Keras Deep Learning Cookbook

How to do it…

1. Create a character table that can handle encoding and decoding. This class has three methods, as follows:
   • __init__()
   • encode()
   • decode()

2. The code is as follows:

class CharTable(object):
    def __init__(self, char):
        self.char = sorted(set(char))
        self.char_indices = dict((ch, i) for i, ch in enumerate(self.char))
        self.indices_char = dict((i, ch) for i, ch in enumerate(self.char))

    def encode(self, C, num_rows):
        x = np.zeros((num_rows, len(self.char)))
        for i, ch in enumerate(C):
            x[i, self.char_indices[ch]] = 1
        return x
    def decode(self, x, calc_argmax=True):
        if calc_argmax:
            x = x.argmax(axis=-1)
        return ''.join(self.indices_char[x] for x in x)

3. Next, we define some utility constants to be used to display the final test results as ...