VGG-M | Delving Deep into Convolutional Nets | Machine Learning library

So basically I am trying to use a pre-trained VGG CNN model. I have downloaded model from the following website:

http://www.vlfeat.org/matconvnet/pretrained/

which has given me a image-vgg-m-2048.mat file. But the guide gives me how to use it in Matlab using MatconvNet Library. I want to implement the same thing in python. For which I am using Keras.

I have written the following code:

I am using a keras based vgg model as an image classifier. From this webpage:

https://machinelearningmastery.com/use-pre-trained-vgg-model-classify-objects-photographs/

But I am interested to find out the parent category of a prediction. For example, if the model predicts a dog, I would like to know its parent category which is animal. I do not know if there is a way to use the Imagenet tree for this problem?

I'm trying to visualize important regions for a classification task with CNN.

I'm using VGG16 + my own top layers (A global average pooling layer and a Dense layer)

I'm using transfer learning method to use per-trained VGG19 model in Keras according to [this tutorial](https://towardsdatascience.com/keras-transfer-learning-for-beginners-6c9b8b7143e ). It shows how to train the model but NOT how to prepare test images for the predictions.

In the comments section it says:

Get an image, preprocess the image using the same preprocess_image function, and call model.predict(image). This will give you the prediction of the model on that image. Using argmax(prediction), you can find the class to which the image belongs.

I can not find a function named preprocess_image used in the code. I did some searches and thought of using the method proposed by this tutorial.

But this give an error saying:

I am failing at training a VGG NET model from scratch. Please allow me to describe my steps so far:

• From two folders contaning my training and validation images, labeled as training_list.txt and validation_list.txt, generated data.mdb files. After inspection these mdb files show valid img data and correct labels.

• Generated a mean_training_image.binaryproto file.

• Uploaded the lmdb dataset to Floydhub cloud, to train it using GPU, with

  floyd run --gpu --env caffe:py2 --data patalanov/datasets/vgg-my-face:input 'caffe train -solver models/Custom_Model/solver.prototxt'

• Downloaded file _iter_3000.caffemodel.

This is what my net prints: