WordEmbeddings.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/rishuatgithub/MLPy/blob/master/WordEmbeddings.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "metadata": {
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      },
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        "import tensorflow as tf\n",
        "import tensorflow_hub as hub\n",
        "import tensorflow_datasets as tfds\n",
        "\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers"
      ],
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    },
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        }
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        "result = embedding_layers(tf.constant([1,2,3]))\n",
        "result.numpy()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.02892378,  0.04330511, -0.04350858,  0.04606298,  0.01532653,\n",
              "         0.02025798, -0.047623  ,  0.04310621,  0.02846254,  0.03304989],\n",
              "       [ 0.01646915, -0.03180424,  0.0108024 , -0.00047391, -0.02628168,\n",
              "         0.03238914, -0.0288126 , -0.02390913, -0.02938774,  0.00302536],\n",
              "       [-0.04173302,  0.04779318, -0.03447205, -0.03592096,  0.03750126,\n",
              "        -0.01336731,  0.02928908,  0.00765145, -0.01379656,  0.01450104]],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qtat8O27Mlyh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "bc8187f1-73ba-4de3-83d2-d7915b096989"
      },
      "source": [
        "result = embedding_layers(tf.constant([[0,1,2],[3,4,5]]))\n",
        "result.shape"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TensorShape([2, 3, 10])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yxkVxz5WBkMt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "ead4eb91-c54a-441a-dc47-2c49cbe9339f"
      },
      "source": [
        "result"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(2, 3, 10), dtype=float32, numpy=\n",
              "array([[[ 0.01391086,  0.03090285,  0.01842237, -0.04916019,\n",
              "          0.00741534, -0.04004639,  0.01717499, -0.02937767,\n",
              "          0.04033489, -0.0262943 ],\n",
              "        [ 0.02892378,  0.04330511, -0.04350858,  0.04606298,\n",
              "          0.01532653,  0.02025798, -0.047623  ,  0.04310621,\n",
              "          0.02846254,  0.03304989],\n",
              "        [ 0.01646915, -0.03180424,  0.0108024 , -0.00047391,\n",
              "         -0.02628168,  0.03238914, -0.0288126 , -0.02390913,\n",
              "         -0.02938774,  0.00302536]],\n",
              "\n",
              "       [[-0.04173302,  0.04779318, -0.03447205, -0.03592096,\n",
              "          0.03750126, -0.01336731,  0.02928908,  0.00765145,\n",
              "         -0.01379656,  0.01450104],\n",
              "        [-0.00446839,  0.00341006, -0.02935089, -0.03999417,\n",
              "          0.04957801, -0.02007909,  0.01906768,  0.02551215,\n",
              "          0.01192757, -0.01508253],\n",
              "        [-0.04063549, -0.04485748, -0.00757761,  0.04632534,\n",
              "         -0.01256894, -0.03113345, -0.03326361,  0.03889633,\n",
              "         -0.02043489, -0.01043113]]], dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "59lK0qIyKEnS",
        "colab_type": "text"
      },
      "source": [
        "From Scratch"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8tPHQ8USBmWO",
        "colab_type": "code",
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            "a0240905afb746d88af4666e14a83d4d",
            "79b5fcd07e5b429e9b51d10e1ca286a5"
          ]
        },
        "outputId": "c215fc87-c6a9-4fba-c543-e0e50007e3af"
      },
      "source": [
        "(train_data, test_data), info = tfds.load(\n",
        "    'imdb_reviews/subwords8k', \n",
        "    split = (tfds.Split.TRAIN, tfds.Split.TEST), \n",
        "    with_info=True, as_supervised=True)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1mDownloading and preparing dataset imdb_reviews/subwords8k/1.0.0 (download: 80.23 MiB, generated: Unknown size, total: 80.23 MiB) to /root/tensorflow_datasets/imdb_reviews/subwords8k/1.0.0...\u001b[0m\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "aa65de88d9de4c1ebfdd599ee0fcc4f1",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Dl Completed...', max=1.0, style=Progre…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5f68751178af4402a876b683c1c11e9d",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Dl Size...', max=1.0, style=ProgressSty…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fdcef364993a4d04ba614d20e88b8d71",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\rShuffling and writing examples to /root/tensorflow_datasets/imdb_reviews/subwords8k/1.0.0.incomplete0UKH3W/imdb_reviews-train.tfrecord\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6ddd705a92034cde9ff940eb34fe594b",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=25000.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\r"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cbc6347efc0b4afeaaddb3cd9453f795",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\rShuffling and writing examples to /root/tensorflow_datasets/imdb_reviews/subwords8k/1.0.0.incomplete0UKH3W/imdb_reviews-test.tfrecord\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5a7e7be4ad634bc498891f84488321b5",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=25000.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\r"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "880e41df61684c64819f393ae2aa4aaf",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\rShuffling and writing examples to /root/tensorflow_datasets/imdb_reviews/subwords8k/1.0.0.incomplete0UKH3W/imdb_reviews-unsupervised.tfrecord\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "affbc96c51594c8b8dbf6af7bd08b76e",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=50000.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\u001b[1mDataset imdb_reviews downloaded and prepared to /root/tensorflow_datasets/imdb_reviews/subwords8k/1.0.0. Subsequent calls will reuse this data.\u001b[0m\n",
            "\r"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Eks5sXolKMUj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "97ab2366-7c02-485c-8ba6-b9e7a4789d30"
      },
      "source": [
        "### Looking at the vocabulary\n",
        "\n",
        "encoder = info.features['text'].encoder\n",
        "encoder.subwords[:20]"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['the_',\n",
              " ', ',\n",
              " '. ',\n",
              " 'a_',\n",
              " 'and_',\n",
              " 'of_',\n",
              " 'to_',\n",
              " 's_',\n",
              " 'is_',\n",
              " 'br',\n",
              " 'in_',\n",
              " 'I_',\n",
              " 'that_',\n",
              " 'this_',\n",
              " 'it_',\n",
              " ' /><',\n",
              " ' />',\n",
              " 'was_',\n",
              " 'The_',\n",
              " 'as_']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kv8nt4sUKdZ4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_batches = train_data.shuffle(1000).padded_batch(10)\n",
        "test_batches = test_data.shuffle(1000).padded_batch(10)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "STuq-CN6Lycy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "cc98204e-d4ef-4447-8530-2afb9d4ac98a"
      },
      "source": [
        "train_batch, train_labels = next(iter(train_batches))\n",
        "train_batch.numpy()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[  12,   31,   33, ...,    0,    0,    0],\n",
              "       [7963, 6389,  884, ...,   14,  204, 7975],\n",
              "       [  12, 5123,   14, ...,    0,    0,    0],\n",
              "       ...,\n",
              "       [  19, 4881,  643, ...,    0,    0,    0],\n",
              "       [  62,   27,    9, ...,    0,    0,    0],\n",
              "       [  19,  118,  874, ...,    0,    0,    0]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NFcurZd_Khoi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "outputId": "e38b8522-7d05-4ec5-930d-2421bbc771d5"
      },
      "source": [
        "embedding_dim=16\n",
        "\n",
        "model = keras.Sequential([\n",
        "  layers.Embedding(encoder.vocab_size, embedding_dim),\n",
        "  layers.GlobalAveragePooling1D(),\n",
        "  layers.Dense(16, activation='relu'),\n",
        "  layers.Dense(1)\n",
        "])\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_1 (Embedding)      (None, None, 16)          130960    \n",
            "_________________________________________________________________\n",
            "global_average_pooling1d (Gl (None, 16)                0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 16)                272       \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 17        \n",
            "=================================================================\n",
            "Total params: 131,249\n",
            "Trainable params: 131,249\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vr8xF9aHSzpe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "5f0ee4c3-4c96-4708-ad3d-d42ebd191b93"
      },
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "history = model.fit(\n",
        "    train_batches,\n",
        "    epochs=10,\n",
        "    validation_data=test_batches, validation_steps=20)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "2500/2500 [==============================] - 13s 5ms/step - loss: 0.4954 - accuracy: 0.7074 - val_loss: 0.3097 - val_accuracy: 0.9100\n",
            "Epoch 2/10\n",
            "2500/2500 [==============================] - 14s 5ms/step - loss: 0.2781 - accuracy: 0.8844 - val_loss: 0.4006 - val_accuracy: 0.8550\n",
            "Epoch 3/10\n",
            "2500/2500 [==============================] - 13s 5ms/step - loss: 0.2243 - accuracy: 0.9122 - val_loss: 0.2750 - val_accuracy: 0.8900\n",
            "Epoch 4/10\n",
            "2500/2500 [==============================] - 13s 5ms/step - loss: 0.1942 - accuracy: 0.9267 - val_loss: 0.2232 - val_accuracy: 0.9100\n",
            "Epoch 5/10\n",
            "2500/2500 [==============================] - 13s 5ms/step - loss: 0.1705 - accuracy: 0.9362 - val_loss: 0.3415 - val_accuracy: 0.8550\n",
            "Epoch 6/10\n",
            "2500/2500 [==============================] - 13s 5ms/step - loss: 0.1534 - accuracy: 0.9452 - val_loss: 0.3112 - val_accuracy: 0.9150\n",
            "Epoch 7/10\n",
            "2500/2500 [==============================] - 14s 5ms/step - loss: 0.1367 - accuracy: 0.9500 - val_loss: 0.3663 - val_accuracy: 0.8700\n",
            "Epoch 8/10\n",
            "2500/2500 [==============================] - 13s 5ms/step - loss: 0.1251 - accuracy: 0.9548 - val_loss: 0.5579 - val_accuracy: 0.8750\n",
            "Epoch 9/10\n",
            "2500/2500 [==============================] - 13s 5ms/step - loss: 0.1138 - accuracy: 0.9600 - val_loss: 0.4988 - val_accuracy: 0.8700\n",
            "Epoch 10/10\n",
            "2500/2500 [==============================] - 13s 5ms/step - loss: 0.1046 - accuracy: 0.9630 - val_loss: 0.5607 - val_accuracy: 0.8600\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D8pa7t1nS80G",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "7adf277f-1e05-4421-c712-d3e0101e02b0"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "history_dict = history.history\n",
        "\n",
        "acc = history_dict['accuracy']\n",
        "val_acc = history_dict['val_accuracy']\n",
        "loss=history_dict['loss']\n",
        "val_loss=history_dict['val_loss']\n",
        "\n",
        "epochs = range(1, len(acc) + 1)\n",
        "\n",
        "plt.figure(figsize=(12,9))\n",
        "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "plt.figure(figsize=(12,9))\n",
        "plt.plot(epochs, acc, 'bo', label='Training acc')\n",
        "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "plt.ylim((0.5,1))\n",
        "plt.show()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zp9tq1j1TQZM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}