Home How to reduce the CNN-SVHN train input to know if the model is going well instead of using the full train dataset and wait hours to see the result?
 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. class SVHN:  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[3] 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[3] 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. Thanks