Character_Level_Recurrent_Seq2Seq_Model: English-French Neural Machine Translation.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyNa6kVvZzLR63AsCWWmm5YZ"
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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    "accelerator": "GPU",
    "gpuClass": "standard"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "Implement basic character-level recurrent seq2seq model to translate short english sentences into short french sentences character by character"
      ],
      "metadata": {
        "id": "uBRmB6nGL5zJ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "qNbFpYMWLPDC"
      },
      "outputs": [],
      "source": [
        "#import libraries\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#download data\n",
        "!!curl -O http://www.manythings.org/anki/fra-eng.zip\n",
        "!!unzip fra-eng.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hQxOR3XnMtfI",
        "outputId": "9e2506ca-3477-4116-b85d-5affc48cd26a"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Archive:  fra-eng.zip',\n",
              " '  inflating: _about.txt              ',\n",
              " '  inflating: fra.txt                 ']"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "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\""
      ],
      "metadata": {
        "id": "MALNRwmpNlYs"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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"
      ],
      "metadata": {
        "id": "Iv7X8kBqNpYb"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "input_texts : the input english sentences\n",
        "\n",
        "input_characters : the characters in the input english sentences\n",
        "\n",
        "target_texts : the target french sentences\n",
        "\n",
        "target_characters : the characters in the target french sentences"
      ],
      "metadata": {
        "id": "lI3Ib0T_Plpo"
      }
    },
    {
      "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])\n",
        "\n",
        "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VkiQ4o6MOu5Q",
        "outputId": "c63809ca-e299-454b-e317-bc27a7d0a2e3"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of samples: 10000\n",
            "Number of unique input tokens: 70\n",
            "Number of unique output tokens: 93\n",
            "Max sequence length for inputs: 14\n",
            "Max sequence length for outputs: 59\n"
          ]
        }
      ]
    },
    {
      "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": "gsoUHQt0QSTM"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "input_token_index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "36Z0QC1mRDS7",
        "outputId": "34ffb2b2-3e6c-4334-b870-ff9675f4357a"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              " 'z': 69}"
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          },
          "metadata": {},
          "execution_count": 12
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "target_token_index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mm-V7_SaRIY_",
        "outputId": "ce7cb4ae-db49-41b0-9d74-4e1c691fa5d0"
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      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "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": "wqIRG6AvRAl5"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "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": "9x5fsQhATt68"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#build model\n",
        "# Define an input sequence and process it.\n",
        "encoder_inputs = keras.Input(shape=(None, num_encoder_tokens))\n",
        "encoder = keras.layers.LSTM(latent_dim, return_state=True)\n",
        "encoder_outputs, state_h, state_c = encoder(encoder_inputs)\n",
        "\n",
        "# We discard `encoder_outputs` and only keep the states.\n",
        "encoder_states = [state_h, state_c]\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",
        "decoder_dense = keras.layers.Dense(num_decoder_tokens, activation=\"softmax\")\n",
        "decoder_outputs = decoder_dense(decoder_outputs)\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)"
      ],
      "metadata": {
        "id": "hfeLr4olStqa"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#train model\n",
        "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\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wpCoAXA4TCds",
        "outputId": "22a5d658-0970-4c95-9afb-7b8de68af04d"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "125/125 [==============================] - 6s 19ms/step - loss: 1.2181 - accuracy: 0.7315 - val_loss: 1.1483 - val_accuracy: 0.7138\n",
            "Epoch 2/100\n",
            "125/125 [==============================] - 1s 12ms/step - loss: 0.9435 - accuracy: 0.7470 - val_loss: 1.0187 - val_accuracy: 0.7278\n",
            "Epoch 3/100\n",
            "125/125 [==============================] - 1s 10ms/step - loss: 0.8492 - accuracy: 0.7648 - val_loss: 0.8756 - val_accuracy: 0.7525\n",
            "Epoch 4/100\n",
            "125/125 [==============================] - 1s 10ms/step - loss: 0.7531 - accuracy: 0.7885 - val_loss: 0.7851 - val_accuracy: 0.7746\n",
            "Epoch 5/100\n",
            "125/125 [==============================] - 1s 11ms/step - loss: 0.6770 - accuracy: 0.8050 - val_loss: 0.7230 - val_accuracy: 0.7904\n",
            "Epoch 6/100\n",
            "125/125 [==============================] - 2s 13ms/step - loss: 0.6335 - accuracy: 0.8155 - val_loss: 0.6941 - val_accuracy: 0.7955\n",
            "Epoch 7/100\n",
            "125/125 [==============================] - 2s 13ms/step - loss: 0.6047 - accuracy: 0.8224 - val_loss: 0.6551 - val_accuracy: 0.8091\n",
            "Epoch 8/100\n",
            "125/125 [==============================] - 1s 11ms/step - loss: 0.5813 - accuracy: 0.8288 - val_loss: 0.6392 - val_accuracy: 0.8121\n",
            "Epoch 9/100\n",
            "125/125 [==============================] - 1s 11ms/step - loss: 0.5610 - accuracy: 0.8346 - val_loss: 0.6218 - val_accuracy: 0.8167\n",
            "Epoch 10/100\n",
            "125/125 [==============================] - 1s 11ms/step - loss: 0.5420 - accuracy: 0.8405 - val_loss: 0.6019 - val_accuracy: 0.8228\n",
            "Epoch 11/100\n",
            "125/125 [==============================] - 1s 11ms/step - loss: 0.5277 - accuracy: 0.8446 - val_loss: 0.5850 - val_accuracy: 0.8287\n",
            "Epoch 12/100\n",
            "125/125 [==============================] - 1s 11ms/step - loss: 0.5103 - accuracy: 0.8497 - val_loss: 0.5696 - val_accuracy: 0.8348\n",
            "Epoch 13/100\n",
            "125/125 [==============================] - 1s 11ms/step - loss: 0.4976 - accuracy: 0.8536 - val_loss: 0.5550 - val_accuracy: 0.8387\n",
            "Epoch 14/100\n",
            "125/125 [==============================] - 1s 11ms/step - loss: 0.4847 - accuracy: 0.8573 - val_loss: 0.5486 - val_accuracy: 0.8397\n",
            "Epoch 15/100\n",
            "125/125 [==============================] - 2s 13ms/step - loss: 0.4736 - accuracy: 0.8605 - val_loss: 0.5330 - val_accuracy: 0.8447\n",
            "Epoch 16/100\n",
            "125/125 [==============================] - 2s 13ms/step - loss: 0.4630 - accuracy: 0.8639 - val_loss: 0.5218 - val_accuracy: 0.8485\n",
            "Epoch 17/100\n",
            "125/125 [==============================] - 1s 10ms/step - loss: 0.4525 - accuracy: 0.8664 - val_loss: 0.5166 - val_accuracy: 0.8507\n",
            "Epoch 18/100\n",
            "125/125 [==============================] - 1s 12ms/step - loss: 0.4438 - accuracy: 0.8694 - val_loss: 0.5065 - val_accuracy: 0.8529\n",
            "Epoch 19/100\n",
            "125/125 [==============================] - 1s 10ms/step - loss: 0.4350 - accuracy: 0.8715 - val_loss: 0.5017 - val_accuracy: 0.8542\n",
            "Epoch 20/100\n",
            "125/125 [==============================] - 1s 11ms/step - loss: 0.4269 - accuracy: 0.8739 - val_loss: 0.4984 - val_accuracy: 0.8547\n",
            "Epoch 21/100\n",
            "125/125 [==============================] - 1s 11ms/step - loss: 0.4195 - accuracy: 0.8758 - val_loss: 0.4888 - val_accuracy: 0.8572\n",
            "Epoch 22/100\n",
            "125/125 [==============================] - 1s 11ms/step - loss: 0.4127 - accuracy: 0.8774 - val_loss: 0.4863 - val_accuracy: 0.8580\n",
            "Epoch 23/100\n",
            "125/125 [==============================] - 1s 11ms/step - loss: 0.4058 - accuracy: 0.8794 - val_loss: 0.4802 - val_accuracy: 0.8604\n",
            "Epoch 24/100\n",
            "125/125 [==============================] - 2s 13ms/step - loss: 0.3990 - accuracy: 0.8812 - val_loss: 0.4765 - val_accuracy: 0.8611\n",
            "Epoch 25/100\n",
            "125/125 [==============================] - 1s 11ms/step - loss: 0.3930 - accuracy: 0.8829 - val_loss: 0.4741 - val_accuracy: 0.8607\n",
            "Epoch 26/100\n",
            "125/125 [==============================] - 1s 10ms/step - loss: 0.3873 - accuracy: 0.8847 - val_loss: 0.4742 - val_accuracy: 0.8618\n",
            "Epoch 27/100\n",
            "125/125 [==============================] - 1s 11ms/step - loss: 0.3807 - accuracy: 0.8862 - val_loss: 0.4648 - val_accuracy: 0.8642\n",
            "Epoch 28/100\n",
            "125/125 [==============================] - 1s 12ms/step - loss: 0.3754 - accuracy: 0.8880 - val_loss: 0.4601 - val_accuracy: 0.8656\n",
            "Epoch 29/100\n",
            "125/125 [==============================] - 1s 11ms/step - loss: 0.3698 - accuracy: 0.8896 - val_loss: 0.4570 - val_accuracy: 0.8661\n",
            "Epoch 30/100\n",
            "125/125 [==============================] - 1s 10ms/step - loss: 0.3641 - accuracy: 0.8911 - val_loss: 0.4557 - val_accuracy: 0.8665\n",
            "Epoch 31/100\n",
            "125/125 [==============================] - 1s 11ms/step - loss: 0.3586 - accuracy: 0.8929 - val_loss: 0.4524 - val_accuracy: 0.8674\n",
            "Epoch 32/100\n",
            "125/125 [==============================] - 1s 12ms/step - loss: 0.3542 - accuracy: 0.8943 - val_loss: 0.4487 - val_accuracy: 0.8693\n",
            "Epoch 33/100\n",
            "125/125 [==============================] - 2s 12ms/step - loss: 0.3488 - accuracy: 0.8956 - val_loss: 0.4478 - val_accuracy: 0.8682\n",
            "Epoch 34/100\n",
            "125/125 [==============================] - 1s 12ms/step - loss: 0.3444 - accuracy: 0.8971 - val_loss: 0.4452 - val_accuracy: 0.8695\n",
            "Epoch 35/100\n",
            "125/125 [==============================] - 1s 11ms/step - loss: 0.3395 - accuracy: 0.8987 - val_loss: 0.4420 - val_accuracy: 0.8706\n",
            "Epoch 36/100\n",
            "125/125 [==============================] - 1s 10ms/step - loss: 0.3348 - accuracy: 0.9000 - val_loss: 0.4407 - val_accuracy: 0.8715\n",
            "Epoch 37/100\n",
            "125/125 [==============================] - 1s 11ms/step - loss: 0.3300 - accuracy: 0.9013 - val_loss: 0.4409 - val_accuracy: 0.8714\n",
            "Epoch 38/100\n",
            "125/125 [==============================] - 1s 10ms/step - loss: 0.3250 - accuracy: 0.9031 - val_loss: 0.4417 - val_accuracy: 0.8713\n",
            "Epoch 39/100\n",
            "125/125 [==============================] - 1s 11ms/step - loss: 0.3205 - accuracy: 0.9041 - val_loss: 0.4410 - val_accuracy: 0.8721\n",
            "Epoch 40/100\n",
            "125/125 [==============================] - 1s 10ms/step - loss: 0.3165 - accuracy: 0.9052 - val_loss: 0.4393 - val_accuracy: 0.8725\n",
            "Epoch 41/100\n",
            "125/125 [==============================] - 2s 12ms/step - loss: 0.3122 - accuracy: 0.9065 - val_loss: 0.4372 - val_accuracy: 0.8732\n",
            "Epoch 42/100\n",
            "125/125 [==============================] - 1s 12ms/step - loss: 0.3082 - accuracy: 0.9075 - val_loss: 0.4417 - val_accuracy: 0.8734\n",
            "Epoch 43/100\n",
            "125/125 [==============================] - 1s 10ms/step - loss: 0.3035 - accuracy: 0.9093 - val_loss: 0.4350 - val_accuracy: 0.8747\n",
            "Epoch 44/100\n",
            "125/125 [==============================] - 1s 11ms/step - loss: 0.2996 - accuracy: 0.9104 - val_loss: 0.4374 - val_accuracy: 0.8748\n",
            "Epoch 45/100\n",
            "125/125 [==============================] - 1s 11ms/step - loss: 0.2956 - accuracy: 0.9114 - val_loss: 0.4365 - val_accuracy: 0.8737\n",
            "Epoch 46/100\n",
            "125/125 [==============================] - 1s 10ms/step - loss: 0.2914 - accuracy: 0.9126 - val_loss: 0.4382 - val_accuracy: 0.8744\n",
            "Epoch 47/100\n",
            "125/125 [==============================] - 1s 11ms/step - loss: 0.2873 - accuracy: 0.9142 - val_loss: 0.4337 - val_accuracy: 0.8761\n",
            "Epoch 48/100\n",
            "125/125 [==============================] - 1s 10ms/step - loss: 0.2833 - accuracy: 0.9152 - val_loss: 0.4337 - val_accuracy: 0.8767\n",
            "Epoch 49/100\n",
            "125/125 [==============================] - 1s 10ms/step - loss: 0.2796 - accuracy: 0.9162 - val_loss: 0.4347 - val_accuracy: 0.8763\n",
            "Epoch 50/100\n",
            "125/125 [==============================] - 1s 12ms/step - loss: 0.2759 - accuracy: 0.9171 - val_loss: 0.4339 - val_accuracy: 0.8771\n",
            "Epoch 51/100\n",
            "125/125 [==============================] - 2s 12ms/step - loss: 0.2725 - accuracy: 0.9184 - val_loss: 0.4366 - val_accuracy: 0.8771\n",
            "Epoch 52/100\n",
            "125/125 [==============================] - 1s 10ms/step - loss: 0.2685 - accuracy: 0.9196 - val_loss: 0.4402 - val_accuracy: 0.8759\n",
            "Epoch 53/100\n",
            "125/125 [==============================] - 1s 11ms/step - loss: 0.2648 - accuracy: 0.9207 - val_loss: 0.4358 - val_accuracy: 0.8777\n",
            "Epoch 54/100\n",
            "125/125 [==============================] - 1s 10ms/step - loss: 0.2614 - accuracy: 0.9214 - val_loss: 0.4368 - val_accuracy: 0.8768\n",
            "Epoch 55/100\n",
            "125/125 [==============================] - 1s 10ms/step - loss: 0.2581 - accuracy: 0.9226 - val_loss: 0.4367 - val_accuracy: 0.8776\n",
            "Epoch 56/100\n",
            "125/125 [==============================] - 1s 10ms/step - loss: 0.2547 - accuracy: 0.9235 - val_loss: 0.4390 - val_accuracy: 0.8763\n",
            "Epoch 57/100\n",
            "125/125 [==============================] - 1s 12ms/step - loss: 0.2512 - accuracy: 0.9245 - val_loss: 0.4381 - val_accuracy: 0.8772\n",
            "Epoch 58/100\n",
            "125/125 [==============================] - 1s 10ms/step - loss: 0.2475 - accuracy: 0.9258 - val_loss: 0.4401 - val_accuracy: 0.8767\n",
            "Epoch 59/100\n",
            "125/125 [==============================] - 2s 13ms/step - loss: 0.2444 - accuracy: 0.9266 - val_loss: 0.4443 - val_accuracy: 0.8770\n",
            "Epoch 60/100\n",
            "125/125 [==============================] - 1s 11ms/step - loss: 0.2413 - accuracy: 0.9276 - val_loss: 0.4423 - val_accuracy: 0.8777\n",
            "Epoch 61/100\n",
            "125/125 [==============================] - 1s 11ms/step - loss: 0.2383 - accuracy: 0.9283 - val_loss: 0.4478 - val_accuracy: 0.8767\n",
            "Epoch 62/100\n",
            "125/125 [==============================] - 1s 10ms/step - loss: 0.2352 - accuracy: 0.9293 - val_loss: 0.4445 - val_accuracy: 0.8773\n",
            "Epoch 63/100\n",
            "125/125 [==============================] - 1s 10ms/step - loss: 0.2321 - accuracy: 0.9302 - val_loss: 0.4507 - val_accuracy: 0.8764\n",
            "Epoch 64/100\n",
            "125/125 [==============================] - 1s 10ms/step - loss: 0.2292 - accuracy: 0.9313 - val_loss: 0.4487 - val_accuracy: 0.8768\n",
            "Epoch 65/100\n",
            "125/125 [==============================] - 1s 10ms/step - loss: 0.2260 - accuracy: 0.9323 - val_loss: 0.4505 - val_accuracy: 0.8772\n",
            "Epoch 66/100\n",
            "125/125 [==============================] - 1s 10ms/step - loss: 0.2230 - accuracy: 0.9329 - val_loss: 0.4478 - val_accuracy: 0.8771\n",
            "Epoch 67/100\n",
            "125/125 [==============================] - 1s 11ms/step - loss: 0.2202 - accuracy: 0.9336 - val_loss: 0.4514 - val_accuracy: 0.8774\n",
            "Epoch 68/100\n",
            "125/125 [==============================] - 1s 12ms/step - loss: 0.2173 - accuracy: 0.9345 - val_loss: 0.4538 - val_accuracy: 0.8777\n",
            "Epoch 69/100\n",
            "125/125 [==============================] - 2s 12ms/step - loss: 0.2147 - accuracy: 0.9351 - val_loss: 0.4543 - val_accuracy: 0.8781\n",
            "Epoch 70/100\n",
            "125/125 [==============================] - 1s 11ms/step - loss: 0.2118 - accuracy: 0.9360 - val_loss: 0.4616 - val_accuracy: 0.8767\n",
            "Epoch 71/100\n",
            "125/125 [==============================] - 1s 10ms/step - loss: 0.2095 - accuracy: 0.9367 - val_loss: 0.4571 - val_accuracy: 0.8775\n",
            "Epoch 72/100\n",
            "125/125 [==============================] - 1s 12ms/step - loss: 0.2060 - accuracy: 0.9379 - val_loss: 0.4595 - val_accuracy: 0.8778\n",
            "Epoch 73/100\n",
            "125/125 [==============================] - 1s 10ms/step - loss: 0.2037 - accuracy: 0.9384 - val_loss: 0.4659 - val_accuracy: 0.8762\n",
            "Epoch 74/100\n",
            "125/125 [==============================] - 1s 10ms/step - loss: 0.2013 - accuracy: 0.9393 - val_loss: 0.4610 - val_accuracy: 0.8781\n",
            "Epoch 75/100\n",
            "125/125 [==============================] - 1s 10ms/step - loss: 0.1988 - accuracy: 0.9400 - val_loss: 0.4676 - val_accuracy: 0.8773\n",
            "Epoch 76/100\n",
            "125/125 [==============================] - 1s 11ms/step - loss: 0.1965 - accuracy: 0.9405 - val_loss: 0.4689 - val_accuracy: 0.8767\n",
            "Epoch 77/100\n",
            "125/125 [==============================] - 2s 13ms/step - loss: 0.1938 - accuracy: 0.9412 - val_loss: 0.4719 - val_accuracy: 0.8775\n",
            "Epoch 78/100\n",
            "125/125 [==============================] - 1s 11ms/step - loss: 0.1911 - accuracy: 0.9421 - val_loss: 0.4739 - val_accuracy: 0.8763\n",
            "Epoch 79/100\n",
            "125/125 [==============================] - 1s 12ms/step - loss: 0.1884 - accuracy: 0.9430 - val_loss: 0.4732 - val_accuracy: 0.8774\n",
            "Epoch 80/100\n",
            "125/125 [==============================] - 1s 11ms/step - loss: 0.1861 - accuracy: 0.9438 - val_loss: 0.4766 - val_accuracy: 0.8780\n",
            "Epoch 81/100\n",
            "125/125 [==============================] - 1s 10ms/step - loss: 0.1844 - accuracy: 0.9439 - val_loss: 0.4756 - val_accuracy: 0.8772\n",
            "Epoch 82/100\n",
            "125/125 [==============================] - 1s 10ms/step - loss: 0.1815 - accuracy: 0.9451 - val_loss: 0.4821 - val_accuracy: 0.8775\n",
            "Epoch 83/100\n",
            "125/125 [==============================] - 1s 10ms/step - loss: 0.1798 - accuracy: 0.9452 - val_loss: 0.4800 - val_accuracy: 0.8777\n",
            "Epoch 84/100\n",
            "125/125 [==============================] - 1s 10ms/step - loss: 0.1764 - accuracy: 0.9466 - val_loss: 0.4854 - val_accuracy: 0.8774\n",
            "Epoch 85/100\n",
            "125/125 [==============================] - 1s 11ms/step - loss: 0.1743 - accuracy: 0.9473 - val_loss: 0.4889 - val_accuracy: 0.8764\n",
            "Epoch 86/100\n",
            "125/125 [==============================] - 2s 13ms/step - loss: 0.1727 - accuracy: 0.9474 - val_loss: 0.4832 - val_accuracy: 0.8777\n",
            "Epoch 87/100\n",
            "125/125 [==============================] - 1s 12ms/step - loss: 0.1703 - accuracy: 0.9484 - val_loss: 0.4886 - val_accuracy: 0.8772\n",
            "Epoch 88/100\n",
            "125/125 [==============================] - 1s 10ms/step - loss: 0.1678 - accuracy: 0.9492 - val_loss: 0.4901 - val_accuracy: 0.8770\n",
            "Epoch 89/100\n",
            "125/125 [==============================] - 1s 10ms/step - loss: 0.1660 - accuracy: 0.9496 - val_loss: 0.4948 - val_accuracy: 0.8774\n",
            "Epoch 90/100\n",
            "125/125 [==============================] - 1s 11ms/step - loss: 0.1636 - accuracy: 0.9502 - val_loss: 0.4955 - val_accuracy: 0.8773\n",
            "Epoch 91/100\n",
            "125/125 [==============================] - 1s 10ms/step - loss: 0.1617 - accuracy: 0.9507 - val_loss: 0.5008 - val_accuracy: 0.8765\n",
            "Epoch 92/100\n",
            "125/125 [==============================] - 1s 11ms/step - loss: 0.1595 - accuracy: 0.9514 - val_loss: 0.5009 - val_accuracy: 0.8773\n",
            "Epoch 93/100\n",
            "125/125 [==============================] - 1s 10ms/step - loss: 0.1576 - accuracy: 0.9522 - val_loss: 0.5057 - val_accuracy: 0.8771\n",
            "Epoch 94/100\n",
            "125/125 [==============================] - 1s 12ms/step - loss: 0.1559 - accuracy: 0.9525 - val_loss: 0.5082 - val_accuracy: 0.8767\n",
            "Epoch 95/100\n",
            "125/125 [==============================] - 2s 13ms/step - loss: 0.1535 - accuracy: 0.9533 - val_loss: 0.5099 - val_accuracy: 0.8766\n",
            "Epoch 96/100\n",
            "125/125 [==============================] - 1s 11ms/step - loss: 0.1518 - accuracy: 0.9536 - val_loss: 0.5083 - val_accuracy: 0.8773\n",
            "Epoch 97/100\n",
            "125/125 [==============================] - 1s 11ms/step - loss: 0.1501 - accuracy: 0.9543 - val_loss: 0.5110 - val_accuracy: 0.8766\n",
            "Epoch 98/100\n",
            "125/125 [==============================] - 1s 12ms/step - loss: 0.1481 - accuracy: 0.9549 - val_loss: 0.5186 - val_accuracy: 0.8759\n",
            "Epoch 99/100\n",
            "125/125 [==============================] - 1s 10ms/step - loss: 0.1463 - accuracy: 0.9553 - val_loss: 0.5178 - val_accuracy: 0.8761\n",
            "Epoch 100/100\n",
            "125/125 [==============================] - 1s 10ms/step - loss: 0.1445 - accuracy: 0.9559 - val_loss: 0.5197 - val_accuracy: 0.8766\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_2_layer_call_fn, lstm_cell_2_layer_call_and_return_conditional_losses, lstm_cell_3_layer_call_fn, lstm_cell_3_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#inference\n",
        "# Define sampling models\n",
        "# Restore the model and construct the encoder and decoder.\n",
        "model = keras.models.load_model(\"s2s\")\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())"
      ],
      "metadata": {
        "id": "fcZGZzQRV3px"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "reverse_input_char_index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kH136XKuWdCj",
        "outputId": "c9fcb531-d919-41e3-ea73-3af518f7b3ea"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0: ' ',\n",
              " 1: '!',\n",
              " 2: '\"',\n",
              " 3: '$',\n",
              " 4: '%',\n",
              " 5: '&',\n",
              " 6: \"'\",\n",
              " 7: ',',\n",
              " 8: '-',\n",
              " 9: '.',\n",
              " 10: '0',\n",
              " 11: '1',\n",
              " 12: '2',\n",
              " 13: '3',\n",
              " 14: '5',\n",
              " 15: '7',\n",
              " 16: '8',\n",
              " 17: '9',\n",
              " 18: ':',\n",
              " 19: '?',\n",
              " 20: 'A',\n",
              " 21: 'B',\n",
              " 22: 'C',\n",
              " 23: 'D',\n",
              " 24: 'E',\n",
              " 25: 'F',\n",
              " 26: 'G',\n",
              " 27: 'H',\n",
              " 28: 'I',\n",
              " 29: 'J',\n",
              " 30: 'K',\n",
              " 31: 'L',\n",
              " 32: 'M',\n",
              " 33: 'N',\n",
              " 34: 'O',\n",
              " 35: 'P',\n",
              " 36: 'Q',\n",
              " 37: 'R',\n",
              " 38: 'S',\n",
              " 39: 'T',\n",
              " 40: 'U',\n",
              " 41: 'V',\n",
              " 42: 'W',\n",
              " 43: 'Y',\n",
              " 44: 'a',\n",
              " 45: 'b',\n",
              " 46: 'c',\n",
              " 47: 'd',\n",
              " 48: 'e',\n",
              " 49: 'f',\n",
              " 50: 'g',\n",
              " 51: 'h',\n",
              " 52: 'i',\n",
              " 53: 'j',\n",
              " 54: 'k',\n",
              " 55: 'l',\n",
              " 56: 'm',\n",
              " 57: 'n',\n",
              " 58: 'o',\n",
              " 59: 'p',\n",
              " 60: 'q',\n",
              " 61: 'r',\n",
              " 62: 's',\n",
              " 63: 't',\n",
              " 64: 'u',\n",
              " 65: 'v',\n",
              " 66: 'w',\n",
              " 67: 'x',\n",
              " 68: 'y',\n",
              " 69: 'z'}"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reverse_target_char_index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pahx0N-dWmJw",
        "outputId": "972cd446-e717-46d7-ab55-54bdc63d6088"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0: '\\t',\n",
              " 1: '\\n',\n",
              " 2: ' ',\n",
              " 3: '!',\n",
              " 4: '%',\n",
              " 5: '&',\n",
              " 6: \"'\",\n",
              " 7: '(',\n",
              " 8: ')',\n",
              " 9: ',',\n",
              " 10: '-',\n",
              " 11: '.',\n",
              " 12: '0',\n",
              " 13: '1',\n",
              " 14: '2',\n",
              " 15: '3',\n",
              " 16: '5',\n",
              " 17: '8',\n",
              " 18: '9',\n",
              " 19: ':',\n",
              " 20: '?',\n",
              " 21: 'A',\n",
              " 22: 'B',\n",
              " 23: 'C',\n",
              " 24: 'D',\n",
              " 25: 'E',\n",
              " 26: 'F',\n",
              " 27: 'G',\n",
              " 28: 'H',\n",
              " 29: 'I',\n",
              " 30: 'J',\n",
              " 31: 'K',\n",
              " 32: 'L',\n",
              " 33: 'M',\n",
              " 34: 'N',\n",
              " 35: 'O',\n",
              " 36: 'P',\n",
              " 37: 'Q',\n",
              " 38: 'R',\n",
              " 39: 'S',\n",
              " 40: 'T',\n",
              " 41: 'U',\n",
              " 42: 'V',\n",
              " 43: 'W',\n",
              " 44: 'Y',\n",
              " 45: 'a',\n",
              " 46: 'b',\n",
              " 47: 'c',\n",
              " 48: 'd',\n",
              " 49: 'e',\n",
              " 50: 'f',\n",
              " 51: 'g',\n",
              " 52: 'h',\n",
              " 53: 'i',\n",
              " 54: 'j',\n",
              " 55: 'k',\n",
              " 56: 'l',\n",
              " 57: 'm',\n",
              " 58: 'n',\n",
              " 59: 'o',\n",
              " 60: 'p',\n",
              " 61: 'q',\n",
              " 62: 'r',\n",
              " 63: 's',\n",
              " 64: 't',\n",
              " 65: 'u',\n",
              " 66: 'v',\n",
              " 67: 'w',\n",
              " 68: 'x',\n",
              " 69: 'y',\n",
              " 70: 'z',\n",
              " 71: '\\xa0',\n",
              " 72: '«',\n",
              " 73: '»',\n",
              " 74: 'À',\n",
              " 75: 'Ç',\n",
              " 76: 'É',\n",
              " 77: 'Ê',\n",
              " 78: 'à',\n",
              " 79: 'â',\n",
              " 80: 'ç',\n",
              " 81: 'è',\n",
              " 82: 'é',\n",
              " 83: 'ê',\n",
              " 84: 'î',\n",
              " 85: 'ï',\n",
              " 86: 'ô',\n",
              " 87: 'ù',\n",
              " 88: 'û',\n",
              " 89: 'œ',\n",
              " 90: '\\u2009',\n",
              " 91: '’',\n",
              " 92: '\\u202f'}"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "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"
      ],
      "metadata": {
        "id": "eM1BE45nWZML"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#generate decoded sentences\n",
        "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)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zS7AUPDvW338",
        "outputId": "ad79a811-e2f3-406b-a633-b2651ce445f9"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 340ms/step\n",
            "1/1 [==============================] - 0s 349ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "-\n",
            "Input sentence: Go.\n",
            "Decoded sentence: Détendez-vous !\n",
            "\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 35ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 35ms/step\n",
            "-\n",
            "Input sentence: Go.\n",
            "Decoded sentence: Détendez-vous !\n",
            "\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 34ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "-\n",
            "Input sentence: Go.\n",
            "Decoded sentence: Détendez-vous !\n",
            "\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "-\n",
            "Input sentence: Go.\n",
            "Decoded sentence: Détendez-vous !\n",
            "\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "-\n",
            "Input sentence: Hi.\n",
            "Decoded sentence: Salut !\n",
            "\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "-\n",
            "Input sentence: Hi.\n",
            "Decoded sentence: Salut !\n",
            "\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "-\n",
            "Input sentence: Run!\n",
            "Decoded sentence: Cours !\n",
            "\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "-\n",
            "Input sentence: Run!\n",
            "Decoded sentence: Cours !\n",
            "\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "-\n",
            "Input sentence: Run!\n",
            "Decoded sentence: Cours !\n",
            "\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "-\n",
            "Input sentence: Run!\n",
            "Decoded sentence: Cours !\n",
            "\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "-\n",
            "Input sentence: Run!\n",
            "Decoded sentence: Cours !\n",
            "\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "-\n",
            "Input sentence: Run!\n",
            "Decoded sentence: Cours !\n",
            "\n",
            "1/1 [==============================] - 0s 20ms/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",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "-\n",
            "Input sentence: Run!\n",
            "Decoded sentence: Cours !\n",
            "\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "-\n",
            "Input sentence: Run!\n",
            "Decoded sentence: Cours !\n",
            "\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "-\n",
            "Input sentence: Run.\n",
            "Decoded sentence: Courez !\n",
            "\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "-\n",
            "Input sentence: Run.\n",
            "Decoded sentence: Courez !\n",
            "\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "-\n",
            "Input sentence: Run.\n",
            "Decoded sentence: Courez !\n",
            "\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "-\n",
            "Input sentence: Run.\n",
            "Decoded sentence: Courez !\n",
            "\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "-\n",
            "Input sentence: Run.\n",
            "Decoded sentence: Courez !\n",
            "\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "-\n",
            "Input sentence: Run.\n",
            "Decoded sentence: Courez !\n",
            "\n"
          ]
        }
      ]
    }
  ]
}