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utils/load_data.py

+# Copyright 2017 Bert Moons
+
+# This file is part of QNN.
+
+# QNN is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+
+# QNN is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+
+# The code for QNN is based on BinaryNet: https://github.com/MatthieuCourbariaux/BinaryNet
+
+# You should have received a copy of the GNU General Public License
+# along with QNN.  If not, see <http://www.gnu.org/licenses/>.
+
+import numpy as np
+from my_datasets import my_cifar10
+from my_datasets import my_mnist
+
+
+def load_dataset(dataset):
+    if (dataset == "CIFAR-10"):
+
+        print('Loading CIFAR-10 dataset...')
+
+        #train_set_size = 45000
+        #train_set = cifar10(which_set="train", start=0, stop=train_set_size)
+        #valid_set = cifar10(which_set="train", start=train_set_size, stop=50000)
+        #test_set = cifar10(which_set="test")
+        (train_set_X,train_set_Y),(valid_set_X,valid_set_Y) = my_cifar10.load_data()
+
+        train_set_X = np.transpose(np.reshape(np.subtract(np.multiply(2. / 255., train_set_X), 1.), (-1, 3, 32, 32)),(0,2,3,1))
+        valid_set_X = np.transpose(np.reshape(np.subtract(np.multiply(2. / 255., valid_set_X), 1.), (-1, 3, 32, 32)),(0,2,3,1))
+        #test_set.X = np.transpose(np.reshape(np.subtract(np.multiply(2. / 255., test_set.X), 1.), (-1, 3, 32, 32)),(0,2,3,1))
+   
+        # flatten targets
+        train_set_Y = np.hstack(train_set_Y)
+        valid_set_Y = np.hstack(valid_set_Y)
+        #test_set.y = np.hstack(test_set.y)
+
+        # Onehot the targets
+        train_set_Y = np.float32(np.eye(10)[train_set_Y])
+        valid_set_Y = np.float32(np.eye(10)[valid_set_Y])
+        #test_set.y = np.float32(np.eye(10)[test_set.y])
+
+        # for hinge loss
+        train_set_Y = 2 * train_set_Y - 1.
+        valid_set_Y = 2 * valid_set_Y - 1.
+        #test_set.y = 2 * test_set.y - 1.
+        
+        # enlarge train data set by mirrroring
+        x_train_flip = train_set_X[:, :, ::-1, :]
+        y_train_flip = train_set_Y
+        train_set_X = np.concatenate((train_set_X, x_train_flip), axis=0)
+        train_set_Y = np.concatenate((train_set_Y, y_train_flip), axis=0)
+
+    elif (dataset == "MNIST"):
+
+        print('Loading MNIST dataset...')
+
+        #train_set_size = 50000
+        #train_set = mnist(which_set="train", start=0, stop=train_set_size)
+        #valid_set = mnist(which_set="train", start=train_set_size, stop=60000)
+        #test_set = mnist(which_set="test")
+        path_to_file = '../my_datasets/mnist.npz'
+        (train_set_X,train_set_Y),(valid_set_X,valid_set_Y) = my_mnist.load_data(path_to_file)
+
+        train_set_X = np.transpose(np.reshape(np.subtract(np.multiply(2. / 255., train_set_X), 1.), (-1, 1, 28, 28)),(0,2,3,1))
+        valid_set_X = np.transpose(np.reshape(np.subtract(np.multiply(2. / 255., valid_set_X), 1.), (-1, 1,  28, 28)),(0,2,3,1))
+        #test_set.X = np.transpose(np.reshape(np.subtract(np.multiply(2. / 255., test_set.X), 1.), (-1, 1,  28, 28)),(0,2,3,1))
+        
+        # flatten targets
+        train_set_Y = np.hstack(train_set_Y)
+        valid_set_Y = np.hstack(valid_set_Y)
+        #test_set.y = np.hstack(test_set.y)
+
+        # Onehot the targets
+        train_set_Y = np.float32(np.eye(10)[train_set_Y])
+        valid_set_Y = np.float32(np.eye(10)[valid_set_Y])
+        #test_set.y = np.float32(np.eye(10)[test_set.y])
+
+        # for hinge loss
+        train_set_Y = 2 * train_set_Y - 1.
+        valid_set_Y = 2 * valid_set_Y - 1.
+        #test_set.y = 2 * test_set.y - 1.
+        
+        # enlarge train data set by mirrroring
+        x_train_flip = train_set_X[:, :, ::-1, :]
+        y_train_flip = train_set_Y
+        train_set_X = np.concatenate((train_set_X, x_train_flip), axis=0)
+        train_set_Y = np.concatenate((train_set_Y, y_train_flip), axis=0)
+
+
+
+
+    else:
+        print("wrong dataset given")
+
+    train_set = (train_set_X,train_set_Y)
+    valid_set = (valid_set_X,valid_set_Y)
+    return train_set, valid_set