fastai_py.ipynb

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    "colab": {
      "name": "fastai_py.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "hGILgIBehFOz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import fastai"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LuK4B1zThJze",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from fastai.text import * "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0sdYbjfvhXNW",
        "colab_type": "code",
        "outputId": "6046aa28-e752-4342-dd7e-9dc455b10bdf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "fastai.text.annealing_no"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function fastai.callback.annealing_no>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bWdN1Y-uiBZd",
        "colab_type": "code",
        "outputId": "51b91942-46ee-41d3-a9e8-85f74719cb16",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "path = untar_data(URLs.IMDB_SAMPLE)\n",
        "path"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PosixPath('/root/.fastai/data/imdb_sample')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5NYLx5gziR54",
        "colab_type": "code",
        "outputId": "14096442-f8c1-40b5-df31-16ddf0396da6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df = pd.read_csv(path/'texts.csv')\n",
        "df.head()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
              "      <th>text</th>\n",
              "      <th>is_valid</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>negative</td>\n",
              "      <td>Un-bleeping-believable! Meg Ryan doesn't even ...</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>positive</td>\n",
              "      <td>This is a extremely well-made film. The acting...</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>negative</td>\n",
              "      <td>Every once in a long while a movie will come a...</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>positive</td>\n",
              "      <td>Name just says it all. I watched this movie wi...</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>negative</td>\n",
              "      <td>This movie succeeds at being one of the most u...</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      label                                               text  is_valid\n",
              "0  negative  Un-bleeping-believable! Meg Ryan doesn't even ...     False\n",
              "1  positive  This is a extremely well-made film. The acting...     False\n",
              "2  negative  Every once in a long while a movie will come a...     False\n",
              "3  positive  Name just says it all. I watched this movie wi...     False\n",
              "4  negative  This movie succeeds at being one of the most u...     False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4KFlG9RLilnm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Language model data\n",
        "data_lm = TextLMDataBunch.from_csv(path, 'texts.csv')\n",
        "# Classifier model data\n",
        "data_clas = TextClasDataBunch.from_csv(path, 'texts.csv', vocab=data_lm.train_ds.vocab, bs=32)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1TPi_DfTio26",
        "colab_type": "code",
        "outputId": "11b99a8e-ac0c-4b16-d7ac-4ce7bb8fb865",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        }
      },
      "source": [
        "learn = language_model_learner(data_lm, AWD_LSTM, drop_mult=0.5)\n",
        "learn.fit_one_cycle(1, 1e-2)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>4.302421</td>\n",
              "      <td>3.922743</td>\n",
              "      <td>0.285108</td>\n",
              "      <td>00:10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "45AIvHEDjBdT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "9526f459-7cfe-4441-d97d-c3c4d301d515"
      },
      "source": [
        "learn.predict(\"Life\", 50)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Life : The comic book of Michael Jackson u II ? ( 1949 ) , Dream Days in the Woods Hotel , a core movie sequence , bears their names , their names in their respective names : A. G.'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7_GTIAbJNmt4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}