In this section, we'll compare our models in the font classification problem. First, we should remind ourselves what the data looks like. Then, we'll inspect the simple logistic dense neural network and convolutional neural network models. You've come a long way in modeling with TensorFlow.
Before we move on from deep learning, however, let's look back and see how models compare on the font classification problem. First, let's look at the data again, so we don't lose sight of the problem. In fact, let's look at one image that includes all the letters and digits from every font, just to see what shapes we have:
# One look at a letter/digit from each font
# Best to reshape as one large array, then plot
all_letters = np.zeros([5*36,62*36])
for font in range(5):
for letter in range(62):
all_letters[font*36:(font+1)*36,
letter*36:(letter+1)*36] = \
train[9*(font*62 + letter)]
