How to convert images as input to a ML classifier?

Question

I want to build a image classifier i gathered images from web and i resized them using PIL libray now i want those images to be converted as input .what operations do i need to perform on these images.I also did covert images in to numpy arrays and stored them in an list named features and what to do next

Answer 1

Well there are a number of decisions to make. One is to partition your images into a training set, a validation set and generally also a test set. I typically use 10% of the images as a validation set and 10% of the images as a test set. Next you need to decide how you want to provide your images to the network. My preference is to use the Keras ImageDataGenerator.flow from directory. This requires you to create 3 directories to store images. I put the test images in a directory called 'test', the validation images in a directory called 'valid' and the training images in a directory called 'train'. Now in each of these directories you need to create identically named class directories. For example if you are trying to classify images of dogs and cats. You would create a 'dogs' sub directory and 'cats' sub directory within the test, train and valid directories. Be sure to name them identically because the names of the sub directories determine the names of your classes. Now populate the class directories with your images. These can be images in standard formats like jpg. Now create 3 generators a train generator, a validation generator and a test generator as in

train_gen=ImageDataGenerator(preprocessing_function=pre_process).flow_from_directory('train', target_size=(height, width), batch_size=train_batch_size, seed=rand_seed, class_mode='categorical', color_mode='rgb')

do the same for the validation generator and the test generator. Documentation for the ImageDataGenerator and flow_from_directory is here.. Now you have your images stored and the data generators set up to provide data to your model in batches based on batch size. So now we can get to actually building a model. You can build your own model however there are excellent models for image processing available for you to use. This is called transfer learning. I like to use a model called MobileNet. I prefer this because it has a small number of trainable parameters (about 4 million) versus other models which have 10's of millions. Keras has this and many other image processing models . Documentation is here. Now you have to modify the final layer of the model to adapt it to your application. MobileNet was trained on the ImageNet data set that had 1000 classes. You need to remove this last layer and make it a dense layer having as many nodes as you have classes and use the softmax activation function. An example for the case of 2 classes is shown below.

 mobile = tf.keras.applications.mobilenet.MobileNet( include_top=Top,
                                                           input_shape=(height,width,3),
                                                           pooling='avg', weights='imagenet',
                                                           alpha=1, depth_multiplier=1)
        x=mobile.layers[-2].output        
        predictions=Dense (2, activation='softmax')(x)
        model = Model(inputs=mobile.input, outputs=predictions)
        for layer in model.layers:
            layer.trainable=True
        model.compile(Adam(lr=.001, loss='categorical_crossentropy', metrics=['accuracy'])

The last line of code compiles your model using the Adam optimizer with a learning rate of .001.Now we can finally get to training the model. I use the modelfit generator as shown below:

data = model.fit_generator(generator = train_gen,validation_data=val_gen,  epochs=epochs, initial_epoch=start_epoch,
                               callbacks = callbacks, verbose=1)

Documentation for the above is here. The model will train on your training set and validate on the validation set. For each epoch (training cycle) you will get a print out of the training loss, training accuracy, validation loss and validation accuracy so you can monitor how your model is performing. The final step is to run your test set to see how well your model performs on data it was not trained on. Do do that use the code below:

resultspmodel.evaluate(test_gen, verbose=0)
print('Model accuracy on Test Set is {0:7.2f} %'.format(results[1]* 100)

That's about it but of course there are a lot of details to fill in. If you are new to Convolutional Neural Networks and machine learning I would recommend an excellent tutorial on YouTube at here. There are about 20 sequential tutorials in the play list. I used this tutorial as a beginner and found it excellent. It will cover all the topics you need to become skilled at using CNN classifiers. Good Luck!