Hands-On Deep Learning Algorithms with Python

Making predictions using the LSTM model

Now we will start making predictions on the test set:

predicted_output = []i = 0while i+batch_size <= len(X_test):       output = session.run([y_hat],feed_dict={input:X_test[i:i+batch_size]})    i += batch_size    predicted_output.append(output)

Print the predicted output:

predicted_output[0]

We will get the result as shown:

[[array([[-0.60426176]], dtype=float32),
  array([[-0.60155034]], dtype=float32),
  array([[-0.60079575]], dtype=float32),
  array([[-0.599668]], dtype=float32),
  array([[-0.5991149]], dtype=float32),
  array([[-0.6008351]], dtype=float32),
  array([[-0.5970466]], dtype=float32)]]

As you can see, the values of test predictions are in a nested list, so we will flatten them:

predicted_values_test = [] ...