mnist_split_into_train_val.py

# shuffle the training dataset (5 times!)
for _ in range(5): 
    indexes = np.random.permutation(len(train_data))

train_data = train_data[indexes]
train_labels_cat = train_labels_cat[indexes]

# now set-aside 10% of the train_data/labels as the
# cross-validation sets
val_perc = 0.10
val_count = int(val_perc * len(train_data))

# first pick validation set from train_data/labels
val_data = train_data[:val_count,:]
val_labels_cat = train_labels_cat[:val_count,:]

# leave rest in training set
train_data2 = train_data[val_count:,:]
train_labels_cat2 = train_labels_cat[val_count:,:]

# NOTE: We will train on train_data2/train_labels_cat2 and 
# cross-validate on val_data/val_labels_cat