tfjs-examples/translation/python/translation.py at master · tensorflow/tfjs-examples

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# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

# http://www.apache.org/licenses/LICENSE-2.0

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

# =============================================================================

"""Train a simple LSTM model for character-level language translation.

This is based on the Keras example at:

https://github.com/keras-team/keras/blob/master/examples/lstm_seq2seq.py

The training data can be downloaded with a command like the following example:

wget http://www.manythings.org/anki/fra-eng.zip

"""

from __future__ import absolute_import

from __future__ import division

from __future__ import print_function

import argparse

import io

import json

import os

from tensorflow.keras.models import Model

from tensorflow.keras.layers import Input, LSTM, Dense

import numpy as np

import tensorflowjs as tfjs

def read_data():

# Vectorize the data.

input_texts = []

target_texts = []

input_characters = set()

target_characters = set()

lines = io.open(FLAGS.data_path, 'r', encoding='utf-8').read().split('\n')

for line in lines[: min(FLAGS.num_samples, len(lines) - 1)]:

input_text, target_text, *_ = line.split('\t')

# We use "tab" as the "start sequence" character for the targets, and "\n"

# as "end sequence" character.

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 output tokens:', num_decoder_tokens)

print('Max sequence length for inputs:', max_encoder_seq_length)

print('Max sequence length for 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)])

# Save the token indices to file.

metadata_json_path = os.path.join(

FLAGS.artifacts_dir, 'metadata.json')

if not os.path.isdir(os.path.dirname(metadata_json_path)):

os.makedirs(os.path.dirname(metadata_json_path))

with io.open(metadata_json_path, 'w', encoding='utf-8') as f:

metadata = {

'input_token_index': input_token_index,

'target_token_index': target_token_index,

'max_encoder_seq_length': max_encoder_seq_length,

'max_decoder_seq_length': max_decoder_seq_length

}

f.write(json.dumps(metadata, ensure_ascii=False))

print('Saved metadata at: %s' % metadata_json_path)

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.

for t, char in enumerate(target_text):

# decoder_target_data is ahead of decoder_input_data by one timestep

decoder_input_data[i, t, target_token_index[char]] = 1.

if t > 0:

# decoder_target_data will be ahead by one timestep

# and will not include the start character.

decoder_target_data[i, t - 1, target_token_index[char]] = 1.

return (input_texts, max_encoder_seq_length, max_decoder_seq_length,

num_encoder_tokens, num_decoder_tokens,

input_token_index, target_token_index,

encoder_input_data, decoder_input_data, decoder_target_data)

def seq2seq_model(num_encoder_tokens, num_decoder_tokens, latent_dim):

"""Create a Keras model for the seq2seq translation.

Args:

num_encoder_tokens: Total number of distinct tokens in the inputs

to the encoder.

num_decoder_tokens: Total number of distinct tokens in the outputs

to/from the decoder

latent_dim: Number of latent dimensions in the LSTMs.

Returns:

encoder_inputs: Instance of `keras.Input`, symbolic tensor as input to

the encoder LSTM.

encoder_states: Instance of `keras.Input`, symbolic tensor for output

states (h and c) from the encoder LSTM.

decoder_inputs: Instance of `keras.Input`, symbolic tensor as input to

the decoder LSTM.

decoder_lstm: `keras.Layer` instance, the decoder LSTM.

decoder_dense: `keras.Layer` instance, the Dense layer in the decoder.

model: `keras.Model` instance, the entire translation model that can be

used in training.

"""

# Define an input sequence and process it.

encoder_inputs = Input(shape=(None, num_encoder_tokens))

encoder = LSTM(latent_dim,

return_state=True)

_, state_h, state_c = encoder(encoder_inputs)

# We discard `encoder_outputs` and only keep the states.

encoder_states = [state_h, state_c]

# Set up the decoder, using `encoder_states` as initial state.

decoder_inputs = Input(shape=(None, num_decoder_tokens))

# We set up our decoder to return full output sequences,

# and to return internal states as well. We don't use the

# return states in the training model, but we will use them in inference.

decoder_lstm = LSTM(FLAGS.latent_dim,

return_sequences=True,

return_state=True)

decoder_outputs, _, _ = decoder_lstm(decoder_inputs,

initial_state=encoder_states)

decoder_dense = Dense(num_decoder_tokens, activation='softmax')

decoder_outputs = decoder_dense(decoder_outputs)

# Define the model that will turn

# `encoder_input_data` & `decoder_input_data` into `decoder_target_data`

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

return (encoder_inputs, encoder_states, decoder_inputs, decoder_lstm,

decoder_dense, model)

def decode_sequence(input_seq,

encoder_model,

decoder_model,

num_decoder_tokens,

target_begin_index,

reverse_target_char_index,

max_decoder_seq_length):

"""Decode (i.e., translate) an encoded sentence.

Args:

input_seq: A `numpy.ndarray` of shape

`(1, max_encoder_seq_length, num_encoder_tokens)`.

encoder_model: A `keras.Model` instance for the encoder.

decoder_model: A `keras.Model` instance for the decoder.

num_decoder_tokens: Number of unique tokens for the decoder.

target_begin_index: An `int`: the index for the beginning token of the

decoder.

reverse_target_char_index: A lookup table for the target characters, i.e.,

a map from `int` index to target character.

max_decoder_seq_length: Maximum allowed sequence length output by the

decoder.

Returns:

The result of the decoding (i.e., translation) as a string.

"""

# Encode the input as state vectors.

states_value = encoder_model.predict(input_seq)

# Generate empty target sequence of length 1.

target_seq = np.zeros((1, 1, num_decoder_tokens))

# Populate the first character of target sequence with the start character.

target_seq[0, 0, target_begin_index] = 1.

# Sampling loop for a batch of sequences

# (to simplify, here we assume a batch of size 1).

stop_condition = False

decoded_sentence = ''

while not stop_condition:

output_tokens, h, c = decoder_model.predict(

[target_seq] + states_value)

# Sample a token

sampled_token_index = np.argmax(output_tokens[0, -1, :])

sampled_char = reverse_target_char_index[sampled_token_index]

decoded_sentence += sampled_char

# Exit condition: either hit max length

# or find stop character.

if (sampled_char == '\n' or

len(decoded_sentence) > max_decoder_seq_length):

stop_condition = True

# Update the target sequence (of length 1).

target_seq = np.zeros((1, 1, num_decoder_tokens))

target_seq[0, 0, sampled_token_index] = 1.

# Update states

states_value = [h, c]

return decoded_sentence

def main():

(input_texts, _, max_decoder_seq_length,

num_encoder_tokens, num_decoder_tokens,

__, target_token_index,

encoder_input_data, decoder_input_data, decoder_target_data) = read_data()

(encoder_inputs, encoder_states, decoder_inputs, decoder_lstm,

decoder_dense, model) = seq2seq_model(

num_encoder_tokens, num_decoder_tokens, FLAGS.latent_dim)

# Run training.

model.compile(optimizer='rmsprop', loss='categorical_crossentropy')

model.fit([encoder_input_data, decoder_input_data], decoder_target_data,

batch_size=FLAGS.batch_size,

epochs=FLAGS.epochs,

validation_split=0.2)

tfjs.converters.save_keras_model(model, FLAGS.artifacts_dir)

# Next: inference mode (sampling).

# Here's the drill:

# 1) encode input and retrieve initial decoder state

# 2) run one step of decoder with this initial state

# and a "start of sequence" token as target.

# Output will be the next target token

# 3) Repeat with the current target token and current states

# Define sampling models

encoder_model = Model(encoder_inputs, encoder_states)

decoder_state_input_h = Input(shape=(FLAGS.latent_dim,))

decoder_state_input_c = Input(shape=(FLAGS.latent_dim,))

decoder_states_inputs = [decoder_state_input_h, decoder_state_input_c]

decoder_outputs, state_h, state_c = decoder_lstm(

decoder_inputs, initial_state=decoder_states_inputs)

decoder_states = [state_h, state_c]

decoder_outputs = decoder_dense(decoder_outputs)

decoder_model = Model(

[decoder_inputs] + decoder_states_inputs,

[decoder_outputs] + decoder_states)

# Reverse-lookup token index to decode sequences back to

# something readable.

reverse_target_char_index = dict(

(i, char) for char, i in target_token_index.items())

target_begin_index = target_token_index['\t']

for seq_index in range(FLAGS.num_test_sentences):

# Take one sequence (part of the training set)

# for trying out decoding.

input_seq = encoder_input_data[seq_index: seq_index + 1]

# Get expected output

target_seq = decoder_target_data[seq_index]

# One-hot to index

target_seq = [

reverse_target_char_index[np.argmax(c)] for c in target_seq

]

# Array to string

target_seq = ''.join(target_seq).replace('\n', '')

decoded_sentence = decode_sequence(

input_seq, encoder_model, decoder_model, num_decoder_tokens,

target_begin_index, reverse_target_char_index, max_decoder_seq_length)

print('-')

print('Input sentence:', input_texts[seq_index])

print('Target sentence:', target_seq)

print('Decoded sentence:', decoded_sentence)

if __name__ == '__main__':

parser = argparse.ArgumentParser(

'Keras seq2seq translation model training and serialization')

parser.add_argument(

'data_path',

type=str,

help='Path to the training data, e.g., ~/ml-data/fra-eng/fra.txt')

parser.add_argument(

'--batch_size',

type=int,

default=64,

help='Training batch size.')

parser.add_argument(

'--epochs',

type=int,

default=20,

help='Number of training epochs.')

parser.add_argument(

'--latent_dim',

type=int,

default=256,

help='Latent dimensionality of the encoding space.')

parser.add_argument(

'--num_samples',

type=int,

default=10000,

help='Number of samples to train on.')

parser.add_argument(

'--num_test_sentences',

type=int,

default=100,

help='Number of example sentences to test at the end of the training.')

parser.add_argument(

'--artifacts_dir',

type=str,

default='/tmp/translation.keras',

help='Local path for saving the TensorFlow.js artifacts.')

FLAGS, _ = parser.parse_known_args()

main()

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