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The code doesn't seem to have anything clearly wrong, but filters of size (25,25) may be somewhat not good.

There are two possibilities:

• Train metrics are great, but test metrics are bad: your model is overfitting (it may be due to little data)
• Train metrics are not good: your model is not good enough

Subquestions:

1 - Yes, you're using filters that are windows sized (25,25) that slide along the input images. The bigger your filters, the less general they can be.

2 - The number 32 refers to how many output "channels" you want for this layer. While your input images have 3 channels, red layer, green layer and blue layer, these convolution layers will produce 32 different channels. The meaning of each channel is up to the hidden mathematics we can't see.

• The number of channels is totally independent from anything.
• The only restrictions are: input channels are 3, output classes are 7.

3 - It's normal to have "a lot" of convolutional layers, one over another. Some well known models have more than 10 convolutional layers.

• Why is it needed? Each convolutional layer is interpreting the results of the previous layer, and producing new results. It's more power to the model. One may be too few.

4 - Generators produce batches with shape (batch_size,image_side1, image_side2, channels).

• steps_per_epoch is necessary because the generators used are infinite (so keras doesn't know when to stop)
• Usually, one uses steps_per_epoch = total_images//batch_size, so one epoch will use exactly all images. But you can play with these numbers as you wish
• Usually, one epoch is one iteration through the entire dataset. (But with generators and steps_per_epoch, that is up to the user)

5 - The image data generator, besides loading data from your folders and making the classes for you, is also a tool for data augmentation.

• If you have too little data, your model will overfit (excellent train results, terrible test results).
• Machine learning needs tons of data to work well
• Data augmentation is a way of creating more data when you don't have enough
  • A shifted, flipped, elongated, etc. image, in the vision of a model, is totally new
  • A model can learn cats looking to the right and yet not learn cats looking to the left, for instance

I needed to change one line: