Python mnist.load_data方法代碼示例

# 需要導入模塊: from keras.datasets import mnist [as 別名]
# 或者: from keras.datasets.mnist import load_data [as 別名]
def get_mnist_data(binarize=False):
    """Puts the MNIST data in the right format."""

    (X_train, y_train), (X_test, y_test) = mnist.load_data()

    if binarize:
        X_test = np.where(X_test >= 10, 1, -1)
        X_train = np.where(X_train >= 10, 1, -1)
    else:
        X_train = (X_train.astype(np.float32) - 127.5) / 127.5
        X_test = (X_test.astype(np.float32) - 127.5) / 127.5

    X_train = np.expand_dims(X_train, axis=-1)
    X_test = np.expand_dims(X_test, axis=-1)

    y_train = np.expand_dims(y_train, axis=-1)
    y_test = np.expand_dims(y_test, axis=-1)

    return (X_train, y_train), (X_test, y_test) 

# 需要導入模塊: from keras.datasets import mnist [as 別名]
# 或者: from keras.datasets.mnist import load_data [as 別名]
def get_mnist_data(binarize=False):
    """Puts the MNIST data in the right format."""

    (X_train, y_train), (X_test, y_test) = mnist.load_data()

    if binarize:
        X_test = np.where(X_test >= 10, 1, -1)
        X_train = np.where(X_train >= 10, 1, -1)
    else:
        X_train = (X_train.astype(np.float32) - 127.5) / 127.5
        X_test = (X_test.astype(np.float32) - 127.5) / 127.5

    X_train = np.expand_dims(X_train, axis=-1)
    X_test = np.expand_dims(X_test, axis=-1)

    y_train = np.eye(10)[y_train]
    y_test = np.eye(10)[y_test]

    return (X_train, y_train), (X_test, y_test) 

# 需要導入模塊: from keras.datasets import mnist [as 別名]
# 或者: from keras.datasets.mnist import load_data [as 別名]
def setup_mnist(self, img_res):

        print ("Setting up MNIST...")

        if not os.path.exists('datasets/mnist_x.npy'):
            # Load the dataset
            (mnist_X, mnist_y), (_, _) = mnist.load_data()

            # Normalize and rescale images
            mnist_X = self.normalize(mnist_X)
            mnist_X = np.array([imresize(x, img_res) for x in mnist_X])
            mnist_X = np.expand_dims(mnist_X, axis=-1)
            mnist_X = np.repeat(mnist_X, 3, axis=-1)

            self.mnist_X, self.mnist_y = mnist_X, mnist_y

            # Save formatted images
            np.save('datasets/mnist_x.npy', self.mnist_X)
            np.save('datasets/mnist_y.npy', self.mnist_y)
        else:
            self.mnist_X = np.load('datasets/mnist_x.npy')
            self.mnist_y = np.load('datasets/mnist_y.npy')

        print ("+ Done.") 

# 需要導入模塊: from keras.datasets import mnist [as 別名]
# 或者: from keras.datasets.mnist import load_data [as 別名]
def nn_model():
    (x_train, y_train), _ = mnist.load_data()
    # 歸一化
    x_train = x_train.reshape(x_train.shape[0], -1) / 255.
    # one-hot
    y_train = np_utils.to_categorical(y=y_train, num_classes=10)
    # constant(value=1.)自定義常數，constant(value=1.)===one()
    # 創建模型:輸入784個神經元，輸出10個神經元
    model = Sequential([
        Dense(units=200, input_dim=784, bias_initializer=constant(value=1.), activation=tanh),
        Dense(units=100, bias_initializer=one(), activation=tanh),
        Dense(units=10, bias_initializer=one(), activation=softmax),
    ])

    opt = SGD(lr=0.2, clipnorm=1.)  # 優化器
    model.compile(optimizer=opt, loss=categorical_crossentropy, metrics=['acc', 'mae'])  # 編譯
    model.fit(x_train, y_train, batch_size=64, epochs=20, callbacks=[RemoteMonitor()])
    model_save(model, './model.h5') 

# 需要導入模塊: from keras.datasets import mnist [as 別名]
# 或者: from keras.datasets.mnist import load_data [as 別名]
def pull_mnist(split=0.1, flatten=True):
    learning, testing = mnist.load_data()
    X = np.concatenate([learning[0], testing[0]]).astype(typing.floatX)
    Y = np.concatenate([learning[1], testing[1]]).astype("uint8")
    X -= X.mean()
    X /= X.std()
    if flatten:
        X = X.reshape(-1, 784)
    else:
        X = X[:, None, ...]
    Y = np.eye(10)[Y]

    if split:
        arg = np.arange(len(X))
        np.random.shuffle(arg)
        div = int(len(X) * split)
        targ, larg = arg[:div], arg[div:]
        return X[larg], Y[larg], X[targ], Y[targ]

    return X, Y 

# 需要導入模塊: from keras.datasets import mnist [as 別名]
# 或者: from keras.datasets.mnist import load_data [as 別名]
def main():

    num_classes = 10
    num_samples = 3 # number of architecture to sample
    metric = 'val_accuracy' # evaluation metric
    resource_type = 'epoch'
    max_resource = 81 # max resource that a configuration can have

    # load and normalize data
    (x_train, y_train),(x_test, y_test) = mnist.load_data()
    x_train, x_test = x_train / 255.0, x_test / 255.0

    # defining searcher and evaluator
    evaluator = SimpleClassifierEvaluator((x_train, y_train), num_classes,
                                        max_num_training_epochs=5)
    searcher = se.RandomSearcher(get_search_space(num_classes))
    hyperband = SimpleArchitectureSearchHyperBand(searcher, hyperband, metric, resource_type)
    (best_config, best_perf) = hyperband.evaluate(max_resource)
    print("Best %s is %f with architecture %d" % (metric, best_perf[0], best_config[0])) 

# 需要導入模塊: from keras.datasets import mnist [as 別名]
# 或者: from keras.datasets.mnist import load_data [as 別名]
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) 

# 需要導入模塊: from keras.datasets import mnist [as 別名]
# 或者: from keras.datasets.mnist import load_data [as 別名]
def test_imdb(self):
        print('imdb')
        (X_train, y_train), (X_test, y_test) = imdb.load_data() 

# 需要導入模塊: from keras.datasets import mnist [as 別名]
# 或者: from keras.datasets.mnist import load_data [as 別名]
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) 

# 需要導入模塊: from keras.datasets import mnist [as 別名]
# 或者: from keras.datasets.mnist import load_data [as 別名]
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) 

# 需要導入模塊: from keras.datasets import mnist [as 別名]
# 或者: from keras.datasets.mnist import load_data [as 別名]
def mnist_dataset_reader():
    (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  # 歸一化
    X_test /= 255

    digit_indices = [np.where(y_train == i)[0] for i in range(10)]
    tr_pairs, tr_y = create_pairs(X_train, digit_indices)

    digit_indices = [np.where(y_test == i)[0] for i in range(10)]
    te_pairs, te_y = create_pairs(X_test, digit_indices)

    input_dim = 784

    return input_dim, tr_pairs, tr_y, te_pairs, te_y 

# 需要導入模塊: from keras.datasets import mnist [as 別名]
# 或者: from keras.datasets.mnist import load_data [as 別名]
def data():
    """Data providing function:

    Make sure to have every relevant import statement included here and return data as
    used in model function below. This function is separated from model() so that hyperopt
    won't reload data for each evaluation run.
    """
    from keras.datasets import mnist
    from keras.utils import np_utils
    (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
    nb_classes = 10
    y_train = np_utils.to_categorical(y_train, nb_classes)
    y_test = np_utils.to_categorical(y_test, nb_classes)
    return x_train, y_train, x_test, y_test 

# 需要導入模塊: from keras.datasets import mnist [as 別名]
# 或者: from keras.datasets.mnist import load_data [as 別名]
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 

# 需要導入模塊: from keras.datasets import mnist [as 別名]
# 或者: from keras.datasets.mnist import load_data [as 別名]
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 

# 需要導入模塊: from keras.datasets import mnist [as 別名]
# 或者: from keras.datasets.mnist import load_data [as 別名]
def load_mnist():
    """
    load and pre-process the MNIST data
    """

    from keras.datasets import mnist
    (x_train, y_train), (x_test, y_test) = mnist.load_data()

    if K.image_data_format() == 'channels_last':
        x_train = x_train.reshape((x_train.shape[0], 28, 28, 1))
        x_test = x_test.reshape((x_test.shape[0], 28, 28, 1))
    else:
        x_train = x_train.reshape((x_train.shape[0], 1, 28, 28))
        x_test = x_test.reshape((x_test.shape[0], 1, 28, 28))

    # standardise the dataset:
    x_train = np.array(x_train).astype('float32') / 255
    x_test = np.array(x_test).astype('float32') / 255

    # shuffle the data:
    perm = np.random.permutation(x_train.shape[0])
    x_train = x_train[perm]
    y_train = y_train[perm]

    return (x_train, y_train), (x_test, y_test)