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I have a CNN built in Keras. I have saved it and am now using the model.predict() function to make predictions from it. Whenever I run the following code,

def prediction(path):
    import keras
    from keras.preprocessing.image import load_img, img_to_array 
    from keras.models import load_model
    import PIL
    import numpy as np
    
    img = load_img(path)
    img = img.resize((224, 224))
    img = img_to_array(img) 

    img = img.reshape( -1,224, 224,3)

    model = load_model('model1.h5')
    pred = model.predict(img)

    return pred

print(prediction('/path/to/image/')

I get an output like this:

[[7.578206e-37 1.000000e+00 0.000000e+00 0.000000e+00]]

I am doing transfer learning using resnet50 with imagenet weights and here is the model.summary().

I have 4 classes. How do I find out where each prediction belongs?

I have looked here as well but it doesn't seem to help me.

Thanks

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  • $\begingroup$ In your last dense layer, I show that dense layer output shape 4. But you mention that you have 2 classes. $\endgroup$ – AIFahim Feb 20 at 17:02
  • $\begingroup$ Oops, I was doing 2 classes earlier, then 4. The 2 just stuck to my mind. Sorry for that, I'll edit it. $\endgroup$ – Amay Agarwal Feb 21 at 6:27
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Model prediction output is a bunch of probabilities. In order to get category name you need use following snippet. It calculates the argmax of predicions and give it to CLASSES list:

print(CLASSES[np.argmax(predictions)])
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Keras included in their library to predict the class label. You can get the class label directly by using model.predict_classes(img).
Ref: https://datascience.stackexchange.com/a/40415/109134

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