Python keras.datasets.fashion_mnist.load_data() Examples

The following are 30 code examples of keras.datasets.fashion_mnist.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.fashion_mnist , or try the search function .
Example #1
Source File: utils.py    From ResNet-Tensorflow with MIT License 7 votes vote down vote up 
def load_cifar100() :
    (train_data, train_labels), (test_data, test_labels) = cifar100.load_data()
    # train_data = train_data / 255.0
    # test_data = test_data / 255.0
    train_data, test_data = normalize(train_data, test_data)

    train_labels = to_categorical(train_labels, 100)
    test_labels = to_categorical(test_labels, 100)

    seed = 777
    np.random.seed(seed)
    np.random.shuffle(train_data)
    np.random.seed(seed)
    np.random.shuffle(train_labels)


    return train_data, train_labels, test_data, test_labels
Example #2
Source File: 05_nn_vis.py    From Practical-Computer-Vision with MIT License 6 votes vote down vote up 
def get_dataset():
    """
    Return processed and reshaped dataset for training
    In this cases Fashion-mnist dataset.
    """
    # load mnist dataset
    (x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()
    
    # test and train datasets
    print("Nb Train:", x_train.shape[0], "Nb test:",x_test.shape[0])
    x_train = x_train.reshape(x_train.shape[0], img_h, img_w, 1)
    x_test = x_test.reshape(x_test.shape[0], img_h, img_w, 1)
    in_shape = (img_h, img_w, 1)

    # normalize inputs
    x_train = x_train.astype('float32')
    x_test = x_test.astype('float32')
    x_train /= 255.0
    x_test /= 255.0

    # convert to one hot vectors 
    y_train = keras.utils.to_categorical(y_train, nb_class)
    y_test = keras.utils.to_categorical(y_test, nb_class)
    return x_train, x_test, y_train, y_test
Example #3
Source File: utils.py    From ResNet-Tensorflow with MIT License 6 votes vote down vote up 
def load_cifar10() :
    (train_data, train_labels), (test_data, test_labels) = cifar10.load_data()
    # train_data = train_data / 255.0
    # test_data = test_data / 255.0

    train_data, test_data = normalize(train_data, test_data)

    train_labels = to_categorical(train_labels, 10)
    test_labels = to_categorical(test_labels, 10)

    seed = 777
    np.random.seed(seed)
    np.random.shuffle(train_data)
    np.random.seed(seed)
    np.random.shuffle(train_labels)


    return train_data, train_labels, test_data, test_labels
Example #4
Source File: utils.py    From ResNet-Tensorflow with MIT License 6 votes vote down vote up 
def load_mnist() :
    (train_data, train_labels), (test_data, test_labels) = mnist.load_data()
    train_data = np.expand_dims(train_data, axis=-1)
    test_data = np.expand_dims(test_data, axis=-1)

    train_data, test_data = normalize(train_data, test_data)

    train_labels = to_categorical(train_labels, 10)
    test_labels = to_categorical(test_labels, 10)

    seed = 777
    np.random.seed(seed)
    np.random.shuffle(train_data)
    np.random.seed(seed)
    np.random.shuffle(train_labels)


    return train_data, train_labels, test_data, test_labels
Example #5
Source File: utils.py    From ResNet-Tensorflow with MIT License 6 votes vote down vote up 
def load_fashion() :
    (train_data, train_labels), (test_data, test_labels) = fashion_mnist.load_data()
    train_data = np.expand_dims(train_data, axis=-1)
    test_data = np.expand_dims(test_data, axis=-1)

    train_data, test_data = normalize(train_data, test_data)

    train_labels = to_categorical(train_labels, 10)
    test_labels = to_categorical(test_labels, 10)

    seed = 777
    np.random.seed(seed)
    np.random.shuffle(train_data)
    np.random.seed(seed)
    np.random.shuffle(train_labels)


    return train_data, train_labels, test_data, test_labels
Example #6
Source File: fashion_mnist_multi_task_learning.py    From Cross-stitch-Networks-for-Multi-task-Learning with MIT License 6 votes vote down vote up 
def load_data():
    # train_X: (60000, 28, 28)
    # train_y: (60000,)
    # test_X: (10000, 28, 28)
    # test_y: (10000,)
    (train_X, train_y_1), (test_X, test_y_1) = fashion_mnist.load_data()
    n_class_1 = 10
    # map to new label
    train_y_2 = list(0 if y in [5, 7, 9] else 1 if y in [3, 6, 8] else 2 for y in train_y_1)
    test_y_2 = list(0 if y in [5, 7, 9] else 1 if y in [3, 6, 8] else 2 for y in test_y_1)
    n_class_2 = 3
    # train_X: (60000, 28, 28, 1)
    # test_X: (10000, 28, 28, 1)
    # train_y: (60000, n_class)
    # test_y: (10000, n_class)
    train_X = np.expand_dims(train_X, axis=3)
    test_X = np.expand_dims(test_X, axis=3)
    train_y_1 = to_categorical(train_y_1, n_class_1)
    test_y_1 = to_categorical(test_y_1, n_class_1)
    train_y_2 = to_categorical(train_y_2, n_class_2)
    test_y_2 = to_categorical(test_y_2, n_class_2)
    return train_X, train_y_1, train_y_2, test_X, test_y_1, test_y_2
Example #7
Source File: 05_nn_mnist.py    From Practical-Computer-Vision with MIT License 6 votes vote down vote up 
def get_dataset():
    """
    Return processed and reshaped dataset for training
    In this cases Fashion-mnist dataset.
    """
    # load mnist dataset
    (x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()
    
    # test and train datasets
    print("Nb Train:", x_train.shape[0], "Nb test:",x_test.shape[0])
    x_train = x_train.reshape(x_train.shape[0], img_h, img_w, 1)
    x_test = x_test.reshape(x_test.shape[0], img_h, img_w, 1)
    in_shape = (img_h, img_w, 1)

    # normalize inputs
    x_train = x_train.astype('float32')
    x_test = x_test.astype('float32')
    x_train /= 255.0
    x_test /= 255.0

    # convert to one hot vectors 
    y_train = keras.utils.to_categorical(y_train, nb_class)
    y_test = keras.utils.to_categorical(y_test, nb_class)
    return x_train, x_test, y_train, y_test
Example #8
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 #9
Source File: capsulenet.py    From CapsNet-Fashion-MNIST with MIT License 5 votes vote down vote up 
def load_mnist():
    # the data, shuffled and split between train and test sets
    from keras.datasets import mnist, fashion_mnist
    (x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()

    x_train = x_train.reshape(-1, 28, 28, 1).astype('float32') / 255.
    x_test = x_test.reshape(-1, 28, 28, 1).astype('float32') / 255.
    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 #10
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 #11
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 #12
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 #13
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 #14
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 #15
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 #16
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 #17
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 #18
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 #19
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 #20
Source File: utils.py    From AnomalyDetectionTransformations with MIT License 5 votes vote down vote up 
def load_fashion_mnist():
    (X_train, y_train), (X_test, y_test) = fashion_mnist.load_data()
    X_train = normalize_minus1_1(cast_to_floatx(np.pad(X_train, ((0, 0), (2, 2), (2, 2)), 'constant')))
    X_train = np.expand_dims(X_train, axis=get_channels_axis())
    X_test = normalize_minus1_1(cast_to_floatx(np.pad(X_test, ((0, 0), (2, 2), (2, 2)), 'constant')))
    X_test = np.expand_dims(X_test, axis=get_channels_axis())
    return (X_train, y_train), (X_test, y_test)
Example #21
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 #22
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 #23
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 #24
Source File: utils.py    From AnomalyDetectionTransformations with MIT License 5 votes vote down vote up 
def load_cifar100(label_mode='coarse'):
    (X_train, y_train), (X_test, y_test) = cifar100.load_data(label_mode=label_mode)
    X_train = normalize_minus1_1(cast_to_floatx(X_train))
    X_test = normalize_minus1_1(cast_to_floatx(X_test))
    return (X_train, y_train), (X_test, y_test)
Example #25
Source File: utils.py    From AnomalyDetectionTransformations with MIT License 5 votes vote down vote up 
def load_cifar10():
    (X_train, y_train), (X_test, y_test) = cifar10.load_data()
    X_train = normalize_minus1_1(cast_to_floatx(X_train))
    X_test = normalize_minus1_1(cast_to_floatx(X_test))
    return (X_train, y_train), (X_test, y_test)
Example #26
Source File: utils.py    From AnomalyDetectionTransformations with MIT License 5 votes vote down vote up 
def load_mnist():
    (X_train, y_train), (X_test, y_test) = mnist.load_data()
    X_train = normalize_minus1_1(cast_to_floatx(np.pad(X_train, ((0, 0), (2, 2), (2, 2)), 'constant')))
    X_train = np.expand_dims(X_train, axis=get_channels_axis())
    X_test = normalize_minus1_1(cast_to_floatx(np.pad(X_test, ((0, 0), (2, 2), (2, 2)), 'constant')))
    X_test = np.expand_dims(X_test, axis=get_channels_axis())
    return (X_train, y_train), (X_test, y_test)
Example #27
Source File: load_datasets.py    From deepcaps with MIT License 5 votes vote down vote up 
def load_cifar10():
    from keras.datasets import cifar10
    (x_train, y_train), (x_test, y_test) = cifar10.load_data()

    x_train = x_train.reshape(-1, 32, 32, 3).astype('float32') / 255.
    x_test = x_test.reshape(-1, 32, 32, 3).astype('float32') / 255.
    # mean = np.mean(x_train, axis=(0,1,2))
    # x_train -= mean
    # x_test -= mean
    
    y_train = to_categorical(y_train.astype('float32'))
    y_test = to_categorical(y_test.astype('float32'))
    
#     return (x_train[0:100], y_train[0:100]), (x_test[0:100], y_test[0:100])
    return (x_train, y_train), (x_test, y_test)
Example #28
Source File: load_datasets.py    From deepcaps with MIT License 5 votes vote down vote up 
def load_cifar100():
    from keras.datasets import cifar100
    (x_train, y_train), (x_test, y_test) = cifar100.load_data()

    x_train = x_train.reshape(-1, 32, 32, 3).astype('float32') / 255.
    x_test = x_test.reshape(-1, 32, 32, 3).astype('float32') / 255.
    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 #29
Source File: load_datasets.py    From deepcaps with MIT License 5 votes vote down vote up 
def load_mnist():
    # the data, shuffled and split between train and test sets
    from keras.datasets import mnist
    (x_train, y_train), (x_test, y_test) = mnist.load_data()

    x_train = x_train.reshape(-1, 28, 28, 1).astype('float32') / 255.
    x_test = x_test.reshape(-1, 28, 28, 1).astype('float32') / 255.
    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 #30
Source File: load_datasets.py    From deepcaps with MIT License 5 votes vote down vote up 
def load_fmnist():
    # the data, shuffled and split between train and test sets
    from keras.datasets import fashion_mnist
    (x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()

    x_train = x_train.reshape(-1, 28, 28, 1).astype('float32') / 255.
    x_test = x_test.reshape(-1, 28, 28, 1).astype('float32') / 255.
    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)