本文整理匯總了Python中keras.datasets.fashion_mnist.load_data方法的典型用法代碼示例。如果您正苦於以下問題：Python fashion_mnist.load_data方法的具體用法？Python fashion_mnist.load_data怎麽用？Python fashion_mnist.load_data使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類keras.datasets.fashion_mnist的用法示例。
在下文中一共展示了fashion_mnist.load_data方法的15個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: load_cifar10
# 需要導入模塊: from keras.datasets import fashion_mnist [as 別名]
# 或者: from keras.datasets.fashion_mnist import load_data [as 別名]
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 示例2: load_cifar100
# 需要導入模塊: from keras.datasets import fashion_mnist [as 別名]
# 或者: from keras.datasets.fashion_mnist import load_data [as 別名]
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 示例3: load_mnist
# 需要導入模塊: from keras.datasets import fashion_mnist [as 別名]
# 或者: from keras.datasets.fashion_mnist import load_data [as 別名]
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 示例4: load_fashion
# 需要導入模塊: from keras.datasets import fashion_mnist [as 別名]
# 或者: from keras.datasets.fashion_mnist import load_data [as 別名]
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 示例5: load_data
# 需要導入模塊: from keras.datasets import fashion_mnist [as 別名]
# 或者: from keras.datasets.fashion_mnist import load_data [as 別名]
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 開發者ID:helloyide，項目名稱:Cross-stitch-Networks-for-Multi-task-Learning，代碼行數:24，代碼來源:fashion_mnist_multi_task_learning.py
示例6: get_dataset
# 需要導入模塊: from keras.datasets import fashion_mnist [as 別名]
# 或者: from keras.datasets.fashion_mnist import load_data [as 別名]
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 示例7: load_mnist
# 需要導入模塊: from keras.datasets import fashion_mnist [as 別名]
# 或者: from keras.datasets.fashion_mnist import load_data [as 別名]
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) 示例8: load_mnist
# 需要導入模塊: from keras.datasets import fashion_mnist [as 別名]
# 或者: from keras.datasets.fashion_mnist import load_data [as 別名]
def load_mnist():
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x = np.concatenate((x_train, x_test))
y = np.concatenate((y_train, y_test))
x = x.reshape((x.shape[0], -1))
x = np.divide(x, 255.)
return x, y 示例9: load_mnist_test
# 需要導入模塊: from keras.datasets import fashion_mnist [as 別名]
# 或者: from keras.datasets.fashion_mnist import load_data [as 別名]
def load_mnist_test():
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x = x_test
y = y_test
x = np.divide(x, 255.)
x = x.reshape((x.shape[0], -1))
return x, y 示例10: load_fashion
# 需要導入模塊: from keras.datasets import fashion_mnist [as 別名]
# 或者: from keras.datasets.fashion_mnist import load_data [as 別名]
def load_fashion():
(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()
x = np.concatenate((x_train, x_test))
y = np.concatenate((y_train, y_test))
x = x.reshape((x.shape[0], -1))
x = np.divide(x, 255.)
y_names = {0: "T-shirt", 1: "Trouser", 2: "Pullover", 3: "Dress", 4: "Coat",
5: "Sandal", 6: "Shirt", 7: "Sneaker", 8: "Bag", 9: "Ankle Boot"}
return x, y, y_names 示例11: test_cifar
# 需要導入模塊: from keras.datasets import fashion_mnist [as 別名]
# 或者: from keras.datasets.fashion_mnist import load_data [as 別名]
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 示例12: test_reuters
# 需要導入模塊: from keras.datasets import fashion_mnist [as 別名]
# 或者: from keras.datasets.fashion_mnist import load_data [as 別名]
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) 示例13: test_mnist
# 需要導入模塊: from keras.datasets import fashion_mnist [as 別名]
# 或者: from keras.datasets.fashion_mnist import load_data [as 別名]
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 示例14: test_imdb
# 需要導入模塊: from keras.datasets import fashion_mnist [as 別名]
# 或者: from keras.datasets.fashion_mnist import load_data [as 別名]
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) 示例15: test_boston_housing
# 需要導入模塊: from keras.datasets import fashion_mnist [as 別名]
# 或者: from keras.datasets.fashion_mnist import load_data [as 別名]
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)