Image_Caption.ipynb

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        "outputId": "b2154cab-c44b-49e4-b808-83cee55f6dae"
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      "source": [
        "import string\n",
        "import numpy as np\n",
        "from PIL import Image\n",
        "import os\n",
        "from pickle import dump, load\n",
        "import numpy as np\n",
        "from keras.applications.xception import Xception, preprocess_input\n",
        "from keras.preprocessing.image import load_img, img_to_array\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "from keras.utils import to_categorical\n",
        "from keras.layers.merge import add\n",
        "from keras.models import Model, load_model\n",
        "from keras.layers import Input, Dense, LSTM, Embedding, Dropout\n",
        "# small library for seeing the progress of loops.\n",
        "from tqdm import tqdm_notebook as tqdm\n",
        "tqdm().pandas()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:17: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
          ],
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        "outputId": "b83342fe-fe96-4aaa-b481-a43e1a11a120"
      },
      "source": [
        "print(\"Downloading Flickr data...\")\n",
        "from urllib.request import urlretrieve\n",
        "import zipfile\n",
        "\n",
        "urlretrieve(\"https://github.com/jbrownlee/Datasets/releases/download/Flickr8k/Flickr8k_Dataset.zip\", \"Flickr8k_Dataset.zip\")\n",
        "zip_ref = zipfile.ZipFile('Flickr8k_Dataset.zip', \"r\")\n",
        "zip_ref.extractall()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading Flickr data...\n"
          ],
          "name": "stdout"
        }
      ]
    },
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        "outputId": "6b6ebc0e-0e76-46f1-b3ea-d1ad71510656"
      },
      "source": [
        "##Downloading Text\n",
        "urlretrieve(\"https://github.com/jbrownlee/Datasets/releases/download/Flickr8k/Flickr8k_text.zip\", \"Flickr8k_text.zip\")\n",
        "zip_ref = zipfile.ZipFile('Flickr8k_text.zip', \"r\")\n",
        "zip_ref.extractall()\n",
        "\n",
        "print(\"Done:\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Done:\n"
          ],
          "name": "stdout"
        }
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          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "c5b438ea-eaea-4d26-c420-5ba62a2cef52"
      },
      "source": [
        "# Loading a text file into memory\n",
        "def load_doc(filename):\n",
        "    # Opening the file as read only\n",
        "    file = open(filename, 'r')\n",
        "    text = file.read()\n",
        "    file.close()\n",
        "    return text\n",
        "# get all imgs with their captions\n",
        "def all_img_captions(filename):\n",
        "    file = load_doc(filename)\n",
        "    captions = file.split('\\n')\n",
        "    descriptions ={}\n",
        "    for caption in captions[:-1]:\n",
        "        img, caption = caption.split('\\t')\n",
        "        if img[:-2] not in descriptions:\n",
        "            descriptions[img[:-2]] = [ caption ]\n",
        "        else:\n",
        "            descriptions[img[:-2]].append(caption)\n",
        "    return descriptions\n",
        "#Data cleaning- lower casing, removing puntuations and words containing numbers\n",
        "def cleaning_text(captions):\n",
        "    table = str.maketrans('','',string.punctuation)\n",
        "    for img,caps in captions.items():\n",
        "        for i,img_caption in enumerate(caps):\n",
        "            img_caption.replace(\"-\",\" \")\n",
        "            desc = img_caption.split()\n",
        "            #converts to lowercase\n",
        "            desc = [word.lower() for word in desc]\n",
        "            #remove punctuation from each token\n",
        "            desc = [word.translate(table) for word in desc]\n",
        "            #remove hanging 's and a \n",
        "            desc = [word for word in desc if(len(word)>1)]\n",
        "            #remove tokens with numbers in them\n",
        "            desc = [word for word in desc if(word.isalpha())]\n",
        "            #convert back to string\n",
        "            img_caption = ' '.join(desc)\n",
        "            captions[img][i]= img_caption\n",
        "    return captions\n",
        "def text_vocabulary(descriptions):\n",
        "    # build vocabulary of all unique words\n",
        "    vocab = set()\n",
        "    for key in descriptions.keys():\n",
        "        [vocab.update(d.split()) for d in descriptions[key]]\n",
        "    return vocab\n",
        "#All descriptions in one file \n",
        "def save_descriptions(descriptions, filename):\n",
        "    lines = list()\n",
        "    for key, desc_list in descriptions.items():\n",
        "        for desc in desc_list:\n",
        "            lines.append(key + '\\t' + desc )\n",
        "    data = \"\\n\".join(lines)\n",
        "    file = open(filename,\"w\")\n",
        "    file.write(data)\n",
        "    file.close()\n",
        "# Set these path according to project folder in you system\n",
        "#dataset_text = \"D:\\dataflair projects\\Project - Image Caption Generator\\Flickr_8k_text\"\n",
        "dataset_images = \"/content/Flicker8k_Dataset\"\n",
        "#we prepare our text data\n",
        "filename = \"/content/Flickr8k.token.txt\"\n",
        "#loading the file that contains all data\n",
        "#mapping them into descriptions dictionary img to 5 captions\n",
        "descriptions = all_img_captions(filename)\n",
        "print(\"Length of descriptions =\" ,len(descriptions))\n",
        "#cleaning the descriptions\n",
        "clean_descriptions = cleaning_text(descriptions)\n",
        "#building vocabulary \n",
        "vocabulary = text_vocabulary(clean_descriptions)\n",
        "print(\"Length of vocabulary = \", len(vocabulary))\n",
        "#saving each description to file \n",
        "save_descriptions(clean_descriptions, \"descriptions.txt\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Length of descriptions = 8092\n",
            "Length of vocabulary =  8763\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uQLYeaTt0O-2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170,
          "referenced_widgets": [
            "63a1ba4896dd47728b90a6af9bc39e05",
            "9c36f88fb74247939b3101a4d69ca40d",
            "96780653e1364d0fbd441656c074caff",
            "376ebaba2eba4374b2852de02c3fde26",
            "301ba8f408a5486792e16774138c0551",
            "f3ea34d63d7e4fa09a61658ab196e864",
            "0ae1f5adae8d48558d2eac7b0b28421c",
            "e8ce45571c194edab5198e8b5df7e435"
          ]
        },
        "outputId": "8355936c-7e4f-4e5d-d1e3-4086517b3253"
      },
      "source": [
        "def extract_features(directory):\n",
        "        model = Xception( include_top=False, pooling='avg' )\n",
        "        features = {}\n",
        "        for img in tqdm(os.listdir(directory)):\n",
        "            filename = directory + \"/\" + img\n",
        "            image = Image.open(filename)\n",
        "            image = image.resize((299,299))\n",
        "            image = np.expand_dims(image, axis=0)\n",
        "            #image = preprocess_input(image)\n",
        "            image = image/127.5\n",
        "            image = image - 1.0\n",
        "            feature = model.predict(image)\n",
        "            features[img] = feature\n",
        "        return features\n",
        "#2048 feature vector\n",
        "features = extract_features(dataset_images)\n",
        "dump(features, open(\"features.p\",\"wb\"))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.4/xception_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "83689472/83683744 [==============================] - 3s 0us/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
            "  after removing the cwd from sys.path.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "63a1ba4896dd47728b90a6af9bc39e05",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=8091.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FBTjK-6Y0Ys0"
      },
      "source": [
        "features = load(open(\"features.p\",\"rb\"))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hQpb_sFZ97az"
      },
      "source": [
        "#load the data \n",
        "def load_photos(filename):\n",
        "    file = load_doc(filename)\n",
        "    photos = file.split(\"\\n\")[:-1]\n",
        "    return photos\n",
        "def load_clean_descriptions(filename, photos): \n",
        "    #loading clean_descriptions\n",
        "    file = load_doc(filename)\n",
        "    descriptions = {}\n",
        "    for line in file.split(\"\\n\"):\n",
        "        words = line.split()\n",
        "        if len(words)<1 :\n",
        "            continue\n",
        "        image, image_caption = words[0], words[1:]\n",
        "        if image in photos:\n",
        "            if image not in descriptions:\n",
        "                descriptions[image] = []\n",
        "            desc = '<start> ' + \" \".join(image_caption) + ' <end>'\n",
        "            descriptions[image].append(desc)\n",
        "    return descriptions\n",
        "def load_features(photos):\n",
        "    #loading all features\n",
        "    all_features = load(open(\"features.p\",\"rb\"))\n",
        "    #selecting only needed features\n",
        "    features = {k:all_features[k] for k in photos}\n",
        "    return features\n",
        "filename = \"/content/Flickr_8k.trainImages.txt\"\n",
        "#train = loading_data(filename)\n",
        "train_imgs = load_photos(filename)\n",
        "train_descriptions = load_clean_descriptions(\"descriptions.txt\", train_imgs)\n",
        "train_features = load_features(train_imgs)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_ekJBSi9-TnE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "0ee50d1b-6e0b-4966-b682-0959403cddde"
      },
      "source": [
        "#converting dictionary to clean list of descriptions\n",
        "def dict_to_list(descriptions):\n",
        "    all_desc = []\n",
        "    for key in descriptions.keys():\n",
        "        [all_desc.append(d) for d in descriptions[key]]\n",
        "    return all_desc\n",
        "#creating tokenizer class \n",
        "#this will vectorise text corpus\n",
        "#each integer will represent token in dictionary\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "def create_tokenizer(descriptions):\n",
        "    desc_list = dict_to_list(descriptions)\n",
        "    tokenizer = Tokenizer()\n",
        "    tokenizer.fit_on_texts(desc_list)\n",
        "    return tokenizer\n",
        "# give each word an index, and store that into tokenizer.p pickle file\n",
        "tokenizer = create_tokenizer(train_descriptions)\n",
        "dump(tokenizer, open('tokenizer.p', 'wb'))\n",
        "vocab_size = len(tokenizer.word_index) + 1\n",
        "vocab_size"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7577"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gyNScWRc-fHC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b0b21342-15a3-44b1-a951-5d1d5de2a52a"
      },
      "source": [
        "#calculate maximum length of descriptions\n",
        "def max_length(descriptions):\n",
        "    desc_list = dict_to_list(descriptions)\n",
        "    return max(len(d.split()) for d in desc_list)\n",
        "    \n",
        "max_length = max_length(descriptions)\n",
        "max_length"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "32"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "djS_hrx--lej",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "db4eda7c-8c31-415b-caac-264270c16694"
      },
      "source": [
        "#create input-output sequence pairs from the image description.\n",
        "#data generator, used by model.fit_generator()\n",
        "def data_generator(descriptions, features, tokenizer, max_length):\n",
        "    while 1:\n",
        "        for key, description_list in descriptions.items():\n",
        "            #retrieve photo features\n",
        "            feature = features[key][0]\n",
        "            input_image, input_sequence, output_word = create_sequences(tokenizer, max_length, description_list, feature)\n",
        "            yield [[input_image, input_sequence], output_word]\n",
        "def create_sequences(tokenizer, max_length, desc_list, feature):\n",
        "    X1, X2, y = list(), list(), list()\n",
        "    # walk through each description for the image\n",
        "    for desc in desc_list:\n",
        "        # encode the sequence\n",
        "        seq = tokenizer.texts_to_sequences([desc])[0]\n",
        "        # split one sequence into multiple X,y pairs\n",
        "        for i in range(1, len(seq)):\n",
        "            # split into input and output pair\n",
        "            in_seq, out_seq = seq[:i], seq[i]\n",
        "            # pad input sequence\n",
        "            in_seq = pad_sequences([in_seq], maxlen=max_length)[0]\n",
        "            # encode output sequence\n",
        "            out_seq = to_categorical([out_seq], num_classes=vocab_size)[0]\n",
        "            # store\n",
        "            X1.append(feature)\n",
        "            X2.append(in_seq)\n",
        "            y.append(out_seq)\n",
        "    return np.array(X1), np.array(X2), np.array(y)\n",
        "#You can check the shape of the input and output for your model\n",
        "[a,b],c = next(data_generator(train_descriptions, features, tokenizer, max_length))\n",
        "a.shape, b.shape, c.shape\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((47, 2048), (47, 32), (47, 7577))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dbo4Vu8a-yH8"
      },
      "source": [
        "from keras.utils import plot_model\n",
        "# define the captioning model\n",
        "def define_model(vocab_size, max_length):\n",
        "    # features from the CNN model squeezed from 2048 to 256 nodes\n",
        "    inputs1 = Input(shape=(2048,))\n",
        "    fe1 = Dropout(0.5)(inputs1)\n",
        "    fe2 = Dense(256, activation='relu')(fe1)\n",
        "    # LSTM sequence model\n",
        "    inputs2 = Input(shape=(max_length,))\n",
        "    se1 = Embedding(vocab_size, 256, mask_zero=True)(inputs2)\n",
        "    se2 = Dropout(0.5)(se1)\n",
        "    se3 = LSTM(256)(se2)\n",
        "    # Merging both models\n",
        "    decoder1 = add([fe2, se3])\n",
        "    decoder2 = Dense(256, activation='relu')(decoder1)\n",
        "    outputs = Dense(vocab_size, activation='softmax')(decoder2)\n",
        "    # tie it together [image, seq] [word]\n",
        "    model = Model(inputs=[inputs1, inputs2], outputs=outputs)\n",
        "    model.compile(loss='categorical_crossentropy', optimizer='adam')\n",
        "    # summarize model\n",
        "    print(model.summary())\n",
        "    plot_model(model, to_file='model.png', show_shapes=True)\n",
        "    return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wM4IKw_c-2_h",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 994
        },
        "outputId": "fd0aac63-59fe-4c5a-f094-953d8b80a42f"
      },
      "source": [
        "# train our model\n",
        "print('Dataset: ', len(train_imgs))\n",
        "print('Descriptions: train=', len(train_descriptions))\n",
        "print('Photos: train=', len(train_features))\n",
        "print('Vocabulary Size:', vocab_size)\n",
        "print('Description Length: ', max_length)\n",
        "model = define_model(vocab_size, max_length)\n",
        "epochs = 10\n",
        "steps = len(train_descriptions)\n",
        "# making a directory models to save our models\n",
        "os.mkdir(\"models\")\n",
        "for i in range(epochs):\n",
        "    generator = data_generator(train_descriptions, train_features, tokenizer, max_length)\n",
        "    model.fit_generator(generator, epochs=1, steps_per_epoch= steps, verbose=1)\n",
        "    model.save(\"models/model_\" + str(i) + \".h5\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Dataset:  6000\n",
            "Descriptions: train= 6000\n",
            "Photos: train= 6000\n",
            "Vocabulary Size: 7577\n",
            "Description Length:  32\n",
            "Model: \"model_1\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_3 (InputLayer)            (None, 32)           0                                            \n",
            "__________________________________________________________________________________________________\n",
            "input_2 (InputLayer)            (None, 2048)         0                                            \n",
            "__________________________________________________________________________________________________\n",
            "embedding_1 (Embedding)         (None, 32, 256)      1939712     input_3[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dropout_1 (Dropout)             (None, 2048)         0           input_2[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dropout_2 (Dropout)             (None, 32, 256)      0           embedding_1[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "dense_1 (Dense)                 (None, 256)          524544      dropout_1[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "lstm_1 (LSTM)                   (None, 256)          525312      dropout_2[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "add_13 (Add)                    (None, 256)          0           dense_1[0][0]                    \n",
            "                                                                 lstm_1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "dense_2 (Dense)                 (None, 256)          65792       add_13[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "dense_3 (Dense)                 (None, 7577)         1947289     dense_2[0][0]                    \n",
            "==================================================================================================\n",
            "Total params: 5,002,649\n",
            "Trainable params: 5,002,649\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/indexed_slices.py:434: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
            "  \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/1\n",
            "6000/6000 [==============================] - 540s 90ms/step - loss: 4.4921\n",
            "Epoch 1/1\n",
            "6000/6000 [==============================] - 530s 88ms/step - loss: 3.6479\n",
            "Epoch 1/1\n",
            "6000/6000 [==============================] - 529s 88ms/step - loss: 3.3584\n",
            "Epoch 1/1\n",
            "6000/6000 [==============================] - 533s 89ms/step - loss: 3.1875\n",
            "Epoch 1/1\n",
            "6000/6000 [==============================] - 534s 89ms/step - loss: 3.0684\n",
            "Epoch 1/1\n",
            "6000/6000 [==============================] - 526s 88ms/step - loss: 2.9770\n",
            "Epoch 1/1\n",
            "6000/6000 [==============================] - 529s 88ms/step - loss: 2.9070\n",
            "Epoch 1/1\n",
            "6000/6000 [==============================] - 525s 88ms/step - loss: 2.8507\n",
            "Epoch 1/1\n",
            "6000/6000 [==============================] - 533s 89ms/step - loss: 2.8033\n",
            "Epoch 1/1\n",
            "6000/6000 [==============================] - 518s 86ms/step - loss: 2.7620\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yqDi-p8X8dJf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 440
        },
        "outputId": "60d6cb5c-f363-41b9-cbd6-b5ffbb8de369"
      },
      "source": [
        "import numpy as np\n",
        "from PIL import Image\n",
        "import matplotlib.pyplot as plt\n",
        "import argparse\n",
        "#ap = argparse.ArgumentParser()\n",
        "#ap.add_argument('-i', '--image', required=True, help=\"Image Path\")\n",
        "#args = vars(ap.parse_args())\n",
        "img_path = '/content/test6.jpg' \n",
        "def extract_features(filename, model):\n",
        "        try:\n",
        "            image = Image.open(filename)\n",
        "        except:\n",
        "            print(\"ERROR: Couldn't open image! Make sure the image path and extension is correct\")\n",
        "        image = image.resize((299,299))\n",
        "        image = np.array(image)\n",
        "        # for images that has 4 channels, we convert them into 3 channels\n",
        "        if image.shape[2] == 4: \n",
        "            image = image[..., :3]\n",
        "        image = np.expand_dims(image, axis=0)\n",
        "        image = image/127.5\n",
        "        image = image - 1.0\n",
        "        feature = model.predict(image)\n",
        "        return feature\n",
        "def word_for_id(integer, tokenizer):\n",
        "  for word, index in tokenizer.word_index.items():\n",
        "     if index == integer:\n",
        "         return word\n",
        "  return None\n",
        "def generate_desc(model, tokenizer, photo, max_length):\n",
        "    in_text = 'start'\n",
        "    for i in range(max_length):\n",
        "        sequence = tokenizer.texts_to_sequences([in_text])[0]\n",
        "        sequence = pad_sequences([sequence], maxlen=max_length)\n",
        "        pred = model.predict([photo,sequence], verbose=0)\n",
        "        pred = np.argmax(pred)\n",
        "        word = word_for_id(pred, tokenizer)\n",
        "        if word is None:\n",
        "            break\n",
        "        in_text += ' ' + word\n",
        "        if word == 'end':\n",
        "            break\n",
        "    return in_text\n",
        "#path = 'Flicker8k_Dataset/111537222_07e56d5a30.jpg'\n",
        "max_length = 32\n",
        "tokenizer = load(open(\"tokenizer.p\",\"rb\"))\n",
        "model = load_model('model_9.h5')\n",
        "xception_model = Xception(include_top=False, pooling=\"avg\")\n",
        "photo = extract_features(img_path, xception_model)\n",
        "img = Image.open(img_path)\n",
        "description = generate_desc(model, tokenizer, photo, max_length)\n",
        "print(\"\\n\\n\")\n",
        "print(description)\n",
        "plt.imshow(img)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/indexed_slices.py:434: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
            "  \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.4/xception_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "83689472/83683744 [==============================] - 7s 0us/step\n",
            "\n",
            "\n",
            "\n",
            "start two girls are playing on the bed end\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f0abf255668>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}