Superhero_Name_Generator.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "21XXWP7Fpt2L"
      },
      "source": [
        "# Superhero Name Generator"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H6P0NU5Cpt2R"
      },
      "source": [
        "Importing the data and creating a tokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "uq4CLmsLpt2P",
        "outputId": "b240ebe3-b1f8-4361-90f3-6bc450a0a4fc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Done\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "print('Done')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Done\n"
          ]
        }
      ],
      "source": [
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras_preprocessing.sequence import pad_sequences\n",
        "from keras.callbacks import EarlyStopping \n",
        "from keras.models import Sequential\n",
        "from keras.layers import Embedding , Conv1D , MaxPool1D , LSTM , Dense\n",
        "print('Done')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "JqhtLuAHpt2R",
        "outputId": "23825e8d-2be9-4989-fe34-9afe5ab5d108"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "jumpa\t\n",
            "doctor fate\t\n",
            "starlight\t\n",
            "isildur\t\n",
            "lasher\t\n",
            "varvara\t\n",
            "the target\t\n",
            "axel\t\n",
            "battra\t\n",
            "changeling\t\n",
            "pyrrh\n"
          ]
        }
      ],
      "source": [
        "with open( 'C:/Users/HP/Downloads/GitHub/Super-Hero-Name-Generator/Names.txt' , 'r') as file :\n",
        "    data = file.read()\n",
        "\n",
        "print(data[:100])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "95Lo1Yqzpt2T"
      },
      "outputs": [],
      "source": [
        "tokenizer = Tokenizer( filters='!\"#$%&()*+,-./:;<=>?@[\\\\]^_`{|}~' , split='\\n' )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "qYfC4sj2pt2V"
      },
      "outputs": [],
      "source": [
        "tokenizer.fit_on_texts(data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EO4-dPM6pt2X",
        "outputId": "f6a3566f-e008-4c35-a110-88e2f8bf3417"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{1: '\\t',\n",
              " 2: 'a',\n",
              " 3: 'e',\n",
              " 4: 'r',\n",
              " 5: 'o',\n",
              " 6: 'n',\n",
              " 7: 'i',\n",
              " 8: ' ',\n",
              " 9: 't',\n",
              " 10: 's',\n",
              " 11: 'l',\n",
              " 12: 'm',\n",
              " 13: 'h',\n",
              " 14: 'd',\n",
              " 15: 'c',\n",
              " 16: 'u',\n",
              " 17: 'g',\n",
              " 18: 'k',\n",
              " 19: 'b',\n",
              " 20: 'p',\n",
              " 21: 'y',\n",
              " 22: 'w',\n",
              " 23: 'f',\n",
              " 24: 'v',\n",
              " 25: 'j',\n",
              " 26: 'z',\n",
              " 27: 'x',\n",
              " 28: 'q'}"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "char_to_index = tokenizer.word_index\n",
        "index_to_char = dict((value , key) for key , value in char_to_index.items())\n",
        "\n",
        "index_to_char"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SMwtIw_Bpt2Z"
      },
      "source": [
        "## Converting between names and sequences"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N7iQLIXzpt2a",
        "outputId": "ea4c530b-165d-4d1d-9e16-d9846a7ddd25"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['jumpa\\t',\n",
              " 'doctor fate\\t',\n",
              " 'starlight\\t',\n",
              " 'isildur\\t',\n",
              " 'lasher\\t',\n",
              " 'varvara\\t',\n",
              " 'the target\\t',\n",
              " 'axel\\t',\n",
              " 'battra\\t',\n",
              " 'changeling\\t']"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "names = data.splitlines()\n",
        "names[:10]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n_-TTfqipt2c",
        "outputId": "7e886014-3dff-406c-c000-9139e13f2a62"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[[25], [16], [12], [20], [2], [1]]"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tokenizer.texts_to_sequences(names[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "P6IsKH1Upt2e"
      },
      "outputs": [],
      "source": [
        "def name_to_seq(name):\n",
        "    return [tokenizer.texts_to_sequences(c)[0][0] for c in name]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TuLUiMP3pt2g",
        "outputId": "0f9e6522-1642-493c-9db4-6b5672986275"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[25, 16, 12, 20, 2, 1]"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "name_to_seq(names[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "yFUYhimKpt2h"
      },
      "outputs": [],
      "source": [
        "def seq_to_name(seq):\n",
        "    return ''.join([index_to_char[i] for i in seq if i != 0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "o21y7uSDxhPj",
        "outputId": "0ffebbba-a435-4f2f-d592-2f4d3f433ef6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'jumpa\\t'"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "seq_to_name(name_to_seq(names[0]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uCbAzsNjpt2m"
      },
      "source": [
        "## Creating sequences and padding all sequences"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "zstNn-0dpt2m"
      },
      "outputs": [],
      "source": [
        "sequences = []\n",
        "\n",
        "for name in names :\n",
        "    seq = name_to_seq(name)\n",
        "    if len(seq) >= 2 :\n",
        "        sequences += [seq[:i] for i in range(2 , len(seq) + 1)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BjRTMysvpt2o",
        "outputId": "456229f1-02ff-4969-a3b2-63a70556212d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[[25, 16],\n",
              " [25, 16, 12],\n",
              " [25, 16, 12, 20],\n",
              " [25, 16, 12, 20, 2],\n",
              " [25, 16, 12, 20, 2, 1],\n",
              " [14, 5],\n",
              " [14, 5, 15],\n",
              " [14, 5, 15, 9],\n",
              " [14, 5, 15, 9, 5],\n",
              " [14, 5, 15, 9, 5, 4]]"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sequences[:10]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SR68pu2tpt2q",
        "outputId": "11e8d3f0-44e2-4dfe-b07b-0cfef05a822a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "33"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "max_len = max([len(x) for x in sequences])\n",
        "max_len"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h_1BtWO7pt2r",
        "outputId": "8d4e9021-051e-4d07-bee9-ce6f75e2701f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
              "        0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0, 25, 16])"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "padded_sequences = pad_sequences( sequences , padding = 'pre' , maxlen = max_len )\n",
        "\n",
        "padded_sequences[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sPlrLRpSpt2t",
        "outputId": "4d67406d-4f80-4310-ed6d-a32e24822cf4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(88278, 33)"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "padded_sequences.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dqph3kRFs7fY"
      },
      "source": [
        "## Creating Training and Test Sets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BE4BIeSnpt2v",
        "outputId": "7a14a664-591b-4565-a16e-40363fd08861"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[(88278, 32), (88278,)]"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x , y = padded_sequences[:,:-1] , padded_sequences[:,-1]\n",
        "[x.shape , y.shape]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q_9B5HKFs7fZ",
        "outputId": "fb2ebda2-01fd-431c-a533-1bb72a5ee428"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[(70622, 32), (70622,), (17656, 32), (17656,)]"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_train , X_test , Y_train , Y_test = train_test_split(x , y , train_size= 0.8 , random_state= 4)\n",
        "\n",
        "[X_train.shape,Y_train.shape,X_test.shape,Y_test.shape]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cgqRZtqnpt2x",
        "outputId": "5b55cb0b-1b3c-454b-91af-2ba1a91141b8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "29"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "num_chars = len(char_to_index.keys()) + 1\n",
        "num_chars"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9qN3gIoys7fZ"
      },
      "source": [
        "## Model Creating"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IYkyHBTbs7fZ",
        "outputId": "c32c20d0-a801-4dfe-b2a9-a66adad4b406"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding (Embedding)       (None, 32, 8)             232       \n",
            "                                                                 \n",
            " conv1d (Conv1D)             (None, 32, 64)            2624      \n",
            "                                                                 \n",
            " max_pooling1d (MaxPooling1D  (None, 16, 64)           0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " lstm (LSTM)                 (None, 32)                12416     \n",
            "                                                                 \n",
            " dense (Dense)               (None, 29)                957       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 16,229\n",
            "Trainable params: 16,229\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model = Sequential([\n",
        "                    Embedding(num_chars , 8 , input_length= max_len - 1 ) ,    \n",
        "                    Conv1D(64 , 5 , strides= 1 , activation= 'tanh' , padding= 'causal') ,\n",
        "                    MaxPool1D(2) ,\n",
        "                    LSTM(32) ,\n",
        "                    Dense(num_chars , activation='softmax')\n",
        "])\n",
        "\n",
        "model.compile(\n",
        "                loss = 'sparse_categorical_crossentropy' ,\n",
        "                optimizer = 'adam' ,\n",
        "                metrics = ['accuracy']\n",
        ")\n",
        "\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AyxfeL3Ss7fZ"
      },
      "source": [
        "## Model training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j0Ssl4qupt22",
        "outputId": "58c0b7af-a512-4e93-e2ef-09c783c9bbc6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "2207/2207 - 16s - loss: 2.7385 - accuracy: 0.1898 - val_loss: 2.5719 - val_accuracy: 0.2293 - 16s/epoch - 7ms/step\n",
            "Epoch 2/50\n",
            "2207/2207 - 18s - loss: 2.5306 - accuracy: 0.2354 - val_loss: 2.4763 - val_accuracy: 0.2546 - 18s/epoch - 8ms/step\n",
            "Epoch 3/50\n",
            "2207/2207 - 19s - loss: 2.4633 - accuracy: 0.2542 - val_loss: 2.4313 - val_accuracy: 0.2581 - 19s/epoch - 8ms/step\n",
            "Epoch 4/50\n",
            "2207/2207 - 20s - loss: 2.4194 - accuracy: 0.2648 - val_loss: 2.3910 - val_accuracy: 0.2716 - 20s/epoch - 9ms/step\n",
            "Epoch 5/50\n",
            "2207/2207 - 23s - loss: 2.3839 - accuracy: 0.2746 - val_loss: 2.3659 - val_accuracy: 0.2843 - 23s/epoch - 10ms/step\n",
            "Epoch 6/50\n",
            "2207/2207 - 21s - loss: 2.3542 - accuracy: 0.2835 - val_loss: 2.3402 - val_accuracy: 0.2960 - 21s/epoch - 10ms/step\n",
            "Epoch 7/50\n",
            "2207/2207 - 20s - loss: 2.3273 - accuracy: 0.2930 - val_loss: 2.3247 - val_accuracy: 0.2908 - 20s/epoch - 9ms/step\n",
            "Epoch 8/50\n",
            "2207/2207 - 23s - loss: 2.3033 - accuracy: 0.2991 - val_loss: 2.3068 - val_accuracy: 0.3037 - 23s/epoch - 10ms/step\n",
            "Epoch 9/50\n",
            "2207/2207 - 23s - loss: 2.2814 - accuracy: 0.3067 - val_loss: 2.2996 - val_accuracy: 0.3071 - 23s/epoch - 10ms/step\n",
            "Epoch 10/50\n",
            "2207/2207 - 22s - loss: 2.2618 - accuracy: 0.3133 - val_loss: 2.2821 - val_accuracy: 0.3092 - 22s/epoch - 10ms/step\n",
            "Epoch 11/50\n",
            "2207/2207 - 21s - loss: 2.2425 - accuracy: 0.3210 - val_loss: 2.2635 - val_accuracy: 0.3175 - 21s/epoch - 10ms/step\n",
            "Epoch 12/50\n",
            "2207/2207 - 19s - loss: 2.2259 - accuracy: 0.3252 - val_loss: 2.2571 - val_accuracy: 0.3199 - 19s/epoch - 9ms/step\n",
            "Epoch 13/50\n",
            "2207/2207 - 20s - loss: 2.2089 - accuracy: 0.3310 - val_loss: 2.2493 - val_accuracy: 0.3259 - 20s/epoch - 9ms/step\n",
            "Epoch 14/50\n",
            "2207/2207 - 19s - loss: 2.1941 - accuracy: 0.3372 - val_loss: 2.2397 - val_accuracy: 0.3246 - 19s/epoch - 9ms/step\n",
            "Epoch 15/50\n",
            "2207/2207 - 17s - loss: 2.1797 - accuracy: 0.3419 - val_loss: 2.2290 - val_accuracy: 0.3337 - 17s/epoch - 8ms/step\n",
            "Epoch 16/50\n",
            "2207/2207 - 18s - loss: 2.1664 - accuracy: 0.3473 - val_loss: 2.2235 - val_accuracy: 0.3355 - 18s/epoch - 8ms/step\n",
            "Epoch 17/50\n",
            "2207/2207 - 18s - loss: 2.1531 - accuracy: 0.3510 - val_loss: 2.2179 - val_accuracy: 0.3390 - 18s/epoch - 8ms/step\n",
            "Epoch 18/50\n",
            "2207/2207 - 19s - loss: 2.1424 - accuracy: 0.3554 - val_loss: 2.2138 - val_accuracy: 0.3380 - 19s/epoch - 8ms/step\n",
            "Epoch 19/50\n",
            "2207/2207 - 19s - loss: 2.1304 - accuracy: 0.3588 - val_loss: 2.2060 - val_accuracy: 0.3402 - 19s/epoch - 8ms/step\n",
            "Epoch 20/50\n",
            "2207/2207 - 18s - loss: 2.1202 - accuracy: 0.3624 - val_loss: 2.2091 - val_accuracy: 0.3405 - 18s/epoch - 8ms/step\n",
            "Epoch 21/50\n",
            "2207/2207 - 20s - loss: 2.1101 - accuracy: 0.3667 - val_loss: 2.2001 - val_accuracy: 0.3418 - 20s/epoch - 9ms/step\n",
            "Epoch 22/50\n",
            "2207/2207 - 21s - loss: 2.1003 - accuracy: 0.3688 - val_loss: 2.1968 - val_accuracy: 0.3453 - 21s/epoch - 10ms/step\n",
            "Epoch 23/50\n",
            "2207/2207 - 20s - loss: 2.0909 - accuracy: 0.3720 - val_loss: 2.1923 - val_accuracy: 0.3465 - 20s/epoch - 9ms/step\n",
            "Epoch 24/50\n",
            "2207/2207 - 18s - loss: 2.0820 - accuracy: 0.3736 - val_loss: 2.1907 - val_accuracy: 0.3479 - 18s/epoch - 8ms/step\n",
            "Epoch 25/50\n",
            "2207/2207 - 17s - loss: 2.0744 - accuracy: 0.3778 - val_loss: 2.1876 - val_accuracy: 0.3494 - 17s/epoch - 8ms/step\n",
            "Epoch 26/50\n",
            "2207/2207 - 19s - loss: 2.0665 - accuracy: 0.3795 - val_loss: 2.1895 - val_accuracy: 0.3506 - 19s/epoch - 9ms/step\n",
            "Epoch 27/50\n",
            "2207/2207 - 20s - loss: 2.0603 - accuracy: 0.3814 - val_loss: 2.1867 - val_accuracy: 0.3539 - 20s/epoch - 9ms/step\n",
            "Epoch 28/50\n",
            "2207/2207 - 19s - loss: 2.0523 - accuracy: 0.3832 - val_loss: 2.1846 - val_accuracy: 0.3540 - 19s/epoch - 9ms/step\n",
            "Epoch 29/50\n",
            "2207/2207 - 20s - loss: 2.0460 - accuracy: 0.3848 - val_loss: 2.1825 - val_accuracy: 0.3564 - 20s/epoch - 9ms/step\n",
            "Epoch 30/50\n",
            "2207/2207 - 18s - loss: 2.0396 - accuracy: 0.3877 - val_loss: 2.1871 - val_accuracy: 0.3565 - 18s/epoch - 8ms/step\n",
            "Epoch 31/50\n",
            "2207/2207 - 18s - loss: 2.0337 - accuracy: 0.3889 - val_loss: 2.1816 - val_accuracy: 0.3564 - 18s/epoch - 8ms/step\n",
            "Epoch 32/50\n",
            "2207/2207 - 18s - loss: 2.0279 - accuracy: 0.3916 - val_loss: 2.1782 - val_accuracy: 0.3615 - 18s/epoch - 8ms/step\n",
            "Epoch 33/50\n",
            "2207/2207 - 20s - loss: 2.0211 - accuracy: 0.3942 - val_loss: 2.1818 - val_accuracy: 0.3602 - 20s/epoch - 9ms/step\n",
            "Epoch 34/50\n",
            "2207/2207 - 19s - loss: 2.0156 - accuracy: 0.3948 - val_loss: 2.1830 - val_accuracy: 0.3600 - 19s/epoch - 9ms/step\n",
            "Epoch 35/50\n",
            "2207/2207 - 20s - loss: 2.0110 - accuracy: 0.3981 - val_loss: 2.1816 - val_accuracy: 0.3585 - 20s/epoch - 9ms/step\n"
          ]
        }
      ],
      "source": [
        "a = model.fit ( X_train , Y_train , validation_data = (X_test,Y_test) , epochs = 50 , verbose = 2 , \n",
        "                callbacks= [EarlyStopping(monitor='val_accuracy' , patience = 3)])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "3V2VhpL1dHw3",
        "outputId": "f7fe10ad-14c3-4723-db78-56a47cba7541"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "epochs_ran = len(a.history['loss'])\n",
        "\n",
        "plt.plot(range(0, epochs_ran) , a.history['val_accuracy'] , label = 'Validation')\n",
        "plt.plot(range(0, epochs_ran) , a.history['accuracy'] , label = 'Training')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5v1BPWJxs7fZ"
      },
      "source": [
        "# Generating Names"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "9f50aTRcpt24"
      },
      "outputs": [],
      "source": [
        "def generate_name(seed):\n",
        "    for i in range(40):\n",
        "        seq = name_to_seq(seed)\n",
        "        padded = pad_sequences( [seq] , padding = 'pre' , maxlen = max_len - 1 , truncating = 'pre')\n",
        "        pred = model.predict(padded)[0]\n",
        "        pred_char = index_to_char[np.argmax(pred)]\n",
        "        seed += pred_char\n",
        "\n",
        "        if pred_char == '\\t' :\n",
        "            break\n",
        "\n",
        "    print(seed)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "faQ0FInlpt26",
        "outputId": "255dd31c-419d-461b-c1e1-e09aadec0490"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1/1 [==============================] - 0s 381ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "desail man\t\n"
          ]
        }
      ],
      "source": [
        "generate_name('desai')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "vanate\t\n"
          ]
        }
      ],
      "source": [
        "generate_name('vana')"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "Superhero Name Generator.ipynb",
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3.10.4 64-bit",
      "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": "3.10.6"
    },
    "vscode": {
      "interpreter": {
        "hash": "52634da84371cba311ea128a5ea7cdc41ff074b781779e754b270ff9f8153cee"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}