I'm trying to concatenate 2 numpy arrays of features predicted by the convolution layers in a vgg16 model.
Basically i have used the bottom layers of a vgg16 model to predict the features for my full dataset and now I want to load the parts of dataset dynamically based on some settings from a dict, to train some models with it.
So, I have 2 array of shape:
(724, 512, 6, 8) and (3376, 512, 6, 8)
Basically the first one contains features predicted from 724 image files (each prediction has shape (512, 6, 8)).
I want to concatenate these 2 arrays into one of shape (4100, 512, 6, 8)
I have tried using:
np.array([np.concatenate(arr, axis=0) for arr in false_train_list])
where false_train_list is the list containing the 2 arrays with the above shapes.
Also tried with np.stack, tf.stack, etc...
All of these result in an array with shape (2,)
Can someone explain why ? I haven't found any good resources to understand how exactly np.concatenate() works..