drcn updated version

dataset.py

@@ -0,0 +1,58 @@
+import gzip
+
+import cPickle as pickle
+import numpy as np
+
+def load_mnist32x32():
+    # dataset='I:\Data\PhD Life\Tutorial\Python\data\mnist.pkl.gz' # linux
+    # dataset='/u/students/gif/Desktop/PhD/Tutorial/dataset/MNIST/mnist32x32.pkl.gz' # linux
+    dataset = '/local/scratch/gif/dataset/MNIST/mnist32x32.pkl.gz' #the-villa
+    # dataset = 'I:\Data\PhD Life\Tutorial\dataset\MNIST\mnist32x32.pkl.gz' # laptop
+    f = gzip.open(dataset,'rb')
+    train_set, valid_set, test_set = pickle.load(f)
+    f.close()
+
+    test_set_x, test_set_y = test_set
+    valid_set_x, valid_set_y = valid_set
+    train_set_x, train_set_y = train_set
+
+
+    train_set_x = train_set_x.reshape(train_set_x.shape[0], 1, 32, 32)
+    valid_set_x = valid_set_x.reshape(valid_set_x.shape[0], 1, 32, 32)
+    test_set_x = test_set_x.reshape(test_set_x.shape[0], 1, 32, 32)
+
+    return (train_set_x.astype('float32'), train_set_y.astype('uint8')), (valid_set_x.astype('float32'), valid_set_y.astype('uint8')), (test_set_x.astype('float32'), test_set_y.astype('uint8'))
+
+def load_usps():
+    # dataset = 'I:\Data\PhD Life\Tutorial\dataset\USPS\usps.pkl.gz' #windows
+    dataset='/u/students/gif/Desktop/PhD/Tutorial/dataset/USPS/usps.pkl.gz' # linux
+    f = gzip.open(dataset,'rb')
+    train_set, test_set = pickle.load(f)
+    f.close()
+
+    train_set_x, train_set_y = train_set
+    test_set_x, test_set_y = test_set
+
+
+
+    train_set_x = train_set_x.reshape(train_set_x.shape[0], 1, 28, 28).astype('float32')
+    test_set_x = test_set_x.reshape(test_set_x.shape[0], 1, 28, 28).astype('float32')
+
+    rval = (train_set_x, train_set_y), (test_set_x, test_set_y)
+    return rval
+
+def load_svhn():
+    # dataset = '/u/students/gif/Desktop/PhD/Tutorial/dataset/SVHN/svhn_gray.pkl.gz' #linux
+    dataset = '/local/scratch/gif/dataset/SVHN/svhn_gray.pkl.gz' #the-villa
+    # dataset = 'I:\Data\PhD Life\Tutorial\dataset\SVHN\svhn_gray.pkl.gz' # laptop
+    f = gzip.open(dataset,'rb')
+    (X_train, y_train), (X_test, y_test) = pickle.load(f)
+    f.close()
+
+    idx10 = np.where(y_train == 10)
+    y_train[idx10] = 0
+
+    idx10 = np.where(y_test == 10)
+    y_test[idx10] = 0
+
+    return (X_train.astype('float32'), y_train.astype('uint8')), (X_test.astype('float32'), y_test.astype('uint8'))