Machine-Learning-Collection/ML/Projects/text_generation_babynames/generating_names.py

"""
Text generation using a character LSTM, specifically we want to
generate new names as inspiration for those having a baby :) 

Although this is for name generation, the code is general in the
way that you can just send in any large text file (shakespear text, etc)
and it will generate it.

Programmed by Aladdin Persson <aladdin.persson at hotmail dot com>
*    2020-05-09 Initial coding

"""

import torch
import torch.nn as nn
import string
import random
import sys
import unidecode

# Device configuration
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Get characters from string.printable
all_characters = string.printable
n_characters = len(all_characters)

# Read large text file (Note can be any text file: not limited to just names)
file = unidecode.unidecode(open("data/names.txt").read())


class RNN(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, output_size):
        super(RNN, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers

        self.embed = nn.Embedding(input_size, hidden_size)
        self.lstm = nn.LSTM(hidden_size, hidden_size, num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_size, output_size)

    def forward(self, x, hidden, cell):
        out = self.embed(x)
        out, (hidden, cell) = self.lstm(out.unsqueeze(1), (hidden, cell))
        out = self.fc(out.reshape(out.shape[0], -1))
        return out, (hidden, cell)

    def init_hidden(self, batch_size):
        hidden = torch.zeros(self.num_layers, batch_size, self.hidden_size).to(device)
        cell = torch.zeros(self.num_layers, batch_size, self.hidden_size).to(device)
        return hidden, cell


class Generator:
    def __init__(self):
        self.chunk_len = 250
        self.num_epochs = 5000
        self.batch_size = 1
        self.print_every = 50
        self.hidden_size = 256
        self.num_layers = 2
        self.lr = 0.003

    def char_tensor(self, string):
        tensor = torch.zeros(len(string)).long()
        for c in range(len(string)):
            tensor[c] = all_characters.index(string[c])
        return tensor

    def get_random_batch(self):
        start_idx = random.randint(0, len(file) - self.chunk_len)
        end_idx = start_idx + self.chunk_len + 1
        text_str = file[start_idx:end_idx]
        text_input = torch.zeros(self.batch_size, self.chunk_len)
        text_target = torch.zeros(self.batch_size, self.chunk_len)

        for i in range(self.batch_size):
            text_input[i, :] = self.char_tensor(text_str[:-1])
            text_target[i, :] = self.char_tensor(text_str[1:])

        return text_input.long(), text_target.long()

    def generate(self, initial_str="A", predict_len=100, temperature=0.85):
        hidden, cell = self.rnn.init_hidden(batch_size=self.batch_size)
        initial_input = self.char_tensor(initial_str)
        predicted = initial_str

        for p in range(len(initial_str) - 1):
            _, (hidden, cell) = self.rnn(
                initial_input[p].view(1).to(device), hidden, cell
            )

        last_char = initial_input[-1]

        for p in range(predict_len):
            output, (hidden, cell) = self.rnn(
                last_char.view(1).to(device), hidden, cell
            )
            output_dist = output.data.view(-1).div(temperature).exp()
            top_char = torch.multinomial(output_dist, 1)[0]
            predicted_char = all_characters[top_char]
            predicted += predicted_char
            last_char = self.char_tensor(predicted_char)

        return predicted

    # input_size, hidden_size, num_layers, output_size
    def train(self):
        self.rnn = RNN(
            n_characters, self.hidden_size, self.num_layers, n_characters
        ).to(device)

        optimizer = torch.optim.Adam(self.rnn.parameters(), lr=self.lr)
        criterion = nn.CrossEntropyLoss()
        writer = SummaryWriter(f"runs/names0")  # for tensorboard

        print("=> Starting training")

        for epoch in range(1, self.num_epochs + 1):
            inp, target = self.get_random_batch()
            hidden, cell = self.rnn.init_hidden(batch_size=self.batch_size)

            self.rnn.zero_grad()
            loss = 0
            inp = inp.to(device)
            target = target.to(device)

            for c in range(self.chunk_len):
                output, (hidden, cell) = self.rnn(inp[:, c], hidden, cell)
                loss += criterion(output, target[:, c])

            loss.backward()
            optimizer.step()
            loss = loss.item() / self.chunk_len

            if epoch % self.print_every == 0:
                print(f"Loss: {loss}")
                print(self.generate())

            writer.add_scalar("Training loss", loss, global_step=epoch)


gennames = Generator()
gennames.train()