DL_Project_Model_ALBERT.ipynb

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            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/507.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.2/507.1 kB\u001b[0m \u001b[31m2.6 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m507.1/507.1 kB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/84.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.1/84.1 kB\u001b[0m \u001b[31m7.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m270.9/270.9 kB\u001b[0m \u001b[31m30.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m9.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m9.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m8.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install --quiet transformers datasets evaluate accelerate"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!gdown 1oUKScMHKFS0UfgVVbb1sPpcF_B4BNco4 -O dataset.zip\n",
        "!unzip -o dataset.zip -d dataset\n",
        "!rm dataset.zip"
      ],
      "metadata": {
        "id": "BnXcsgssaGaf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c0168b14-9608-489d-84af-6d05c5559b41"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1oUKScMHKFS0UfgVVbb1sPpcF_B4BNco4\n",
            "To: /content/dataset.zip\n",
            "\r  0% 0.00/503k [00:00<?, ?B/s]\r100% 503k/503k [00:00<00:00, 149MB/s]\n",
            "Archive:  dataset.zip\n",
            "  inflating: dataset/test.csv        \n",
            "  inflating: dataset/train.csv       \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer, DataCollatorWithPadding, AutoModelForSequenceClassification, TrainingArguments, Trainer, pipeline, AutoConfig\n",
        "import evaluate\n",
        "from datasets import load_dataset\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "RJzTvK7ScZkT"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
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      "source": [
        "MODEL_NAME=\"HooshvareLab/bert-fa-base-uncased\"\n",
        "MODEL_NAME=\"FacebookAI/xlm-roberta-large\"\n",
        "MODEL_NAME=\"pedramyazdipoor/persian_xlm_roberta_large\"\n",
        "MODEL_NAME=\"wietsedv/xlm-roberta-base-ft-udpos28-fa\"\n",
        "MODEL_NAME=\"HooshvareLab/albert-fa-zwnj-base-v2\""
      ],
      "metadata": {
        "id": "7HdyAr5HuVMK"
      },
      "execution_count": 4,
      "outputs": []
    },
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      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)"
      ],
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      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
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            "text/plain": [
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      "cell_type": "code",
      "source": [
        "# def preprocess_function(dataframe):\n",
        "#     return tokenizer(dataframe[\"text\"].tolist(), padding=True, truncation=True, return_tensors=\"pt\")\n",
        "def preprocess_function(data):\n",
        "    for i in range(len(data[\"label\"])):\n",
        "      data[\"label\"][i]=label2id[data[\"label\"][i]]\n",
        "    # return tokenizer(data[\"text\"],add_special_tokens = True,\n",
        "    #                  return_token_type_ids = True,\n",
        "    #                  return_tensors = 'pt',\n",
        "    #                  padding = True,\n",
        "    #                  return_offsets_mapping = True,\n",
        "    #                  truncation = 'only_first',\n",
        "    #                  max_length = 32)\n",
        "    return tokenizer(data[\"text\"], truncation=True,padding=True,max_length=50, add_special_tokens = True)"
      ],
      "metadata": {
        "id": "fiaVrgnEdM34"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "TRAIN_FILE=\"dataset/train.csv\"\n",
        "TEST_FILE=\"dataset/test.csv\""
      ],
      "metadata": {
        "id": "NhGLpwbdoTWK"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_df= pd.read_csv(TRAIN_FILE)\n",
        "# test_df= pd.read_csv(TEST_FILE)"
      ],
      "metadata": {
        "id": "agljSqNGiifs"
      },
      "execution_count": 8,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "dataset = load_dataset('csv', data_files={'train': TRAIN_FILE,'test': TEST_FILE})"
      ],
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        "id": "fXvK7jK5oGHK",
        "colab": {
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      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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        {
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              "Generating test split: 0 examples [00:00, ? examples/s]"
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      "cell_type": "code",
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        "id2label = {}\n",
        "label2id = {}\n",
        "#TODO: use dataset[\"train\"][\"label\"].unique()\n",
        "emotions=train_df[\"label\"].unique()\n",
        "index=0\n",
        "for emotion in emotions:\n",
        "  id2label[index]=emotion\n",
        "  label2id[emotion]=index\n",
        "  index+=1\n",
        "num_classes=len(emotions)"
      ],
      "metadata": {
        "id": "OGmvyyafeJqD"
      },
      "execution_count": 10,
      "outputs": []
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        "tokenized_dataset = dataset.map(preprocess_function, batched=True)"
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            "text/plain": [
              "Map:   0%|          | 0/6125 [00:00<?, ? examples/s]"
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      "execution_count": 12,
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            "text/plain": [
              "{'text': '\" کیفیتش خیلی خوبه با شک خریدم ولی واقعا راضیم بعد از حدود 2 ماه استفاده « متاسفانه باخبر شدیم » که فردی در ایرانشهر به حداقل 41 دختر تجاوز کرده . امیدواریم با همکاری نمایندگان محترم مجلس و دستگاه قضا ، دیگه به این راحتی باخبر نشیم .',\n",
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
      ],
      "metadata": {
        "id": "WgBxUHNkeBWg"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "config = AutoConfig.from_pretrained(\"HooshvareLab/albert-fa-zwnj-base-v2\")\n",
        "config.update({\n",
        "    \"num_labels\":num_classes,\n",
        "    \"id2label\":id2label,\n",
        "    \"label2id\":label2id\n",
        "})"
      ],
      "metadata": {
        "id": "cETafsH_plPO"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = AutoModelForSequenceClassification.from_pretrained(\n",
        "    MODEL_NAME, config=config\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104,
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      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/44.9M [00:00<?, ?B/s]"
            ],
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of AlbertForSequenceClassification were not initialized from the model checkpoint at HooshvareLab/albert-fa-zwnj-base-v2 and are newly initialized: ['classifier.weight', 'classifier.bias', 'albert.pooler.bias', 'albert.pooler.weight']\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": [
        "accuracy = evaluate.load(\"accuracy\")\n",
        "def compute_metrics(eval_pred):\n",
        "    predictions, labels = eval_pred\n",
        "    predictions = np.argmax(predictions, axis=1)\n",
        "    return accuracy.compute(predictions=predictions, references=labels)"
      ],
      "metadata": {
        "id": "Xz2HN_Kqg7UK",
        "colab": {
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      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/4.20k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0e7a90ef919b4d6ea58f4708aef4e86d"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "training_args = TrainingArguments(\n",
        "    output_dir=\"my_awesome_model\",\n",
        "    learning_rate=3e-5,\n",
        "    # max_seq_len = 256,\n",
        "    gradient_accumulation_steps = 8,\n",
        "    per_device_train_batch_size=4,\n",
        "    per_device_eval_batch_size=4,\n",
        "    num_train_epochs=2,\n",
        "    weight_decay=0.01,\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    save_strategy=\"epoch\",\n",
        "    load_best_model_at_end=True,\n",
        "    push_to_hub=False,\n",
        ")\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=\"my_awesome_model\",\n",
        "    learning_rate=2e-5,\n",
        "    per_device_train_batch_size=16,\n",
        "    per_device_eval_batch_size=16,\n",
        "    num_train_epochs=5,\n",
        "    weight_decay=0.01,\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    save_strategy=\"epoch\",\n",
        "    load_best_model_at_end=True,\n",
        "    push_to_hub=False,\n",
        ")"
      ],
      "metadata": {
        "id": "RpGciXzjg8tA"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=tokenized_dataset[\"train\"],\n",
        "    eval_dataset=tokenized_dataset[\"test\"],\n",
        "    tokenizer=tokenizer,\n",
        "    data_collator=data_collator,\n",
        "    compute_metrics=compute_metrics,\n",
        ")"
      ],
      "metadata": {
        "id": "xL9hNiVrgJhg"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "4Jut8nzQgsMc",
        "outputId": "67cf832d-cb5a-406b-fad6-91643fafc103"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You're using a AlbertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1915' max='1915' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1915/1915 05:03, Epoch 5/5]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>No log</td>\n",
              "      <td>1.371873</td>\n",
              "      <td>0.475239</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>1.276700</td>\n",
              "      <td>1.303931</td>\n",
              "      <td>0.528236</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.838200</td>\n",
              "      <td>1.384774</td>\n",
              "      <td>0.507385</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.581800</td>\n",
              "      <td>1.425434</td>\n",
              "      <td>0.523023</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.581800</td>\n",
              "      <td>1.456195</td>\n",
              "      <td>0.526499</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=1915, training_loss=0.7921786714160411, metrics={'train_runtime': 305.7964, 'train_samples_per_second': 100.148, 'train_steps_per_second': 6.262, 'total_flos': 71507920312500.0, 'train_loss': 0.7921786714160411, 'epoch': 5.0})"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classifier = pipeline(\n",
        "    \"text-classification\",\n",
        "    model=model,\n",
        "    tokenizer=tokenizer,\n",
        "    return_all_scores=True,\n",
        "    device=0\n",
        ")\n",
        "def predict(text):\n",
        "  return classifier(text)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V5a8-Kyqjwbb",
        "outputId": "ed888de4-4993-4739-de49-a7a1508bfdc5"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/text_classification.py:105: UserWarning: `return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text=\"امروز چه روز غم انگیزیه\""
      ],
      "metadata": {
        "id": "b1QIIkl-krIx"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer(text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l72Z6ZGVBPoM",
        "outputId": "1f38b817-0f8f-4147-99ca-3fe9d736672f"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': [2, 459, 261, 202, 4384, 5717, 184, 3], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1]}"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predict(text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C1DYrMSCkxtY",
        "outputId": "8da69786-6260-4dc8-bc70-487a3af9ef1b"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[{'label': 'SAD', 'score': 0.3667246401309967},\n",
              "  {'label': 'HATE', 'score': 0.018979720771312714},\n",
              "  {'label': 'OTHER', 'score': 0.024096760898828506},\n",
              "  {'label': 'FEAR', 'score': 0.01760365255177021},\n",
              "  {'label': 'ANGRY', 'score': 0.010263434611260891},\n",
              "  {'label': 'HAPPY', 'score': 0.5012698173522949},\n",
              "  {'label': 'SURPRISE', 'score': 0.06106196343898773}]]"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history=pd.DataFrame(trainer.state.log_history)"
      ],
      "metadata": {
        "id": "syrYWIIAmMPz"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "val_acc=history[[\"epoch\",\"eval_accuracy\"]].dropna()\n",
        "val_loss=history[[\"epoch\",\"eval_loss\"]].dropna()\n",
        "train_loss=history[[\"epoch\",\"loss\"]].dropna()\n",
        "learning_rates=history[[\"epoch\",\"learning_rate\"]].dropna()"
      ],
      "metadata": {
        "id": "ztzzKteWnZhX"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(val_acc[\"epoch\"],val_acc[\"eval_accuracy\"], label=\"Accuracy\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.title(\"Val Accuracy per Epoch\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "39cdQ38DokBJ",
        "outputId": "c8d0b7de-9c80-48f3-d5ab-28b59a111eb4"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(val_loss[\"epoch\"],val_loss[\"eval_loss\"], label=\"Val Loss\")\n",
        "plt.plot(train_loss[\"epoch\"],train_loss[\"loss\"], label=\"Train Loss\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.title(\"Loss per Epoch\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "SgrWvFByo1XD",
        "outputId": "52a0a42a-3ada-4cc4-9a8e-eb4908be11d5"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(learning_rates[\"epoch\"],learning_rates[\"learning_rate\"], label=\"Learning rate\")\n",
        "plt.xlabel(\"alpha\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.title(\"Learning rate per Epoch\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "Gqwa-WfGpHnd",
        "outputId": "2787d106-bd90-4367-bcfa-a08a7867bfd4"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}