ya variance should be close but validation loss should be greater and it is less than train loss, following is code for loading and splitting dataset
import os

import numpy as np
from pandas.io.parsers import read_csv
from sklearn.utils import shuffle
FTRAIN = 'all/training/training.csv'
FTEST = 'all/test/test.csv'

def load(test=False, cols=None):
    """Loads data from FTEST if *test* is True, otherwise from FTRAIN.
    Pass a list of *cols* if you're only interested in a subset of the
    target columns.
    """
    fname = FTEST if test else FTRAIN
    df = read_csv(os.path.expanduser(fname))  # load pandas dataframe

    # The Image column has pixel values separated by space; convert
    # the values to numpy arrays:
    df['Image'] = df['Image'].apply(lambda im: np.fromstring(im, sep=' '))

    if cols:  # get a subset of columns
        df = df[list(cols) + ['Image']]

    print(df.count())  # prints the number of values for each column
    df = df.dropna()  # drop all rows that have missing values in them

    X = np.vstack(df['Image'].values) / 255.  # scale pixel values to [0, 1]
    X = X.astype(np.float32)

    if not test:  # only FTRAIN has any target columns
        y = df[df.columns[:-1]].values
        y = (y - 48) / 48  # scale target coordinates to [-1, 1]
        X, y = shuffle(X, y, random_state=42)  # shuffle train data
        y = y.astype(np.float32)
    else:
        y = None

    return X, y


X, y = load()
print("X.shape == {}; X.min == {:.3f}; X.max == {:.3f}".format(
    X.shape, X.min(), X.max()))
print("y.shape == {}; y.min == {:.3f}; y.max == {:.3f}".format(
    y.shape, y.min(), y.max()))
import numpy as np
from sklearn.model_selection import train_test_split
X_train, X_valid, y_train, y_valid = train_test_split( X, y, test_size=0.2, random_state=42)


Blockquote