Split tensor into training and test sets

As elham mentioned, you can use scikit-learn to do this easily. scikit-learn is an open source library for machine learning. There are tons of tools for data preparation including the model_selection module, which handles comparing, validating and choosing parameters.

The model_selection.train_test_split() method is specifically designed to split your data into train and test sets randomly and by percentage.

X_train, X_test, y_train, y_test = train_test_split(features,
                                                    labels,
                                                    test_size=0.33,
                                                    random_state=42)

test_size is the percentage to reserve for testing and random_state is to seed the random sampling.

I typically use this to provide train and validation data sets, and keep true test data separately. You could just run train_test_split twice to do this as well. I.e. split the data into (Train + Validation) and Test, then split Train + Validation into two separate tensors.

import sklearn.model_selection as sk

X_train, X_test, y_train, y_test = 
sk.train_test_split(features,labels,test_size=0.33, random_state = 42)

Something like the following should work: tf.split_v(tf.random_shuffle(...

Edit: For tensorflow>0.12 This should now be called as tf.split(tf.random.shuffle(...

Reference

See docs for tf.split and for tf.random.shuffle for examples.