generate.py

import numpy as np
import pickle
import tqdm
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, LSTM, Dropout, Activation
import os

sequence_length = 100
# dataset file path
FILE_PATH = "data/wonderland.txt"
# FILE_PATH = "data/python_code.py"
BASENAME = os.path.basename(FILE_PATH)
# load vocab dictionaries
char2int = pickle.load(open(f"{BASENAME}-char2int.pickle", "rb"))
int2char = pickle.load(open(f"{BASENAME}-int2char.pickle", "rb"))

sequence_length = 100
vocab_size = len(char2int)

# building the model
model = Sequential([
    LSTM(256, input_shape=(sequence_length, vocab_size), return_sequences=True),
    Dropout(0.3),
    LSTM(256),
    Dense(vocab_size, activation="softmax"),
])

# load the optimal weights
model.load_weights(f"results/{BASENAME}-{sequence_length}.h5")
# specify the feed to first characters to generate
seed = "alice is pretty"
s = seed
n_chars = 400
# generate 400 characters
generated = ""
for i in tqdm.tqdm(range(n_chars), "Generating text"):
    # make the input sequence
    X = np.zeros((1, sequence_length, vocab_size))
    for t, char in enumerate(seed):
        X[0, (sequence_length - len(seed)) + t, char2int[char]] = 1
    # predict the next character
    predicted = model.predict(X, verbose=0)[0]
    # converting the vector to an integer
    next_index = np.argmax(predicted)
    # converting the integer to a character
    next_char = int2char[next_index]
    # add the character to results
    generated += next_char
    # shift seed and the predicted character
    seed = seed[1:] + next_char

print("Seed:", s)
print("Generated text:")
print(generated)