Saving extracted features in CNN - machine-learning

Question

I've just started learning machine learning algorithms. I would like to train VGG-16 network for my own dataset. I am using tflearn.DNN to simulate the VGG net.
I want to save the output (which is a tensor) of fully connected layer, that extracts 4096 features, into a file. I wanted to know how to save these features.
When i ran the following lines:
feed_dict = feed_dict_builder(X, Y, model.inputs, model.targets)
output = model.predictor.evaluate(feed_dict, convnet1)
print(output)
output.save('features.npy')
I got the following exception and error:
Exception in thread Thread-48:
Traceback (most recent call last):
File "/home/anupama/anaconda3/lib/python3.6/threading.py", line 916, in _bootstrap_inner
self.run()
File "/home/anupama/anaconda3/lib/python3.6/threading.py", line 864, in run
self._target(*self._args, **self._kwargs)
File "/home/anupama/anaconda3/lib/python3.6/site-packages/tflearn/data_flow.py", line 187, in fill_feed_dict_queue
data = self.retrieve_data(batch_ids)
File "/home/anupama/anaconda3/lib/python3.6/site-packages/tflearn/data_flow.py", line 222, in retrieve_data
utils.slice_array(self.feed_dict[key], batch_ids)
File "/home/anupama/anaconda3/lib/python3.6/site-packages/tflearn/utils.py", line 180, in slice_array
return [x[start] for x in X]
File "/home/anupama/anaconda3/lib/python3.6/site-packages/tflearn/utils.py", line 180, in <listcomp>
return [x[start] for x in X]
IndexError: index 2 is out of bounds for axis 1 with size 2
[0.0]
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-23-f2d62c020964> in <module>()
4 output = model.predictor.evaluate(feed_dict, convnet1)
5 print(output)
----> 6 output.save('/home/anupama/Internship/feats')
AttributeError: 'list' object has no attribute 'save'

Answer 1

You should save the FC layer of the network as a separate tensor and use DNN.predictor to evaluate it. Sample code:
import tflearn
from tflearn.utils import feed_dict_builder
# VGG model definition
...
previous_layer = ...
fc_layer1 = tflearn.fully_connected(previous_layer, 4096, activation='relu', name='fc1')
fc_layer2 = tflearn.fully_connected(fc_layer1, 4096, activation='relu', name='fc2')
network = ...
# Training
model = tflearn.DNN(network)
model.fit(x, y)
# Evaluation
feed_dict = feed_dict_builder(x, y, model.inputs, model.targets)
output = model.predictor.evaluate(feed_dict, [fc_layer2])
np.save('features.npy', output)