Get Class Labels from Keras Functional Model

Get order of class labels for Keras predict function

noobalert, to get the top 2 predictions, as you requested to the Matias Valdenegro 's question in the comments section, you can do the following code:

prediction1 = model.predict(your_data)
# sorting the predictions in descending order
sorting = (-prediction1).argsort()

# getting the top 2 predictions
sorted_ = sorting[0][:2]

for value in sorted_:
    # you can get your classes from the encoder(your_classes = encoder.classes_) 
    # or from a dictionary that you created before.
    # And then we access them with the predicted index.
    predicted_label = your_classes[value]

    # just some rounding steps
    prob = (prediction1[0][value]) * 100
    prob = "%.2f" % round(prob,2)
    print("I have %s%% sure that it belongs to %s." % (prob, predicted_label)

Keras: how to get predicted labels for more than two classes

For a multi-class classification problem with k labels, you can retrieve the index of the predicted classes by using model.predict_classes(). Toy example:

import keras
import numpy as np

# Simpel model, 3 output nodes
model = keras.Sequential()
model.add(keras.layers.Dense(3, input_shape=(10,), activation='softmax'))

# 10 random input data points
x = np.random.rand(10, 10)
model.predict_classes(x)
> array([1, 1, 2, 1, 2, 1, 2, 1, 1, 1])

If you have the labels in a list, you can use the predicted classes to get the predicted labels:

labels = ['label1', 'label2', 'label3']
[labels[i] for i in model.predict_classes(x)]
> ['label2', 'label2', 'label3', 'label2', 'label3', 'label2', 'label3', 'label2', 'label2', 'label2']

Under the hood, model.predict_classes return the index of the maximum predicted class probability for each row in the predictions:

model.predict_classes(x)
> array([1, 1, 2, 1, 2, 1, 2, 1, 1, 1])
model.predict(x).argmax(axis=-1) # same thing
> array([1, 1, 2, 1, 2, 1, 2, 1, 1, 1])

Attaching class labels to a Keras model

So I tried my hand at a solution myself and this seems to work. I was hoping for something simpler though.

Opening the model file a second time is not really optimal I think. If anyone can do better, by all means, do.

import h5py

from keras.models import load_model
from keras.models import save_model

def load_model_ext(filepath, custom_objects=None):
    model = load_model(filepath, custom_objects=None)
    f = h5py.File(filepath, mode='r')
    meta_data = None
    if 'my_meta_data' in f.attrs:
        meta_data = f.attrs.get('my_meta_data')
    f.close()
    return model, meta_data
   

def save_model_ext(model, filepath, overwrite=True, meta_data=None):
    save_model(model, filepath, overwrite)
    if meta_data is not None:
        f = h5py.File(filepath, mode='a')
        f.attrs['my_meta_data'] = meta_data
        f.close()

Since h5 files do not accept python containers, you should consider converting the meta data into a string. Assuming that your meta data exists in the form of a dictionary or a list, you can use json to do the conversion. This would also allow you to store more complex data structures within your model.

Full usage example:

import json
import keras

# prepare model and label lookup
model = keras.Sequential();
model.add(keras.layers.Dense(10, input_dim=8, activation='relu'));
model.add(keras.layers.Dense(3, activation='softmax'))
model.compile()

filepath = r".\mymodel.h5"

labels = ["dog", "cat", "automobile"]

# save
labels_string = json.dumps(labels)
save_model_ext(model, filepath, meta_data=labels_string)

# load
loaded_model, loaded_labels_string = load_model_ext(filepath)
loaded_labels = json.loads(loaded_labels_string)

# label of class 0: "dog"
print(loaded_labels[0])

If you prefer to have a dictionary for your classes, be aware that json will convert numeric dictionary keys to strings, so you will have to convert them back to numbers after loading.

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