Python keras.datasets.mnist.load_data() Examples
The following are 30
code examples of keras.datasets.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.mnist
, or try the search function
.
![](https://www.programcreek.com/common/static/images/search.png)
Example #1
Source File: datasets.py From super-simple-distributed-keras with MIT License | 7 votes |
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 #2
Source File: DataSampler.py From MassImageRetrieval with Apache License 2.0 | 6 votes |
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 # 归一化 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
Example #3
Source File: data_loader.py From Keras-GAN with MIT License | 6 votes |
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.")
Example #4
Source File: reversing_gan.py From gandlf with MIT License | 6 votes |
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)
Example #5
Source File: mnist_gan.py From gandlf with MIT License | 6 votes |
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)
Example #6
Source File: utils.py From RelativisticGAN-Tensorflow with MIT License | 6 votes |
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 #7
Source File: main.py From DiscriminativeActiveLearning with MIT License | 6 votes |
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)
Example #8
Source File: utils.py From RelativisticGAN-Tensorflow with MIT License | 6 votes |
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 #9
Source File: HandWritingRecognition.py From Jtyoui with MIT License | 6 votes |
def nn_model(): (x_train, y_train), _ = mnist.load_data() # 归一化 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.)自定义常数,constant(value=1.)===one() # 创建模型:输入784个神经元,输出10个神经元 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.) # 优化器 model.compile(optimizer=opt, loss=categorical_crossentropy, metrics=['acc', 'mae']) # 编译 model.fit(x_train, y_train, batch_size=64, epochs=20, callbacks=[RemoteMonitor()]) model_save(model, './model.h5')
Example #10
Source File: utils.py From Self-Attention-GAN-Tensorflow with MIT License | 6 votes |
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 #11
Source File: xp_elm.py From brainforge with GNU General Public License v3.0 | 6 votes |
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
Example #12
Source File: test_hyperband.py From deep_architect with MIT License | 6 votes |
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]))
Example #13
Source File: train.py From neural-network-genetic-algorithm with MIT License | 6 votes |
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 #14
Source File: train.py From neural-network-genetic-algorithm with MIT License | 6 votes |
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 #15
Source File: datasets.py From super-simple-distributed-keras with MIT License | 6 votes |
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 #16
Source File: test_datasets.py From CAPTCHA-breaking with MIT License | 6 votes |
def test_imdb(self): print('imdb') (X_train, y_train), (X_test, y_test) = imdb.load_data()
Example #17
Source File: test_datasets.py From CAPTCHA-breaking with MIT License | 6 votes |
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 #18
Source File: hyperparam_optimization.py From elephas with MIT License | 6 votes |
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
Example #19
Source File: utils.py From Self-Attention-GAN-Tensorflow with MIT License | 6 votes |
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 #20
Source File: train.py From Generative-Adversarial-Networks-Cookbook with MIT License | 5 votes |
def load_MNIST(self,model_type=3): allowed_types = [-1,0,1,2,3,4,5,6,7,8,9] if self.model_type not in allowed_types: print('ERROR: Only Integer Values from -1 to 9 are allowed') (self.X_train, self.Y_train), (_, _) = mnist.load_data() if self.model_type!=-1: self.X_train = self.X_train[np.where(self.Y_train==int(self.model_type))[0]] # Rescale -1 to 1 # Find Normalize Function from CV Class self.X_train = ( np.float32(self.X_train) - 127.5) / 127.5 self.X_train = np.expand_dims(self.X_train, axis=3) return
Example #21
Source File: datasets.py From n2d with GNU General Public License v3.0 | 5 votes |
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
Example #22
Source File: datasets.py From n2d with GNU General Public License v3.0 | 5 votes |
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
Example #23
Source File: utils.py From SphereGAN-Tensorflow with MIT License | 5 votes |
def load_mnist(): (train_data, train_labels), (test_data, test_labels) = mnist.load_data() x = np.concatenate((train_data, test_data), axis=0) x = np.expand_dims(x, axis=-1) return x
Example #24
Source File: train.py From Generative-Adversarial-Networks-Cookbook with MIT License | 5 votes |
def load_2D_encoded_MNIST(self): (_, self.Y_train_2D), (_, self.Y_test_2D) = mnist.load_data() self.X_train_2D_encoded = np.load('x_train_encoded.npy') self.X_test_2D_encoded = np.load('x_test_encoded.npy') return
Example #25
Source File: conv_mnist_data_loader.py From Keras-Project-Template with Apache License 2.0 | 5 votes |
def __init__(self, config): super(ConvMnistDataLoader, self).__init__(config) (self.X_train, self.y_train), (self.X_test, self.y_test) = mnist.load_data() self.X_train = self.X_train.reshape((-1, 28, 28, 1)) self.X_test = self.X_test.reshape((-1, 28, 28, 1))
Example #26
Source File: datasets.py From n2d with GNU General Public License v3.0 | 5 votes |
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
Example #27
Source File: DataSampler.py From MassImageRetrieval with Apache License 2.0 | 5 votes |
def __init__(self, dataset_name="mnist"): self.X_train = None self.y_train = None self.X_test = None self.y_test = None self.grouped = None self.num_classes = None self.train_colors = None self.train_colored_x = None self.test_colors = None self.test_colored_x = None self.epoch_id = 0 self.m_AvgSampler = None self.m_InverseSampler = None if dataset_name == "mnist": self.num_classes = 10 (X_train, y_train), (X_test, y_test) = mnist.load_data() X_train = X_train.reshape(60000, 28, 28, 1) X_test = X_test.reshape(10000, 28, 28, 1) X_train = X_train.astype('float32') X_test = X_test.astype('float32') y_train = y_train.astype("int32") y_test = y_test.astype("int32") X_train /= 255 # 归一化 X_test /= 255 self.X_train, self.y_train = X_train, keras.utils.to_categorical(y_train, self.num_classes) self.X_test, self.y_test = X_test, keras.utils.to_categorical(y_test, self.num_classes) self.y_train = self.y_train.astype("int32") self.y_test = self.y_test.astype("int32") print(self.X_train.shape, self.X_train.dtype) print(self.y_train.shape, self.y_train.dtype) self.shuffle_train_samples()
Example #28
Source File: simple_mnist_data_loader.py From Keras-Project-Template with Apache License 2.0 | 5 votes |
def __init__(self, config): super(SimpleMnistDataLoader, self).__init__(config) (self.X_train, self.y_train), (self.X_test, self.y_test) = mnist.load_data() self.X_train = self.X_train.reshape((-1, 28 * 28)) self.X_test = self.X_test.reshape((-1, 28 * 28))
Example #29
Source File: train.py From Generative-Adversarial-Networks-Cookbook with MIT License | 5 votes |
def load_MNIST(self,model_type=3): allowed_types = [-1,0,1,2,3,4,5,6,7,8,9] if self.model_type not in allowed_types: print('ERROR: Only Integer Values from -1 to 9 are allowed') (self.X_train, self.Y_train), (_, _) = mnist.load_data() if self.model_type!=-1: self.X_train = self.X_train[np.where(self.Y_train==int(self.model_type))[0]] # Rescale -1 to 1 # Find Normalize Function from CV Class self.X_train = ( np.float32(self.X_train) - 127.5) / 127.5 self.X_train = np.expand_dims(self.X_train, axis=3) return
Example #30
Source File: mnist.py From keras-deepcv with MIT License | 5 votes |
def get_data(num_classes=10): """ Get the MNIST dataset. Will download dataset if first time and will be downloaded to ~/.keras/datasets/mnist.npz Parameters: None Returns: train_data - training data split train_labels - training labels test_data - test data split test_labels - test labels """ print('[INFO] Loading the MNIST dataset...') (train_data, train_labels), (test_data, test_labels) = mnist.load_data() # Reshape the data from (samples, height, width) to # (samples, height, width, depth) where depth is 1 channel (grayscale) train_data = train_data[:, :, :, np.newaxis] test_data = test_data[:, :, :, np.newaxis] # Normalize the data train_data = train_data / 255.0 test_data = test_data / 255.0 # Transform labels to one hot labels # Example: '0' will become [1, 0, 0, 0, 0, 0, 0, 0, 0] # '1' will become [0, 1, 0, 0, 0, 0, 0, 0, 0] # and so on... train_labels = np_utils.to_categorical(train_labels, num_classes) test_labels = np_utils.to_categorical(test_labels, num_classes) return train_data, train_labels, test_data, test_labels