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  3. Image Processing with Keras in Python

Exercise

Convolutional network for image classification

Convolutional networks for classification are constructed from a sequence of convolutional layers (for image processing) and fully connected (Dense) layers (for readout). In this exercise, you will construct a small convolutional network for classification of the data from the fashion dataset.

Instructions

100 XP
  • Add a Conv2D layer to construct the input layer of the network. Use a kernel size of 3 by 3. You can use the img_rows and img_cols objects available in your workspace to define the input_shape of this layer.
  • Add a Flatten layer to translate between the image processing and classification part of your network.
  • Add a Dense layer to classify the 3 different categories of clothing in the dataset.