demo_output.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "b934bc87",
      "metadata": {
        "id": "b934bc87"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "5f50edca",
      "metadata": {
        "id": "5f50edca"
      },
      "outputs": [],
      "source": [
        "import translator"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "03d4aeb4",
      "metadata": {
        "id": "03d4aeb4"
      },
      "outputs": [],
      "source": [
        "translate = translator.AdditiveAttentionTranslator()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2e3999d0",
      "metadata": {
        "id": "2e3999d0"
      },
      "source": [
        "Hyperparameter settings:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "e38f48db",
      "metadata": {
        "id": "e38f48db"
      },
      "outputs": [],
      "source": [
        "translate.encoder_input_words = 20\n",
        "translate.vocab_size = 1000\n",
        "translate.embedding_size = 128\n",
        "translate.epochs = 100\n",
        "translate.batch_size = 200\n",
        "translate.optimizer = tf.keras.optimizers.Adam(learning_rate = 0.001, beta_1 = 0.05)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "74ae89c3",
      "metadata": {
        "id": "74ae89c3"
      },
      "source": [
        "Build Encoder, Decoder, Attention Layer from the given hyper-parameters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "940e4143",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "940e4143",
        "outputId": "ee3f7b00-26a1-46cf-b3f7-662cd561212a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "\n",
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 20)]              0         \n",
            "                                                                 \n",
            " encoder (Encoder)           ((None, 20, 128),         227072    \n",
            "                              (None, 128))                       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 227,072\n",
            "Trainable params: 227,072\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Model: \"model_1\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_2 (InputLayer)           [(None, 1)]          0           []                               \n",
            "                                                                                                  \n",
            " embedding (Embedding)          (None, 1, 128)       128000      ['input_2[0][0]']                \n",
            "                                                                                                  \n",
            " tf.__operators__.getitem (Slic  (None, 128)         0           ['embedding[0][0]']              \n",
            " ingOpLambda)                                                                                     \n",
            "                                                                                                  \n",
            " input_3 (InputLayer)           [(None, 128)]        0           []                               \n",
            "                                                                                                  \n",
            " input_4 (InputLayer)           [(None, 20, 128)]    0           []                               \n",
            "                                                                                                  \n",
            " decoder (Decoder)              ((None, 1000),       3520360     ['tf.__operators__.getitem[0][0]'\n",
            "                                 (None, 128))                    , 'input_3[0][0]',               \n",
            "                                                                  'input_4[0][0]']                \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 3,648,360\n",
            "Trainable params: 3,648,360\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(None, None)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "translate.get_enc_dec()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "68d6ab7c",
      "metadata": {
        "id": "68d6ab7c"
      },
      "source": [
        "We need some data to train and test our model on. Here we are generating our random data for our convinence."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "f3968e84",
      "metadata": {
        "id": "f3968e84"
      },
      "outputs": [],
      "source": [
        "keys_train = np.random.randint(1000, size = (5000, 20))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "d84c9ead",
      "metadata": {
        "id": "d84c9ead"
      },
      "outputs": [],
      "source": [
        "Y = np.eye(1000)[np.random.choice(1000, 50000)].reshape(5000, 10, 1000)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "33e76818",
      "metadata": {
        "id": "33e76818"
      },
      "outputs": [],
      "source": [
        "query = []\n",
        "for instance in Y:\n",
        "    for word in instance:\n",
        "        query.append(np.argmax(word, axis = -1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "bfc91da9",
      "metadata": {
        "id": "bfc91da9"
      },
      "outputs": [],
      "source": [
        "query = np.array(query)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "7eda27b6",
      "metadata": {
        "id": "7eda27b6"
      },
      "outputs": [],
      "source": [
        "query = query.reshape(5000, 10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "ec4026c1",
      "metadata": {
        "id": "ec4026c1"
      },
      "outputs": [],
      "source": [
        "query = np.concatenate([np.zeros(5000).reshape(5000, 1), query], axis = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "9a27d234",
      "metadata": {
        "id": "9a27d234"
      },
      "outputs": [],
      "source": [
        "query = query[:, :10]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "135d819f",
      "metadata": {
        "id": "135d819f"
      },
      "source": [
        "Now we set these dataset into the translate object and train the model and make predictions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "f96d9820",
      "metadata": {
        "id": "f96d9820"
      },
      "outputs": [],
      "source": [
        "translate.X1 = keys_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "8ad9dc74",
      "metadata": {
        "id": "8ad9dc74"
      },
      "outputs": [],
      "source": [
        "translate.X2 = query"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "2d231f8b",
      "metadata": {
        "id": "2d231f8b"
      },
      "outputs": [],
      "source": [
        "translate.Y = Y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "410f9fbe",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "410f9fbe",
        "outputId": "c0e3c3bf-f2cb-4030-b423-cd0b7a71601a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r  0%|          | 0/25 [00:00<?, ?it/s]/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:1082: UserWarning: \"`categorical_crossentropy` received `from_logits=True`, but the `output` argument was produced by a sigmoid or softmax activation and thus does not represent logits. Was this intended?\"\n",
            "  return dispatch_target(*args, **kwargs)\n",
            "100%|██████████| 25/25 [00:17<00:00,  1.46it/s]\n"
          ]
        },
        {
          "output_type": "stream",
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            "Epoch: 1/100 : Error 1727.1259\n"
          ]
        },
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            "Epoch: 2/100 : Error 1713.1003\n"
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            "Epoch: 3/100 : Error 1662.192\n"
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            "Epoch: 22/100 : Error 689.0376\n"
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            "Epoch: 23/100 : Error 620.34625\n"
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            "Epoch: 24/100 : Error 550.21216\n"
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            "Epoch: 25/100 : Error 475.91025\n"
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            "Epoch: 26/100 : Error 404.52127\n"
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            "Epoch: 28/100 : Error 275.31152\n"
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            "Epoch: 29/100 : Error 218.3002\n"
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            "Epoch: 30/100 : Error 168.45787\n"
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            "Epoch: 31/100 : Error 127.952515\n"
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            "Epoch: 32/100 : Error 97.05601\n"
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            "Epoch: 33/100 : Error 74.57121\n"
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            "Epoch: 34/100 : Error 58.95012\n"
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            "Epoch: 35/100 : Error 48.046154\n"
          ]
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            "Epoch: 36/100 : Error 40.19772\n"
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            "Epoch: 37/100 : Error 34.351772\n"
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            "Epoch: 38/100 : Error 29.853779\n"
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        },
        {
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 100/100 : Error 1.7213042\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "translate.train_translator()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "217d27b9",
      "metadata": {
        "id": "217d27b9"
      },
      "source": [
        "Inferencing & results of testing the wrt, to the random training data generated."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "275185bf",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "275185bf",
        "outputId": "05faf0fe-89a9-406a-cb7c-e43a3495704e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([633, 704, 893, 367, 302, 856, 680, 287, 791, 402, 331, 645, 994,\n",
              "       618, 330, 214, 810, 521, 416, 167])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "keys_train[10]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "2eb567a1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2eb567a1",
        "outputId": "848b9222-2569-4f5f-c2f7-4bd03e6a7b4a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "query[10][0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "362489d4",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "362489d4",
        "outputId": "576d9a0a-6257-4c37-b070-c1ac3aa257ec"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 612, 760, 52, 106, 879, 916, 837, 48, 83, 62]"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "output, states = translate.translate_sentence(keys = keys_train[10].reshape(1, 20), query_start = query[10, 0].reshape(1, 1))\n",
        "output"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "907aa1d6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "907aa1d6",
        "outputId": "9932626b-6e74-427c-cb6c-ad6e50af47da"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([  0., 612., 760.,  52., 106., 879., 916., 837.,  48.,  83.])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "query[10]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "fc330e4f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fc330e4f",
        "outputId": "652899d2-8027-4ece-891e-544031a02d02"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([612, 760,  52, 106, 879, 916, 837,  48,  83,  62])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "np.argmax(Y[10], axis = -1)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "04711f5a",
      "metadata": {
        "id": "04711f5a"
      },
      "source": [
        "Graphing Results and inferences"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "a85d5003",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 644
        },
        "id": "a85d5003",
        "outputId": "289ed71d-d641-4d24-a7dd-15cc46f34a97"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fee7006f690>]"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "y_plot = translate.loss_history\n",
        "plt.figure(figsize=(10, 10))\n",
        "plt.xlabel(\"Epochs : \", fontsize = 15)\n",
        "plt.ylabel(\"Loss (Categorical Crossentropy with Logits) : \", fontsize = 15)\n",
        "plt.title(\"Epochs Vs Loss :\")\n",
        "plt.plot(y_plot)"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "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": "3.9.5"
    },
    "colab": {
      "provenance": [],
      "collapsed_sections": []
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "nbformat": 4,
  "nbformat_minor": 5
}