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| # -*- coding: utf-8 -*- | |
| """translation.ipynb | |
| Automatically generated by Colaboratory. | |
| Original file is located at | |
| https://colab.research.google.com/drive/1PADMvkToYgpdhvQYlZw4q8O-gLvsvGmK | |
| """ | |
| import pathlib | |
| import random | |
| import string | |
| import re | |
| import numpy as np | |
| import tensorflow as tf | |
| from tensorflow import keras | |
| from tensorflow.keras import layers | |
| # googled fix to "cannot find TextVectorization" | |
| from tensorflow.keras.layers.experimental.preprocessing import TextVectorization | |
| import os | |
| import gdown | |
| from os.path import exists | |
| text_file = keras.utils.get_file( | |
| fname = "spa-eng.zip", | |
| origin = "http://storage.googleapis.com/download.tensorflow.org/data/spa-eng.zip", | |
| extract = True, | |
| ) | |
| text_file = pathlib.Path(text_file).parent / "spa-eng" / "spa.txt" | |
| # change: added utf-8 encoding | |
| with open(text_file, encoding="utf-8") as f: | |
| lines = f.read().split("\n")[:-1] | |
| text_pairs = [] | |
| for line in lines: | |
| eng, spa = line.split("\t") | |
| spa = "[start] " + spa + " [end]" | |
| text_pairs.append((eng, spa)) | |
| for _ in range(5): | |
| print(random.choice(text_pairs)) | |
| random.shuffle(text_pairs) | |
| num_val_samples = int(0.15 * len(text_pairs)) | |
| num_train_samples = len(text_pairs) - 2 * num_val_samples | |
| train_pairs = text_pairs[:num_train_samples] | |
| val_pairs = text_pairs[num_train_samples : num_train_samples + num_val_samples] | |
| test_pairs = text_pairs[num_train_samples + num_val_samples :] | |
| print(f"{len(text_pairs)} total pairs") | |
| print(f"{len(train_pairs)} training pairs") | |
| print(f"{len(val_pairs)} validation pairs") | |
| print(f"{len(test_pairs)} test pairs") | |
| strip_chars = string.punctuation + "¿" | |
| strip_chars = strip_chars.replace("[", "") | |
| strip_chars = strip_chars.replace("]", "") | |
| vocab_size = 15000 | |
| sequence_length = 20 | |
| batch_size = 64 | |
| def custom_standardization(input_string): | |
| lowercase = tf.strings.lower(input_string) | |
| return tf.strings.regex_replace(lowercase, "[%s]" % re.escape(strip_chars), "") | |
| eng_vectorization = TextVectorization( | |
| max_tokens=vocab_size, | |
| output_mode="int", | |
| output_sequence_length=sequence_length, | |
| ) | |
| spa_vectorization = TextVectorization( | |
| max_tokens=vocab_size, | |
| output_mode="int", | |
| output_sequence_length=sequence_length + 1, | |
| standardize=custom_standardization, | |
| ) | |
| train_eng_texts = [pair[0] for pair in train_pairs] | |
| train_spa_texts = [pair[1] for pair in train_pairs] | |
| eng_vectorization.adapt(train_eng_texts) | |
| spa_vectorization.adapt(train_spa_texts) | |
| def format_dataset(eng, spa): | |
| eng = eng_vectorization(eng) | |
| spa = spa_vectorization(spa) | |
| return ( | |
| { | |
| "encoder_inputs": eng, | |
| "decoder_inputs": spa[:, :-1], | |
| }, | |
| spa[:, 1:], | |
| ) | |
| def make_dataset(pairs): | |
| eng_texts, spa_texts = zip(*pairs) | |
| eng_texts = list(eng_texts) | |
| spa_texts = list(spa_texts) | |
| dataset = tf.data.Dataset.from_tensor_slices((eng_texts, spa_texts)) | |
| dataset = dataset.batch(batch_size) | |
| dataset = dataset.map(format_dataset) | |
| return dataset.shuffle(2048).prefetch(16).cache() | |
| train_ds = make_dataset(train_pairs) | |
| val_ds = make_dataset(val_pairs) | |
| for inputs, targets in train_ds.take(1): | |
| print(f'inputs["encoder_inputs"].shape: {inputs["encoder_inputs"].shape}') | |
| print(f'inputs["decoder_inputs"].shape: {inputs["decoder_inputs"].shape}') | |
| print(f"targets.shape: {targets.shape}") | |
| class TransformerEncoder(layers.Layer): | |
| def __init__(self, embed_dim, dense_dim, num_heads, **kwargs): | |
| super(TransformerEncoder, self).__init__(**kwargs) | |
| self.embed_dim = embed_dim | |
| self.dense_dim = dense_dim | |
| self.num_heads = num_heads | |
| self.attention = layers.MultiHeadAttention( | |
| num_heads=num_heads, key_dim=embed_dim | |
| ) | |
| self.dense_proj = keras.Sequential( | |
| [ | |
| layers.Dense(dense_dim, activation="relu"), | |
| layers.Dense(embed_dim), | |
| ] | |
| ) | |
| self.layernorm_1 = layers.LayerNormalization() | |
| self.layernorm_2 = layers.LayerNormalization() | |
| self.supports_masking = True | |
| def call(self, inputs, mask=None): | |
| if mask is not None: | |
| padding_mask = tf.cast(mask[:, tf.newaxis, tf.newaxis, :], dtype="int32") | |
| attention_output = self.attention( | |
| query=inputs, value=inputs, key=inputs, attention_mask=padding_mask | |
| ) | |
| proj_input = self.layernorm_1(inputs + attention_output) | |
| proj_output = self.dense_proj(proj_input) | |
| return self.layernorm_2(proj_input + proj_output) | |
| class PositionalEmbedding(layers.Layer): | |
| def __init__(self, sequence_length, vocab_size, embed_dim, **kwargs): | |
| super(PositionalEmbedding, self).__init__(**kwargs) | |
| self.token_embeddings = layers.Embedding( | |
| input_dim=vocab_size, output_dim=embed_dim | |
| ) | |
| self.position_embeddings = layers.Embedding( | |
| input_dim=sequence_length, output_dim=embed_dim | |
| ) | |
| self.sequence_length = sequence_length | |
| self.vocab_size = vocab_size | |
| self.embed_dim = embed_dim | |
| def call(self, inputs): | |
| length = tf.shape(inputs)[-1] | |
| positions = tf.range(start=0, limit=length, delta=1) | |
| embedded_tokens = self.token_embeddings(inputs) | |
| embedded_positions = self.position_embeddings(positions) | |
| return embedded_tokens + embedded_positions | |
| def compute_mask(self, inputs, mask=None): | |
| return tf.math.not_equal(inputs, 0) | |
| class TransformerDecoder(layers.Layer): | |
| def __init__(self, embed_dim, latent_dim, num_heads, **kwargs): | |
| super(TransformerDecoder, self).__init__(**kwargs) | |
| self.embed_dim = embed_dim | |
| self.latent_dim = latent_dim | |
| self.num_heads = num_heads | |
| self.attention_1 = layers.MultiHeadAttention( | |
| num_heads=num_heads, key_dim=embed_dim | |
| ) | |
| self.attention_2 = layers.MultiHeadAttention( | |
| num_heads=num_heads, key_dim=embed_dim | |
| ) | |
| self.dense_proj = keras.Sequential( | |
| [ | |
| layers.Dense(latent_dim, activation="relu"), | |
| layers.Dense(embed_dim), | |
| ] | |
| ) | |
| self.layernorm_1 = layers.LayerNormalization() | |
| self.layernorm_2 = layers.LayerNormalization() | |
| self.layernorm_3 = layers.LayerNormalization() | |
| self.supports_masking = True | |
| def call(self, inputs, encoder_outputs, mask=None): | |
| causal_mask = self.get_causal_attention_mask(inputs) | |
| if mask is not None: | |
| padding_mask = tf.cast(mask[:, tf.newaxis, :], dtype="int32") | |
| padding_mask = tf.minimum(padding_mask, causal_mask) | |
| attention_output_1 = self.attention_1( | |
| query=inputs, value=inputs, key=inputs, attention_mask=causal_mask | |
| ) | |
| out_1 = self.layernorm_1(inputs + attention_output_1) | |
| attention_output_2 = self.attention_2( | |
| query=out_1, | |
| value=encoder_outputs, | |
| key=encoder_outputs, | |
| attention_mask=padding_mask, | |
| ) | |
| out_2 = self.layernorm_2(out_1 + attention_output_2) | |
| proj_output = self.dense_proj(out_2) | |
| return self.layernorm_3(out_2 + proj_output) | |
| def get_causal_attention_mask(self, inputs): | |
| input_shape = tf.shape(inputs) | |
| batch_size, sequence_length = input_shape[0], input_shape[1] | |
| i = tf.range(sequence_length)[:, tf.newaxis] | |
| j = tf.range(sequence_length) | |
| mask = tf.cast(i >= j, dtype="int32") | |
| mask = tf.reshape(mask, (1, input_shape[1], input_shape[1])) | |
| mult = tf.concat( | |
| [tf.expand_dims(batch_size, -1), tf.constant([1, 1], dtype=tf.int32)], | |
| axis=0, | |
| ) | |
| return tf.tile(mask, mult) | |
| embed_dim = 256 | |
| latent_dim = 2048 | |
| num_heads = 8 | |
| encoder_inputs = keras.Input(shape=(None,), dtype="int64", name="encoder_inputs") | |
| x = PositionalEmbedding(sequence_length, vocab_size, embed_dim)(encoder_inputs) | |
| encoder_outputs = TransformerEncoder(embed_dim, latent_dim, num_heads)(x) | |
| encoder = keras.Model(encoder_inputs, encoder_outputs) | |
| decoder_inputs = keras.Input(shape=(None,), dtype="int64", name="decoder_inputs") | |
| encoded_seq_inputs = keras.Input(shape=(None, embed_dim), name="decoder_state_inputs") | |
| x = PositionalEmbedding(sequence_length, vocab_size, embed_dim)(decoder_inputs) | |
| x = TransformerDecoder(embed_dim, latent_dim, num_heads)(x, encoded_seq_inputs) | |
| x = layers.Dropout(0.5)(x) | |
| decoder_outputs = layers.Dense(vocab_size, activation="softmax")(x) | |
| decoder = keras.Model([decoder_inputs, encoded_seq_inputs], decoder_outputs) | |
| decoder_outputs = decoder([decoder_inputs, encoder_outputs]) | |
| transformer = keras.Model( | |
| [encoder_inputs, decoder_inputs], decoder_outputs, name="transformer" | |
| ) | |
| transformer.summary() | |
| # load weights using gdown | |
| print(os.listdir()) | |
| if not exists("./EngToSpanishckpts"): | |
| gdown.download_folder("https://drive.google.com/drive/folders/1DwN-MlL6MMh7qVJbwoLrWBSMVBN5zbBi?usp=sharing") | |
| transformer.load_weights("./EngToSpanishckpts/cp.ckpt") | |
| spa_vocab = spa_vectorization.get_vocabulary() | |
| spa_index_lookup = dict(zip(range(len(spa_vocab)), spa_vocab)) | |
| max_decoded_sentence_length = 20 | |
| def decode_sequence(input_sentence): | |
| tokenized_input_sentence = eng_vectorization([input_sentence]) | |
| decoded_sentence = "[start]" | |
| for i in range(max_decoded_sentence_length): | |
| tokenized_target_sentence = spa_vectorization([decoded_sentence])[:, :-1] | |
| predictions = transformer([tokenized_input_sentence, tokenized_target_sentence]) | |
| sampled_token_index = np.argmax(predictions[0, i, :]) | |
| sampled_token = spa_index_lookup[sampled_token_index] | |
| decoded_sentence += " " + sampled_token | |
| if sampled_token == "[end]": | |
| break | |
| return decoded_sentence | |
| transformer.compile( | |
| "rmsprop", loss="sparse_categorical_crossentropy" | |
| ) | |