Inference.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "mount_file_id": "1OcWUdMLPn4DUqxnHXzX-i9SNjEG6V0ed",
      "authorship_tag": "ABX9TyOssri3CxykKoQxqNpBaRp/",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sanchit45/Practice/blob/main/Inference.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d2oXw1Rcf3Iz",
        "outputId": "680e32ad-b163-4174-ca83-56bbf6e94a00"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cpu')"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "nltk.download('wordnet')\n",
        "import random\n",
        "import re\n",
        "import csv\n",
        "from collections import Counter\n",
        "from functools import partial\n",
        "from pathlib import Path\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from IPython.core.display import display, HTML\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer # TF-IDF\n",
        "from sklearn.metrics import classification_report\n",
        "from tqdm import tqdm, tqdm_notebook\n",
        "\n",
        "# PyTorch modules\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "from torch import optim\n",
        "from torch.optim.lr_scheduler import CosineAnnealingLR\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from torch.utils.data.dataset import random_split\n",
        "# nltk text processors\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import wordpunct_tokenize\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "\n",
        "%matplotlib inline\n",
        "%config InlineBackend.figure_formats = ['svg']\n",
        "plt.style.use('ggplot')\n",
        "tqdm.pandas()\n",
        "\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "device"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "class FeedfowardTextClassifier(nn.Module):\n",
        "    def __init__(self, device, vocab_size, hidden1, hidden2, num_labels, batch_size):\n",
        "        super(FeedfowardTextClassifier, self).__init__()\n",
        "        self.device = device\n",
        "        self.batch_size = batch_size\n",
        "        self.fc1 = nn.Linear(vocab_size, hidden1)\n",
        "        self.fc2 = nn.Linear(hidden1, hidden2)\n",
        "        self.fc3 = nn.Linear(hidden2, num_labels)\n",
        "\n",
        "    def forward(self, x):\n",
        "        batch_size = len(x)\n",
        "        if batch_size != self.batch_size:\n",
        "            self.batch_size = batch_size\n",
        "        x = torch.FloatTensor(x)\n",
        "        x = F.relu(self.fc1(x))\n",
        "        x = F.relu(self.fc2(x))\n",
        "        return torch.sigmoid(self.fc3(x))\n",
        "\n"
      ],
      "metadata": {
        "id": "KwuXIy3J79tF"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model=torch.load(\"/content/drive/MyDrive/bow.pth\")\n",
        "\n",
        "token_idx_mapping=model[\"token2idx\"]\n",
        "\n",
        "bow_model = FeedfowardTextClassifier(\n",
        "    vocab_size=len(token_idx_mapping),\n",
        "    hidden1=100,\n",
        "    hidden2=50,\n",
        "    num_labels=2,\n",
        "    device=device,\n",
        "    batch_size=528,\n",
        ")\n",
        "\n",
        "\n",
        "bow_model.load_state_dict(model[\"state_dict\"])\n",
        "token_idx_mapping=model[\"token2idx\"]\n",
        "idx_token_mapping=model[\"index2token\"]\n",
        "print(bow_model)\n",
        "print(model[\"state_dict\"])\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "RZCeCptaf_vF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5de58c37-2183-4c50-fd43-188710100790"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "FeedfowardTextClassifier(\n",
            "  (fc1): Linear(in_features=1001, out_features=100, bias=True)\n",
            "  (fc2): Linear(in_features=100, out_features=50, bias=True)\n",
            "  (fc3): Linear(in_features=50, out_features=2, bias=True)\n",
            ")\n",
            "OrderedDict([('fc1.weight', tensor([[ 0.0641,  0.0777,  0.0736,  ..., -0.0120,  0.0778,  0.0525],\n",
            "        [ 0.0108,  0.0332,  0.0205,  ...,  0.0196,  0.0811,  0.0330],\n",
            "        [ 0.0385,  0.0363,  0.0224,  ..., -0.0252,  0.0674,  0.0468],\n",
            "        ...,\n",
            "        [-0.0169,  0.0522, -0.0166,  ...,  0.0073, -0.1093, -0.0380],\n",
            "        [ 0.0412,  0.0564,  0.0108,  ...,  0.0010,  0.0973,  0.0652],\n",
            "        [ 0.0522,  0.0629,  0.0614,  ...,  0.0130,  0.0807,  0.0153]])), ('fc1.bias', tensor([ 0.1516, -0.0235, -0.0126,  0.1621,  0.1890, -0.0100,  0.0177, -0.0038,\n",
            "         0.1844,  0.0191,  0.1438, -0.0268,  0.1655,  0.1424, -0.0088,  0.1266,\n",
            "        -0.0149,  0.1494, -0.0268, -0.0415,  0.1577,  0.1955, -0.0312,  0.1441,\n",
            "         0.0134,  0.1635, -0.0129, -0.0006, -0.0392,  0.1374,  0.0815, -0.0284,\n",
            "        -0.0372,  0.1256, -0.0046,  0.1263, -0.0021, -0.0031,  0.1666,  0.1318,\n",
            "         0.1748, -0.0235, -0.0113,  0.1577,  0.1764, -0.0418,  0.1985,  0.0011,\n",
            "         0.1286,  0.0003,  0.0092, -0.0190,  0.1538, -0.0131, -0.0257,  0.0412,\n",
            "         0.1660,  0.1364, -0.0123, -0.0176,  0.0010,  0.0683, -0.0101,  0.1356,\n",
            "         0.1348, -0.0073, -0.0297,  0.0048,  0.1192, -0.0377, -0.0225,  0.0698,\n",
            "         0.0054,  0.0387, -0.0028, -0.0203,  0.1546, -0.0049, -0.0368,  0.0029,\n",
            "         0.0535, -0.0246,  0.0227, -0.0214,  0.1341,  0.0152, -0.0214,  0.1828,\n",
            "         0.0167,  0.0261,  0.0019,  0.0129, -0.0393, -0.0222,  0.1220, -0.0156,\n",
            "        -0.0046,  0.1622, -0.0135,  0.1019])), ('fc2.weight', tensor([[-0.0009,  0.0691, -0.0061,  ..., -0.0125, -0.0497,  0.0171],\n",
            "        [ 0.0574,  0.0357,  0.1850,  ..., -0.1763,  0.0296,  0.0415],\n",
            "        [ 0.0888, -0.1652, -0.1152,  ...,  0.0689, -0.1314,  0.0317],\n",
            "        ...,\n",
            "        [ 0.0522,  0.0959,  0.1603,  ..., -0.1666,  0.0266,  0.0918],\n",
            "        [ 0.1451,  0.0095,  0.1040,  ..., -0.0018,  0.0703,  0.1030],\n",
            "        [ 0.0909, -0.1567, -0.1130,  ...,  0.2051, -0.0170,  0.0962]])), ('fc2.bias', tensor([-0.0952,  0.0583,  0.0702,  0.0970, -0.0771, -0.0140,  0.0481, -0.0786,\n",
            "         0.0871, -0.0383, -0.0989,  0.1007,  0.1372,  0.0631, -0.0539,  0.0111,\n",
            "         0.0458, -0.0723,  0.0471,  0.0109,  0.0978,  0.0913,  0.1427,  0.0962,\n",
            "         0.0551,  0.0504, -0.0860,  0.0875,  0.1323,  0.0986, -0.0560, -0.0186,\n",
            "         0.0868, -0.0094, -0.0374,  0.0252,  0.0578,  0.0504, -0.1001,  0.0689,\n",
            "         0.0503, -0.0639,  0.0086,  0.0872,  0.0899,  0.0606,  0.1024, -0.0206,\n",
            "        -0.0636,  0.0927])), ('fc3.weight', tensor([[ 0.1036, -0.1349,  0.1535,  0.2534,  0.1018, -0.2792, -0.1729, -0.2809,\n",
            "         -0.0762, -0.1080, -0.0304,  0.2792,  0.3222, -0.1937,  0.0067, -0.2276,\n",
            "         -0.2002,  0.1160, -0.1804, -0.1340,  0.3016, -0.0546,  0.1546,  0.2769,\n",
            "          0.2378, -0.2121,  0.0523, -0.1221,  0.0939,  0.2682, -0.2137, -0.0166,\n",
            "         -0.2132, -0.1239,  0.0444,  0.0005, -0.0818, -0.0844,  0.0393, -0.0764,\n",
            "         -0.1519, -0.2586, -0.2240, -0.0493, -0.0410, -0.0172,  0.2522, -0.2151,\n",
            "         -0.0805,  0.2371],\n",
            "        [-0.0469,  0.1692, -0.3003, -0.1693,  0.0580,  0.0204,  0.0977,  0.0448,\n",
            "          0.1486,  0.1134,  0.0028, -0.2857, -0.1327,  0.1828, -0.0789,  0.2077,\n",
            "          0.1039,  0.0295,  0.1280, -0.1332, -0.2520,  0.1778, -0.2842, -0.2033,\n",
            "         -0.2871,  0.2008, -0.1146,  0.2140, -0.3136, -0.1811,  0.1098, -0.0269,\n",
            "          0.0226,  0.0795,  0.0456,  0.2498,  0.1454,  0.1424, -0.1027,  0.1556,\n",
            "          0.0394,  0.0533,  0.2207,  0.2238,  0.1800,  0.2094, -0.1223,  0.0894,\n",
            "          0.1945, -0.1664]])), ('fc3.bias', tensor([ 0.1394, -0.1215]))])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tfidf_model = FeedfowardTextClassifier(\n",
        "    vocab_size=len(token_idx_mapping),\n",
        "    hidden1=100,\n",
        "    hidden2=50,\n",
        "    num_labels=2,\n",
        "    device=device,\n",
        "    batch_size=528,\n",
        ")\n",
        "tfidf_model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KONGwXnt-qV5",
        "outputId": "73e3f007-7596-4703-85fa-4589709282a2"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FeedfowardTextClassifier(\n",
              "  (fc1): Linear(in_features=1001, out_features=100, bias=True)\n",
              "  (fc2): Linear(in_features=100, out_features=50, bias=True)\n",
              "  (fc3): Linear(in_features=50, out_features=2, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tfidf=torch.load(\"/content/drive/MyDrive/tfidf.pth\")\n",
        "tfidf_model.load_state_dict(tfidf[\"state_dict\"])\n",
        "print(tfidf_model)"
      ],
      "metadata": {
        "id": "X6qhHj6ggNFH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "00c52409-4a3e-4615-a506-6938cc68d907"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "FeedfowardTextClassifier(\n",
            "  (fc1): Linear(in_features=1001, out_features=100, bias=True)\n",
            "  (fc2): Linear(in_features=100, out_features=50, bias=True)\n",
            "  (fc3): Linear(in_features=50, out_features=2, bias=True)\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def build_vocab(corpus):\n",
        "    vocab = {}\n",
        "    for token in corpus:\n",
        "        if token not in vocab.keys():\n",
        "              vocab[token] = len(vocab)\n",
        "    return vocab\n",
        "\n",
        "def build_index2token(vocab):\n",
        "    index2token = {}\n",
        "    for token in vocab.keys():\n",
        "        index2token[vocab[token]] = token\n",
        "    return index2token\n",
        "\n",
        "def tokenize(text, stop_words, lemmatizer):\n",
        "    text = re.sub(r'[^\\w\\s]', '', text) # remove special characters\n",
        "    text = text.lower() # lowercase\n",
        "    tokens = wordpunct_tokenize(text) # tokenize\n",
        "    tokens = [lemmatizer.lemmatize(token) for token in tokens] # noun lemmatizer\n",
        "    tokens = [lemmatizer.lemmatize(token, \"v\") for token in tokens] # verb lemmatizer\n",
        "    tokens = [token for token in tokens if token not in stop_words] # remove stopwords\n",
        "    return tokens\n",
        "\n",
        "def build_bow_vector(sequence, idx2token):\n",
        "    vector = [0] * len(idx2token)\n",
        "    for token_idx in sequence:\n",
        "        if token_idx not in idx2token:\n",
        "            raise ValueError('Wrong sequence index found!')\n",
        "        else:\n",
        "            vector[token_idx] += 1\n",
        "    return vector\n",
        "\n",
        "def preprocess(data,token_idx_mapping,idx_token_mapping,feature):\n",
        "\n",
        "        stop_words = set(stopwords.words('english'))\n",
        "        lemmatizer = WordNetLemmatizer()\n",
        "        tokens=tokenize(data, stop_words, lemmatizer)\n",
        "        vocab=build_vocab(tokens)\n",
        "        index2token=build_index2token(vocab)\n",
        "        doc=list(vocab.keys())\n",
        "        sequence=[token_idx_mapping[token] for token in doc if token in token_idx_mapping]\n",
        "        idx2token=idx_token_mapping\n",
        "        bow_vector=build_bow_vector(sequence, idx2token)\n",
        "        \"\"\"vectorizer = TfidfVectorizer(\n",
        "            analyzer='word',\n",
        "            tokenizer=lambda doc: doc,\n",
        "            preprocessor=lambda doc: doc,\n",
        "            token_pattern=None,\n",
        "        )\n",
        "        tfidf_vectors = vectorizer.fit_transform(tokens).toarray()\n",
        "        tfidf_vector = [vector.tolist() for vector in tfidf_vectors]\"\"\"\n",
        "        vectorizer = TfidfVectorizer()\n",
        "\n",
        "        # Fit and transform the input text to obtain TF-IDF vectors\n",
        "        tfidf_vectors = vectorizer.fit_transform(tokens).toarray()\n",
        "\n",
        "\n",
        "        # Convert the TF-IDF vectors to a list of lists\n",
        "        tfidf_vector = tfidf_vectors.tolist()\n",
        "        if feature ==\"bow\":\n",
        "            return bow_vector\n",
        "        elif feature==\"tfidf\":\n",
        "            #return tfidf_vector\n",
        "            print(tfidf_vector)"
      ],
      "metadata": {
        "id": "yLdfzBhqg33w"
      },
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data=\"Quora is a place to gain and share knowledge. It's a platform to ask questions and connect with people who contribute unique insights and quality answers \""
      ],
      "metadata": {
        "id": "bXs4jeEX-ABZ"
      },
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "preprocess(data,token_idx_mapping,idx_token_mapping,feature=\"bow\")"
      ],
      "metadata": {
        "id": "Pfpty-TyEOoz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e22b0755-e7fc-496d-aea7-8ad7126b720b"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "r3Y5ZZtBCt18"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#use BOw model(just below this text cell) ,few input issues in tfidf"
      ],
      "metadata": {
        "id": "dAXON2IvCuEK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bow_model.eval()\n",
        "bow_model.to(device)\n",
        "y_pred = []\n",
        "\n",
        "\n",
        "with torch.no_grad():\n",
        "\n",
        "        inputs = preprocess(data,token_idx_mapping,idx_token_mapping,feature=\"bow\")\n",
        "        probs = bow_model([inputs])\n",
        "\n",
        "\n",
        "        probs = probs.detach().cpu().numpy()\n",
        "        predictions = np.argmax(probs, axis=1)\n",
        "        y_pred.extend(predictions)\n",
        "\n",
        "\n",
        "print(y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "esm5puNChJRd",
        "outputId": "00a9e473-4c48-4a4c-8fbd-d5f0605bb544"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# working on this one\n",
        "tfidf_model.eval()\n",
        "tfidf_model.to(device)\n",
        "y_pred = []\n",
        "\n",
        "\n",
        "with torch.no_grad():\n",
        "\n",
        "        inputs = preprocess(data,token_idx_mapping,idx_token_mapping,feature=\"tfidf\")\n",
        "        probs = tfidf_model([inputs])\n",
        "\n",
        "\n",
        "        probs = probs.detach().cpu().numpy()\n",
        "        predictions = np.argmax(probs, axis=1)\n",
        "        y_pred.extend(predictions)\n",
        "\n",
        "\n",
        "print(y_pred)"
      ],
      "metadata": {
        "id": "byLj-3hbiO-j",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "ebf2a6a8-5298-4d0a-d3d9-081f74b2da3f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  (0, 16)\t0.39028945195301484\n",
            "  (0, 8)\t0.455453973865905\n",
            "  (0, 15)\t0.657873467373268\n",
            "  (0, 11)\t0.455453973865905\n",
            "  (1, 7)\t0.20259317165969928\n",
            "  (1, 14)\t0.40518634331939857\n",
            "  (1, 17)\t0.20259317165969928\n",
            "  (1, 4)\t0.20259317165969928\n",
            "  (1, 0)\t0.40518634331939857\n",
            "  (1, 12)\t0.40518634331939857\n",
            "  (1, 5)\t0.17104307638950533\n",
            "  (1, 9)\t0.17104307638950533\n",
            "  (1, 13)\t0.20259317165969928\n",
            "  (1, 1)\t0.34208615277901067\n",
            "  (1, 16)\t0.1465709212673861\n",
            "  (1, 8)\t0.34208615277901067\n",
            "  (1, 11)\t0.17104307638950533\n",
            "  (2, 7)\t0.21211528370190072\n",
            "  (2, 14)\t0.42423056740380144\n",
            "  (2, 17)\t0.21211528370190072\n",
            "  (2, 4)\t0.21211528370190072\n",
            "  (2, 0)\t0.42423056740380144\n",
            "  (2, 12)\t0.42423056740380144\n",
            "  (2, 5)\t0.17908229767263645\n",
            "  (2, 9)\t0.17908229767263645\n",
            "  (2, 13)\t0.21211528370190072\n",
            "  (2, 1)\t0.17908229767263645\n",
            "  (2, 16)\t0.15345992311775974\n",
            "  (2, 8)\t0.3581645953452729\n",
            "  (2, 11)\t0.17908229767263645\n",
            "  (3, 6)\t0.657873467373268\n",
            "  (3, 5)\t0.455453973865905\n",
            "  (3, 1)\t0.455453973865905\n",
            "  (3, 16)\t0.39028945195301484\n",
            "  (4, 10)\t0.6817217130641468\n",
            "  (4, 2)\t0.5590215568768956\n",
            "  (4, 9)\t0.4719644106114538\n",
            "  (5, 3)\t0.7732623667832087\n",
            "  (5, 2)\t0.6340862024337309\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-112-6bb822af1cdb>\u001b[0m in \u001b[0;36m<cell line: 6>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m         \u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpreprocess\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtoken_idx_mapping\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0midx_token_mapping\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfeature\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"tfidf\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m         \u001b[0mprobs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtfidf_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1499\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1500\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1502\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1503\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-5-797f098411ca>\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     12\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mbatch_size\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatch_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFloatTensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfc1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfc2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: must be real number, not NoneType"
          ]
        }
      ]
    }
  ]
}