Character_level_recurrent_sequence_to_sequence_model.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LM8KcgV-QQp6"
      },
      "source": [
        "# Character-level recurrent sequence-to-sequence model\n",
        "\n",
        "Reference:\n",
        "\n",
        "**Author:** [fchollet](https://twitter.com/fchollet)<br>\n",
        "**Date created:** 2017/09/29<br>\n",
        "**Last modified:** 2020/04/26<br>\n",
        "**Description:** Character-level recurrent sequence-to-sequence model."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "[Dataset source](http://www.manythings.org/anki/)"
      ],
      "metadata": {
        "id": "68FTC9CuQ9Qm"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IAJguYa3QQp8"
      },
      "source": [
        "## Introduction\n",
        "\n",
        "This example demonstrates how to implement a basic character-level\n",
        "recurrent sequence-to-sequence model. We apply it to translating\n",
        "short English sentences into short French sentences,\n",
        "character-by-character. Note that it is fairly unusual to\n",
        "do character-level machine translation, as word-level\n",
        "models are more common in this domain.\n",
        "\n",
        "**Summary of the algorithm**\n",
        "\n",
        "- We start with input sequences from a domain (e.g. English sentences)\n",
        "    and corresponding target sequences from another domain\n",
        "    (e.g. French sentences).\n",
        "- An encoder LSTM turns input sequences to 2 state vectors\n",
        "    (we keep the last LSTM state and discard the outputs).\n",
        "- A decoder LSTM is trained to turn the target sequences into\n",
        "    the same sequence but offset by one timestep in the future,\n",
        "    a training process called \"teacher forcing\" in this context.\n",
        "    It uses as initial state the state vectors from the encoder.\n",
        "    Effectively, the decoder learns to generate `targets[t+1...]`\n",
        "    given `targets[...t]`, conditioned on the input sequence.\n",
        "- In inference mode, when we want to decode unknown input sequences, we:\n",
        "    - Encode the input sequence into state vectors\n",
        "    - Start with a target sequence of size 1\n",
        "        (just the start-of-sequence character)\n",
        "    - Feed the state vectors and 1-char target sequence\n",
        "        to the decoder to produce predictions for the next character\n",
        "    - Sample the next character using these predictions\n",
        "        (we simply use argmax).\n",
        "    - Append the sampled character to the target sequence\n",
        "    - Repeat until we generate the end-of-sequence character or we\n",
        "        hit the character limit.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KeeC5ztgQQp8"
      },
      "source": [
        "## Setup\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6xdkkRLEQQp9"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q14KfVLmQQp9"
      },
      "source": [
        "## Download the data\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "w5t40D-GQQp9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9c5ec410-a2a1-40c5-9ae8-11504f168079"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Archive:  fra-eng.zip',\n",
              " '  inflating: _about.txt              ',\n",
              " '  inflating: fra.txt                 ']"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "!!curl -O http://www.manythings.org/anki/fra-eng.zip\n",
        "!!unzip fra-eng.zip\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4TkHZmEiQQp-"
      },
      "source": [
        "## Configuration\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hMVtOdFpQQp-"
      },
      "outputs": [],
      "source": [
        "batch_size = 64  # Batch size for training.\n",
        "epochs = 100  # Number of epochs to train for.\n",
        "latent_dim = 256  # Latent dimensionality of the encoding space.\n",
        "num_samples = 10000  # Number of samples to train on.\n",
        "# Path to the data txt file on disk.\n",
        "data_path = \"fra.txt\"\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jNSrcwfpQQp-"
      },
      "source": [
        "## Prepare the data\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "1H16vyz1jqiN"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZFVWe2QZQQp_"
      },
      "outputs": [],
      "source": [
        "# Vectorize the data.\n",
        "input_texts = []\n",
        "target_texts = []\n",
        "input_characters = set()\n",
        "target_characters = set()\n",
        "with open(data_path, \"r\", encoding=\"utf-8\") as f:\n",
        "    lines = f.read().split(\"\\n\")\n",
        "for line in lines[: min(num_samples, len(lines) - 1)]:\n",
        "    input_text, target_text, _ = line.split(\"\\t\")\n",
        "    # We use \"tab\" as the \"start sequence\" character\n",
        "    # for the targets, and \"\\n\" as \"end sequence\" character.\n",
        "    target_text = \"\\t\" + target_text + \"\\n\"\n",
        "    input_texts.append(input_text)\n",
        "    target_texts.append(target_text)\n",
        "    for char in input_text:\n",
        "        if char not in input_characters:\n",
        "            input_characters.add(char)\n",
        "    for char in target_text:\n",
        "        if char not in target_characters:\n",
        "            target_characters.add(char)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_characters = sorted(list(input_characters))\n",
        "target_characters = sorted(list(target_characters))\n",
        "num_encoder_tokens = len(input_characters)\n",
        "num_decoder_tokens = len(target_characters)\n",
        "max_encoder_seq_length = max([len(txt) for txt in input_texts])\n",
        "max_decoder_seq_length = max([len(txt) for txt in target_texts])"
      ],
      "metadata": {
        "id": "hD51vZNWUPrM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Number of samples:\", len(input_texts))\n",
        "print(\"Number of unique input tokens:\", num_encoder_tokens)\n",
        "print(\"Number of unique output tokens:\", num_decoder_tokens)\n",
        "print(\"Max sequence length for inputs:\", max_encoder_seq_length)\n",
        "print(\"Max sequence length for outputs:\", max_decoder_seq_length)"
      ],
      "metadata": {
        "id": "-AeadQesUR9Z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "224692e7-bff5-4060-a8a0-31e30ae5cd9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of samples: 10000\n",
            "Number of unique input tokens: 71\n",
            "Number of unique output tokens: 93\n",
            "Max sequence length for inputs: 15\n",
            "Max sequence length for outputs: 59\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Input Tokens"
      ],
      "metadata": {
        "id": "nVY1aB8ZV9x-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "input_token_index = dict([(char, i) for i, char in enumerate(input_characters)])\n",
        "target_token_index = dict([(char, i) for i, char in enumerate(target_characters)])"
      ],
      "metadata": {
        "id": "8YjpYzmFUfVJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "target_token_index"
      ],
      "metadata": {
        "id": "BgJ2ZJegVpAt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Encoder and Decoder"
      ],
      "metadata": {
        "id": "YgYi0aGRWBUZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# refer to blog for more on the parameters: https://blog.keras.io/a-ten-minute-introduction-to-sequence-to-sequence-learning-in-keras.html\n",
        "encoder_input_data = np.zeros(\n",
        "    (len(input_texts), max_encoder_seq_length, num_encoder_tokens), dtype=\"float32\"\n",
        ")\n",
        "decoder_input_data = np.zeros(\n",
        "    (len(input_texts), max_decoder_seq_length, num_decoder_tokens), dtype=\"float32\"\n",
        ")\n",
        "decoder_target_data = np.zeros(\n",
        "    (len(input_texts), max_decoder_seq_length, num_decoder_tokens), dtype=\"float32\"\n",
        ")"
      ],
      "metadata": {
        "id": "BiDSK2X0VnZT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###One-Hot Representation"
      ],
      "metadata": {
        "id": "cTpNTC7QXYEJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#You can simply use the keras One-hot encoder to do same thing: https://machinelearningmastery.com/how-to-one-hot-encode-sequence-data-in-python/\n",
        "\n",
        "for i, (input_text, target_text) in enumerate(zip(input_texts, target_texts)):\n",
        "    for t, char in enumerate(input_text):\n",
        "        encoder_input_data[i, t, input_token_index[char]] = 1.0\n",
        "    encoder_input_data[i, t + 1 :, input_token_index[\" \"]] = 1.0\n",
        "    for t, char in enumerate(target_text):\n",
        "        # decoder_target_data is ahead of decoder_input_data by one timestep\n",
        "        decoder_input_data[i, t, target_token_index[char]] = 1.0\n",
        "        if t > 0:\n",
        "            # decoder_target_data will be ahead by one timestep\n",
        "            # and will not include the start character.\n",
        "            decoder_target_data[i, t - 1, target_token_index[char]] = 1.0\n",
        "    decoder_input_data[i, t + 1 :, target_token_index[\" \"]] = 1.0\n",
        "    decoder_target_data[i, t:, target_token_index[\" \"]] = 1.0"
      ],
      "metadata": {
        "id": "qW5EFoZtUj2t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0Uu8LdbHQQp_"
      },
      "source": [
        "## Build the model\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uPb8vMq4QQp_"
      },
      "outputs": [],
      "source": [
        "# Define an input sequence and process it.\n",
        "encoder_inputs = keras.Input(shape=(None, num_encoder_tokens)) #encoder input shape\n",
        "encoder = keras.layers.LSTM(latent_dim, return_state=True) #specify the latent_dim(your timestamp)\n",
        "encoder_outputs, state_h, state_c = encoder(encoder_inputs) #encoder returns encoder output, hidden state and cell state\n",
        "\n",
        "# We discard `encoder_outputs` and only keep the states.\n",
        "encoder_states = [state_h, state_c] #only keep the hidden state and cell state\n",
        "\n",
        "\n",
        "# Set up the decoder, using `encoder_states` as initial state.\n",
        "decoder_inputs = keras.Input(shape=(None, num_decoder_tokens))\n",
        "\n",
        "# We set up our decoder to return full output sequences,\n",
        "# and to return internal states as well. We don't use the\n",
        "# return states in the training model, but we will use them in inference.\n",
        "decoder_lstm = keras.layers.LSTM(latent_dim, return_sequences=True, return_state=True)\n",
        "decoder_outputs, _, _ = decoder_lstm(decoder_inputs, initial_state=encoder_states)\n",
        "\n",
        "decoder_dense = keras.layers.Dense(num_decoder_tokens, activation=\"softmax\") #adding dense layer\n",
        "decoder_outputs = decoder_dense(decoder_outputs) #getting all the output\n",
        "\n",
        "# Define the model that will turn\n",
        "# `encoder_input_data` & `decoder_input_data` into `decoder_target_data`\n",
        "model = keras.Model([encoder_inputs, decoder_inputs], decoder_outputs)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QmEsP5YuQQqA"
      },
      "source": [
        "## Train the model\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QVz1Alm4QQqA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4f2c2be9-29b4-4334-aeae-a8f2938164f4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "125/125 [==============================] - 51s 384ms/step - loss: 1.1448 - accuracy: 0.7310 - val_loss: 1.0205 - val_accuracy: 0.7166\n",
            "Epoch 2/100\n",
            "125/125 [==============================] - 47s 379ms/step - loss: 0.8323 - accuracy: 0.7762 - val_loss: 0.8413 - val_accuracy: 0.7679\n",
            "Epoch 3/100\n",
            "125/125 [==============================] - 48s 381ms/step - loss: 0.6694 - accuracy: 0.8139 - val_loss: 0.7072 - val_accuracy: 0.7956\n",
            "Epoch 4/100\n",
            "125/125 [==============================] - 48s 381ms/step - loss: 0.5768 - accuracy: 0.8329 - val_loss: 0.6389 - val_accuracy: 0.8134\n",
            "Epoch 5/100\n",
            "125/125 [==============================] - 48s 384ms/step - loss: 0.5265 - accuracy: 0.8460 - val_loss: 0.5963 - val_accuracy: 0.8281\n",
            "Epoch 6/100\n",
            "125/125 [==============================] - 48s 383ms/step - loss: 0.4904 - accuracy: 0.8558 - val_loss: 0.5612 - val_accuracy: 0.8363\n",
            "Epoch 7/100\n",
            "125/125 [==============================] - 48s 388ms/step - loss: 0.4619 - accuracy: 0.8634 - val_loss: 0.5360 - val_accuracy: 0.8438\n",
            "Epoch 8/100\n",
            "125/125 [==============================] - 48s 387ms/step - loss: 0.4385 - accuracy: 0.8695 - val_loss: 0.5184 - val_accuracy: 0.8474\n",
            "Epoch 9/100\n",
            "125/125 [==============================] - 48s 385ms/step - loss: 0.4182 - accuracy: 0.8752 - val_loss: 0.5091 - val_accuracy: 0.8504\n",
            "Epoch 10/100\n",
            "125/125 [==============================] - 48s 383ms/step - loss: 0.3996 - accuracy: 0.8803 - val_loss: 0.4916 - val_accuracy: 0.8551\n",
            "Epoch 11/100\n",
            "125/125 [==============================] - 48s 383ms/step - loss: 0.3827 - accuracy: 0.8851 - val_loss: 0.4785 - val_accuracy: 0.8585\n",
            "Epoch 12/100\n",
            "125/125 [==============================] - 48s 386ms/step - loss: 0.3674 - accuracy: 0.8893 - val_loss: 0.4747 - val_accuracy: 0.8605\n",
            "Epoch 13/100\n",
            "125/125 [==============================] - 49s 388ms/step - loss: 0.3529 - accuracy: 0.8937 - val_loss: 0.4635 - val_accuracy: 0.8633\n",
            "Epoch 14/100\n",
            "125/125 [==============================] - 48s 387ms/step - loss: 0.3398 - accuracy: 0.8972 - val_loss: 0.4632 - val_accuracy: 0.8629\n",
            "Epoch 15/100\n",
            "125/125 [==============================] - 48s 381ms/step - loss: 0.3272 - accuracy: 0.9016 - val_loss: 0.4528 - val_accuracy: 0.8677\n",
            "Epoch 16/100\n",
            "125/125 [==============================] - 47s 380ms/step - loss: 0.3154 - accuracy: 0.9049 - val_loss: 0.4505 - val_accuracy: 0.8689\n",
            "Epoch 17/100\n",
            "125/125 [==============================] - 48s 383ms/step - loss: 0.3041 - accuracy: 0.9079 - val_loss: 0.4468 - val_accuracy: 0.8705\n",
            "Epoch 18/100\n",
            "125/125 [==============================] - 47s 380ms/step - loss: 0.2937 - accuracy: 0.9112 - val_loss: 0.4438 - val_accuracy: 0.8715\n",
            "Epoch 19/100\n",
            "125/125 [==============================] - 47s 380ms/step - loss: 0.2831 - accuracy: 0.9142 - val_loss: 0.4427 - val_accuracy: 0.8722\n",
            "Epoch 20/100\n",
            "125/125 [==============================] - 47s 379ms/step - loss: 0.2735 - accuracy: 0.9175 - val_loss: 0.4435 - val_accuracy: 0.8721\n",
            "Epoch 21/100\n",
            "125/125 [==============================] - 47s 375ms/step - loss: 0.2643 - accuracy: 0.9203 - val_loss: 0.4390 - val_accuracy: 0.8751\n",
            "Epoch 22/100\n",
            "125/125 [==============================] - 47s 376ms/step - loss: 0.2556 - accuracy: 0.9223 - val_loss: 0.4412 - val_accuracy: 0.8751\n",
            "Epoch 23/100\n",
            "125/125 [==============================] - 47s 377ms/step - loss: 0.2470 - accuracy: 0.9249 - val_loss: 0.4417 - val_accuracy: 0.8760\n",
            "Epoch 24/100\n",
            "125/125 [==============================] - 47s 378ms/step - loss: 0.2388 - accuracy: 0.9275 - val_loss: 0.4434 - val_accuracy: 0.8753\n",
            "Epoch 25/100\n",
            "125/125 [==============================] - 47s 380ms/step - loss: 0.2313 - accuracy: 0.9298 - val_loss: 0.4437 - val_accuracy: 0.8755\n",
            "Epoch 26/100\n",
            "125/125 [==============================] - 47s 378ms/step - loss: 0.2234 - accuracy: 0.9322 - val_loss: 0.4487 - val_accuracy: 0.8758\n",
            "Epoch 27/100\n",
            "125/125 [==============================] - 47s 379ms/step - loss: 0.2169 - accuracy: 0.9342 - val_loss: 0.4469 - val_accuracy: 0.8770\n",
            "Epoch 28/100\n",
            "125/125 [==============================] - 47s 377ms/step - loss: 0.2099 - accuracy: 0.9362 - val_loss: 0.4476 - val_accuracy: 0.8757\n",
            "Epoch 29/100\n",
            "125/125 [==============================] - 47s 377ms/step - loss: 0.2033 - accuracy: 0.9382 - val_loss: 0.4535 - val_accuracy: 0.8768\n",
            "Epoch 30/100\n",
            "125/125 [==============================] - 48s 383ms/step - loss: 0.1967 - accuracy: 0.9402 - val_loss: 0.4598 - val_accuracy: 0.8761\n",
            "Epoch 31/100\n",
            "125/125 [==============================] - 48s 381ms/step - loss: 0.1909 - accuracy: 0.9417 - val_loss: 0.4629 - val_accuracy: 0.8765\n",
            "Epoch 32/100\n",
            "125/125 [==============================] - 47s 379ms/step - loss: 0.1851 - accuracy: 0.9436 - val_loss: 0.4689 - val_accuracy: 0.8750\n",
            "Epoch 33/100\n",
            "125/125 [==============================] - 47s 380ms/step - loss: 0.1795 - accuracy: 0.9451 - val_loss: 0.4717 - val_accuracy: 0.8761\n",
            "Epoch 34/100\n",
            "125/125 [==============================] - 48s 382ms/step - loss: 0.1736 - accuracy: 0.9472 - val_loss: 0.4724 - val_accuracy: 0.8762\n",
            "Epoch 35/100\n",
            "125/125 [==============================] - 48s 383ms/step - loss: 0.1693 - accuracy: 0.9483 - val_loss: 0.4816 - val_accuracy: 0.8756\n",
            "Epoch 36/100\n",
            "125/125 [==============================] - 47s 379ms/step - loss: 0.1641 - accuracy: 0.9498 - val_loss: 0.4820 - val_accuracy: 0.8770\n",
            "Epoch 37/100\n",
            "125/125 [==============================] - 47s 379ms/step - loss: 0.1597 - accuracy: 0.9511 - val_loss: 0.4898 - val_accuracy: 0.8760\n",
            "Epoch 38/100\n",
            "125/125 [==============================] - 47s 378ms/step - loss: 0.1547 - accuracy: 0.9527 - val_loss: 0.4895 - val_accuracy: 0.8764\n",
            "Epoch 39/100\n",
            "125/125 [==============================] - 47s 378ms/step - loss: 0.1503 - accuracy: 0.9538 - val_loss: 0.4969 - val_accuracy: 0.8752\n",
            "Epoch 40/100\n",
            "125/125 [==============================] - 47s 380ms/step - loss: 0.1461 - accuracy: 0.9549 - val_loss: 0.4994 - val_accuracy: 0.8759\n",
            "Epoch 41/100\n",
            "125/125 [==============================] - 48s 381ms/step - loss: 0.1422 - accuracy: 0.9564 - val_loss: 0.5034 - val_accuracy: 0.8763\n",
            "Epoch 42/100\n",
            "125/125 [==============================] - 47s 380ms/step - loss: 0.1389 - accuracy: 0.9571 - val_loss: 0.5073 - val_accuracy: 0.8758\n",
            "Epoch 43/100\n",
            "125/125 [==============================] - 47s 375ms/step - loss: 0.1347 - accuracy: 0.9586 - val_loss: 0.5198 - val_accuracy: 0.8744\n",
            "Epoch 44/100\n",
            "125/125 [==============================] - 47s 374ms/step - loss: 0.1315 - accuracy: 0.9594 - val_loss: 0.5213 - val_accuracy: 0.8756\n",
            "Epoch 45/100\n",
            "125/125 [==============================] - 47s 374ms/step - loss: 0.1276 - accuracy: 0.9605 - val_loss: 0.5203 - val_accuracy: 0.8756\n",
            "Epoch 46/100\n",
            "125/125 [==============================] - 47s 373ms/step - loss: 0.1245 - accuracy: 0.9615 - val_loss: 0.5353 - val_accuracy: 0.8748\n",
            "Epoch 47/100\n",
            "125/125 [==============================] - 50s 399ms/step - loss: 0.1215 - accuracy: 0.9623 - val_loss: 0.5351 - val_accuracy: 0.8757\n",
            "Epoch 48/100\n",
            "125/125 [==============================] - 49s 395ms/step - loss: 0.1185 - accuracy: 0.9630 - val_loss: 0.5428 - val_accuracy: 0.8754\n",
            "Epoch 49/100\n",
            "125/125 [==============================] - 48s 382ms/step - loss: 0.1155 - accuracy: 0.9642 - val_loss: 0.5431 - val_accuracy: 0.8751\n",
            "Epoch 50/100\n",
            "125/125 [==============================] - 47s 379ms/step - loss: 0.1128 - accuracy: 0.9648 - val_loss: 0.5476 - val_accuracy: 0.8755\n",
            "Epoch 51/100\n",
            "125/125 [==============================] - 48s 384ms/step - loss: 0.1099 - accuracy: 0.9657 - val_loss: 0.5555 - val_accuracy: 0.8737\n",
            "Epoch 52/100\n",
            "125/125 [==============================] - 51s 407ms/step - loss: 0.1073 - accuracy: 0.9663 - val_loss: 0.5618 - val_accuracy: 0.8746\n",
            "Epoch 53/100\n",
            "125/125 [==============================] - 51s 407ms/step - loss: 0.1047 - accuracy: 0.9670 - val_loss: 0.5687 - val_accuracy: 0.8751\n",
            "Epoch 54/100\n",
            "125/125 [==============================] - 50s 402ms/step - loss: 0.1024 - accuracy: 0.9677 - val_loss: 0.5673 - val_accuracy: 0.8752\n",
            "Epoch 55/100\n",
            "125/125 [==============================] - 50s 398ms/step - loss: 0.1001 - accuracy: 0.9684 - val_loss: 0.5686 - val_accuracy: 0.8746\n",
            "Epoch 56/100\n",
            "125/125 [==============================] - 49s 390ms/step - loss: 0.0980 - accuracy: 0.9691 - val_loss: 0.5783 - val_accuracy: 0.8746\n",
            "Epoch 57/100\n",
            "125/125 [==============================] - 51s 406ms/step - loss: 0.0960 - accuracy: 0.9697 - val_loss: 0.5827 - val_accuracy: 0.8750\n",
            "Epoch 58/100\n",
            "125/125 [==============================] - 49s 395ms/step - loss: 0.0937 - accuracy: 0.9702 - val_loss: 0.5860 - val_accuracy: 0.8746\n",
            "Epoch 59/100\n",
            "125/125 [==============================] - 50s 398ms/step - loss: 0.0920 - accuracy: 0.9707 - val_loss: 0.5911 - val_accuracy: 0.8740\n",
            "Epoch 60/100\n",
            "125/125 [==============================] - 48s 384ms/step - loss: 0.0895 - accuracy: 0.9715 - val_loss: 0.5987 - val_accuracy: 0.8738\n",
            "Epoch 61/100\n",
            "125/125 [==============================] - 48s 385ms/step - loss: 0.0879 - accuracy: 0.9720 - val_loss: 0.6027 - val_accuracy: 0.8749\n",
            "Epoch 62/100\n",
            "125/125 [==============================] - 48s 383ms/step - loss: 0.0860 - accuracy: 0.9725 - val_loss: 0.6017 - val_accuracy: 0.8732\n",
            "Epoch 63/100\n",
            "125/125 [==============================] - 48s 381ms/step - loss: 0.0842 - accuracy: 0.9731 - val_loss: 0.6125 - val_accuracy: 0.8737\n",
            "Epoch 64/100\n",
            "125/125 [==============================] - 47s 380ms/step - loss: 0.0831 - accuracy: 0.9732 - val_loss: 0.6189 - val_accuracy: 0.8736\n",
            "Epoch 65/100\n",
            "125/125 [==============================] - 48s 382ms/step - loss: 0.0808 - accuracy: 0.9738 - val_loss: 0.6228 - val_accuracy: 0.8727\n",
            "Epoch 66/100\n",
            "125/125 [==============================] - 48s 381ms/step - loss: 0.0796 - accuracy: 0.9743 - val_loss: 0.6261 - val_accuracy: 0.8732\n",
            "Epoch 67/100\n",
            "125/125 [==============================] - 48s 385ms/step - loss: 0.0778 - accuracy: 0.9747 - val_loss: 0.6269 - val_accuracy: 0.8730\n",
            "Epoch 68/100\n",
            "125/125 [==============================] - 48s 386ms/step - loss: 0.0763 - accuracy: 0.9753 - val_loss: 0.6323 - val_accuracy: 0.8730\n",
            "Epoch 69/100\n",
            "125/125 [==============================] - 48s 386ms/step - loss: 0.0749 - accuracy: 0.9756 - val_loss: 0.6351 - val_accuracy: 0.8746\n",
            "Epoch 70/100\n",
            "125/125 [==============================] - 48s 387ms/step - loss: 0.0734 - accuracy: 0.9761 - val_loss: 0.6443 - val_accuracy: 0.8729\n",
            "Epoch 71/100\n",
            "125/125 [==============================] - 49s 390ms/step - loss: 0.0719 - accuracy: 0.9765 - val_loss: 0.6438 - val_accuracy: 0.8728\n",
            "Epoch 72/100\n",
            "125/125 [==============================] - 54s 433ms/step - loss: 0.0708 - accuracy: 0.9769 - val_loss: 0.6465 - val_accuracy: 0.8738\n",
            "Epoch 73/100\n",
            "125/125 [==============================] - 55s 438ms/step - loss: 0.0695 - accuracy: 0.9772 - val_loss: 0.6533 - val_accuracy: 0.8733\n",
            "Epoch 74/100\n",
            "125/125 [==============================] - 55s 442ms/step - loss: 0.0680 - accuracy: 0.9777 - val_loss: 0.6538 - val_accuracy: 0.8729\n",
            "Epoch 75/100\n",
            "125/125 [==============================] - 52s 414ms/step - loss: 0.0671 - accuracy: 0.9779 - val_loss: 0.6576 - val_accuracy: 0.8735\n",
            "Epoch 76/100\n",
            "125/125 [==============================] - 51s 406ms/step - loss: 0.0659 - accuracy: 0.9779 - val_loss: 0.6632 - val_accuracy: 0.8726\n",
            "Epoch 77/100\n",
            "125/125 [==============================] - 51s 408ms/step - loss: 0.0645 - accuracy: 0.9783 - val_loss: 0.6687 - val_accuracy: 0.8729\n",
            "Epoch 78/100\n",
            "125/125 [==============================] - 52s 413ms/step - loss: 0.0637 - accuracy: 0.9789 - val_loss: 0.6692 - val_accuracy: 0.8733\n",
            "Epoch 79/100\n",
            "125/125 [==============================] - 51s 412ms/step - loss: 0.0627 - accuracy: 0.9790 - val_loss: 0.6725 - val_accuracy: 0.8726\n",
            "Epoch 80/100\n",
            "125/125 [==============================] - 51s 410ms/step - loss: 0.0617 - accuracy: 0.9792 - val_loss: 0.6748 - val_accuracy: 0.8717\n",
            "Epoch 81/100\n",
            "125/125 [==============================] - 52s 414ms/step - loss: 0.0607 - accuracy: 0.9795 - val_loss: 0.6782 - val_accuracy: 0.8726\n",
            "Epoch 82/100\n",
            "125/125 [==============================] - 51s 412ms/step - loss: 0.0594 - accuracy: 0.9800 - val_loss: 0.6907 - val_accuracy: 0.8727\n",
            "Epoch 83/100\n",
            "125/125 [==============================] - 50s 403ms/step - loss: 0.0586 - accuracy: 0.9800 - val_loss: 0.6888 - val_accuracy: 0.8724\n",
            "Epoch 84/100\n",
            "125/125 [==============================] - 50s 398ms/step - loss: 0.0576 - accuracy: 0.9804 - val_loss: 0.6943 - val_accuracy: 0.8723\n",
            "Epoch 85/100\n",
            "125/125 [==============================] - 49s 389ms/step - loss: 0.0564 - accuracy: 0.9808 - val_loss: 0.7038 - val_accuracy: 0.8709\n",
            "Epoch 86/100\n",
            "125/125 [==============================] - 49s 388ms/step - loss: 0.0558 - accuracy: 0.9811 - val_loss: 0.7068 - val_accuracy: 0.8728\n",
            "Epoch 87/100\n",
            "125/125 [==============================] - 49s 395ms/step - loss: 0.0551 - accuracy: 0.9811 - val_loss: 0.7071 - val_accuracy: 0.8721\n",
            "Epoch 88/100\n",
            "125/125 [==============================] - 48s 385ms/step - loss: 0.0538 - accuracy: 0.9814 - val_loss: 0.7076 - val_accuracy: 0.8711\n",
            "Epoch 89/100\n",
            "125/125 [==============================] - 49s 389ms/step - loss: 0.0533 - accuracy: 0.9816 - val_loss: 0.7091 - val_accuracy: 0.8725\n",
            "Epoch 90/100\n",
            "125/125 [==============================] - 48s 385ms/step - loss: 0.0523 - accuracy: 0.9821 - val_loss: 0.7184 - val_accuracy: 0.8725\n",
            "Epoch 91/100\n",
            "125/125 [==============================] - 48s 385ms/step - loss: 0.0519 - accuracy: 0.9822 - val_loss: 0.7183 - val_accuracy: 0.8720\n",
            "Epoch 92/100\n",
            "125/125 [==============================] - 48s 383ms/step - loss: 0.0513 - accuracy: 0.9822 - val_loss: 0.7220 - val_accuracy: 0.8717\n",
            "Epoch 93/100\n",
            "125/125 [==============================] - 48s 384ms/step - loss: 0.0506 - accuracy: 0.9823 - val_loss: 0.7251 - val_accuracy: 0.8720\n",
            "Epoch 94/100\n",
            "125/125 [==============================] - 52s 420ms/step - loss: 0.0498 - accuracy: 0.9826 - val_loss: 0.7284 - val_accuracy: 0.8717\n",
            "Epoch 95/100\n",
            "125/125 [==============================] - 52s 420ms/step - loss: 0.0493 - accuracy: 0.9827 - val_loss: 0.7316 - val_accuracy: 0.8711\n",
            "Epoch 96/100\n",
            "125/125 [==============================] - 54s 434ms/step - loss: 0.0484 - accuracy: 0.9830 - val_loss: 0.7336 - val_accuracy: 0.8715\n",
            "Epoch 97/100\n",
            "125/125 [==============================] - 50s 399ms/step - loss: 0.0480 - accuracy: 0.9833 - val_loss: 0.7369 - val_accuracy: 0.8721\n",
            "Epoch 98/100\n",
            "125/125 [==============================] - 50s 402ms/step - loss: 0.0474 - accuracy: 0.9831 - val_loss: 0.7373 - val_accuracy: 0.8716\n",
            "Epoch 99/100\n",
            "125/125 [==============================] - 51s 408ms/step - loss: 0.0470 - accuracy: 0.9834 - val_loss: 0.7374 - val_accuracy: 0.8722\n",
            "Epoch 100/100\n",
            "125/125 [==============================] - 51s 406ms/step - loss: 0.0463 - accuracy: 0.9835 - val_loss: 0.7464 - val_accuracy: 0.8724\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses, lstm_cell_1_layer_call_fn, lstm_cell_1_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Assets written to: s2s/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: s2s/assets\n",
            "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7f7c5d107a10> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
            "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7f7c5d169110> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n"
          ]
        }
      ],
      "source": [
        "model.compile(\n",
        "    optimizer=\"rmsprop\", loss=\"categorical_crossentropy\", metrics=[\"accuracy\"]\n",
        ")\n",
        "model.fit(\n",
        "    [encoder_input_data, decoder_input_data],\n",
        "    decoder_target_data,\n",
        "    batch_size=batch_size,\n",
        "    epochs=epochs,\n",
        "    validation_split=0.2,\n",
        ")\n",
        "# Save model\n",
        "model.save(\"s2s\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Se2BQjAJQQqA"
      },
      "source": [
        "#Student Assignment\n",
        "\n",
        "Review the following code and provide your understanding of each line of code and what it is doing. \n",
        "\n",
        "Submit your report for instructor review.\n",
        "\n",
        "You can refer to [keras documentation](https://blog.keras.io/a-ten-minute-introduction-to-sequence-to-sequence-learning-in-keras.html) for more\n",
        "\n",
        "#### Run inference (sampling)\n",
        "\n",
        "1. encode input and retrieve initial decoder state\n",
        "2. run one step of decoder with this initial state\n",
        "and a \"start of sequence\" token as target.\n",
        "Output will be the next target token.\n",
        "3. Repeat with the current target token and current states\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "crO5HHlCQQqA"
      },
      "outputs": [],
      "source": [
        "# Define sampling models\n",
        "# Restore the model and construct the encoder and decoder.\n",
        "model = keras.models.load_model(\"s2s\") #reading saved model\n",
        "\n",
        "encoder_inputs = model.input[0]  # input_1\n",
        "encoder_outputs, state_h_enc, state_c_enc = model.layers[2].output  # lstm_1\n",
        "encoder_states = [state_h_enc, state_c_enc]\n",
        "encoder_model = keras.Model(encoder_inputs, encoder_states)\n",
        "\n",
        "decoder_inputs = model.input[1]  # input_2\n",
        "decoder_state_input_h = keras.Input(shape=(latent_dim,))\n",
        "decoder_state_input_c = keras.Input(shape=(latent_dim,))\n",
        "decoder_states_inputs = [decoder_state_input_h, decoder_state_input_c]\n",
        "decoder_lstm = model.layers[3]\n",
        "decoder_outputs, state_h_dec, state_c_dec = decoder_lstm(\n",
        "    decoder_inputs, initial_state=decoder_states_inputs\n",
        ")\n",
        "decoder_states = [state_h_dec, state_c_dec]\n",
        "decoder_dense = model.layers[4]\n",
        "decoder_outputs = decoder_dense(decoder_outputs)\n",
        "decoder_model = keras.Model(\n",
        "    [decoder_inputs] + decoder_states_inputs, [decoder_outputs] + decoder_states\n",
        ")\n",
        "\n",
        "# Reverse-lookup token index to decode sequences back to\n",
        "# something readable.\n",
        "reverse_input_char_index = dict((i, char) for char, i in input_token_index.items())\n",
        "reverse_target_char_index = dict((i, char) for char, i in target_token_index.items())\n",
        "\n",
        "\n",
        "def decode_sequence(input_seq):\n",
        "    # Encode the input as state vectors.\n",
        "    states_value = encoder_model.predict(input_seq)\n",
        "\n",
        "    # Generate empty target sequence of length 1.\n",
        "    target_seq = np.zeros((1, 1, num_decoder_tokens))\n",
        "    # Populate the first character of target sequence with the start character.\n",
        "    target_seq[0, 0, target_token_index[\"\\t\"]] = 1.0\n",
        "\n",
        "    # Sampling loop for a batch of sequences\n",
        "    # (to simplify, here we assume a batch of size 1).\n",
        "    stop_condition = False\n",
        "    decoded_sentence = \"\"\n",
        "    while not stop_condition:\n",
        "        output_tokens, h, c = decoder_model.predict([target_seq] + states_value)\n",
        "\n",
        "        # Sample a token\n",
        "        sampled_token_index = np.argmax(output_tokens[0, -1, :])\n",
        "        sampled_char = reverse_target_char_index[sampled_token_index]\n",
        "        decoded_sentence += sampled_char\n",
        "\n",
        "        # Exit condition: either hit max length\n",
        "        # or find stop character.\n",
        "        if sampled_char == \"\\n\" or len(decoded_sentence) > max_decoder_seq_length:\n",
        "            stop_condition = True\n",
        "\n",
        "        # Update the target sequence (of length 1).\n",
        "        target_seq = np.zeros((1, 1, num_decoder_tokens))\n",
        "        target_seq[0, 0, sampled_token_index] = 1.0\n",
        "\n",
        "        # Update states\n",
        "        states_value = [h, c]\n",
        "    return decoded_sentence\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "at-0sZ2DQQqB"
      },
      "source": [
        "You can now generate decoded sentences as such:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6PcUPb38QQqB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b14409a6-a7b4-4d5f-f596-0aa510d7e371"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-\n",
            "Input sentence: Go.\n",
            "Decoded sentence: Va !\n",
            "\n",
            "-\n",
            "Input sentence: Go.\n",
            "Decoded sentence: Va !\n",
            "\n",
            "-\n",
            "Input sentence: Go.\n",
            "Decoded sentence: Va !\n",
            "\n",
            "-\n",
            "Input sentence: Hi.\n",
            "Decoded sentence: Salut.\n",
            "\n",
            "-\n",
            "Input sentence: Hi.\n",
            "Decoded sentence: Salut.\n",
            "\n",
            "-\n",
            "Input sentence: Run!\n",
            "Decoded sentence: Cours !\n",
            "\n",
            "-\n",
            "Input sentence: Run!\n",
            "Decoded sentence: Cours !\n",
            "\n",
            "-\n",
            "Input sentence: Run!\n",
            "Decoded sentence: Cours !\n",
            "\n",
            "-\n",
            "Input sentence: Run!\n",
            "Decoded sentence: Cours !\n",
            "\n",
            "-\n",
            "Input sentence: Run!\n",
            "Decoded sentence: Cours !\n",
            "\n",
            "-\n",
            "Input sentence: Run!\n",
            "Decoded sentence: Cours !\n",
            "\n",
            "-\n",
            "Input sentence: Run!\n",
            "Decoded sentence: Cours !\n",
            "\n",
            "-\n",
            "Input sentence: Run!\n",
            "Decoded sentence: Cours !\n",
            "\n",
            "-\n",
            "Input sentence: Run.\n",
            "Decoded sentence: Cours !\n",
            "\n",
            "-\n",
            "Input sentence: Run.\n",
            "Decoded sentence: Cours !\n",
            "\n",
            "-\n",
            "Input sentence: Run.\n",
            "Decoded sentence: Cours !\n",
            "\n",
            "-\n",
            "Input sentence: Run.\n",
            "Decoded sentence: Cours !\n",
            "\n",
            "-\n",
            "Input sentence: Run.\n",
            "Decoded sentence: Cours !\n",
            "\n",
            "-\n",
            "Input sentence: Run.\n",
            "Decoded sentence: Cours !\n",
            "\n",
            "-\n",
            "Input sentence: Run.\n",
            "Decoded sentence: Cours !\n",
            "\n"
          ]
        }
      ],
      "source": [
        "for seq_index in range(20):\n",
        "    # Take one sequence (part of the training set)\n",
        "    # for trying out decoding.\n",
        "    input_seq = encoder_input_data[seq_index : seq_index + 1]\n",
        "    decoded_sentence = decode_sequence(input_seq)\n",
        "    print(\"-\")\n",
        "    print(\"Input sentence:\", input_texts[seq_index])\n",
        "    print(\"Decoded sentence:\", decoded_sentence)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xELm6oDE4BsV"
      },
      "execution_count": null,
      "outputs": []
    }
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