Does my model make sense? It’s looking thicc but I don’t know

I’ve built my first model and I’ve not very experienced so I’m
unsure if it’s structured correctly.

I have the VGG16 model on top (frozen) and I connect this to a
dene layer that I train on categorical data (6 classes)

_________________________________________________________________ Model: "model" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_1 (InputLayer) [(None, 150, 150, 3)] 0 _________________________________________________________________ block1_conv1 (Conv2D) (None, 150, 150, 64) 1792 _________________________________________________________________ block1_conv2 (Conv2D) (None, 150, 150, 64) 36928 _________________________________________________________________ block1_pool (MaxPooling2D) (None, 75, 75, 64) 0 _________________________________________________________________ block2_conv1 (Conv2D) (None, 75, 75, 128) 73856 _________________________________________________________________ block2_conv2 (Conv2D) (None, 75, 75, 128) 147584 _________________________________________________________________ block2_pool (MaxPooling2D) (None, 37, 37, 128) 0 _________________________________________________________________ block3_conv1 (Conv2D) (None, 37, 37, 256) 295168 _________________________________________________________________ block3_conv2 (Conv2D) (None, 37, 37, 256) 590080 _________________________________________________________________ block3_conv3 (Conv2D) (None, 37, 37, 256) 590080 _________________________________________________________________ block3_pool (MaxPooling2D) (None, 18, 18, 256) 0 _________________________________________________________________ block4_conv1 (Conv2D) (None, 18, 18, 512) 1180160 _________________________________________________________________ block4_conv2 (Conv2D) (None, 18, 18, 512) 2359808 _________________________________________________________________ block4_conv3 (Conv2D) (None, 18, 18, 512) 2359808 _________________________________________________________________ block4_pool (MaxPooling2D) (None, 9, 9, 512) 0 _________________________________________________________________ block5_conv1 (Conv2D) (None, 9, 9, 512) 2359808 _________________________________________________________________ block5_conv2 (Conv2D) (None, 9, 9, 512) 2359808 _________________________________________________________________ block5_conv3 (Conv2D) (None, 9, 9, 512) 2359808 _________________________________________________________________ block5_pool (MaxPooling2D) (None, 4, 4, 512) 0 _________________________________________________________________ flatten (Flatten) (None, 8192) 0 _________________________________________________________________ dense (Dense) (None, 128) 1048704 _________________________________________________________________ dense_1 (Dense) (None, 6) 774 ================================================================= Total params: 15,764,166 Trainable params: 1,049,478 Non-trainable params: 14,714,688 _________________________________________________________________ 

I want to apply what the model has learnt thus far to a binary
classification problem. So, once trained on my categorical data, I
freeze `dense` and remove `dense_1`, then I add in `dense_2`,
`dense_3`, `dense_4` (the latter having 1 output).

continued from before.... block5_pool (MaxPooling2D) (None, 4, 4, 512) 0 _________________________________________________________________ flatten (Flatten) (None, 8192) 0 _________________________________________________________________ dense (Dense) (None, 128) 1048704 _________________________________________________________________ dense_2 (Dense) (None, 128) 16512 _________________________________________________________________ dense_3 (Dense) (None, 128) 16512 _________________________________________________________________ dense_4 (Dense) (None, 1) 129 ================================================================= Total params: 15,796,545 Trainable params: 33,153 Non-trainable params: 15,763,392 

Then I train it on my binary data (I have setup augmentation and
preprocessing, etc.)

Does this network make sense though? I don’t have the deep
understanding many people here do, so not really sure. Any input
would be appreciated.