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YasuThompson/translator_wp_7.ipynb Secret

Created March 17, 2021 14:49
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translator_wp_7.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"import time\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.ticker as ticker\n",
"from sklearn.model_selection import train_test_split\n",
"import unicodedata\n",
"import re\n",
"import os\n",
"import io\n",
"import time\n",
"from bpemb import BPEmb"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"bpemb_de = BPEmb(lang='de', vs=10000, dim=100)\n",
"bpemb_en = BPEmb(lang='en', vs=10000, dim=100)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"path_to_file = \"./datasets/deu.txt\"\n",
"\n",
"lines = io.open(path_to_file, encoding='UTF-8').read().strip().split('\\n')\n",
"\n",
"temp_list = []\n",
"corpus = []\n",
"\n",
"for i in range(len(lines)):\n",
" temp_list = lines[i].split('\\t')[:-1]\n",
" corpus.append(temp_list)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"en, de = np.array(corpus).T\n",
"\n",
"en_encoded = []\n",
"de_encoded = []\n",
"\n",
"cnt_en = 0\n",
"cnt_de = 0\n",
"\n",
"for i in range(len(en)):\n",
" en_encoded_temp = bpemb_en.encode_ids(en[i])\n",
" de_encoded_temp = bpemb_de.encode_ids(de[i])\n",
"\n",
" if (len(en_encoded_temp)<=40) and (len(de_encoded_temp)<=40):\n",
" en_encoded.append([10000] + en_encoded_temp + [10001])\n",
" de_encoded.append([10000] + de_encoded_temp + [10001])\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"en_padded = tf.keras.preprocessing.sequence.pad_sequences(en_encoded, padding='post')\n",
"de_padded = tf.keras.preprocessing.sequence.pad_sequences(de_encoded, padding='post')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def create_padding_mask(seq):\n",
" seq = tf.cast(tf.math.equal(seq, 0), tf.float32)\n",
"\n",
" # add extra dimensions to add the padding\n",
" # to the attention logits.\n",
" return seq[:, tf.newaxis, tf.newaxis, :] # (batch_size, 1, 1, seq_len)\n",
"\n",
"def create_look_ahead_mask(size):\n",
" mask = 1 - tf.linalg.band_part(tf.ones((size, size)), -1, 0)\n",
" return mask # (seq_len, seq_len)\n",
"\n",
"def create_masks(inp, tar):\n",
" # Encoder padding mask\n",
" enc_padding_mask = create_padding_mask(inp)\n",
"\n",
" # Used in the 2nd attention block in the decoder.\n",
" # This padding mask is used to mask the encoder outputs.\n",
" dec_padding_mask = create_padding_mask(inp)\n",
"\n",
" # Used in the 1st attention block in the decoder.\n",
" # It is used to pad and mask future tokens in the input received by \n",
" # the decoder.\n",
" look_ahead_mask = create_look_ahead_mask(tf.shape(tar)[1])\n",
" dec_target_padding_mask = create_padding_mask(tar)\n",
" combined_mask = tf.maximum(dec_target_padding_mask, look_ahead_mask)\n",
"\n",
" return enc_padding_mask, combined_mask, dec_padding_mask\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"en_sample_batch, de_sample_batch = np.stack((en_padded[0], en_padded[-1])), np.stack((de_padded[0], de_padded[-1]))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[10000, 4766, 9935, 10001, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0],\n",
" [10000, 3077, 2557, 9915, 1682, 9940, 2468, 2259, 344,\n",
" 10, 2161, 148, 9937, 105, 4001, 9940, 2377, 148,\n",
" 1715, 713, 443, 200, 2229, 8612, 9940, 369, 178,\n",
" 4416, 89, 28, 2648, 9935, 10001, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0]], dtype=int32)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"de_sample_batch"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"enc_padding_mask, combined_mask, dec_padding_mask = create_masks(en_sample_batch, de_sample_batch)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(TensorShape([2, 1, 1, 41]), TensorShape([2, 1, 1, 41]))"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Importantly enc_padding_mask and dec_padding_mask have the same shape. \n",
"enc_padding_mask.shape, dec_padding_mask.shape"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"\n",
"\n",
" [[[0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]\n",
" [0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]\n",
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]
}
],
"source": [
"# When the input target sentences in the batch are like above, \n",
"# the resulting look ahead mask looks like stairs. \n",
"# You can see that the number the steps of the \"stairs\" depends \n",
"# on the number of non-zero elements of the inputs. \n",
"tf.print(combined_mask, summarize=-1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"collapsed_sections": [
"s_qNSzzyaCbD"
],
"name": "transformer.ipynb",
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
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