Training and classifying

We are now going to build a neural network that will take an image as input and try to predict which (single) letter is in the image.

We will use the training set of single letters we created earlier. The dataset itself is quite simple. We have a 20-by-20-pixel image, each pixel 1 (black) or 0 (white). These represent the 400 features that we will use as inputs into the neural network. The outputs will be 26 values between 0 and 1, where higher values indicate a higher likelihood that the associated letter (the first neuron is A, the second is B, and so on) is the letter represented by the input image.

We are going to use the scikit-learn's MLPClassifier for our neural network in this chapter.

Note

You will need a recent version of scikit-learn to use MLPClassifier. If the below import statement fails, try again after updating scikit-learn. You can do this using the following Anaconda command:  conda update scikit-learn

As for other scikit-learn classifiers, we import...