Hello,
I made two models for the multi-class classification of images. The first model consists of 3( Conv2D → Maxpooling2d) to a flattened layer, followed by two dense layers.
the second model - I use inceptiveV3 as my base model then I pass the output of the ‘mixed7’ layer into a flattened layer and then into two dense layers.
base_model = InceptionV3(input_shape = ( 150 , 150 , 3), include_top = False)
base_model.trainable = False
there are about 14k image shapes (150, 150, 3) images in the training set and about 3k images in the validation set.
I have two big issues with these models:
1: they both take so long to run about 2-4 min per epoch. I run for 20- 25 epochs
- for the second model both the training and validation loss values change significantly from one run to another like 0.7 points or more. so every time I train the model I get different loss values.
any idea how I can shorten the training time and stabilize the loss values for the second model?