Sequence_Learning_LSTM.ipynb

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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "A4wHqpHzANxb"
      },
      "outputs": [],
      "source": [
        "alphabets = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "list(enumerate(alphabets))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vK_ZMHeDAtHf",
        "outputId": "cc108ef2-81f7-4465-b1fd-1e3e11cbaa25"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(0, 'A'),\n",
              " (1, 'B'),\n",
              " (2, 'C'),\n",
              " (3, 'D'),\n",
              " (4, 'E'),\n",
              " (5, 'F'),\n",
              " (6, 'G'),\n",
              " (7, 'H'),\n",
              " (8, 'I'),\n",
              " (9, 'J'),\n",
              " (10, 'K'),\n",
              " (11, 'L'),\n",
              " (12, 'M'),\n",
              " (13, 'N'),\n",
              " (14, 'O'),\n",
              " (15, 'P'),\n",
              " (16, 'Q'),\n",
              " (17, 'R'),\n",
              " (18, 'S'),\n",
              " (19, 'T'),\n",
              " (20, 'U'),\n",
              " (21, 'V'),\n",
              " (22, 'W'),\n",
              " (23, 'X'),\n",
              " (24, 'Y'),\n",
              " (25, 'Z')]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "char_to_int = dict((c, i) for i, c in enumerate(alphabets))"
      ],
      "metadata": {
        "id": "UuJhwTKjBLsL"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "int_to_char = dict((i, c) for i, c in enumerate(alphabets))"
      ],
      "metadata": {
        "id": "loxxC1L0BmL5"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "seq_length = 1"
      ],
      "metadata": {
        "id": "H-0i5v-dBnVi"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = []\n",
        "y = []\n",
        "for i in range(len(alphabets) - seq_length):\n",
        "  seq_in = alphabets[i:i + seq_length]\n",
        "  seq_out = alphabets[i + seq_length]\n",
        "  x.append([char_to_int[char] for char in seq_in])\n",
        "  y.append(char_to_int[seq_out])\n",
        "  print(seq_in,'->',seq_out)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CjyyA3hnB-Kd",
        "outputId": "7862ee12-3f89-4877-af33-7ecce3b98881"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "A -> B\n",
            "B -> C\n",
            "C -> D\n",
            "D -> E\n",
            "E -> F\n",
            "F -> G\n",
            "G -> H\n",
            "H -> I\n",
            "I -> J\n",
            "J -> K\n",
            "K -> L\n",
            "L -> M\n",
            "M -> N\n",
            "N -> O\n",
            "O -> P\n",
            "P -> Q\n",
            "Q -> R\n",
            "R -> S\n",
            "S -> T\n",
            "T -> U\n",
            "U -> V\n",
            "V -> W\n",
            "W -> X\n",
            "X -> Y\n",
            "Y -> Z\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XYWb0wzMCYmQ",
        "outputId": "740dae2a-6576-4198-d541-3eb00f43d35f"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1,\n",
              " 2,\n",
              " 3,\n",
              " 4,\n",
              " 5,\n",
              " 6,\n",
              " 7,\n",
              " 8,\n",
              " 9,\n",
              " 10,\n",
              " 11,\n",
              " 12,\n",
              " 13,\n",
              " 14,\n",
              " 15,\n",
              " 16,\n",
              " 17,\n",
              " 18,\n",
              " 19,\n",
              " 20,\n",
              " 21,\n",
              " 22,\n",
              " 23,\n",
              " 24,\n",
              " 25]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "WQzPXSmNDhmE"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = np.reshape(x, (len(x), seq_length, 1))"
      ],
      "metadata": {
        "id": "1RSbrFBVFCH_"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bdC9seAnFEPY",
        "outputId": "3e2ea259-b7b1-4d6c-c558-e8cfb0e9feca"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(25, 1, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Feature scaling\n",
        "x = x / 25"
      ],
      "metadata": {
        "id": "WaEuahWIFM9O"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.utils import to_categorical"
      ],
      "metadata": {
        "id": "PMU2olw3FczN"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = to_categorical(y)"
      ],
      "metadata": {
        "id": "zlb4AcbLGM0a"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XAEpSqOIGpy-",
        "outputId": "a00557f5-4e36-43de-ab48-3a8d6aee94fb"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
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            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1nFNFyIMGqcc",
        "outputId": "65b8274e-0aca-4b3e-daea-5b3cddb8ca29"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(25, 1, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NAjbdWyAHpTl",
        "outputId": "7caca5dd-e448-43e6-a0ec-83a5af2e1a7f"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(25, 26)"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, LSTM"
      ],
      "metadata": {
        "id": "zTtqi3eVHp8H"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(LSTM(32, input_shape=(x.shape[1], x.shape[2])))\n",
        "\n",
        "model.add(Dense(26, activation='softmax'))"
      ],
      "metadata": {
        "id": "gdxvt-6UIBvF"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6shxP6YqID7L",
        "outputId": "cd6563f1-bcd3-43ce-adcf-f95874cc5d45"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm (LSTM)                 (None, 32)                4352      \n",
            "                                                                 \n",
            " dense (Dense)               (None, 26)                858       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 5,210\n",
            "Trainable params: 5,210\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Compile the model\n",
        "model.compile(loss='categorical_crossentropy', \n",
        "              optimizer='adam', metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "EAx-gQHlIsow"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model\n",
        "model.fit(x, y, epochs=500, batch_size=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "shv2Qz7qI2Pb",
        "outputId": "7940dc9a-0bee-4631-cdfc-8115981b7d20"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.3789 - accuracy: 0.9200\n",
            "Epoch 2/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.3747 - accuracy: 0.9200\n",
            "Epoch 3/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.3749 - accuracy: 0.9200\n",
            "Epoch 4/500\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 1.3750 - accuracy: 0.8800\n",
            "Epoch 5/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.3741 - accuracy: 0.8800\n",
            "Epoch 6/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.3731 - accuracy: 0.9200\n",
            "Epoch 7/500\n",
            "25/25 [==============================] - 0s 5ms/step - loss: 1.3738 - accuracy: 0.9200\n",
            "Epoch 8/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.3735 - accuracy: 0.9200\n",
            "Epoch 9/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.3734 - accuracy: 0.8800\n",
            "Epoch 10/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.3710 - accuracy: 0.9200\n",
            "Epoch 11/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.3711 - accuracy: 0.9200\n",
            "Epoch 12/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.3698 - accuracy: 0.9200\n",
            "Epoch 13/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.3708 - accuracy: 0.8800\n",
            "Epoch 14/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.3709 - accuracy: 0.9200\n",
            "Epoch 15/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.3696 - accuracy: 0.9200\n",
            "Epoch 16/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.3722 - accuracy: 0.9200\n",
            "Epoch 17/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.3728 - accuracy: 0.9200\n",
            "Epoch 18/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.3694 - accuracy: 0.8800\n",
            "Epoch 19/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.3708 - accuracy: 0.9200\n",
            "Epoch 20/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.3673 - accuracy: 0.9200\n",
            "Epoch 21/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.3690 - accuracy: 0.9200\n",
            "Epoch 22/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.3671 - accuracy: 0.8800\n",
            "Epoch 23/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.3709 - accuracy: 0.8800\n",
            "Epoch 24/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.3661 - accuracy: 0.9200\n",
            "Epoch 25/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.3651 - accuracy: 0.9200\n",
            "Epoch 26/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.3653 - accuracy: 0.9200\n",
            "Epoch 27/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.3662 - accuracy: 0.9200\n",
            "Epoch 28/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.3653 - accuracy: 0.8800\n",
            "Epoch 29/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.3636 - accuracy: 0.9200\n",
            "Epoch 30/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.3630 - accuracy: 0.9200\n",
            "Epoch 31/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.3630 - accuracy: 0.9200\n",
            "Epoch 32/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.3645 - accuracy: 0.8800\n",
            "Epoch 33/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.3658 - accuracy: 0.9200\n",
            "Epoch 34/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.3656 - accuracy: 0.9200\n",
            "Epoch 35/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.3637 - accuracy: 0.9200\n",
            "Epoch 36/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.3598 - accuracy: 0.9200\n",
            "Epoch 37/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.3602 - accuracy: 0.9200\n",
            "Epoch 38/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.3618 - accuracy: 0.9200\n",
            "Epoch 39/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.3600 - accuracy: 0.9200\n",
            "Epoch 40/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.3626 - accuracy: 0.9200\n",
            "Epoch 41/500\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 1.3615 - accuracy: 0.8800\n",
            "Epoch 42/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.3607 - accuracy: 0.9200\n",
            "Epoch 43/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.3606 - accuracy: 0.9200\n",
            "Epoch 44/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.3578 - accuracy: 0.9200\n",
            "Epoch 45/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.3590 - accuracy: 0.9200\n",
            "Epoch 46/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.3571 - accuracy: 0.8800\n",
            "Epoch 47/500\n",
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            "Epoch 401/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2295 - accuracy: 0.9600\n",
            "Epoch 402/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2309 - accuracy: 0.9200\n",
            "Epoch 403/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2264 - accuracy: 0.9600\n",
            "Epoch 404/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2329 - accuracy: 0.9200\n",
            "Epoch 405/500\n",
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            "Epoch 406/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2245 - accuracy: 0.9200\n",
            "Epoch 407/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2260 - accuracy: 0.9200\n",
            "Epoch 408/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2283 - accuracy: 0.9600\n",
            "Epoch 409/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2263 - accuracy: 0.9600\n",
            "Epoch 410/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2276 - accuracy: 0.9200\n",
            "Epoch 411/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2261 - accuracy: 0.9600\n",
            "Epoch 412/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2254 - accuracy: 0.9600\n",
            "Epoch 413/500\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 1.2289 - accuracy: 0.9600\n",
            "Epoch 414/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2258 - accuracy: 0.9600\n",
            "Epoch 415/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2252 - accuracy: 0.9600\n",
            "Epoch 416/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2229 - accuracy: 0.9600\n",
            "Epoch 417/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2273 - accuracy: 0.9200\n",
            "Epoch 418/500\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 1.2238 - accuracy: 0.9600\n",
            "Epoch 419/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2273 - accuracy: 0.9600\n",
            "Epoch 420/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2229 - accuracy: 0.9600\n",
            "Epoch 421/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2211 - accuracy: 0.9600\n",
            "Epoch 422/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2237 - accuracy: 0.9600\n",
            "Epoch 423/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2218 - accuracy: 0.9600\n",
            "Epoch 424/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2209 - accuracy: 0.9200\n",
            "Epoch 425/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2216 - accuracy: 0.9200\n",
            "Epoch 426/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2232 - accuracy: 0.9600\n",
            "Epoch 427/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2240 - accuracy: 0.9200\n",
            "Epoch 428/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2227 - accuracy: 0.9200\n",
            "Epoch 429/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2196 - accuracy: 0.9200\n",
            "Epoch 430/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2200 - accuracy: 0.9200\n",
            "Epoch 431/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2240 - accuracy: 0.9200\n",
            "Epoch 432/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2213 - accuracy: 0.9200\n",
            "Epoch 433/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2198 - accuracy: 0.9600\n",
            "Epoch 434/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2233 - accuracy: 0.9600\n",
            "Epoch 435/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2184 - accuracy: 0.9600\n",
            "Epoch 436/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2193 - accuracy: 0.9600\n",
            "Epoch 437/500\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 1.2177 - accuracy: 0.9200\n",
            "Epoch 438/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2193 - accuracy: 0.9200\n",
            "Epoch 439/500\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 1.2202 - accuracy: 0.9600\n",
            "Epoch 440/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2175 - accuracy: 0.9200\n",
            "Epoch 441/500\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 1.2168 - accuracy: 0.9200\n",
            "Epoch 442/500\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 1.2163 - accuracy: 0.9200\n",
            "Epoch 443/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2155 - accuracy: 0.9600\n",
            "Epoch 444/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2195 - accuracy: 0.9600\n",
            "Epoch 445/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2188 - accuracy: 0.9200\n",
            "Epoch 446/500\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 1.2166 - accuracy: 0.9600\n",
            "Epoch 447/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2141 - accuracy: 0.9200\n",
            "Epoch 448/500\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 1.2138 - accuracy: 0.9200\n",
            "Epoch 449/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2155 - accuracy: 0.9200\n",
            "Epoch 450/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2163 - accuracy: 0.9200\n",
            "Epoch 451/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2156 - accuracy: 0.9600\n",
            "Epoch 452/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2167 - accuracy: 0.9200\n",
            "Epoch 453/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2168 - accuracy: 0.9200\n",
            "Epoch 454/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2131 - accuracy: 0.9600\n",
            "Epoch 455/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2140 - accuracy: 0.9600\n",
            "Epoch 456/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2120 - accuracy: 0.9600\n",
            "Epoch 457/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2132 - accuracy: 0.9600\n",
            "Epoch 458/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2137 - accuracy: 0.9600\n",
            "Epoch 459/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2112 - accuracy: 0.9600\n",
            "Epoch 460/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2121 - accuracy: 0.9600\n",
            "Epoch 461/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2115 - accuracy: 0.9200\n",
            "Epoch 462/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2119 - accuracy: 0.9200\n",
            "Epoch 463/500\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 1.2125 - accuracy: 0.9200\n",
            "Epoch 464/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2105 - accuracy: 0.9200\n",
            "Epoch 465/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2100 - accuracy: 0.9600\n",
            "Epoch 466/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2103 - accuracy: 0.9200\n",
            "Epoch 467/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2110 - accuracy: 0.9200\n",
            "Epoch 468/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2109 - accuracy: 0.9600\n",
            "Epoch 469/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2072 - accuracy: 0.9600\n",
            "Epoch 470/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2081 - accuracy: 0.9600\n",
            "Epoch 471/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2096 - accuracy: 0.9200\n",
            "Epoch 472/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2095 - accuracy: 0.9200\n",
            "Epoch 473/500\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 1.2084 - accuracy: 0.9600\n",
            "Epoch 474/500\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 1.2090 - accuracy: 0.9600\n",
            "Epoch 475/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2093 - accuracy: 0.9200\n",
            "Epoch 476/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2050 - accuracy: 0.9200\n",
            "Epoch 477/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2054 - accuracy: 0.9600\n",
            "Epoch 478/500\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 1.2063 - accuracy: 0.9600\n",
            "Epoch 479/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2091 - accuracy: 0.9600\n",
            "Epoch 480/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2065 - accuracy: 0.9600\n",
            "Epoch 481/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2067 - accuracy: 0.9200\n",
            "Epoch 482/500\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 1.2072 - accuracy: 0.9200\n",
            "Epoch 483/500\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 1.2074 - accuracy: 0.9200\n",
            "Epoch 484/500\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 1.2051 - accuracy: 0.9200\n",
            "Epoch 485/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2049 - accuracy: 0.9200\n",
            "Epoch 486/500\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 1.2054 - accuracy: 0.9200\n",
            "Epoch 487/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2059 - accuracy: 0.9600\n",
            "Epoch 488/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2046 - accuracy: 0.9600\n",
            "Epoch 489/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2046 - accuracy: 0.9200\n",
            "Epoch 490/500\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 1.2005 - accuracy: 0.9600\n",
            "Epoch 491/500\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 1.2027 - accuracy: 0.9600\n",
            "Epoch 492/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2028 - accuracy: 0.9200\n",
            "Epoch 493/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2032 - accuracy: 0.9200\n",
            "Epoch 494/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2034 - accuracy: 0.9200\n",
            "Epoch 495/500\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 1.2036 - accuracy: 0.9200\n",
            "Epoch 496/500\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 1.2013 - accuracy: 0.9200\n",
            "Epoch 497/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.1988 - accuracy: 0.9200\n",
            "Epoch 498/500\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 1.2001 - accuracy: 0.9600\n",
            "Epoch 499/500\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 1.2025 - accuracy: 0.9600\n",
            "Epoch 500/500\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 1.2017 - accuracy: 0.9600\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fc28602ddc0>"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(x, y, batch_size=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uQftJVlfI7Ox",
        "outputId": "4095758c-de6c-439a-e47b-9f321ae3e68e"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "25/25 [==============================] - 1s 9ms/step - loss: 1.3590 - accuracy: 0.9200\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.3589717149734497, 0.9200000166893005]"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new = 'R'\n",
        "new = char_to_int[new]\n",
        "dx = np.reshape(new, (1, len([new]),1))\n",
        "dx = dx / 25\n",
        "pred = model.predict(dx, verbose=False)\n",
        "print(int_to_char[np.argmax(pred)])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TxOuAciDKMQK",
        "outputId": "ea0feab2-31fb-41c3-cdd2-1ff88fdc03ae"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "S\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3e1B2t_uKRD7"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}