learning-text-preprocessing-keras.ipynb

{"cells":[{"source":"<a href=\"https://www.kaggle.com/sid9300/learning-text-preprocessing-keras?scriptVersionId=83115659\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown","outputs":[],"execution_count":0},{"cell_type":"markdown","id":"23673c9f","metadata":{"papermill":{"duration":0.013994,"end_time":"2021-12-23T16:28:11.367296","exception":false,"start_time":"2021-12-23T16:28:11.353302","status":"completed"},"tags":[]},"source":["## Importing Libraries"]},{"cell_type":"code","execution_count":1,"id":"0ea939b6","metadata":{"execution":{"iopub.execute_input":"2021-12-23T16:28:11.398819Z","iopub.status.busy":"2021-12-23T16:28:11.397781Z","iopub.status.idle":"2021-12-23T16:28:22.123634Z","shell.execute_reply":"2021-12-23T16:28:22.124103Z","shell.execute_reply.started":"2021-12-23T16:27:39.638328Z"},"papermill":{"duration":10.74271,"end_time":"2021-12-23T16:28:22.124387","exception":false,"start_time":"2021-12-23T16:28:11.381677","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Collecting tensor-sensor\r\n","  Downloading tensor-sensor-1.0.tar.gz (23 kB)\r\n","  Preparing metadata (setup.py) ... \u001b[?25l-\b \bdone\r\n","\u001b[?25hCollecting graphviz>=0.14.1\r\n","  Downloading graphviz-0.19.1-py3-none-any.whl (46 kB)\r\n","     |████████████████████████████████| 46 kB 2.3 MB/s             \r\n","\u001b[?25hRequirement already satisfied: numpy in /opt/conda/lib/python3.7/site-packages (from tensor-sensor) (1.19.5)\r\n","Requirement already satisfied: IPython in /opt/conda/lib/python3.7/site-packages (from tensor-sensor) (7.29.0)\r\n","Requirement already satisfied: matplotlib in /opt/conda/lib/python3.7/site-packages (from tensor-sensor) (3.5.0)\r\n","Requirement already satisfied: pickleshare in /opt/conda/lib/python3.7/site-packages (from IPython->tensor-sensor) (0.7.5)\r\n","Requirement already satisfied: pygments in /opt/conda/lib/python3.7/site-packages (from IPython->tensor-sensor) (2.10.0)\r\n","Requirement already satisfied: jedi>=0.16 in /opt/conda/lib/python3.7/site-packages (from IPython->tensor-sensor) (0.18.1)\r\n","Requirement already satisfied: pexpect>4.3 in /opt/conda/lib/python3.7/site-packages (from IPython->tensor-sensor) (4.8.0)\r\n","Requirement already satisfied: backcall in /opt/conda/lib/python3.7/site-packages (from IPython->tensor-sensor) (0.2.0)\r\n","Requirement already satisfied: setuptools>=18.5 in /opt/conda/lib/python3.7/site-packages (from IPython->tensor-sensor) (59.1.1)\r\n","Requirement already satisfied: matplotlib-inline in /opt/conda/lib/python3.7/site-packages (from IPython->tensor-sensor) (0.1.3)\r\n","Requirement already satisfied: traitlets>=4.2 in /opt/conda/lib/python3.7/site-packages (from IPython->tensor-sensor) (5.1.1)\r\n","Requirement already satisfied: prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0 in /opt/conda/lib/python3.7/site-packages (from IPython->tensor-sensor) (3.0.22)\r\n","Requirement already satisfied: decorator in /opt/conda/lib/python3.7/site-packages (from IPython->tensor-sensor) (5.1.0)\r\n","Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.7/site-packages (from matplotlib->tensor-sensor) (21.0)\r\n","Requirement already satisfied: pyparsing>=2.2.1 in /opt/conda/lib/python3.7/site-packages (from matplotlib->tensor-sensor) (3.0.6)\r\n","Requirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.7/site-packages (from matplotlib->tensor-sensor) (2.8.0)\r\n","Requirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.7/site-packages (from matplotlib->tensor-sensor) (4.28.2)\r\n","Requirement already satisfied: pillow>=6.2.0 in /opt/conda/lib/python3.7/site-packages (from matplotlib->tensor-sensor) (8.2.0)\r\n","Requirement already satisfied: setuptools-scm>=4 in /opt/conda/lib/python3.7/site-packages (from matplotlib->tensor-sensor) (6.3.2)\r\n","Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.7/site-packages (from matplotlib->tensor-sensor) (1.3.2)\r\n","Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.7/site-packages (from matplotlib->tensor-sensor) (0.11.0)\r\n","Requirement already satisfied: parso<0.9.0,>=0.8.0 in /opt/conda/lib/python3.7/site-packages (from jedi>=0.16->IPython->tensor-sensor) (0.8.2)\r\n","Requirement already satisfied: ptyprocess>=0.5 in /opt/conda/lib/python3.7/site-packages (from pexpect>4.3->IPython->tensor-sensor) (0.7.0)\r\n","Requirement already satisfied: wcwidth in /opt/conda/lib/python3.7/site-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->IPython->tensor-sensor) (0.2.5)\r\n","Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.7/site-packages (from python-dateutil>=2.7->matplotlib->tensor-sensor) (1.16.0)\r\n","Requirement already satisfied: tomli>=1.0.0 in /opt/conda/lib/python3.7/site-packages (from setuptools-scm>=4->matplotlib->tensor-sensor) (1.2.2)\r\n","Building wheels for collected packages: tensor-sensor\r\n","  Building wheel for tensor-sensor (setup.py) ... \u001b[?25l-\b \b\\\b \bdone\r\n","\u001b[?25h  Created wheel for tensor-sensor: filename=tensor_sensor-1.0-py3-none-any.whl size=26915 sha256=5fc3827c482d8cb70712c1ea25c8f26455e6fb65f3478b32dec72b914ed5cb71\r\n","  Stored in directory: /root/.cache/pip/wheels/f4/cd/e7/b6ea52c9dc8351069412939cd428731e1bd19385c0671a1020\r\n","Successfully built tensor-sensor\r\n","Installing collected packages: graphviz, tensor-sensor\r\n","  Attempting uninstall: graphviz\r\n","    Found existing installation: graphviz 0.8.4\r\n","    Uninstalling graphviz-0.8.4:\r\n","      Successfully uninstalled graphviz-0.8.4\r\n","\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\r\n","mxnet 1.8.0.post0 requires graphviz<0.9.0,>=0.8.1, but you have graphviz 0.19.1 which is incompatible.\u001b[0m\r\n","Successfully installed graphviz-0.19.1 tensor-sensor-1.0\r\n","\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\r\n"]}],"source":["!pip install tensor-sensor"]},{"cell_type":"code","execution_count":2,"id":"e31a16bd","metadata":{"execution":{"iopub.execute_input":"2021-12-23T16:28:22.164925Z","iopub.status.busy":"2021-12-23T16:28:22.163886Z","iopub.status.idle":"2021-12-23T16:28:28.083632Z","shell.execute_reply":"2021-12-23T16:28:28.082649Z","shell.execute_reply.started":"2021-12-23T16:27:48.177884Z"},"papermill":{"duration":5.942194,"end_time":"2021-12-23T16:28:28.08378","exception":false,"start_time":"2021-12-23T16:28:22.141586","status":"completed"},"tags":[]},"outputs":[],"source":["import tsensor\n","import numpy as np\n","\n","from tensorflow.keras.preprocessing.text import Tokenizer\n","from tensorflow.keras.preprocessing.sequence import pad_sequences\n","from tensorflow.keras.layers import Embedding, GlobalAveragePooling1D\n","\n","import warnings\n","warnings.filterwarnings(\"ignore\")"]},{"cell_type":"markdown","id":"40c61942","metadata":{"papermill":{"duration":0.017629,"end_time":"2021-12-23T16:28:28.118449","exception":false,"start_time":"2021-12-23T16:28:28.10082","status":"completed"},"tags":[]},"source":["## Tokenization"]},{"cell_type":"code","execution_count":3,"id":"d234c0c4","metadata":{"execution":{"iopub.execute_input":"2021-12-23T16:28:28.155925Z","iopub.status.busy":"2021-12-23T16:28:28.154993Z","iopub.status.idle":"2021-12-23T16:28:28.158998Z","shell.execute_reply":"2021-12-23T16:28:28.159401Z","shell.execute_reply.started":"2021-12-23T16:27:48.185726Z"},"papermill":{"duration":0.024308,"end_time":"2021-12-23T16:28:28.159569","exception":false,"start_time":"2021-12-23T16:28:28.135261","status":"completed"},"tags":[]},"outputs":[],"source":["# Tokenising sentences\n","sentences = [\n","    'The quick brown fox jumps over the lazy dog.'\n","]\n","\n","tokenizer = Tokenizer()\n","tokenizer.fit_on_texts(sentences)"]},{"cell_type":"code","execution_count":4,"id":"784549c9","metadata":{"execution":{"iopub.execute_input":"2021-12-23T16:28:28.198127Z","iopub.status.busy":"2021-12-23T16:28:28.197485Z","iopub.status.idle":"2021-12-23T16:28:28.201028Z","shell.execute_reply":"2021-12-23T16:28:28.201427Z","shell.execute_reply.started":"2021-12-23T16:27:48.199619Z"},"papermill":{"duration":0.02527,"end_time":"2021-12-23T16:28:28.201612","exception":false,"start_time":"2021-12-23T16:28:28.176342","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Word Index       : {'the': 1, 'quick': 2, 'brown': 3, 'fox': 4, 'jumps': 5, 'over': 6, 'lazy': 7, 'dog': 8}\n"]}],"source":["print(\"Word Index       :\", tokenizer.word_index)"]},{"cell_type":"code","execution_count":5,"id":"478f3ffb","metadata":{"execution":{"iopub.execute_input":"2021-12-23T16:28:28.243241Z","iopub.status.busy":"2021-12-23T16:28:28.242208Z","iopub.status.idle":"2021-12-23T16:28:28.246351Z","shell.execute_reply":"2021-12-23T16:28:28.246778Z","shell.execute_reply.started":"2021-12-23T16:27:48.211449Z"},"papermill":{"duration":0.02825,"end_time":"2021-12-23T16:28:28.246968","exception":false,"start_time":"2021-12-23T16:28:28.218718","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Sentence Sequence : [[1 2 3 4 5 6 1 7 8]]\n"]}],"source":["train_sequence = tokenizer.texts_to_sequences(sentences)\n","train_sequence = np.array(train_sequence)\n","print(\"Sentence Sequence :\", train_sequence)"]},{"cell_type":"markdown","id":"54f80ae6","metadata":{"papermill":{"duration":0.017128,"end_time":"2021-12-23T16:28:28.281389","exception":false,"start_time":"2021-12-23T16:28:28.264261","status":"completed"},"tags":[]},"source":["## Creating Embedding Layer"]},{"cell_type":"code","execution_count":6,"id":"6ae639de","metadata":{"execution":{"iopub.execute_input":"2021-12-23T16:28:28.320738Z","iopub.status.busy":"2021-12-23T16:28:28.318898Z","iopub.status.idle":"2021-12-23T16:28:28.349028Z","shell.execute_reply":"2021-12-23T16:28:28.34847Z","shell.execute_reply.started":"2021-12-23T16:27:48.223554Z"},"papermill":{"duration":0.05052,"end_time":"2021-12-23T16:28:28.349186","exception":false,"start_time":"2021-12-23T16:28:28.298666","status":"completed"},"tags":[]},"outputs":[],"source":["# Create a random embedding layer\n","embedding = Embedding(input_dim=len(train_sequence[0]), output_dim=128)"]},{"cell_type":"code","execution_count":7,"id":"50cac3e7","metadata":{"execution":{"iopub.execute_input":"2021-12-23T16:28:28.40295Z","iopub.status.busy":"2021-12-23T16:28:28.402121Z","iopub.status.idle":"2021-12-23T16:28:28.450317Z","shell.execute_reply":"2021-12-23T16:28:28.450847Z","shell.execute_reply.started":"2021-12-23T16:27:48.234737Z"},"papermill":{"duration":0.08435,"end_time":"2021-12-23T16:28:28.451031","exception":false,"start_time":"2021-12-23T16:28:28.366681","status":"completed"},"tags":[]},"outputs":[{"name":"stderr","output_type":"stream","text":["\n","User settings:\n","\n","   KMP_AFFINITY=granularity=fine,verbose,compact,1,0\n","   KMP_BLOCKTIME=0\n","   KMP_DUPLICATE_LIB_OK=True\n","   KMP_SETTINGS=1\n","   KMP_WARNINGS=0\n","\n","Effective settings:\n","\n","   KMP_ABORT_DELAY=0\n","   KMP_ADAPTIVE_LOCK_PROPS='1,1024'\n","   KMP_ALIGN_ALLOC=64\n","   KMP_ALL_THREADPRIVATE=128\n","   KMP_ATOMIC_MODE=2\n","   KMP_BLOCKTIME=0\n","   KMP_CPUINFO_FILE: value is not defined\n","   KMP_DETERMINISTIC_REDUCTION=false\n","   KMP_DEVICE_THREAD_LIMIT=2147483647\n","   KMP_DISP_NUM_BUFFERS=7\n","   KMP_DUPLICATE_LIB_OK=true\n","   KMP_ENABLE_TASK_THROTTLING=true\n","   KMP_FORCE_REDUCTION: value is not defined\n","   KMP_FOREIGN_THREADS_THREADPRIVATE=true\n","   KMP_FORKJOIN_BARRIER='2,2'\n","   KMP_FORKJOIN_BARRIER_PATTERN='hyper,hyper'\n","   KMP_GTID_MODE=3\n","   KMP_HANDLE_SIGNALS=false\n","   KMP_HOT_TEAMS_MAX_LEVEL=1\n","   KMP_HOT_TEAMS_MODE=0\n","   KMP_INIT_AT_FORK=true\n","   KMP_LIBRARY=throughput\n","   KMP_LOCK_KIND=queuing\n","   KMP_MALLOC_POOL_INCR=1M\n","   KMP_NUM_LOCKS_IN_BLOCK=1\n","   KMP_PLAIN_BARRIER='2,2'\n","   KMP_PLAIN_BARRIER_PATTERN='hyper,hyper'\n","   KMP_REDUCTION_BARRIER='1,1'\n","   KMP_REDUCTION_BARRIER_PATTERN='hyper,hyper'\n","   KMP_SCHEDULE='static,balanced;guided,iterative'\n","   KMP_SETTINGS=true\n","   KMP_SPIN_BACKOFF_PARAMS='4096,100'\n","   KMP_STACKOFFSET=64\n","   KMP_STACKPAD=0\n","   KMP_STACKSIZE=8M\n","   KMP_STORAGE_MAP=false\n","   KMP_TASKING=2\n","   KMP_TASKLOOP_MIN_TASKS=0\n","   KMP_TASK_STEALING_CONSTRAINT=1\n","   KMP_TEAMS_THREAD_LIMIT=4\n","   KMP_TOPOLOGY_METHOD=all\n","   KMP_USE_YIELD=1\n","   KMP_VERSION=false\n","   KMP_WARNINGS=false\n","   OMP_AFFINITY_FORMAT='OMP: pid %P tid %i thread %n bound to OS proc set {%A}'\n","   OMP_ALLOCATOR=omp_default_mem_alloc\n","   OMP_CANCELLATION=false\n","   OMP_DEFAULT_DEVICE=0\n","   OMP_DISPLAY_AFFINITY=false\n","   OMP_DISPLAY_ENV=false\n","   OMP_DYNAMIC=false\n","   OMP_MAX_ACTIVE_LEVELS=1\n","   OMP_MAX_TASK_PRIORITY=0\n","   OMP_NESTED: deprecated; max-active-levels-var=1\n","   OMP_NUM_THREADS: value is not defined\n","   OMP_PLACES: value is not defined\n","   OMP_PROC_BIND='intel'\n","   OMP_SCHEDULE='static'\n","   OMP_STACKSIZE=8M\n","   OMP_TARGET_OFFLOAD=DEFAULT\n","   OMP_THREAD_LIMIT=2147483647\n","   OMP_WAIT_POLICY=PASSIVE\n","   KMP_AFFINITY='verbose,warnings,respect,granularity=fine,compact,1,0'\n","\n","2021-12-23 16:28:28.399169: I tensorflow/core/common_runtime/process_util.cc:146] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.\n"]}],"source":["# Get the embeddings of the train sample\n","train_sample = embedding(train_sequence)"]},{"cell_type":"code","execution_count":8,"id":"ae0f52c7","metadata":{"execution":{"iopub.execute_input":"2021-12-23T16:28:28.48949Z","iopub.status.busy":"2021-12-23T16:28:28.488863Z","iopub.status.idle":"2021-12-23T16:28:28.493921Z","shell.execute_reply":"2021-12-23T16:28:28.494373Z","shell.execute_reply.started":"2021-12-23T16:27:48.250116Z"},"papermill":{"duration":0.025604,"end_time":"2021-12-23T16:28:28.494558","exception":false,"start_time":"2021-12-23T16:28:28.468954","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Shape of Input          : (1, 9)\n","Shape of Embedded Input : (1, 9, 128)\n"]}],"source":["print(\"Shape of Input          :\", train_sequence.shape)\n","print(\"Shape of Embedded Input :\", train_sample.shape)"]},{"cell_type":"code","execution_count":9,"id":"913fa57e","metadata":{"execution":{"iopub.execute_input":"2021-12-23T16:28:28.533702Z","iopub.status.busy":"2021-12-23T16:28:28.533151Z","iopub.status.idle":"2021-12-23T16:28:29.207204Z","shell.execute_reply":"2021-12-23T16:28:29.206619Z","shell.execute_reply.started":"2021-12-23T16:27:48.262051Z"},"papermill":{"duration":0.694141,"end_time":"2021-12-23T16:28:29.207331","exception":false,"start_time":"2021-12-23T16:28:28.51319","status":"completed"},"tags":[]},"outputs":[{"data":{"image/svg+xml":["<svg height=\"36.568242pt\" version=\"1.1\" viewBox=\"0 0 268.119 36.568242\" width=\"268.119pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n"," <metadata>\n","  <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n","   <cc:Work>\n","    <dc:type 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GlobalAveragePooling1D()(train_sample)"]},{"cell_type":"markdown","id":"506114e0","metadata":{"papermill":{"duration":0.020635,"end_time":"2021-12-23T16:28:29.653233","exception":false,"start_time":"2021-12-23T16:28:29.632598","status":"completed"},"tags":[]},"source":["## Create Word Embeddings for More than One Sentence"]},{"cell_type":"code","execution_count":13,"id":"e173b298","metadata":{"execution":{"iopub.execute_input":"2021-12-23T16:28:29.704554Z","iopub.status.busy":"2021-12-23T16:28:29.697492Z","iopub.status.idle":"2021-12-23T16:28:29.709182Z","shell.execute_reply":"2021-12-23T16:28:29.708366Z","shell.execute_reply.started":"2021-12-23T16:27:48.859416Z"},"papermill":{"duration":0.035157,"end_time":"2021-12-23T16:28:29.709404","exception":false,"start_time":"2021-12-23T16:28:29.674247","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Sentence 1 : The quick brown fox jumps over the lazy dog.\n","Sequence   : [1, 2, 3, 4, 5, 6, 1, 7, 8]\n","---------------------------------------------------------\n","Sentence 2 : The quick brown fox.\n","Sequence   : [1, 2, 3, 4]\n","---------------------------------------------------------\n","Sentence 3 : The lazy dog\n","Sequence   : [1, 7, 8]\n","---------------------------------------------------------\n","Sentence 4 : The dog\n","Sequence   : [1, 8]\n","---------------------------------------------------------\n","Sentence 5 : Dog and the fox\n","Sequence   : [8, 1, 4]\n","---------------------------------------------------------\n","Sentence 6 : Hello, world!\n","Sequence   : []\n","---------------------------------------------------------\n"]}],"source":["# More than one sentence\n","\n","test_corpus = [\n","    'The quick brown fox jumps over the lazy dog.',\n","    'The quick brown fox.',\n","    'The lazy dog',\n","    'The dog',\n","    'Dog and the fox',\n","    'Hello, world!'\n","]\n","\n","encoded_sentences = tokenizer.texts_to_sequences(test_corpus)\n","i = 1\n","for sentence, encoded_sentence in zip(test_corpus, encoded_sentences):\n","    print(\"Sentence\",str(i) + \" :\", sentence)\n","    print(\"Sequence   :\", encoded_sentence)\n","    print(\"---------------------------------------------------------\")\n","    i+=1"]},{"cell_type":"markdown","id":"a3c0d88f","metadata":{"papermill":{"duration":0.020854,"end_time":"2021-12-23T16:28:29.755084","exception":false,"start_time":"2021-12-23T16:28:29.73423","status":"completed"},"tags":[]},"source":["## Padding Sequences"]},{"cell_type":"code","execution_count":14,"id":"612160c2","metadata":{"execution":{"iopub.execute_input":"2021-12-23T16:28:29.801144Z","iopub.status.busy":"2021-12-23T16:28:29.800549Z","iopub.status.idle":"2021-12-23T16:28:29.805368Z","shell.execute_reply":"2021-12-23T16:28:29.80611Z","shell.execute_reply.started":"2021-12-23T16:27:48.872391Z"},"papermill":{"duration":0.029907,"end_time":"2021-12-23T16:28:29.806325","exception":false,"start_time":"2021-12-23T16:28:29.776418","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Length : [9, 4, 3, 2, 3, 0]\n"]}],"source":["# Length of each sentence in the corpus\n","print(\"Length :\", [len(sentence) for sentence in encoded_sentences])"]},{"cell_type":"code","execution_count":15,"id":"bbf12c05","metadata":{"execution":{"iopub.execute_input":"2021-12-23T16:28:29.854087Z","iopub.status.busy":"2021-12-23T16:28:29.853495Z","iopub.status.idle":"2021-12-23T16:28:29.8594Z","shell.execute_reply":"2021-12-23T16:28:29.858547Z","shell.execute_reply.started":"2021-12-23T16:27:48.884655Z"},"papermill":{"duration":0.030902,"end_time":"2021-12-23T16:28:29.859579","exception":false,"start_time":"2021-12-23T16:28:29.828677","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Max Length : 9\n"]}],"source":["# Length of the longest sentence\n","print(\"Max Length :\", max([len(sentence) for sentence in encoded_sentences]))"]},{"cell_type":"code","execution_count":16,"id":"7129bb6d","metadata":{"execution":{"iopub.execute_input":"2021-12-23T16:28:29.906577Z","iopub.status.busy":"2021-12-23T16:28:29.906031Z","iopub.status.idle":"2021-12-23T16:28:29.9092Z","shell.execute_reply":"2021-12-23T16:28:29.909657Z","shell.execute_reply.started":"2021-12-23T16:27:48.896042Z"},"papermill":{"duration":0.027943,"end_time":"2021-12-23T16:28:29.909825","exception":false,"start_time":"2021-12-23T16:28:29.881882","status":"completed"},"tags":[]},"outputs":[],"source":["MAX_SEQUENCE_LENGTH = 9"]},{"cell_type":"code","execution_count":17,"id":"bc658a83","metadata":{"execution":{"iopub.execute_input":"2021-12-23T16:28:29.956128Z","iopub.status.busy":"2021-12-23T16:28:29.955527Z","iopub.status.idle":"2021-12-23T16:28:29.962237Z","shell.execute_reply":"2021-12-23T16:28:29.962743Z","shell.execute_reply.started":"2021-12-23T16:27:48.907729Z"},"papermill":{"duration":0.031329,"end_time":"2021-12-23T16:28:29.962938","exception":false,"start_time":"2021-12-23T16:28:29.931609","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["array([[1, 2, 3, 4, 5, 6, 1, 7, 8],\n","       [0, 0, 0, 0, 0, 1, 2, 3, 4],\n","       [0, 0, 0, 0, 0, 0, 1, 7, 8],\n","       [0, 0, 0, 0, 0, 0, 0, 1, 8],\n","       [0, 0, 0, 0, 0, 0, 8, 1, 4],\n","       [0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=int32)"]},"execution_count":17,"metadata":{},"output_type":"execute_result"}],"source":["# Padding sequence that are shorter than the longest sequence\n","X = pad_sequences(encoded_sentences, maxlen=MAX_SEQUENCE_LENGTH)\n","X"]}],"metadata":{"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":"3.7.12"},"papermill":{"default_parameters":{},"duration":30.413044,"end_time":"2021-12-23T16:28:33.50782","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2021-12-23T16:28:03.094776","version":"2.3.3"}},"nbformat":4,"nbformat_minor":5}