I’ve created a multi-class image classifier using CNN. I am using the keras module specifically and I am using generators to fit and then predict 4 different classes of images. My test_generator has 394 examples (all four classes combined), but my model.predict yields (6304, 4) predictions.
Here’s the model summary:
Model: "sequential_2"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
IP (Conv2D) (None, 64, 64, 32) 320
Convolution0 (Conv2D) (None, 64, 64, 64) 18496
PL0 (MaxPooling2D) (None, 32, 32, 64) 0
Convolution1 (Conv2D) (None, 32, 32, 128) 73856
PL1 (MaxPooling2D) (None, 16, 16, 128) 0
Convolution2 (Conv2D) (None, 16, 16, 256) 295168
PL2 (MaxPooling2D) (None, 8, 8, 256) 0
FL (Flatten) (None, 16384) 0
FC (Dense) (None, 128) 2097280
OP (Dense) (None, 4) 516
=================================================================
Total params: 2,485,636
Trainable params: 2,485,636
Non-trainable params: 0
_________________________________________________________________
Here’s how I created the test_generator: test_generator = core_imageDataGenerator(test_directory) and the result of len(test_generator.classes) is 394.
Here’s how I made the predictions: predictions = model.predict(test_generator) and the result of predictions.shape is [6304, 4] and not [394, 4]. What could be the reason for this? Am I doing something wrong?
I am posting this here because I think this is a bug of some sort. Also , what are my options here because my next step is to create a classification report with a variety of metrics.