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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Machine Translation Project (English to French)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Fetching 30 files: 100%|██████████| 30/30 [00:00<?, ?it/s]\n"
]
},
{
"ename": "OSError",
"evalue": "SavedModel file does not exist at: C:\\Users\\prajw\\.cache\\huggingface\\hub\\models--prajwath--NullClass_Task-1\\snapshots\\db56a5e525e47ee9bde30fd099bea336bba3908f\\{saved_model.pbtxt|saved_model.pb}",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mOSError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[6], line 3\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mhuggingface_hub\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m from_pretrained_keras\n\u001b[1;32m----> 3\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mfrom_pretrained_keras\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mprajwath/NullClass_Task-1\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
"File \u001b[1;32mc:\\Users\\prajw\\anaconda3\\envs\\TestNullClass\\lib\\site-packages\\huggingface_hub\\keras_mixin.py:293\u001b[0m, in \u001b[0;36mfrom_pretrained_keras\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 240\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfrom_pretrained_keras\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mKerasModelHubMixin\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m 241\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 242\u001b[0m \u001b[38;5;124;03m Instantiate a pretrained Keras model from a pre-trained model from the Hub.\u001b[39;00m\n\u001b[0;32m 243\u001b[0m \u001b[38;5;124;03m The model is expected to be in `SavedModel` format.\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 291\u001b[0m \u001b[38;5;124;03m </Tip>\u001b[39;00m\n\u001b[0;32m 292\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 293\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m KerasModelHubMixin\u001b[38;5;241m.\u001b[39mfrom_pretrained(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
"File \u001b[1;32mc:\\Users\\prajw\\anaconda3\\envs\\TestNullClass\\lib\\site-packages\\huggingface_hub\\utils\\_validators.py:114\u001b[0m, in \u001b[0;36mvalidate_hf_hub_args.<locals>._inner_fn\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 111\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m check_use_auth_token:\n\u001b[0;32m 112\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m smoothly_deprecate_use_auth_token(fn_name\u001b[38;5;241m=\u001b[39mfn\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, has_token\u001b[38;5;241m=\u001b[39mhas_token, kwargs\u001b[38;5;241m=\u001b[39mkwargs)\n\u001b[1;32m--> 114\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
"File \u001b[1;32mc:\\Users\\prajw\\anaconda3\\envs\\TestNullClass\\lib\\site-packages\\huggingface_hub\\hub_mixin.py:558\u001b[0m, in \u001b[0;36mModelHubMixin.from_pretrained\u001b[1;34m(cls, pretrained_model_name_or_path, force_download, resume_download, proxies, token, cache_dir, local_files_only, revision, **model_kwargs)\u001b[0m\n\u001b[0;32m 555\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_hub_mixin_inject_config \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mconfig\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m model_kwargs:\n\u001b[0;32m 556\u001b[0m model_kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mconfig\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m config\n\u001b[1;32m--> 558\u001b[0m instance \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_from_pretrained(\n\u001b[0;32m 559\u001b[0m model_id\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mstr\u001b[39m(model_id),\n\u001b[0;32m 560\u001b[0m revision\u001b[38;5;241m=\u001b[39mrevision,\n\u001b[0;32m 561\u001b[0m cache_dir\u001b[38;5;241m=\u001b[39mcache_dir,\n\u001b[0;32m 562\u001b[0m force_download\u001b[38;5;241m=\u001b[39mforce_download,\n\u001b[0;32m 563\u001b[0m proxies\u001b[38;5;241m=\u001b[39mproxies,\n\u001b[0;32m 564\u001b[0m resume_download\u001b[38;5;241m=\u001b[39mresume_download,\n\u001b[0;32m 565\u001b[0m local_files_only\u001b[38;5;241m=\u001b[39mlocal_files_only,\n\u001b[0;32m 566\u001b[0m token\u001b[38;5;241m=\u001b[39mtoken,\n\u001b[0;32m 567\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mmodel_kwargs,\n\u001b[0;32m 568\u001b[0m )\n\u001b[0;32m 570\u001b[0m \u001b[38;5;66;03m# Implicitly set the config as instance attribute if not already set by the class\u001b[39;00m\n\u001b[0;32m 571\u001b[0m \u001b[38;5;66;03m# This way `config` will be available when calling `save_pretrained` or `push_to_hub`.\u001b[39;00m\n\u001b[0;32m 572\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m config \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m (\u001b[38;5;28mgetattr\u001b[39m(instance, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_hub_mixin_config\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;129;01min\u001b[39;00m (\u001b[38;5;28;01mNone\u001b[39;00m, {})):\n",
"File \u001b[1;32mc:\\Users\\prajw\\anaconda3\\envs\\TestNullClass\\lib\\site-packages\\huggingface_hub\\keras_mixin.py:494\u001b[0m, in \u001b[0;36mKerasModelHubMixin._from_pretrained\u001b[1;34m(cls, model_id, revision, cache_dir, force_download, proxies, resume_download, local_files_only, token, config, **model_kwargs)\u001b[0m\n\u001b[0;32m 491\u001b[0m storage_folder \u001b[38;5;241m=\u001b[39m model_id\n\u001b[0;32m 493\u001b[0m \u001b[38;5;66;03m# TODO: change this in a future PR. We are not returning a KerasModelHubMixin instance here...\u001b[39;00m\n\u001b[1;32m--> 494\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mkeras\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodels\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstorage_folder\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 496\u001b[0m \u001b[38;5;66;03m# For now, we add a new attribute, config, to store the config loaded from the hub/a local dir.\u001b[39;00m\n\u001b[0;32m 497\u001b[0m model\u001b[38;5;241m.\u001b[39mconfig \u001b[38;5;241m=\u001b[39m config\n",
"File \u001b[1;32mc:\\Users\\prajw\\anaconda3\\envs\\TestNullClass\\lib\\site-packages\\keras\\utils\\traceback_utils.py:70\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 67\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[0;32m 68\u001b[0m \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[0;32m 69\u001b[0m \u001b[38;5;66;03m# `tf.debugging.disable_traceback_filtering()`\u001b[39;00m\n\u001b[1;32m---> 70\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 71\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m 72\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
"File \u001b[1;32mc:\\Users\\prajw\\anaconda3\\envs\\TestNullClass\\lib\\site-packages\\tensorflow\\python\\saved_model\\loader_impl.py:115\u001b[0m, in \u001b[0;36mparse_saved_model\u001b[1;34m(export_dir)\u001b[0m\n\u001b[0;32m 113\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mIOError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot parse file \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpath_to_pbtxt\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mstr\u001b[39m(e)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 114\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 115\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mIOError\u001b[39;00m(\n\u001b[0;32m 116\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSavedModel file does not exist at: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mexport_dir\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mos\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39msep\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 117\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m{{\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mconstants\u001b[38;5;241m.\u001b[39mSAVED_MODEL_FILENAME_PBTXT\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m|\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 118\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconstants\u001b[38;5;241m.\u001b[39mSAVED_MODEL_FILENAME_PB\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m}}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
"\u001b[1;31mOSError\u001b[0m: SavedModel file does not exist at: C:\\Users\\prajw\\.cache\\huggingface\\hub\\models--prajwath--NullClass_Task-1\\snapshots\\db56a5e525e47ee9bde30fd099bea336bba3908f\\{saved_model.pbtxt|saved_model.pb}"
]
}
],
"source": [
"from huggingface_hub import from_pretrained_keras\n",
"\n",
"model = from_pretrained_keras(\"prajwath/NullClass_Task-1\")\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting huggingface_hub\n",
" Using cached huggingface_hub-0.23.4-py3-none-any.whl.metadata (12 kB)\n",
"Collecting filelock (from huggingface_hub)\n",
" Using cached filelock-3.15.4-py3-none-any.whl.metadata (2.9 kB)\n",
"Collecting fsspec>=2023.5.0 (from huggingface_hub)\n",
" Using cached fsspec-2024.6.1-py3-none-any.whl.metadata (11 kB)\n",
"Requirement already satisfied: packaging>=20.9 in c:\\users\\prajw\\anaconda3\\envs\\testnullclass\\lib\\site-packages (from huggingface_hub) (24.0)\n",
"Collecting pyyaml>=5.1 (from huggingface_hub)\n",
" Using cached PyYAML-6.0.1-cp39-cp39-win_amd64.whl.metadata (2.1 kB)\n",
"Requirement already satisfied: requests in c:\\users\\prajw\\anaconda3\\envs\\testnullclass\\lib\\site-packages (from huggingface_hub) (2.32.3)\n",
"Collecting tqdm>=4.42.1 (from huggingface_hub)\n",
" Using cached tqdm-4.66.4-py3-none-any.whl.metadata (57 kB)\n",
"Requirement already satisfied: typing-extensions>=3.7.4.3 in c:\\users\\prajw\\anaconda3\\envs\\testnullclass\\lib\\site-packages (from huggingface_hub) (4.12.2)\n",
"Requirement already satisfied: colorama in c:\\users\\prajw\\anaconda3\\envs\\testnullclass\\lib\\site-packages (from tqdm>=4.42.1->huggingface_hub) (0.4.6)\n",
"Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\prajw\\anaconda3\\envs\\testnullclass\\lib\\site-packages (from requests->huggingface_hub) (3.3.2)\n",
"Requirement already satisfied: idna<4,>=2.5 in c:\\users\\prajw\\anaconda3\\envs\\testnullclass\\lib\\site-packages (from requests->huggingface_hub) (2.10)\n",
"Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\prajw\\anaconda3\\envs\\testnullclass\\lib\\site-packages (from requests->huggingface_hub) (2.2.1)\n",
"Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\prajw\\anaconda3\\envs\\testnullclass\\lib\\site-packages (from requests->huggingface_hub) (2024.6.2)\n",
"Downloading huggingface_hub-0.23.4-py3-none-any.whl (402 kB)\n",
" ---------------------------------------- 0.0/402.6 kB ? eta -:--:--\n",
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"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"pip install huggingface_hub\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import collections\n",
"import numpy as np\n",
"import json\n",
"\n",
"from keras.preprocessing.text import Tokenizer\n",
"from keras.utils import pad_sequences\n",
"from keras.models import Model, Sequential\n",
"from keras.layers import Input, Dense, Embedding, GRU, LSTM, Bidirectional, Dropout, Activation, TimeDistributed, RepeatVector\n",
"from keras.optimizers import Adam\n",
"from keras.losses import sparse_categorical_crossentropy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Verify access to the GPU"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[name: \"/device:CPU:0\"\n",
"device_type: \"CPU\"\n",
"memory_limit: 268435456\n",
"locality {\n",
"}\n",
"incarnation: 753238729468120299\n",
"xla_global_id: -1\n",
", name: \"/device:GPU:0\"\n",
"device_type: \"GPU\"\n",
"memory_limit: 1733715559\n",
"locality {\n",
" bus_id: 1\n",
" links {\n",
" }\n",
"}\n",
"incarnation: 17911561745832575813\n",
"physical_device_desc: \"device: 0, name: NVIDIA GeForce RTX 2050, pci bus id: 0000:01:00.0, compute capability: 8.6\"\n",
"xla_global_id: 416903419\n",
"]\n"
]
}
],
"source": [
"from tensorflow.python.client import device_lib\n",
"print(device_lib.list_local_devices())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Dataset\n",
"For our machine translation project, we opt for a dataset featuring a limited vocabulary, specifically designed to facilitate a more manageable and efficient training process. Unlike the extensive [WMT](http://www.statmt.org/) datasets, our chosen dataset ensures a quicker training time and demands fewer computational resources. This strategic decision aims to balance the learning experience while still achieving meaningful results within practical time constraints.\n",
"### Load Data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def load_data(path):\n",
" input_file = path\n",
" with open(input_file, \"r\") as f:\n",
" data = f.read()\n",
" return data.split('\\n')\n",
"\n",
"english_sentences = load_data('data/english')\n",
"french_sentences = load_data('data/french')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Sample Data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['new jersey is sometimes quiet during autumn , and it is snowy in april .',\n",
" 'the united states is usually chilly during july , and it is usually freezing in november .',\n",
" 'california is usually quiet during march , and it is usually hot in june .',\n",
" 'the united states is sometimes mild during june , and it is cold in september .',\n",
" 'your least liked fruit is the grape , but my least liked is the apple .']"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"english_sentences[:5]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By examining the sentences, it's apparent that they have undergone preprocessing: punctuation has been delimited with spaces, and all the text has been converted to lowercase. This preprocessing serves a crucial purpose in text preparation. Firstly, delimiting punctuation with spaces ensures that each punctuation mark is treated as a separate token, aiding the model in understanding sentence structure. Secondly, converting the entire text to lowercase standardizes the input, preventing the model from distinguishing between words solely based on their casing. This uniformity facilitates more effective training and generalization, enhancing the model's ability to grasp patterns and generate accurate translations."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Structure of the Dataset"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1823250 English words.\n",
"227 unique English words.\n",
"10 Most common words in the English dataset:\n",
"\"is\" \",\" \".\" \"in\" \"it\" \"during\" \"the\" \"but\" \"and\" \"sometimes\"\n",
"\n",
"1961295 French words.\n",
"355 unique French words.\n",
"10 Most common words in the French dataset:\n",
"\"est\" \".\" \",\" \"en\" \"il\" \"les\" \"mais\" \"et\" \"la\" \"parfois\"\n"
]
}
],
"source": [
"english_words_counter = collections.Counter([word for sentence in english_sentences for word in sentence.split()])\n",
"french_words_counter = collections.Counter([word for sentence in french_sentences for word in sentence.split()])\n",
"\n",
"print('{} English words.'.format(len([word for sentence in english_sentences for word in sentence.split()])))\n",
"print('{} unique English words.'.format(len(english_words_counter)))\n",
"print('10 Most common words in the English dataset:')\n",
"print('\"' + '\" \"'.join(list(zip(*english_words_counter.most_common(10)))[0]) + '\"')\n",
"\n",
"print()\n",
"print('{} French words.'.format(len([word for sentence in french_sentences for word in sentence.split()])))\n",
"print('{} unique French words.'.format(len(french_words_counter)))\n",
"print('10 Most common words in the French dataset:')\n",
"print('\"' + '\" \"'.join(list(zip(*french_words_counter.most_common(10)))[0]) + '\"')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Preprocess\n",
"1. Tokenize the words into ids\n",
"2. Add padding to make all the sequences the same length."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'the': 1, 'quick': 2, 'a': 3, 'brown': 4, 'fox': 5, 'jumps': 6, 'over': 7, 'lazy': 8, 'dog': 9, 'by': 10, 'jove': 11, 'my': 12, 'study': 13, 'of': 14, 'lexicography': 15, 'won': 16, 'prize': 17, 'this': 18, 'is': 19, 'short': 20, 'sentence': 21}\n",
"\n",
"Sequence 1 in x\n",
" Input: The quick brown fox jumps over the lazy dog .\n",
" Output: [1, 2, 4, 5, 6, 7, 1, 8, 9]\n",
"Sequence 2 in x\n",
" Input: By Jove , my quick study of lexicography won a prize .\n",
" Output: [10, 11, 12, 2, 13, 14, 15, 16, 3, 17]\n",
"Sequence 3 in x\n",
" Input: This is a short sentence .\n",
" Output: [18, 19, 3, 20, 21]\n"
]
}
],
"source": [
"def tokenize(x):\n",
" tokenizer = Tokenizer()\n",
" tokenizer.fit_on_texts(x)\n",
" return tokenizer.texts_to_sequences(x), tokenizer\n",
"\n",
"text_sentences = [\n",
" 'The quick brown fox jumps over the lazy dog .',\n",
" 'By Jove , my quick study of lexicography won a prize .',\n",
" 'This is a short sentence .']\n",
"\n",
"text_tokenized, text_tokenizer = tokenize(text_sentences)\n",
"print(text_tokenizer.word_index)\n",
"print()\n",
"for sample_i, (sent, token_sent) in enumerate(zip(text_sentences, text_tokenized)):\n",
" print('Sequence {} in x'.format(sample_i + 1))\n",
" print(' Input: {}'.format(sent))\n",
" print(' Output: {}'.format(token_sent))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sequence 1 in x\n",
" Input: [1 2 4 5 6 7 1 8 9]\n",
" Output: [1 2 4 5 6 7 1 8 9 0]\n",
"Sequence 2 in x\n",
" Input: [10 11 12 2 13 14 15 16 3 17]\n",
" Output: [10 11 12 2 13 14 15 16 3 17]\n",
"Sequence 3 in x\n",
" Input: [18 19 3 20 21]\n",
" Output: [18 19 3 20 21 0 0 0 0 0]\n"
]
}
],
"source": [
"def pad(x, length=None):\n",
" if length is None:\n",
" length = max([len(sentence) for sentence in x])\n",
" return pad_sequences(x, maxlen=length, padding='post')\n",
"\n",
"test_pad = pad(text_tokenized)\n",
"for sample_i, (token_sent, pad_sent) in enumerate(zip(text_tokenized, test_pad)):\n",
" print('Sequence {} in x'.format(sample_i + 1))\n",
" print(' Input: {}'.format(np.array(token_sent)))\n",
" print(' Output: {}'.format(pad_sent))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data Preprocessed\n",
"Max English sentence length: 15\n",
"Max French sentence length: 21\n",
"English vocabulary size: 199\n",
"French vocabulary size: 344\n"
]
}
],
"source": [
"def preprocess(x,y):\n",
" preprocess_x, x_tk = tokenize(x)\n",
" preprocess_y, y_tk = tokenize(y)\n",
" \n",
" preprocess_x = pad(preprocess_x)\n",
" preprocess_y = pad(preprocess_y)\n",
" \n",
" preprocess_y = preprocess_y.reshape(*preprocess_y.shape, 1)\n",
" \n",
" return preprocess_x, preprocess_y, x_tk, y_tk\n",
"\n",
"preproc_english_sentences, preproc_french_sentences, english_tokenizer, french_tokenizer = preprocess(english_sentences, french_sentences)\n",
"\n",
"max_english_sequence_length = preproc_english_sentences.shape[1]\n",
"max_french_sequence_length = preproc_french_sentences.shape[1]\n",
"english_vocab_size = len(english_tokenizer.word_index)\n",
"french_vocab_size = len(french_tokenizer.word_index)\n",
"\n",
"print('Data Preprocessed')\n",
"print(\"Max English sentence length:\", max_english_sequence_length)\n",
"print(\"Max French sentence length:\", max_french_sequence_length)\n",
"print(\"English vocabulary size:\", english_vocab_size)\n",
"print(\"French vocabulary size:\", french_vocab_size)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Models\n",
"- Model 1 is a simple RNN\n",
"- Model 2 is a Bidirectional RNN\n",
"- Model 3 is an Embedding RNN\n",
"\n",
"### Ids Back to Text\n",
"The neural network will be translating the input to words ids, which isn't the final form we want. We want the French translation. The function `logits_to_text` will bridge the gab between the logits from the neural network to the French translation. You'll be using this function to better understand the output of the neural network."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"def logits_to_text(logits, tokenizer):\n",
" index_to_words = {id: word for word, id in tokenizer.word_index.items()}\n",
" index_to_words[0] = '<PAD>'\n",
" \n",
" return ' '.join([index_to_words[prediction] for prediction in np.argmax(logits, 1)])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Model 1: RNN\n",
"\n",
"A basic RNN model is a good baseline for sequence data. In this model, you'll build a RNN that translates English to French."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/10\n",
"108/108 [==============================] - 21s 132ms/step - loss: 1.9204 - accuracy: 0.5445 - val_loss: nan - val_accuracy: 0.6271\n",
"Epoch 2/10\n",
"108/108 [==============================] - 13s 119ms/step - loss: 1.2280 - accuracy: 0.6415 - val_loss: nan - val_accuracy: 0.6736\n",
"Epoch 3/10\n",
"108/108 [==============================] - 13s 119ms/step - loss: 1.0781 - accuracy: 0.6701 - val_loss: nan - val_accuracy: 0.7027\n",
"Epoch 4/10\n",
"108/108 [==============================] - 13s 122ms/step - loss: 0.9893 - accuracy: 0.6861 - val_loss: nan - val_accuracy: 0.7056\n",
"Epoch 5/10\n",
"108/108 [==============================] - 13s 122ms/step - loss: 0.9328 - accuracy: 0.6960 - val_loss: nan - val_accuracy: 0.7206\n",
"Epoch 6/10\n",
"108/108 [==============================] - 12s 113ms/step - loss: 0.8917 - accuracy: 0.7037 - val_loss: nan - val_accuracy: 0.7074\n",
"Epoch 7/10\n",
"108/108 [==============================] - 12s 109ms/step - loss: 0.8539 - accuracy: 0.7123 - val_loss: nan - val_accuracy: 0.7419\n",
"Epoch 8/10\n",
"108/108 [==============================] - 12s 114ms/step - loss: 0.8136 - accuracy: 0.7258 - val_loss: nan - val_accuracy: 0.7366\n",
"Epoch 9/10\n",
"108/108 [==============================] - 13s 117ms/step - loss: 0.7947 - accuracy: 0.7312 - val_loss: nan - val_accuracy: 0.7469\n",
"Epoch 10/10\n",
"108/108 [==============================] - 13s 117ms/step - loss: 0.7671 - accuracy: 0.7396 - val_loss: nan - val_accuracy: 0.7694\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x1d1836779d0>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def simple_model(input_shape, output_sequence_length, english_vocab_size, french_vocab_size):\n",
" \n",
" #Hyperparameters\n",
" learning_rate = 0.005\n",
" \n",
" # Build the layers\n",
" model = Sequential()\n",
" model.add(GRU(256, input_shape=input_shape[1:], return_sequences=True))\n",
" model.add(TimeDistributed(Dense(1024, activation='relu')))\n",
" model.add(Dropout(0.5))\n",
" model.add(TimeDistributed(Dense(french_vocab_size, activation='softmax')))\n",
" \n",
" # Compile model\n",
" model.compile(loss = sparse_categorical_crossentropy,\n",
" optimizer = Adam(learning_rate),\n",
" metrics = ['accuracy'])\n",
" \n",
" return model\n",
"\n",
"tmp_x = pad(preproc_english_sentences, max_french_sequence_length)\n",
"tmp_x = tmp_x.reshape((-1, preproc_french_sentences.shape[-2], 1))\n",
"\n",
"#Train the neural network\n",
"simple_rnn_model = simple_model(\n",
" tmp_x.shape,\n",
" max_french_sequence_length,\n",
" english_vocab_size,\n",
" french_vocab_size)\n",
"\n",
"simple_rnn_model.fit(tmp_x, preproc_french_sentences, batch_size=1024, epochs=10, validation_split=0.2)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Prediciton:\n",
"1/1 [==============================] - 0s 278ms/step\n",
"new jersey est parfois calme en mois de il et il est en en <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD>\n",
"\n",
"Correct Translation:\n",
"[\"new jersey est parfois calme pendant l' automne , et il est neigeux en avril .\"]\n",
"\n",
"Original text:\n",
"['new jersey is sometimes quiet during autumn , and it is snowy in april .']\n"
]
}
],
"source": [
"# Print prediction(s)\n",
"print(\"Prediciton:\")\n",
"print(logits_to_text(simple_rnn_model.predict(tmp_x[:1])[0], french_tokenizer))\n",
"\n",
"print(\"\\nCorrect Translation:\")\n",
"print(french_sentences[:1])\n",
"\n",
"print('\\nOriginal text:')\n",
"print(english_sentences[:1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Model 2: Bidirectional RNNs\n",
"\n",
"One restriction of a RNN is that it can't see the future input, only the past. This is where bidirectional recurrent neural networks come in. They are able to see the future data."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"sequential_1\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
" bidirectional (Bidirectiona (None, 21, 256) 100608 \n",
" l) \n",
" \n",
" time_distributed_2 (TimeDis (None, 21, 1024) 263168 \n",
" tributed) \n",
" \n",
" dropout_1 (Dropout) (None, 21, 1024) 0 \n",
" \n",
" time_distributed_3 (TimeDis (None, 21, 344) 352600 \n",
" tributed) \n",
" \n",
"=================================================================\n",
"Total params: 716,376\n",
"Trainable params: 716,376\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n",
"None\n",
"Epoch 1/10\n",
"108/108 [==============================] - 15s 121ms/step - loss: 1.7581 - accuracy: 0.5748 - val_loss: nan - val_accuracy: 0.6500\n",
"Epoch 2/10\n",
"108/108 [==============================] - 13s 117ms/step - loss: 1.1684 - accuracy: 0.6552 - val_loss: nan - val_accuracy: 0.6823\n",
"Epoch 3/10\n",
"108/108 [==============================] - 13s 118ms/step - loss: 1.0394 - accuracy: 0.6766 - val_loss: nan - val_accuracy: 0.6961\n",
"Epoch 4/10\n",
"108/108 [==============================] - 13s 118ms/step - loss: 0.9543 - accuracy: 0.6900 - val_loss: nan - val_accuracy: 0.7073\n",
"Epoch 5/10\n",
"108/108 [==============================] - 13s 118ms/step - loss: 0.8969 - accuracy: 0.6993 - val_loss: nan - val_accuracy: 0.7155\n",
"Epoch 6/10\n",
"108/108 [==============================] - 13s 118ms/step - loss: 0.8589 - accuracy: 0.7062 - val_loss: nan - val_accuracy: 0.7109\n",
"Epoch 7/10\n",
"108/108 [==============================] - 13s 118ms/step - loss: 0.8371 - accuracy: 0.7107 - val_loss: nan - val_accuracy: 0.7233\n",
"Epoch 8/10\n",
"108/108 [==============================] - 13s 118ms/step - loss: 0.7975 - accuracy: 0.7195 - val_loss: nan - val_accuracy: 0.7446\n",
"Epoch 9/10\n",
"108/108 [==============================] - 13s 118ms/step - loss: 0.7670 - accuracy: 0.7282 - val_loss: nan - val_accuracy: 0.7498\n",
"Epoch 10/10\n",
"108/108 [==============================] - 13s 117ms/step - loss: 0.7318 - accuracy: 0.7401 - val_loss: nan - val_accuracy: 0.7604\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x1d184e5adf0>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def bd_model(input_shape, output_sequence_length, english_vocab_size, french_vocab_size):\n",
" \n",
" #Hyperparameters\n",
" learning_rate = 0.005\n",
" \n",
" # Build the layers\n",
" model = Sequential()\n",
" model.add(Bidirectional(GRU(128, return_sequences=True), input_shape=input_shape[1:]))\n",
" model.add(TimeDistributed(Dense(1024, activation='relu')))\n",
" model.add(Dropout(0.5))\n",
" model.add(TimeDistributed(Dense(french_vocab_size, activation='softmax')))\n",
" \n",
" # Compile model\n",
" model.compile(loss = sparse_categorical_crossentropy,\n",
" optimizer = Adam(learning_rate),\n",
" metrics = ['accuracy'])\n",
" \n",
" return model\n",
"\n",
"tmp_x = pad(preproc_english_sentences, max_french_sequence_length)\n",
"tmp_x = tmp_x.reshape((-1, preproc_french_sentences.shape[-2], 1))\n",
"\n",
"# Train the neural network\n",
"bd_rnn_model = bd_model(\n",
" tmp_x.shape,\n",
" max_french_sequence_length,\n",
" english_vocab_size,\n",
" french_vocab_size)\n",
"\n",
"print(bd_rnn_model.summary())\n",
"\n",
"bd_rnn_model.fit(tmp_x, preproc_french_sentences, batch_size=1024, epochs=10, validation_split=0.2)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Prediciton:\n",
"1/1 [==============================] - 1s 569ms/step\n",
"new jersey est parfois chaud en mois de il et est est en en <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD>\n",
"\n",
"Correct Translation:\n",
"[\"new jersey est parfois calme pendant l' automne , et il est neigeux en avril .\"]\n",
"\n",
"Original text:\n",
"['new jersey is sometimes quiet during autumn , and it is snowy in april .']\n"
]
}
],
"source": [
"# Print prediction(s)\n",
"print(\"Prediciton:\")\n",
"print(logits_to_text(bd_rnn_model.predict(tmp_x[:1])[0], french_tokenizer))\n",
"\n",
"print(\"\\nCorrect Translation:\")\n",
"print(french_sentences[:1])\n",
"\n",
"print('\\nOriginal text:')\n",
"print(english_sentences[:1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Model 3: Embedding\n",
"\n",
"You've turned the words into ids, but there's a better representation of a word. This is called word embeddings. An embedding is a vector representation of the word that is close to similar words in n-dimensional space, where the n represents the size of the embedding vectors."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"sequential_2\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
" embedding (Embedding) (None, 21, 256) 50944 \n",
" \n",
" bidirectional_1 (Bidirectio (None, 21, 512) 789504 \n",
" nal) \n",
" \n",
" time_distributed_4 (TimeDis (None, 21, 1024) 525312 \n",
" tributed) \n",
" \n",
" dropout_2 (Dropout) (None, 21, 1024) 0 \n",
" \n",
" time_distributed_5 (TimeDis (None, 21, 344) 352600 \n",
" tributed) \n",
" \n",
"=================================================================\n",
"Total params: 1,718,360\n",
"Trainable params: 1,718,360\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n",
"None\n",
"Epoch 1/10\n",
"108/108 [==============================] - 21s 168ms/step - loss: 1.3566 - accuracy: 0.6890 - val_loss: nan - val_accuracy: 0.8730\n",
"Epoch 2/10\n",
"108/108 [==============================] - 16s 151ms/step - loss: 0.3153 - accuracy: 0.9007 - val_loss: nan - val_accuracy: 0.9377\n",
"Epoch 3/10\n",
"108/108 [==============================] - 17s 154ms/step - loss: 0.1827 - accuracy: 0.9428 - val_loss: nan - val_accuracy: 0.9572\n",
"Epoch 4/10\n",
"108/108 [==============================] - 17s 154ms/step - loss: 0.1322 - accuracy: 0.9589 - val_loss: nan - val_accuracy: 0.9685\n",
"Epoch 5/10\n",
"108/108 [==============================] - 17s 154ms/step - loss: 0.1035 - accuracy: 0.9680 - val_loss: nan - val_accuracy: 0.9734\n",
"Epoch 6/10\n",
"108/108 [==============================] - 17s 156ms/step - loss: 0.0864 - accuracy: 0.9734 - val_loss: nan - val_accuracy: 0.9764\n",
"Epoch 7/10\n",
"108/108 [==============================] - 17s 156ms/step - loss: 0.0755 - accuracy: 0.9767 - val_loss: nan - val_accuracy: 0.9774\n",
"Epoch 8/10\n",
"108/108 [==============================] - 17s 157ms/step - loss: 0.0659 - accuracy: 0.9795 - val_loss: nan - val_accuracy: 0.9805\n",
"Epoch 9/10\n",
"108/108 [==============================] - 17s 159ms/step - loss: 0.0604 - accuracy: 0.9812 - val_loss: nan - val_accuracy: 0.9813\n",
"Epoch 10/10\n",
"108/108 [==============================] - 17s 158ms/step - loss: 0.0559 - accuracy: 0.9827 - val_loss: nan - val_accuracy: 0.9825\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x1d183c75460>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def bidirectional_embed_model(input_shape, output_sequence_length, english_vocab_size, french_vocab_size):\n",
" \n",
" # Hyperparameters\n",
" learning_rate = 0.005\n",
" \n",
" # Build the layers\n",
" model = Sequential()\n",
" model.add(Embedding(english_vocab_size, 256, input_length=input_shape[1], input_shape=input_shape[1:]))\n",
" model.add(Bidirectional(GRU(256, return_sequences=True)))\n",
" model.add(TimeDistributed(Dense(1024, activation='relu')))\n",
" model.add(Dropout(0.5))\n",
" model.add(TimeDistributed(Dense(french_vocab_size, activation='softmax')))\n",
" \n",
" # Compile model\n",
" model.compile(loss = sparse_categorical_crossentropy,\n",
" optimizer = Adam(learning_rate),\n",
" metrics = ['accuracy'])\n",
" \n",
" return model\n",
"\n",
"tmp_x = pad(preproc_english_sentences, max_french_sequence_length)\n",
"tmp_x = tmp_x.reshape((-1, preproc_french_sentences.shape[-2]))\n",
"\n",
"# Build the model\n",
"embed_rnn_model = bidirectional_embed_model(\n",
" tmp_x.shape,\n",
" max_french_sequence_length,\n",
" english_vocab_size,\n",
" french_vocab_size)\n",
"\n",
"print(embed_rnn_model.summary())\n",
"\n",
"embed_rnn_model.fit(tmp_x, preproc_french_sentences, batch_size=1024, epochs=10, validation_split=0.2)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Prediciton:\n",
"1/1 [==============================] - 0s 479ms/step\n",
"new jersey est parfois calme pendant l' automne et il est neigeux en avril <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD>\n",
"\n",
"Correct Translation:\n",
"[\"new jersey est parfois calme pendant l' automne , et il est neigeux en avril .\"]\n",
"\n",
"Original text:\n",
"['new jersey is sometimes quiet during autumn , and it is snowy in april .']\n"
]
}
],
"source": [
"# Print prediction(s)\n",
"print(\"Prediciton:\")\n",
"print(logits_to_text(embed_rnn_model.predict(tmp_x[:1])[0], french_tokenizer))\n",
"\n",
"print(\"\\nCorrect Translation:\")\n",
"print(french_sentences[:1])\n",
"\n",
"print('\\nOriginal text:')\n",
"print(english_sentences[:1])"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:absl:Found untraced functions such as gru_cell_5_layer_call_fn, gru_cell_5_layer_call_and_return_conditional_losses, gru_cell_6_layer_call_fn, gru_cell_6_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:tensorflow:Assets written to: english_to_french_model\\assets\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:tensorflow:Assets written to: english_to_french_model\\assets\n"
]
}
],
"source": [
"embed_rnn_model.save('english_to_french_model')\n",
"# Serialize English Tokenizer to JSON\n",
"with open('english_tokenizer.json', 'w', encoding='utf8') as f:\n",
" f.write(json.dumps(english_tokenizer.to_json(), ensure_ascii=False))\n",
" \n",
"# Serialize French Tokenizer to JSON\n",
"with open('french_tokenizer.json', 'w', encoding='utf8') as f:\n",
" f.write(json.dumps(french_tokenizer.to_json(), ensure_ascii=False))\n",
" \n",
"# Save max lengths\n",
"max_french_sequence_length_json = max_french_sequence_length\n",
"with open('sequence_length.json', 'w', encoding='utf8') as f:\n",
" f.write(json.dumps(max_french_sequence_length_json, ensure_ascii=False))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
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"kernelspec": {
"display_name": "Python 3",
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|