I am newbie in Tensorflow and I would like to train the SVHN model with just few inputs so the model will train-finish soon instead of waiting hours by using the full SVHN train dataset (train_32x32.mat). I would like to use few input images and scale up to see if the model is working properly. I was following the next script but it uses the full train/test dataset. All answers will be appreciate it, thanks.
def __init__(self, file_path, n_classes, use_extra=False, gray=False):
self.n_classes = n_classes
# # Load Train Set
train = sio.loadmat(file_path + "/train_32x32.mat")
self.train_labels = self.__one_hot_encode(train['y'])
self.train_examples = train['X'].shape
self.train_data = self.__store_data(train['X'].astype("float32"), self.train_examples, gray)
# Load Test Set
test = sio.loadmat("../res/test_32x32.mat")
self.test_labels = self.__one_hot_encode(test['y'])
self.test_examples = test['X'].shape
self.test_data = self.__store_data(test['X'].astype("float32"), self.test_examples, gray)
To sum up, I would like to use few inputs (around 1000) for training/testing and then scale up.