Seq2Seq Fre-Eng Keras.py

import numpy as np
import tensorflow as tf
from tensorflow import keras
from keras.models import Model
from keras.layers import Input, LSTM, Dense

batch_size = 128
epochs = 100
latent_dim = 256
num_samples = 100000
data_path = './Translation Data/fra.txt'

input_texts = []
target_texts = []
input_characters = set()
target_characters = set()

with open(data_path, 'r', encoding='utf-8') as f:
    lines = f.read().split('\n')

for line in lines[:min(num_samples, len(lines) - 1)]:
    input_text, target_text, _ = line.split('\t')
    # print('input_text', input_text, 'target_text', target_text)
    target_text = '\t' + target_text + '\n'
    input_texts.append(input_text)
    target_texts.append(target_text)
    for char in input_text:
        if char not in input_characters:
            input_characters.add(char)
    for char in target_text:
        if char not in target_characters:
            target_characters.add(char)

input_characters = sorted(list(input_characters))
target_characters = sorted(list(target_characters))
num_encoder_tokens = len(input_characters)
num_decoder_tokens = len(target_characters)
max_encoder_seq_length = max([len(txt) for txt in input_texts])
max_decoder_seq_length = max([len(txt) for txt in target_texts])

print('Number of samples:', len(input_texts))
print('Number of unique input tokens:', num_encoder_tokens)
print('Number of unique target tokens:', num_decoder_tokens)
print('Max sequence length of inputs:', max_encoder_seq_length)
print('Max sequence length of outputs:', max_decoder_seq_length)

input_token_index = dict([
    (char, i) for i, char in enumerate(input_characters)
])

target_token_index = dict([
    (char, i) for i, char in enumerate(target_characters)
])

print(input_token_index)
print(target_token_index)

encoder_input_data = np.zeros((len(input_texts), max_encoder_seq_length, num_encoder_tokens), dtype='float32')
decoder_input_data = np.zeros((len(input_texts), max_decoder_seq_length, num_decoder_tokens), dtype='float32')
decoder_target_data = np.zeros((len(input_texts), max_decoder_seq_length, num_decoder_tokens), dtype='float32')

for i, (input_text, target_text) in enumerate(zip(input_texts, target_texts)):
    for t, char in enumerate(input_text):
        encoder_input_data[i, t, input_token_index[char]] = 1.
    encoder_input_data[i, t + 1:, input_token_index[' ']] = 1.
    for t, char in enumerate(target_text):
        decoder_input_data[i, t, target_token_index[char]] = 1.
        if t > 0:
            decoder_target_data[i, t - 1, target_token_index[char]] = 1.
    decoder_input_data[i, t + 1:, target_token_index[' ']] = 1.
    decoder_target_data[i, t:, target_token_index[' ']] = 1.


encoder_inputs = Input(shape=(None, num_encoder_tokens))
encoder = LSTM(latent_dim, return_state=True)
encoder_outputs, state_h, state_c = encoder(encoder_inputs)
encoder_states = [state_h, state_c]

decoder_inputs = Input(shape=(None, num_decoder_tokens))
decoder = LSTM(latent_dim, return_sequences=True, return_state=True)
decoder_outputs, _, _ = decoder(decoder_inputs, initial_state=encoder_states)
decoder_dense = Dense(num_decoder_tokens, activation='softmax')
decoder_outputs = decoder_dense(decoder_outputs)

model = Model([encoder_inputs, decoder_inputs], decoder_outputs)

model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])

checkpointer = tf.keras.callbacks.ModelCheckpoint('./Pretrained Model/Seq2Seq Fre-Eng Keras.h5', verbose=1, save_best_only=True)

earlystopping = tf.keras.callbacks.EarlyStopping(patience=5, monitor='val_loss')

model.fit([encoder_input_data, decoder_input_data], decoder_target_data,
          batch_size=batch_size, epochs=epochs, validation_split=0.2, callbacks=[checkpointer, earlystopping])