0)
    # y = np.concatenate((train_labels, test_labels), axis=0).astype(np.int)

    seed = 777
    np.random.seed(seed)
    np.random.shuffle(x)
    # np.random.seed(seed)
    # np.random.shuffle(y)

    x = np.asarray([scipy.misc.imresize(x_img, [size, size]) for x_img in x])

    return x
Example #4
Source File: train.py    From neural-network-genetic-algorithm with MIT License 6 votes vote down vote up 
def get_mnist():
    """Retrieve the MNIST dataset and process the data."""
    # Set defaults.
    nb_classes = 10
    batch_size = 128
    input_shape = (784,)

    # Get the data.
    (x_train, y_train), (x_test, y_test) = mnist.load_data()
    x_train = x_train.reshape(60000, 784)
    x_test = x_test.reshape(10000, 784)
    x_train = x_train.astype('float32')
    x_test = x_test.astype('float32')
    x_train /= 255
    x_test /= 255

    # convert class vectors to binary class matrices
    y_train = to_categorical(y_train, nb_classes)
    y_test = to_categorical(y_test, nb_classes)

    return (nb_classes, batch_size, input_shape, x_train, x_test, y_train, y_test)
Example #5
Source File: train.py    From neural-network-genetic-algorithm with MIT License 6 votes vote down vote up 
def get_cifar10():
    """Retrieve the CIFAR dataset and process the data."""
    # Set defaults.
    nb_classes = 10
    batch_size = 64
    input_shape = (3072,)

    # Get the data.
    (x_train, y_train), (x_test, y_test) = cifar10.load_data()
    x_train = x_train.reshape(50000, 3072)
    x_test = x_test.reshape(10000, 3072)
    x_train = x_train.astype('float32')
    x_test = x_test.astype('float32')
    x_train /= 255
    x_test /= 255

    # convert class vectors to binary class matrices
    y_train = to_categorical(y_train, nb_classes)
    y_test = to_categorical(y_test, nb_classes)

    return (nb_classes, batch_size, input_shape, x_train, x_test, y_train, y_test)
Example #6
Source File: 3leveldcnet.py    From Multi-level-DCNet with GNU General Public License v3.0 6 votes vote down vote up 
def load_dataset():
    # Load the dataset from Keras
    from keras.datasets import cifar10
    (x_train, y_train), (x_test, y_test) = cifar10.load_data()

    # Preprocessing the dataset
    x_train = x_train.astype('float32')
    x_test = x_test.astype('float32')
    x_train= preprocess_input(x_train)
    x_test= preprocess_input(x_test)
    x_train = x_train.reshape(-1, 32, 32, 3).astype('float32') 
    x_test = x_test.reshape(-1, 32, 32, 3).astype('float32')
    y_train = to_categorical(y_train.astype('float32'))
    y_test = to_categorical(y_test.astype('float32'))

    return (x_train, y_train), (x_test, y_test)
Example #7
Source File: mobilenet_pseudo_cifar.py    From Pseudo-Label-Keras with MIT License 6 votes vote down vote up 
def __init__(self, model, n_labeled_sample, batch_size):
        self.n_labeled_sample = n_labeled_sample
        self.batch_size = batch_size
        self.model = model
        self.n_classes = 10
        # labeled_unlabeledの作成
        (X_train, y_train), (self.X_test, self.y_test) = cifar10.load_data()
        indices = np.arange(X_train.shape[0])
        np.random.shuffle(indices)
        self.X_train_labeled = X_train[indices[:n_labeled_sample]]
        self.y_train_labeled = y_train[indices[:n_labeled_sample]]
        self.X_train_unlabeled = X_train[indices[n_labeled_sample:]]
        self.y_train_unlabeled_groundtruth = y_train[indices[n_labeled_sample:]]
        # unlabeledの予測値
        self.y_train_unlabeled_prediction = np.random.randint(
            10, size=(self.y_train_unlabeled_groundtruth.shape[0], 1))
        # steps_per_epoch
        self.train_steps_per_epoch = X_train.shape[0] // batch_size
        self.test_stepes_per_epoch = self.X_test.shape[0] // batch_size
        # unlabeledの重み
        self.alpha_t = 0.0
        # labeled/unlabeledの一致率推移
        self.unlabeled_accuracy = []
        self.labeled_accuracy = []
Example #8
Source File: pseudo_pretrain_cifar.py    From Pseudo-Label-Keras with MIT License 6 votes vote down vote up 
def __init__(self, model, n_labeled_sample, batch_size):
        self.n_labeled_sample = n_labeled_sample
        self.batch_size = batch_size
        self.model = model
        self.n_classes = 10
        # labeled_unlabeledの作成
        (X_train, y_train), (self.X_test, self.y_test) = cifar10.load_data()
        indices = np.arange(X_train.shape[0])
        np.random.shuffle(indices)
        self.X_train_labeled = X_train[indices[:n_labeled_sample]]
        self.y_train_labeled = y_train[indices[:n_labeled_sample]]
        self.X_train_unlabeled = X_train[indices[n_labeled_sample:]]
        self.y_train_unlabeled_groundtruth = y_train[indices[n_labeled_sample:]]
        # unlabeledの予測値
        self.y_train_unlabeled_prediction = np.random.randint(
            10, size=(self.y_train_unlabeled_groundtruth.shape[0], 1))
        # steps_per_epoch
        self.train_steps_per_epoch = X_train.shape[0] // batch_size
        self.test_stepes_per_epoch = self.X_test.shape[0] // batch_size
        # unlabeledの重み
        self.alpha_t = 0.05
        # labeled/unlabeledの一致率推移
        self.unlabeled_accuracy = []
        self.labeled_accuracy = []
Example #9
Source File: mobilenet_transfer_pseudo_cifar.py    From Pseudo-Label-Keras with MIT License 6 votes vote down vote up 
def __init__(self, model, n_labeled_sample, batch_size):
        self.n_labeled_sample = n_labeled_sample
        self.batch_size = batch_size
        self.model = model
        self.n_classes = 10
        # labeled_unlabeledの作成
        (X_train, y_train), (self.X_test, self.y_test) = cifar10.load_data()
        indices = np.arange(X_train.shape[0])
        np.random.shuffle(indices)
        self.X_train_labeled = X_train[indices[:n_labeled_sample]]
        self.y_train_labeled = y_train[indices[:n_labeled_sample]]
        self.X_train_unlabeled = X_train[indices[n_labeled_sample:]]
        self.y_train_unlabeled_groundtruth = y_train[indices[n_labeled_sample:]]
        # unlabeledの予測値
        self.y_train_unlabeled_prediction = np.random.randint(
            10, size=(self.y_train_unlabeled_groundtruth.shape[0], 1))
        # steps_per_epoch
        self.train_steps_per_epoch = X_train.shape[0] // batch_size
        self.test_stepes_per_epoch = self.X_test.shape[0] // batch_size
        # unlabeledの重み
        self.alpha_t = 0.0
        # labeled/unlabeledの一致率推移
        self.unlabeled_accuracy = []
        self.labeled_accuracy = []
Example #10
Source File: datasets.py    From super-simple-distributed-keras with MIT License 6 votes vote down vote up 
def get_cifar10():
    """Retrieve the CIFAR dataset and process the data."""
    # Set defaults.
    nb_classes = 10
    batch_size = 64
    input_shape = (3072,)

    # Get the data.
    (x_train, y_train), (x_test, y_test) = cifar10.load_data()
    x_train = x_train.reshape(50000, 3072)
    x_test = x_test.reshape(10000, 3072)
    x_train = x_train.astype('float32')
    x_test = x_test.astype('float32')
    x_train /= 255
    x_test /= 255

    # convert class vectors to binary class matrices
    y_train = to_categorical(y_train, nb_classes)
    y_test = to_categorical(y_test, nb_classes)

    return (nb_classes, batch_size, input_shape, x_train, x_test, y_train, y_test)
Example #11
Source File: pseudo_cifar.py    From Pseudo-Label-Keras with MIT License 6 votes vote down vote up 
def __init__(self, model, n_labeled_sample, batch_size):
        self.n_labeled_sample = n_labeled_sample
        self.batch_size = batch_size
        self.model = model
        self.n_classes = 10
        # labeled_unlabeledの作成
        (X_train, y_train), (self.X_test, self.y_test) = cifar10.load_data()
        indices = np.arange(X_train.shape[0])
        np.random.shuffle(indices)
        self.X_train_labeled = X_train[indices[:n_labeled_sample]]
        self.y_train_labeled = y_train[indices[:n_labeled_sample]]
        self.X_train_unlabeled = X_train[indices[n_labeled_sample:]]
        self.y_train_unlabeled_groundtruth = y_train[indices[n_labeled_sample:]]
        # unlabeledの予測値
        self.y_train_unlabeled_prediction = np.random.randint(
            10, size=(self.y_train_unlabeled_groundtruth.shape[0], 1))
        # steps_per_epoch
        self.train_steps_per_epoch = X_train.shape[0] // batch_size
        self.test_stepes_per_epoch = self.X_test.shape[0] // batch_size
        # unlabeledの重み
        self.alpha_t = 0.0
        # labeled/unlabeledの一致率推移
        self.unlabeled_accuracy = []
        self.labeled_accuracy = []
Example #12
Source File: utils.py    From Self-Attention-GAN-Tensorflow with MIT License 6 votes vote down vote up 
def load_mnist(size=64):
    (train_data, train_labels), (test_data, test_labels) = mnist.load_data()
    train_data = normalize(train_data)
    test_data = normalize(test_data)

    x = np.concatenate((train_data, test_data), axis=0)
    # y = np.concatenate((train_labels, test_labels), axis=0).astype(np.int)

    seed = 777
    np.random.seed(seed)
    np.random.shuffle(x)
    # np.random.seed(seed)
    # np.random.shuffle(y)
    # x = np.expand_dims(x, axis=-1)

    x = np.asarray([scipy.misc.imresize(x_img, [size, size]) for x_img in x])
    x = np.expand_dims(x, axis=-1)
    return x
Example #13
Source File: test_datasets.py    From CAPTCHA-breaking with 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 #14
Source File: utils.py    From Self-Attention-GAN-Tensorflow with MIT License 6 votes vote down vote up 
def load_cifar10(size=64) :
    (train_data, train_labels), (test_data, test_labels) = cifar10.load_data()
    train_data = normalize(train_data)
    test_data = normalize(test_data)

    x = np.concatenate((train_data, test_data), axis=0)
    # y = np.concatenate((train_labels, test_labels), axis=0).astype(np.int)

    seed = 777
    np.random.seed(seed)
    np.random.shuffle(x)
    # np.random.seed(seed)
    # np.random.shuffle(y)

    x = np.asarray([scipy.misc.imresize(x_img, [size, size]) for x_img in x])

    return x
Example #15
Source File: test_datasets.py    From CAPTCHA-breaking with 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 #16
Source File: utils.py    From RelativisticGAN-Tensorflow with MIT License 6 votes vote down vote up 
def load_mnist(size=64):
    (train_data, train_labels), (test_data, test_labels) = mnist.load_data()
    train_data = normalize(train_data)
    test_data = normalize(test_data)

    x = np.concatenate((train_data, test_data), axis=0)
    # y = np.concatenate((train_labels, test_labels), axis=0).astype(np.int)

    seed = 777
    np.random.seed(seed)
    np.random.shuffle(x)
    # np.random.seed(seed)
    # np.random.shuffle(y)
    # x = np.expand_dims(x, axis=-1)

    x = np.asarray([scipy.misc.imresize(x_img, [size, size]) for x_img in x])
    x = np.expand_dims(x, axis=-1)
    return x
Example #17
Source File: test_datasets.py    From DeepLearning_Wavelet-LSTM with 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 #18
Source File: test_datasets.py    From DeepLearning_Wavelet-LSTM with 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 #19
Source File: test_datasets.py    From DeepLearning_Wavelet-LSTM with 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 #20
Source File: test_datasets.py    From DeepLearning_Wavelet-LSTM with 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 #21
Source File: test_datasets.py    From DeepLearning_Wavelet-LSTM with 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 File: test_datasets.py    From DeepLearning_Wavelet-LSTM with 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 #23
Source File: test_datasets.py    From DeepLearning_Wavelet-LSTM with 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 #24
Source File: test_datasets.py    From DeepLearning_Wavelet-LSTM with 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 File: test_datasets.py    From DeepLearning_Wavelet-LSTM with 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 #26
Source File: test_datasets.py    From DeepLearning_Wavelet-LSTM with 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 #27
Source File: test_datasets.py    From DeepLearning_Wavelet-LSTM with 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 #28
Source File: test_datasets.py    From DeepLearning_Wavelet-LSTM with 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 File: test_datasets.py    From DeepLearning_Wavelet-LSTM with 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 File: test_datasets.py    From DeepLearning_Wavelet-LSTM with 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)