Welcome_To_Colab.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/thomasjv799/Beta-VAE-on-Animal-Face/blob/master/Welcome_To_Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install transformers datasets evaluate"
      ],
      "metadata": {
        "id": "QmY5xsi9UHka",
        "outputId": "f772bd30-5143-4550-fd1b-efd9ad917522",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "from transformers import AutoTokenizer\n",
        "from transformers import DataCollatorWithPadding\n",
        "import evaluate\n",
        "from sklearn.metrics import accuracy_score, f1_score\n",
        "import numpy as np\n",
        "from transformers import create_optimizer\n",
        "from transformers.keras_callbacks import KerasMetricCallback\n",
        "import tensorflow as tf\n",
        "from transformers import TFAutoModelForSequenceClassification\n",
        "from transformers import TFDistilBertForSequenceClassification\n",
        "from transformers import TFDistilBertPreTrainedModel, TFDistilBertMainLayer\n",
        "from transformers import TFMobileBertForSequenceClassification\n",
        "# hgf_model = \"google/mobilebert-uncased\"\n",
        "hgf_2 = \"distilbert/distilbert-base-uncased\"\n",
        "# hgf_mob = \"mobilebert_uncased\""
      ],
      "metadata": {
        "id": "8N7JY6SmUHhB"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def preprocess_function(examples):\n",
        "    return tokenizer(examples[\"text\"], truncation=True)\n",
        "\n",
        "\n",
        "def compute_metrics(eval_pred):\n",
        "    predictions, labels = eval_pred\n",
        "    predictions = np.argmax(predictions, axis=1)\n",
        "\n",
        "    accuracy = accuracy_score(labels, predictions)\n",
        "    f1 = f1_score(labels, predictions, average='weighted')  # Use 'weighted', 'micro', or 'macro' based on your needs.\n",
        "\n",
        "    return {\n",
        "        \"accuracy\": accuracy,\n",
        "        \"f1\": f1\n",
        "    }\n"
      ],
      "metadata": {
        "id": "lyT9VdBfUHeF"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ds = load_dataset(\"csv\", data_files=\"output_5_classes_updated2.csv\",split=\"train\")\n",
        "ds = ds.train_test_split(test_size=0.1)"
      ],
      "metadata": {
        "id": "bEFyiLrYUMsj"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(hgf_2)"
      ],
      "metadata": {
        "id": "mqPM2uwIURP0",
        "outputId": "a3f179ea-f2b9-4a5d-8d21-513401a943a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
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            "f6c1913486274698b526315575cf49e2"
          ]
        }
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
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              "version_minor": 0,
              "model_id": "8305a025a5ff475e95e00afff6b3b9b0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f56abd294adc46f5b2cb8e6da40d2dc2"
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
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          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_query = ds.map(preprocess_function, batched=True)"
      ],
      "metadata": {
        "id": "T2CMVJ2AUSOh",
        "outputId": "845e2d3b-61ff-4d7f-d533-26d5b4bb1afb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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        }
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/13011 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4a116c54b3374b73a5498fe577e463e5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1446 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8bdc387cad1746748fc2aae6cc683d01"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"tf\")"
      ],
      "metadata": {
        "id": "gnWog2z8UTbi"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy = evaluate.load(\"accuracy\")"
      ],
      "metadata": {
        "id": "w690AFOAUUfi"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "id2label = {0: \"vehicle\", 1: \"user_manual\",2:\"generic\",3:\"productivity\",4:\"entertainment\"}\n",
        "label2id = {\"vehicle\": 0, \"user_manual\": 1,\"generic\":2,\"productivity\":3,\"entertainment\":4}"
      ],
      "metadata": {
        "id": "rMJvlhK-UVp_"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 16\n",
        "num_epochs = 10\n",
        "batches_per_epoch = len(tokenized_query[\"train\"]) // batch_size\n",
        "total_train_steps = int(batches_per_epoch * num_epochs)\n",
        "optimizer, schedule = create_optimizer(init_lr=2e-5, num_warmup_steps=0, num_train_steps=total_train_steps)"
      ],
      "metadata": {
        "id": "4xrii5FZUWl_"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# model_1 = \"mobilebert_uncased\"\n",
        "# model_2 = \"distillbert-uncased\""
      ],
      "metadata": {
        "id": "zM6bLWjiUXy2"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "base_model = TFAutoModelForSequenceClassification.from_pretrained(\n",
        "    pretrained_model_name_or_path = hgf_2,\n",
        "    num_labels=5,\n",
        "    id2label=id2label,\n",
        "    label2id=label2id\n",
        ")\n",
        "\n",
        "# model = ClippedModel(base_model, clip_value=1.0)\n",
        "# model.compile(optimizer=optimizer)"
      ],
      "metadata": {
        "id": "hRN8qbVPUaDC",
        "outputId": "7ad85ce5-176e-4536-e56f-a562eee1afaf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the PyTorch model were not used when initializing the TF 2.0 model TFDistilBertForSequenceClassification: ['vocab_transform.weight', 'vocab_transform.bias', 'vocab_layer_norm.weight', 'vocab_layer_norm.bias', 'vocab_projector.bias']\n",
            "- This IS expected if you are initializing TFDistilBertForSequenceClassification from a PyTorch model trained on another task or with another architecture (e.g. initializing a TFBertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing TFDistilBertForSequenceClassification from a PyTorch model that you expect to be exactly identical (e.g. initializing a TFBertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights or buffers of the TF 2.0 model TFDistilBertForSequenceClassification were not initialized from the PyTorch model and are newly initialized: ['pre_classifier.weight', 'pre_classifier.bias', 'classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from transformers import TFDistilBertPreTrainedModel\n",
        "from transformers.models.distilbert.modeling_tf_distilbert import TFMultiHeadSelfAttention\n",
        "\n",
        "class TFClampedMultiHeadSelfAttention(TFMultiHeadSelfAttention):\n",
        "    def __init__(self, config, clamp_value, **kwargs):\n",
        "        super().__init__(config, **kwargs)\n",
        "        self.clamp_value = clamp_value\n",
        "\n",
        "    def call(self, query, key, value, mask, head_mask=None, output_attentions=False, training=False):\n",
        "        # Call original attention\n",
        "        outputs = super().call(\n",
        "            query, key, value, mask,\n",
        "            head_mask=head_mask,\n",
        "            output_attentions=output_attentions,\n",
        "            training=training\n",
        "        )\n",
        "\n",
        "        # Apply clamping to the attention output\n",
        "        if isinstance(outputs, tuple):\n",
        "            clamped = tf.clip_by_value(outputs[0], -self.clamp_value, self.clamp_value)\n",
        "            return (clamped,) + outputs[1:]\n",
        "        return tf.clip_by_value(outputs, -self.clamp_value, self.clamp_value)\n",
        "\n",
        "class TFClampedDistilBertModel(TFDistilBertPreTrainedModel):\n",
        "    def __init__(self, base_model, clamp_value=1.0):\n",
        "        super().__init__(base_model.config)\n",
        "        self.clamp_value = clamp_value\n",
        "\n",
        "        # Create new transformer with clamped attention\n",
        "        self.base_model = tf.keras.models.clone_model(base_model)\n",
        "\n",
        "        # Replace attention layers with clamped versions\n",
        "        for layer in self.base_model.distilbert.transformer.layer:\n",
        "            original = layer.attention\n",
        "            # Create new clamped attention layer\n",
        "            layer.attention = TFClampedMultiHeadSelfAttention(\n",
        "                config=base_model.config,\n",
        "                clamp_value=clamp_value,\n",
        "                name=original.name\n",
        "            )\n",
        "            # Copy weights from original attention layer\n",
        "            layer.attention.set_weights(original.get_weights())\n",
        "\n",
        "    def call(self, inputs, training=False):\n",
        "        # Handle both dictionary and direct inputs\n",
        "        if isinstance(inputs, dict):\n",
        "            outputs = self.base_model(\n",
        "                input_ids=inputs.get('input_ids'),\n",
        "                attention_mask=inputs.get('attention_mask'),\n",
        "                training=training\n",
        "            )\n",
        "        else:\n",
        "            outputs = self.base_model(inputs, training=training)\n",
        "        return outputs\n",
        "\n",
        "    def prepare_tf_dataset(self, dataset, shuffle=True, batch_size=16, collate_fn=None):\n",
        "        return self.base_model.prepare_tf_dataset(\n",
        "            dataset,\n",
        "            shuffle=shuffle,\n",
        "            batch_size=batch_size,\n",
        "            collate_fn=collate_fn\n",
        "        )\n",
        "\n",
        "# Usage:\n",
        "# clamped_model = TFClampedDistilBertModel(base_model, clamp_value=1.0)"
      ],
      "metadata": {
        "id": "PHWWB24jbMdd"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clamped_model = TFClampedDistilBertModel(base_model, clamp_value=1.0)"
      ],
      "metadata": {
        "id": "qkEwUknxc4jM"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clamped_model.compile(optimizer=optimizer)"
      ],
      "metadata": {
        "id": "SqEUvlNlbWa5"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tf_train_set = clamped_model.prepare_tf_dataset(\n",
        "    tokenized_query[\"train\"],\n",
        "    shuffle=True,\n",
        "    batch_size=16,\n",
        "    collate_fn=data_collator,\n",
        ")\n",
        "\n",
        "tf_validation_set = clamped_model.prepare_tf_dataset(\n",
        "    tokenized_query[\"test\"],\n",
        "    shuffle=False,\n",
        "    batch_size=16,\n",
        "    collate_fn=data_collator,\n",
        ")\n"
      ],
      "metadata": {
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      },
      "execution_count": 78,
      "outputs": []
    },
    {
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        "metric_callback = KerasMetricCallback(metric_fn=compute_metrics, eval_dataset=tf_validation_set)"
      ],
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      },
      "execution_count": 79,
      "outputs": []
    },
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      "source": [
        "early_stopping = tf.keras.callbacks.EarlyStopping(\n",
        "    monitor='val_loss',  # Monitor validation loss\n",
        "    patience=3,          # Number of epochs with no improvement after which training will stop\n",
        "    mode='min',         # Stop when monitored value stops decreasing\n",
        "    restore_best_weights=True,  # Restore model weights from the epoch with the best value\n",
        "    min_delta=1e-4      # Minimum change to qualify as an improvement\n",
        ")"
      ],
      "metadata": {
        "id": "zBVnyUrIVKJ1"
      },
      "execution_count": 80,
      "outputs": []
    },
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      "source": [
        "callbacks = [metric_callback,early_stopping]"
      ],
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      },
      "execution_count": 81,
      "outputs": []
    },
    {
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      "source": [
        "history = clamped_model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=3, callbacks=callbacks,verbose=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5MORTS4CVMrf",
        "outputId": "152cd73f-efb1-49a7-860b-bf6dc11ac154"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/3\n",
            "813/813 [==============================] - 60s 74ms/step - loss: -388.0922 - val_loss: -623.3708 - accuracy: 0.1936 - f1: 0.0628\n",
            "Epoch 2/3\n",
            "813/813 [==============================] - 62s 76ms/step - loss: -903.2974 - val_loss: -1197.9688 - accuracy: 0.1936 - f1: 0.0628\n",
            "Epoch 3/3\n",
            "813/813 [==============================] - 60s 73ms/step - loss: -1507.2650 - val_loss: -1819.3340 - accuracy: 0.1936 - f1: 0.0628\n"
          ]
        }
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        "# @title Default title text\n",
        "model.save_pretrained(\"distilbert_1epoch\")"
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      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'model' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
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            "\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
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        "model"
      ],
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        "id": "VPAis_65Z0ME",
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        "colab": {
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        }
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "<transformers.models.mobilebert.modeling_tf_mobilebert.TFMobileBertForSequenceClassification at 0x7d7011438160>"
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          },
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          "execution_count": 37
        }
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