girishp92/human-activity-recognition-using-recurrent-neural-nets-rnn-lstm-and-tensorflow-on-smartphones

This was my Master's project where i was involved using a dataset from Wireless Sensor Data Mining Lab (WISDM) to build a machine learning model to predict basic human activities using a smartphone accelerometer, Using Tensorflow framework, recurrent neural nets and multiple stacks of Long-short-term memory units(LSTM) for building a deep network. After the model was trained, it was saved and exported to an android application and the predictions were made using the model and the interface to speak out the results using text-to-speech API.

human-activity-recognition-using-recurrent-neural-nets-rnn-lstm-and-tensorflow-on-smartphones's Issues

the problem of training set and testing set

Hello, in the process of iterative training, I see that the testing set is also being iterated. Do you use the testing set as validation set, or do the iterative testing set have other meanings? Thank you.
The relevant code is as follows：
`for i in range(1, N_EPOCHS + 1):
for start, end in zip(range(0, train_count, BATCH_SIZE),
range(BATCH_SIZE, train_count + 1, BATCH_SIZE)):
sess.run(optimizer, feed_dict={X:X_train[start:end],
Y:y_train[start:end]})

_, acc_train, loss_train = sess.run([pred_softmax, accuracy, loss], feed_dict={
    X: X_train, Y:y_train})
_, acc_test, loss_test = sess.run([pred_softmax, accuracy, loss], feed_dict={
    X: X_test, Y:y_test})`