deep_learning/time_space/TF2Keras_ImageCaptioning_Attention.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"TF2Keras_ImageCaptioning_Attention.ipynb","provenance":[],"collapsed_sections":[],"toc_visible":true,"authorship_tag":"ABX9TyOvHE3tyFC+5N/LU0ibHMLU"},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"markdown","metadata":{"id":"P0eNxTGM1kT8","colab_type":"text"},"source":["Image captioning is a task to describe the image. For example, `a surfer riding on a wave` is generated given the below image.\n","\n","![](https://tensorflow.org/images/surf.jpg)\n","\n","License: Public Domain\n","\n","To accomplish this goal, you'll use an `attention-based model`. It would help us to take a look at what parts of the image that the model focuses on when it generates the caption.\n","\n","![](https://tensorflow.org/images/imcap_prediction.png)\n","\n","Refer to Tensorflow.Org (2020)\n","\n","The model used here is similar to [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://arxiv.org/abs/1502.03044)."]},{"cell_type":"code","metadata":{"id":"UG-mqaN50udo","colab_type":"code","colab":{}},"source":["!pip install -q tf-nightly tqdm"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"RbDknGTo07mT","colab_type":"code","outputId":"5dc337e2-d7b9-48a5-eb5a-d32658c60862","executionInfo":{"status":"ok","timestamp":1582269471031,"user_tz":-480,"elapsed":2490,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAzLB0C3whTHAdHpq24UrEWqGtbhJElQxTU5_b_4g=s64","userId":"04300517850278510646"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["import tensorflow as tf\n","import matplotlib.pyplot as plt\n","from sklearn.model_selection import train_test_split\n","from sklearn.utils import shuffle\n","from IPython.display import clear_output\n","\n","import re\n","import numpy as np\n","import os\n","import json\n","from glob import glob\n","import cv2\n","import pickle\n","import tqdm\n","\n","print(\"Tensorflow Version: {}\".format(tf.__version__))\n","print(\"GPU {} available.\".format(\"is\" if tf.config.experimental.list_physical_devices(\"GPU\") else \"not\"))"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Tensorflow Version: 2.2.0-dev20200218\n","GPU is available.\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"fEOz6xAK8ITZ","colab_type":"code","outputId":"0bae38d0-2f80-483d-c894-d891b407649c","executionInfo":{"status":"ok","timestamp":1582269494322,"user_tz":-480,"elapsed":25774,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAzLB0C3whTHAdHpq24UrEWqGtbhJElQxTU5_b_4g=s64","userId":"04300517850278510646"}},"colab":{"base_uri":"https://localhost:8080/","height":122}},"source":["from google.colab import drive\n","drive.mount('/content/gdrive')  # for saving checkpoints"],"execution_count":2,"outputs":[{"output_type":"stream","text":["Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n","\n","Enter your authorization code:\n","··········\n","Mounted at /content/gdrive\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"milIjn1q5MsG","colab_type":"text"},"source":["# Data Preprocessing"]},{"cell_type":"markdown","metadata":{"id":"c-0WxB5f8BCv","colab_type":"text"},"source":["You will use the MS-COCO dataset, which contains 82000 images and at least 5 captions for each image."]},{"cell_type":"markdown","metadata":{"id":"KHX9sTvz9nn4","colab_type":"text"},"source":["## Downloading Caption Datasets"]},{"cell_type":"code","metadata":{"id":"vVWHZBSz1aql","colab_type":"code","outputId":"106cd528-49cc-46d4-8faa-5c6d5915e314","executionInfo":{"status":"ok","timestamp":1582269504570,"user_tz":-480,"elapsed":36013,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAzLB0C3whTHAdHpq24UrEWqGtbhJElQxTU5_b_4g=s64","userId":"04300517850278510646"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["annotation_folder = 'annotations'\n","\n","if not os.path.exists(os.path.join(os.path.abspath('.'), annotation_folder)):\n","  annotation_zip = tf.keras.utils.get_file(\n","    'captions.zip', \n","    origin='http://images.cocodataset.org/annotations/annotations_trainval2014.zip', \n","    cache_subdir=os.path.abspath('.'),\n","    extract=True)\n","  annotation_file = os.path.join(os.path.dirname(annotation_zip), \n","                                 'annotations', \n","                                 'captions_train2014.json')\n","  os.remove(annotation_zip)"],"execution_count":3,"outputs":[{"output_type":"stream","text":["Downloading data from http://images.cocodataset.org/annotations/annotations_trainval2014.zip\n","252878848/252872794 [==============================] - 3s 0us/step\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"SQzvmcX6BjQ8","colab_type":"code","outputId":"a04f8590-fc16-47bf-a83c-4848c8958842","executionInfo":{"status":"ok","timestamp":1582269506405,"user_tz":-480,"elapsed":37839,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAzLB0C3whTHAdHpq24UrEWqGtbhJElQxTU5_b_4g=s64","userId":"04300517850278510646"}},"colab":{"base_uri":"https://localhost:8080/","height":170}},"source":["!ls -al /content/annotations/"],"execution_count":4,"outputs":[{"output_type":"stream","text":["total 825044\n","drwxr-xr-x 2 root root      4096 Feb 21 07:18 .\n","drwxr-xr-x 1 root root      4096 Feb 21 07:18 ..\n","-rw-r--r-- 1 root root  66782097 Feb 21 07:18 captions_train2014.json\n","-rw-r--r-- 1 root root  32421077 Feb 21 07:18 captions_val2014.json\n","-rw-r--r-- 1 root root 332556225 Feb 21 07:18 instances_train2014.json\n","-rw-r--r-- 1 root root 160682675 Feb 21 07:18 instances_val2014.json\n","-rw-r--r-- 1 root root 170733465 Feb 21 07:18 person_keypoints_train2014.json\n","-rw-r--r-- 1 root root  81637509 Feb 21 07:18 person_keypoints_val2014.json\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"K29QGwX0-6rJ","colab_type":"text"},"source":["## Downloading Image Datasets"]},{"cell_type":"code","metadata":{"id":"Y8RvfB3J-97X","colab_type":"code","outputId":"95aa9b61-8137-461b-e338-6929f9fe16cd","executionInfo":{"status":"ok","timestamp":1582270217300,"user_tz":-480,"elapsed":748726,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAzLB0C3whTHAdHpq24UrEWqGtbhJElQxTU5_b_4g=s64","userId":"04300517850278510646"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["img_folder = \"train2014\"\n","\n","if not os.path.exists(os.path.join(os.path.abspath('.'), img_folder)):\n","  img_zip = tf.keras.utils.get_file(\n","    \"train2014.zip\", \n","    origin='http://images.cocodataset.org/zips/train2014.zip',\n","    cache_subdir=os.path.abspath('.'),\n","    extract=True)\n","  PATH = os.path.join(os.path.dirname(img_zip), img_folder)\n","  os.remove(img_zip)\n","else:\n","  PATH = os.path.join(os.path.abspath('.'), img_folder)"],"execution_count":5,"outputs":[{"output_type":"stream","text":["Downloading data from http://images.cocodataset.org/zips/train2014.zip\n","13510574080/13510573713 [==============================] - 324s 0us/step\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"PSCSXoJVRxEL","colab_type":"code","outputId":"b902b01c-07f6-4f1f-b8b8-e4d47c964b0f","executionInfo":{"status":"ok","timestamp":1582270217304,"user_tz":-480,"elapsed":748721,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAzLB0C3whTHAdHpq24UrEWqGtbhJElQxTU5_b_4g=s64","userId":"04300517850278510646"}},"colab":{"base_uri":"https://localhost:8080/","height":102}},"source":["# first several imgs\n","next(os.walk(PATH))[2][:5]"],"execution_count":6,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['COCO_train2014_000000106658.jpg',\n"," 'COCO_train2014_000000305975.jpg',\n"," 'COCO_train2014_000000411446.jpg',\n"," 'COCO_train2014_000000386111.jpg',\n"," 'COCO_train2014_000000033425.jpg']"]},"metadata":{"tags":[]},"execution_count":6}]},{"cell_type":"code","metadata":{"id":"mDhqM6CwIv4r","colab_type":"code","outputId":"42a010e2-b626-4899-b204-d6f8bb4904ea","executionInfo":{"status":"ok","timestamp":1582270221186,"user_tz":-480,"elapsed":752595,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAzLB0C3whTHAdHpq24UrEWqGtbhJElQxTU5_b_4g=s64","userId":"04300517850278510646"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["!ls -al {PATH} | wc -l"],"execution_count":7,"outputs":[{"output_type":"stream","text":["82786\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"eXwsDYwHOzk5","colab_type":"text"},"source":["## Limit the Size of the Training Dataset\n","\n","This notebook is an end-to-end example. To speed up the training, we are going to use [MS-COCO](http://cocodataset.org/#home) dataset that we take 30000 captions for 20000 images as the training dataset (An image could have more than 1 caption)."]},{"cell_type":"code","metadata":{"id":"HWRvxt7hIzQp","colab_type":"code","colab":{}},"source":["# read the annotations\n","with open(annotation_file) as fin:\n","  annotations = json.load(fin)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"HdA7uG9AQ5Sj","colab_type":"text"},"source":["Let's take a look at the annotation content."]},{"cell_type":"code","metadata":{"id":"EeJi3USvQq51","colab_type":"code","outputId":"020996cc-7df2-4f40-da37-1a14ac7823ce","executionInfo":{"status":"ok","timestamp":1582270223854,"user_tz":-480,"elapsed":755249,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAzLB0C3whTHAdHpq24UrEWqGtbhJElQxTU5_b_4g=s64","userId":"04300517850278510646"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["annotations.keys()"],"execution_count":9,"outputs":[{"output_type":"execute_result","data":{"text/plain":["dict_keys(['info', 'images', 'licenses', 'annotations'])"]},"metadata":{"tags":[]},"execution_count":9}]},{"cell_type":"code","metadata":{"id":"oMM0g2VtQwRD","colab_type":"code","outputId":"0f8248cc-1210-45e3-89af-f46eacbb49cc","executionInfo":{"status":"ok","timestamp":1582270223855,"user_tz":-480,"elapsed":755233,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAzLB0C3whTHAdHpq24UrEWqGtbhJElQxTU5_b_4g=s64","userId":"04300517850278510646"}},"colab":{"base_uri":"https://localhost:8080/","height":272}},"source":["annotations['annotations'][:5]"],"execution_count":10,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[{'caption': 'A very clean and well decorated empty bathroom',\n","  'id': 48,\n","  'image_id': 318556},\n"," {'caption': 'A panoramic view of a kitchen and all of its appliances.',\n","  'id': 67,\n","  'image_id': 116100},\n"," {'caption': 'A blue and white bathroom with butterfly themed wall tiles.',\n","  'id': 126,\n","  'image_id': 318556},\n"," {'caption': 'A panoramic photo of a kitchen and dining room',\n","  'id': 148,\n","  'image_id': 116100},\n"," {'caption': 'A graffiti-ed stop sign across the street from a red car ',\n","  'id': 173,\n","  'image_id': 379340}]"]},"metadata":{"tags":[]},"execution_count":10}]},{"cell_type":"code","metadata":{"id":"wOaJXzCxRAoL","colab_type":"code","outputId":"b015b152-f37f-440b-9f6f-74e7129749a8","executionInfo":{"status":"ok","timestamp":1582270223855,"user_tz":-480,"elapsed":755218,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAzLB0C3whTHAdHpq24UrEWqGtbhJElQxTU5_b_4g=s64","userId":"04300517850278510646"}},"colab":{"base_uri":"https://localhost:8080/","height":425}},"source":["annotations['images'][:3]"],"execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[{'coco_url': 'http://images.cocodataset.org/train2014/COCO_train2014_000000057870.jpg',\n","  'date_captured': '2013-11-14 16:28:13',\n","  'file_name': 'COCO_train2014_000000057870.jpg',\n","  'flickr_url': 'http://farm4.staticflickr.com/3153/2970773875_164f0c0b83_z.jpg',\n","  'height': 480,\n","  'id': 57870,\n","  'license': 5,\n","  'width': 640},\n"," {'coco_url': 'http://images.cocodataset.org/train2014/COCO_train2014_000000384029.jpg',\n","  'date_captured': '2013-11-14 16:29:45',\n","  'file_name': 'COCO_train2014_000000384029.jpg',\n","  'flickr_url': 'http://farm3.staticflickr.com/2422/3577229611_3a3235458a_z.jpg',\n","  'height': 429,\n","  'id': 384029,\n","  'license': 5,\n","  'width': 640},\n"," {'coco_url': 'http://images.cocodataset.org/train2014/COCO_train2014_000000222016.jpg',\n","  'date_captured': '2013-11-14 16:37:59',\n","  'file_name': 'COCO_train2014_000000222016.jpg',\n","  'flickr_url': 'http://farm2.staticflickr.com/1431/1118526611_09172475e5_z.jpg',\n","  'height': 640,\n","  'id': 222016,\n","  'license': 1,\n","  'width': 480}]"]},"metadata":{"tags":[]},"execution_count":11}]},{"cell_type":"code","metadata":{"id":"xNIe46haRFic","colab_type":"code","colab":{}},"source":["all_captions = []\n","all_img_names = []\n","\n","for annot in annotations['annotations']:\n","  caption = '{} {} {}'.format('<start>', annot['caption'], '<end>')\n","  image_id = annot['image_id']\n","  coco_img_path = \"{}/COCO_train2014_{:012d}.jpg\".format(PATH, image_id)\n","\n","  all_img_names.append(coco_img_path)\n","  all_captions.append(caption)\n","\n","# shuffle captions and image_names together\n","captions, img_names = shuffle(all_captions, all_img_names, random_state=1)\n","\n","# select the first 30000 elements as the training dataset\n","num_examples = 30000\n","train_caption = captions[:num_examples]\n","train_img_names = img_names[:num_examples]"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"tsmi4SWwSkwC","colab_type":"code","outputId":"dba5cb37-060c-455a-bdbf-a0ebfc4f0968","executionInfo":{"status":"ok","timestamp":1582270224887,"user_tz":-480,"elapsed":756225,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAzLB0C3whTHAdHpq24UrEWqGtbhJElQxTU5_b_4g=s64","userId":"04300517850278510646"}},"colab":{"base_uri":"https://localhost:8080/","height":88}},"source":["for cap, name in zip(train_caption[:3], train_img_names[:3]):\n","  print(\"The caption: `{}` in the image `{}`.\".format(cap, name))"],"execution_count":13,"outputs":[{"output_type":"stream","text":["The caption: `<start> A skateboarder performing a trick on a skateboard ramp. <end>` in the image `/content/train2014/COCO_train2014_000000324909.jpg`.\n","The caption: `<start> a person soaring through the air on skis <end>` in the image `/content/train2014/COCO_train2014_000000511972.jpg`.\n","The caption: `<start> a wood door with some boards laid against it <end>` in the image `/content/train2014/COCO_train2014_000000508809.jpg`.\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"VgCznoBTTl2Q","colab_type":"code","outputId":"8c835524-2cd0-4e0f-ee37-1a8f4fda4623","executionInfo":{"status":"ok","timestamp":1582270224888,"user_tz":-480,"elapsed":756211,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAzLB0C3whTHAdHpq24UrEWqGtbhJElQxTU5_b_4g=s64","userId":"04300517850278510646"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["len(all_captions), len(train_caption)"],"execution_count":14,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(414113, 30000)"]},"metadata":{"tags":[]},"execution_count":14}]},{"cell_type":"markdown","metadata":{"id":"0v8f2fIZUP2e","colab_type":"text"},"source":["# Preprocessing the Images using InceptionV3"]},{"cell_type":"markdown","metadata":{"id":"sDJYjfF0W-Ja","colab_type":"text"},"source":["Next, we are going to preprocess the images. We use the pre-trained InceptionV3 on ImageNet to extract features from the last convolutional layer.\n","\n","The preprocessing includes:\n","* Resize an image to the shape of `(299, 299)`. This shape is the same as the input of the InceptionV3 model that is further used as the image encoder.\n","* Normalize an image using the `tf.keras.applications.inception_v3.preprocess_input` API to scale the RGB channel value in the range from -1 to 1."]},{"cell_type":"code","metadata":{"id":"_W3Agx_VUEuw","colab_type":"code","colab":{}},"source":["def load_image(image_path):\n","  img = tf.io.read_file(image_path)\n","  img = tf.image.decode_jpeg(img, channels=3)\n","  img = tf.image.resize(img, (299, 299))\n","\n","  # normalize the channel value to the range from -1 to 1\n","  img = tf.keras.applications.inception_v3.preprocess_input(img)\n","  return img, image_path"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"ed14OtejXigU","colab_type":"text"},"source":["## Instantiate the InceptionV3 with the Pre-Trained Weights"]},{"cell_type":"markdown","metadata":{"id":"JNL-YeQ_bko3","colab_type":"text"},"source":["We use the pre-trained InceptionV3 model. We use its last convolutional layer of the InceptionV3 as the feature extractor. The shape of the layer is (8, 8, 2048). You use the last convolutional layer for the attention mechanism. This layer is also the bottleneck.\n","\n","* You forward each image through the pre-trained model and store the feature vector in a dictionary `(image_name -> feature_vector)`.\n","* After all images are passed through the model, save all of the features in a pickle object."]},{"cell_type":"code","metadata":{"id":"asfnkeb6XhWY","colab_type":"code","outputId":"7b1d64e9-3002-49b5-c2b3-68f6cf6425b1","executionInfo":{"status":"ok","timestamp":1582270242486,"user_tz":-480,"elapsed":773783,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAzLB0C3whTHAdHpq24UrEWqGtbhJElQxTU5_b_4g=s64","userId":"04300517850278510646"}},"colab":{"base_uri":"https://localhost:8080/","height":71}},"source":["image_model = tf.keras.applications.InceptionV3(\n","  include_top=False, weights='imagenet')\n","\n","new_input = image_model.input\n","hidden_layer = image_model.layers[-1].output\n","\n","image_feature_extract_model = tf.keras.Model(new_input, hidden_layer)"],"execution_count":16,"outputs":[{"output_type":"stream","text":["Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5\n","87916544/87910968 [==============================] - 3s 0us/step\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"-zXtJlXkdGWw","colab_type":"text"},"source":["## Cached the Features extracted from the InceptionV3"]},{"cell_type":"code","metadata":{"id":"WXTjXYz-bTao","colab_type":"code","outputId":"0140b0c0-7a2b-4f95-d5cb-d6fab5791f3a","executionInfo":{"status":"ok","timestamp":1582270633707,"user_tz":-480,"elapsed":1164967,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAzLB0C3whTHAdHpq24UrEWqGtbhJElQxTU5_b_4g=s64","userId":"04300517850278510646"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# get unique image names\n","encoded_train = sorted(set(train_img_names))\n","\n","image_dataset = tf.data.Dataset.from_tensor_slices(encoded_train)\n","image_dataset = image_dataset.map(\n","  load_image, num_parallel_calls=tf.data.experimental.AUTOTUNE).batch(16)\n","\n","for img, path in tqdm.tqdm(image_dataset):\n","  batch_features = image_feature_extract_model(img)\n","  batch_features = tf.reshape(batch_features,\n","                              (batch_features.shape[0], -1, batch_features.shape[3]))\n","  \n","  for bottleFeatures, each_path in zip(batch_features, path):\n","    path_of_features = each_path.numpy().decode(\"UTF-8\")\n","    np.save(path_of_features, bottleFeatures.numpy())"],"execution_count":17,"outputs":[{"output_type":"stream","text":["1622it [06:31,  3.92it/s]\n"],"name":"stderr"}]},{"cell_type":"code","metadata":{"id":"LoAEVp-SfZZA","colab_type":"code","outputId":"047a07d0-3d63-4c93-a4e9-95c759a636e1","executionInfo":{"status":"ok","timestamp":1582270633993,"user_tz":-480,"elapsed":1165225,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAzLB0C3whTHAdHpq24UrEWqGtbhJElQxTU5_b_4g=s64","userId":"04300517850278510646"}},"colab":{"base_uri":"https://localhost:8080/","height":187}},"source":["[item for item in next(os.walk(PATH))[2] if item[-3:] == \"npy\"][:10]"],"execution_count":18,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['COCO_train2014_000000478809.jpg.npy',\n"," 'COCO_train2014_000000136092.jpg.npy',\n"," 'COCO_train2014_000000526840.jpg.npy',\n"," 'COCO_train2014_000000104021.jpg.npy',\n"," 'COCO_train2014_000000294116.jpg.npy',\n"," 'COCO_train2014_000000204667.jpg.npy',\n"," 'COCO_train2014_000000193287.jpg.npy',\n"," 'COCO_train2014_000000014089.jpg.npy',\n"," 'COCO_train2014_000000208940.jpg.npy',\n"," 'COCO_train2014_000000296344.jpg.npy']"]},"metadata":{"tags":[]},"execution_count":18}]},{"cell_type":"markdown","metadata":{"id":"maYg_d_Thm_y","colab_type":"text"},"source":["# Preprocessing and Tokenizing the Captions"]},{"cell_type":"markdown","metadata":{"id":"5x6zPGrjjzd6","colab_type":"text"},"source":["Next, we are going to preprocess the captions into tokens. There are several steps:\n","\n","* Tokenize the caption into tokens. For example, the tokens are like `man`, `surfing`, etc.\n","* Limit the token (or vocabulary) size up to 5000 to save the memory. The other words are replaced with 'UNK' (unknown).\n","* Create both word-to-index and index-to-word mappings.\n","* Pad the sequence of tokens into the same length (the same as the longest sequence by default)."]},{"cell_type":"code","metadata":{"id":"oGTiapPAg-LA","colab_type":"code","colab":{}},"source":["# find the longest sequence length of the batch tensors\n","def longest_length(tensor):\n","  return max([len(t) for t in tensor])"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"JUHxezshkDDT","colab_type":"code","colab":{}},"source":["# choose the top 5K words from the vocabularies\n","top_k = 5000\n","tokenizer = tf.keras.preprocessing.text.Tokenizer(\n","  num_words=top_k, oov_token='<unk>', filters='!\"#$%&()*+.,-/:;=?@[\\]^_`{|}~ ')\n","\n","# generate the token list\n","tokenizer.fit_on_texts(train_caption)\n","\n","# convert the sequence to the tokenized vectors (token number)\n","train_seqs = tokenizer.texts_to_sequences(train_caption)\n","\n","# pad the sequence\n","pad_seqs = tf.keras.preprocessing.sequence.pad_sequences(train_seqs, padding=\"post\")"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Snf4UcX5mHPC","colab_type":"code","outputId":"bd79c14b-bfa6-4793-c329-c2a24f52291c","executionInfo":{"status":"ok","timestamp":1582270635694,"user_tz":-480,"elapsed":1166877,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAzLB0C3whTHAdHpq24UrEWqGtbhJElQxTU5_b_4g=s64","userId":"04300517850278510646"}},"colab":{"base_uri":"https://localhost:8080/","height":204}},"source":["pad_seqs.shape, pad_seqs[:2]"],"execution_count":21,"outputs":[{"output_type":"execute_result","data":{"text/plain":["((30000, 49),\n"," array([[   3,    2,  351,  687,    2,  280,    5,    2,   84,  339,    4,\n","            0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n","            0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n","            0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n","            0,    0,    0,    0,    0],\n","        [   3,    2,   31, 2356,  112,    7,  137,    5,  159,    4,    0,\n","            0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n","            0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n","            0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n","            0,    0,    0,    0,    0]], dtype=int32))"]},"metadata":{"tags":[]},"execution_count":21}]},{"cell_type":"code","metadata":{"id":"oJ7ZmzXrk3fJ","colab_type":"code","colab":{}},"source":["# tokens start from 1, set <pad> to 0\n","tokenizer.word_index['<pad>'] = 0\n","tokenizer.index_word[0] = '<pad>'"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"jJOsAY6ClSoh","colab_type":"code","outputId":"12357028-2d60-4f1f-beb7-ce42ffb5638f","executionInfo":{"status":"ok","timestamp":1582270635697,"user_tz":-480,"elapsed":1166821,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAzLB0C3whTHAdHpq24UrEWqGtbhJElQxTU5_b_4g=s64","userId":"04300517850278510646"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# calculate the max_length of the sequence, it is used to store attention weights\n","max_length = longest_length(train_seqs)\n","assert max_length == len(pad_seqs[0])\n","print(max_length)"],"execution_count":23,"outputs":[{"output_type":"stream","text":["49\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"82o0jnKtma3T","colab_type":"text"},"source":["# Splitting the Dataset into the Training and Testing"]},{"cell_type":"code","metadata":{"id":"Nuo1U7fUl6QU","colab_type":"code","outputId":"26fa3360-d484-4d03-f0bf-be67a7d0bddf","executionInfo":{"status":"ok","timestamp":1582270635697,"user_tz":-480,"elapsed":1166794,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAzLB0C3whTHAdHpq24UrEWqGtbhJElQxTU5_b_4g=s64","userId":"04300517850278510646"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["img_name_train, img_name_val, pad_train, pad_val = train_test_split(\n","  train_img_names, pad_seqs, test_size=0.2, random_state=0)\n","\n","len(img_name_train), len(img_name_val), len(pad_train), len(pad_val)"],"execution_count":24,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(24000, 6000, 24000, 6000)"]},"metadata":{"tags":[]},"execution_count":24}]},{"cell_type":"markdown","metadata":{"id":"YfJjvIJepiTW","colab_type":"text"},"source":["## Create a tf.data.Dataset for Training"]},{"cell_type":"markdown","metadata":{"id":"e5zs493OpqPV","colab_type":"text"},"source":["Next, when the images and captions are prepared, we now can create a `tf.data.Dataset` for the training."]},{"cell_type":"code","metadata":{"id":"ixXuJD5npgfN","colab_type":"code","colab":{}},"source":["BATCH_SIZE = 64\n","BUFFER_SIZE = 1000\n","embedding_dim = 256\n","units = 512\n","vocab_size = top_k + 1\n","num_steps_per_epoch = len(img_name_train) // BATCH_SIZE"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"VwACagcwqvgn","colab_type":"code","colab":{}},"source":["# shape of the vector extracted from InceptionV3 is (8x8, 2048)\n","# these two variables represent that vector shape\n","feature_shape = 2048\n","attention_features_shape = 64"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"DxgrLLBFrPe1","colab_type":"code","colab":{}},"source":["# load the numpy files which extracted features from InceptionV3\n","def map_func(img_path, pad):\n","  img_tensor = np.load(\"{}.npy\".format(img_path.decode(\"UTF-8\")))\n","  return img_tensor, pad"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"XCk0j0uarsyu","colab_type":"code","outputId":"54d487c3-542b-4409-9960-455e91940927","executionInfo":{"status":"ok","timestamp":1582270648985,"user_tz":-480,"elapsed":379,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAzLB0C3whTHAdHpq24UrEWqGtbhJElQxTU5_b_4g=s64","userId":"04300517850278510646"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["dataset = tf.data.Dataset.from_tensor_slices((img_name_train, pad_train))\n","\n","# load the image tensor\n","dataset = dataset.map(\n","  lambda item1, item2: \n","    tf.numpy_function(map_func, [item1, item2], [tf.float32, tf.int32])\n",")\n","\n","# shuffle and batch\n","dataset = dataset.shuffle(BUFFER_SIZE).prefetch(tf.data.experimental.AUTOTUNE).batch(BATCH_SIZE)\n","\n","clear_output()\n","for _data in dataset.take(1):\n","  print(\"Image Shape (batch_size, 8x8 (InceptionV3[1]xInceptionV3[2]), 2048 (InceptionV3[3])): {}\".format(_data[0].shape))\n","  print(\"Padded Sequence (batch_size, padded_sequence_length): {}\".format(_data[1].shape))"],"execution_count":28,"outputs":[{"output_type":"stream","text":["Image Shape (batch_size, 8x8 (InceptionV3[1]xInceptionV3[2]), 2048 (InceptionV3[3])): (64, 64, 2048)\n","Padded Sequence (batch_size, padded_sequence_length): (64, 49)\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"aMFmWdNgYCrf","colab_type":"text"},"source":["# Model\n","\n","The model of image captioning is similar to the seq2seq one which is composed of an encoder and a decoder. The decoder one is identical to the one in the seq2seq model, which is a gated RNN cell empowered by an attention mechanism. The encoder is a modified pre-trained InceptionV3 model and generates convolutional features.\n","\n","The model architecture of image captioning is inspired by the [Show, Attend and Tell](https://arxiv.org/pdf/1502.03044.pdf) paper.\n","\n","* In this mode, you extract the image features from the last convolutional layer of a pre-trained InceptionV3 model giving us a vector in shape (8, 8, 2048).\n","* You further squash the vector into the shape of (8x8, 2048). You can regard the squashed vector as time-series data in the shape of (timestamp, features).\n","* The squashed vector is further passed through a CNN encoder (here it consists of a single dense layer).\n","* The gated RNN attends over the image to predict the next word."]},{"cell_type":"markdown","metadata":{"id":"RSqWVVldvJX_","colab_type":"text"},"source":["## Building the Model"]},{"cell_type":"code","metadata":{"id":"WtTNtHnZbsYN","colab_type":"code","colab":{}},"source":["class BahdanauAttention(tf.keras.Model):\n","  def __init__(self, units):\n","    \"\"\"\n","    Args:\n","      units: the number for the potential timestamp\n","    \"\"\"\n","    super(BahdanauAttention, self).__init__()\n","    self.W1 = tf.keras.layers.Dense(units)\n","    self.W2 = tf.keras.layers.Dense(units)\n","    self.V = tf.keras.layers.Dense(1)\n","\n","  def call(self, features, hidden):\n","    \"\"\"\n","    Args:\n","      features: the encoder output in the shape of \n","                (batch_size, sequence_length, hidden_size == encoder_units)\n","      hidden: the hidden state of the last timestamp in the shape of\n","              (batch_size, hidden_size == encoder_units)\n","\n","    Returns:\n","      context_vector: the context vector in the shape of \n","                      (batch_size, 1, units == hidden_size)\n","      attention_weights: the attention weights in the shape of\n","                         (batch_size, sequence_length, 1)\n","    \"\"\"\n","    # (batch_size, 1, hidden_size)\n","    hidden_state_time_axis = tf.expand_dims(hidden, axis=1)\n","\n","    # (batch_size, sequence_length == 64, unit)\n","    weights_vector = tf.nn.tanh(self.W1(features) + self.W2(hidden_state_time_axis))\n","\n","    # (batch_size, sequence_length == 64, 1)\n","    attention_weights = tf.nn.softmax(self.V(weights_vector), axis=1)\n","\n","    # (batch_size, sequence_length, units == hidden_size)\n","    context_vector = features * attention_weights\n","    context_vector = tf.reduce_sum(context_vector, axis=1)\n","\n","    return context_vector, attention_weights"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"h0hpii_uqgdE","colab_type":"code","colab":{}},"source":["class CNN_Encoder(tf.keras.Model):\n","  def __init__(self, embedding_dims):\n","    super(CNN_Encoder, self).__init__()\n","    # (batch_size, 64 = 8x8, 2048) => (batch_size, 64, embedding_dims)\n","    self.dense = tf.keras.layers.Dense(embedding_dim)\n","\n","  def call(self, x):\n","    x = self.dense(x)\n","    x = tf.nn.relu(x)  # output: (batch_size, 64, embedding_dims)\n","    return x"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"dkgXOAmerOzI","colab_type":"code","colab":{}},"source":["class RNN_Decoder(tf.keras.Model):\n","  def __init__(self, vocab_size, embedding_dims, units):\n","    super(RNN_Decoder, self).__init__()\n","    self.units = units\n","    self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dims)\n","    self.gru = tf.keras.layers.GRU(units, \n","                                   return_sequences=True, \n","                                   return_state=True, \n","                                   recurrent_initializer='glorot_uniform')\n","    self.fc1 = tf.keras.layers.Dense(units)\n","    self.fc2 = tf.keras.layers.Dense(vocab_size)\n","    self.attention = BahdanauAttention(units)\n","\n","  def call(self, x, features, hidden):\n","    # get the information from the Encoder\n","    # context_vector: (batch_size, units)\n","    # attention_weights: (batch_size, 1 == sequence_length, 1)\n","    context_vector, attention_weights = self.attention(features, hidden)\n","\n","    # get the embedding vector, (batch_size, sequence_length == 1, embedding_dims)\n","    embed = self.embedding(x)\n","\n","    # concatenate the features and context, (batch_size, 1, embedding_dims + units)\n","    concatenate = tf.concat([tf.expand_dims(context_vector, axis=1), embed], axis=-1)\n","\n","    # output: (batch_size, 1, units)\n","    # state: (batch_size, hidden_size)\n","    output, state = self.gru(concatenate)\n","\n","    # (batch_size, 1, hidden_size == units)\n","    fc1 = self.fc1(output)\n","\n","    # reshape the fc1 to (batch_size * sequence_length, hidden_size)\n","    # we want to connect the hidden with the vocab_size\n","    x = tf.reshape(fc1, (-1, fc1.shape[2]))\n","\n","    # (batch_size * sequence_length, vocab_size)\n","    vocab = self.fc2(x)\n","\n","    return vocab, state, attention_weights\n","\n","  def reset_state(self, batch_size):\n","    return tf.zeros((batch_size, self.units))"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"zsuDSyiCtI_K","colab_type":"code","colab":{}},"source":["encoder = CNN_Encoder(embedding_dim)\n","decoder = RNN_Decoder(vocab_size, embedding_dim, units)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"-fcbTUCmzMgj","colab_type":"code","colab":{}},"source":["optimizer = tf.keras.optimizers.Adam()\n","loss_object = tf.keras.losses.SparseCategoricalCrossentropy(\n","  from_logits=True, reduction='none')\n","\n","def loss_function(real, pred):\n","  mask = tf.math.logical_not(tf.math.equal(real, 0))\n","  loss = loss_object(real, pred)\n","\n","  mask = tf.cast(mask, dtype=loss.dtype)\n","  loss *= mask\n","\n","  return tf.reduce_mean(loss)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"IQl4RUlI1XEV","colab_type":"text"},"source":["## Checkpoint"]},{"cell_type":"code","metadata":{"id":"TYjlNmxMz9wD","colab_type":"code","colab":{}},"source":["ckpt_path = \"./ckpt/train\"\n","ckpt = tf.train.Checkpoint(encoder=encoder, \n","                           decoder=decoder,\n","                           optimizer=optimizer)\n","ckpt_manager = tf.train.CheckpointManager(ckpt, ckpt_path, max_to_keep=5)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"D34Pmxjc1x22","colab_type":"code","colab":{}},"source":["start_epoch = 0\n","if ckpt_manager.latest_checkpoint:\n","  start_epoch = int(ckpt_manager.latest_checkpoint.split('-')[-1])\n","  # restore the latest checkpoint in ckpt_path\n","  ckpt.restore(ckpt_manager.latest_checkpoint)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"bGTLb3m03Cbw","colab_type":"text"},"source":["## Training\n","\n","* Pass the extracted features through the encoder from the `.npy` file and get the encoder output `(batch_size, 64, 2048)` and the hidden state `(batch_size, embedding_dim)`.\n","* Pass the decoder input which is a start token in shape (batch_size, 1), the encoder output, and the encoder hidden state which is as the initial decoder state into the decoder.\n","* The decoder returns the predictions and the decoder state. The decoder state is further passed back into the decoder and the predictions are used to calculate the loss.\n","* In decoder training, use teacher forcing to decide the next input token to the decoder.\n","* Calculate the gradients given the loss and apply to the variable using the backpropagate."]},{"cell_type":"code","metadata":{"id":"yMgHHR4G1-Ye","colab_type":"code","colab":{}},"source":["loss_plot = []"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"b-0M5h-Pzzt_","colab_type":"code","colab":{}},"source":["@tf.function\n","def train_step(images, targets):\n","  \"\"\"\n","  Args:\n","    images: (batch_size, 64, 2048)\n","    targets: (batch_size, padded_sequence_length)\n","\n","  Returns:\n","    total_loss: the total loss \n","    loss: total / batch_size\n","  \"\"\"\n","  total_loss = 0\n","\n","  # initializing the hidden state on every batch\n","  # because the captions are not related from image to image\n","  hidden = decoder.reset_state(batch_size=targets.shape[0])\n","\n","  # the start token for the decoder, (batch_size, 1)\n","  dec_inputs = tf.expand_dims([tokenizer.word_index['<start>']] * targets.shape[0], axis=1)\n","\n","  with tf.GradientTape() as tape:\n","    # features == encoder_output in shape (64, embedding_dims)\n","    features = encoder(images)\n","\n","    for t in range(1, targets.shape[1]):\n","      # vocab: (batch_size * sequence_length, vocab_size)\n","      vocab, hidden, attention_weights = decoder(dec_inputs, features, hidden)\n","\n","      # total loss is the sum along the time axis\n","      total_loss += loss_function(targets[:, t], vocab)\n","\n","      # teacher forcing\n","      dec_inputs = tf.expand_dims(targets[:, t], axis=1)\n","  \n","  # average loss along time axis\n","  loss = total_loss / int(targets.shape[1])\n","  trainable_variables = encoder.trainable_variables + decoder.trainable_variables\n","  gradients = tape.gradient(total_loss, trainable_variables)\n","  optimizer.apply_gradients(zip(gradients, trainable_variables))\n","  return total_loss, loss"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"sHwC8oPl2aMc","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":238},"outputId":"30322ca5-81c4-49cf-8a1b-f65d3cf1d9b1","executionInfo":{"status":"ok","timestamp":1582281354863,"user_tz":-480,"elapsed":817728,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAzLB0C3whTHAdHpq24UrEWqGtbhJElQxTU5_b_4g=s64","userId":"04300517850278510646"}}},"source":["START_EPOCH = 0\n","EPOCHS = 20\n","\n","loss_history = []\n","\n","for epoch in range(START_EPOCH, EPOCHS):\n","  total_loss = 0.0\n","\n","  for batch, (images, sequences) in enumerate(dataset): \n","    # get the batch loss and the time-series loss of the batch loss\n","    batch_total_loss, time_loss = train_step(images, sequences)\n","    total_loss += time_loss\n","\n","    if batch % 100 == 0:\n","      print(\"Epoch {}, Batch {}, Batch_Loss {:.8f}\".format(epoch+1, batch, time_loss))\n","\n","  epoch_loss = total_loss / batch\n","  loss_history.append(epoch_loss)\n","\n","  if epoch % 5 == 0:\n","    ckpt_manager.save()\n","\n","  print(\"Epoch: {}, Epoch Loss: {:.8f}\".format(epoch+1, epoch_loss))"],"execution_count":57,"outputs":[{"output_type":"stream","text":["Epoch 18, Batch 200, Loss 0.26982018\n","Epoch 18, Batch 300, Loss 0.29293090\n","Epoch: 17, Epoch Loss: 0.29865655\n","Epoch 19, Batch 0, Loss 0.28283918\n","Epoch 19, Batch 100, Loss 0.28289935\n","Epoch 19, Batch 200, Loss 0.28469875\n","Epoch 19, Batch 300, Loss 0.27943051\n","Epoch: 18, Epoch Loss: 0.27991861\n","Epoch 20, Batch 0, Loss 0.25334436\n","Epoch 20, Batch 100, Loss 0.27258274\n","Epoch 20, Batch 200, Loss 0.25415236\n","Epoch 20, Batch 300, Loss 0.24588595\n","Epoch: 19, Epoch Loss: 0.26398703\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Itx5suMa3da2","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":295},"outputId":"f9330243-53b7-4f2f-cd5a-7ed6b2db56c5","executionInfo":{"status":"ok","timestamp":1582281466798,"user_tz":-480,"elapsed":1190,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAzLB0C3whTHAdHpq24UrEWqGtbhJElQxTU5_b_4g=s64","userId":"04300517850278510646"}}},"source":["plt.plot(loss_history)\n","plt.xlabel(\"Epoch\")\n","plt.ylabel(\"Value\")\n","plt.title(\"Loss History\")\n","plt.show()"],"execution_count":65,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYIAAAEWCAYAAABrDZDcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n9XDdb5bz60Wbqa9XV5GIhENBEJLRQwfw8r0XcvlZg/jhXzZw09yVl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MWVh5m3qpinC0uZ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\nAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"lfJqWCB0Ba09","colab_type":"text"},"source":["# Caption\n","\n","* The evaluation function is similar to the training loop but without the teacher forcing method. The decoder input at each time is the previous prediction along with the previous state and the encoder output.\n","* Stop prediction while the decoder prediction is the end token.\n","* Store the attention weights for every time step."]},{"cell_type":"code","metadata":{"id":"5jOmh_kpVMTS","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"outputId":"a333a22a-5177-462c-bc31-5a94e7e11bd9","executionInfo":{"status":"ok","timestamp":1582281622945,"user_tz":-480,"elapsed":707,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAzLB0C3whTHAdHpq24UrEWqGtbhJElQxTU5_b_4g=s64","userId":"04300517850278510646"}}},"source":["tf.expand_dims(load_image(image_path)[0], axis=0).shape"],"execution_count":80,"outputs":[{"output_type":"execute_result","data":{"text/plain":["TensorShape([1, 299, 299, 3])"]},"metadata":{"tags":[]},"execution_count":80}]},{"cell_type":"code","metadata":{"id":"tK96T4aWBdrq","colab_type":"code","colab":{}},"source":["def evaluate(image):\n","  \"\"\"\n","  Args:\n","    image: the image path\n","\n","  Returns:\n","    result\n","    attention_plots\n","  \"\"\"\n","  # save attentions\n","  attention_plot = np.zeros((max_length, attention_features_shape))\n","\n","  # decoder initialization\n","  hidden = decoder.reset_state(batch_size=1)\n","\n","  # image preprocessing and feature extraction using the encoder\n","  img = tf.expand_dims(load_image(image)[0], axis=0)\n","  features = image_feature_extract_model(img)\n","  features = tf.reshape(features, (features.shape[0], -1, features.shape[3]))  # (batch_size, 64, 2048)\n","\n","  # (batch_size = 1, 64, embedding_dims)\n","  enc_output = encoder(features)\n","\n","  # start from a start token\n","  dec_input = tf.expand_dims([tokenizer.word_index['<start>']], axis=0)\n","\n","  # save the results\n","  result = []\n","\n","  for t in range(0, max_length):\n","    # generate the vocab, hidden state and the attention weights\n","    vocab, hidden, attention_weights = decoder(dec_input, enc_output, hidden)\n","\n","    # save the attention weights\n","    attention_plot[t] = tf.reshape(attention_weights, (-1, )).numpy()\n","\n","    # get the prediction vocabulary\n","    predicted_id = tf.random.categorical(vocab, 1)[0][0].numpy()\n","    token = tokenizer.index_word[predicted_id]\n","    result.append(token)\n","\n","    if token == \"<end>\":\n","      break\n","\n","    # for the next input\n","    dec_input = tf.expand_dims([predicted_id], axis=0)\n","\n","  attention_plot = attention_plot[:len(result), :]\n","  return result, attention_plot"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"VBTbo2ApEFzs","colab_type":"code","colab":{}},"source":["def plot_attention(image, result, attention_plot):\n","  temp_img = cv2.imread(image)\n","\n","  fig = plt.figure(figsize=(10, 10))\n","  \n","  result_length = len(result)\n","  for l in range(result_length):\n","    attentions = np.resize(attention_plot[l], (8, 8))\n","    ax = fig.add_subplot(result_length // 2, result_length // 2, l+1)\n","    ax.set_title(result[l])\n","    img = ax.imshow(temp_img)\n","    ax.imshow(attentions, cmap='gray', alpha=0.6, extent=img.get_extent())\n","\n","  plt.tight_layout()\n","  plt.show()"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"rcsiSn7sRDW0","colab_type":"text"},"source":["Validate the model on the validation data."]},{"cell_type":"code","metadata":{"id":"1-HRFEunELWP","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":417},"outputId":"53c630a6-fdb9-4ec1-9799-cefc99206cdc","executionInfo":{"status":"ok","timestamp":1582281795484,"user_tz":-480,"elapsed":2973,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAzLB0C3whTHAdHpq24UrEWqGtbhJElQxTU5_b_4g=s64","userId":"04300517850278510646"}}},"source":["idx = np.random.randint(0, len(img_name_val))\n","image_path = img_name_val[idx]\n","\n","real_caption = ' '.join([tokenizer.index_word[i] for i in pad_val[idx] if i not in [0]])\n","result, attention_plots = evaluate(image_path)\n","\n","print(\"Real: {}\".format(real_caption))\n","print(\"Prediction: {}\".format(result))\n","plot_attention(image_path, result, attention_plots)"],"execution_count":93,"outputs":[{"output_type":"stream","text":["Real: <start> a fire hydrant on the side walk next to some buildings <end>\n","Prediction: ['a', 'park', 'area', 'shows', 'a', 'professional', 'pool', '<end>']\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAApwAAAFuCAYAAAAs+cgqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n205n9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PGjJ5/kwQcfRBwH11ikHBdVyOOFIYEJwOqDwqC1NpA5PSAuwxuuo/927X4VT6Dt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\nNJ0uoebVs68gddDyt1WZD2tNW7oOIWqW0xgiEesClpwi6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Vghszbt6gZjc/b2tqlyv28z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cq35Jd7u9Lk3dcPL8RQpX4pxOb5yN9p+r7r4UAae1jl6vhyBMp3NSbCnLkpQvnMYI0XvN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\nRTHlbMfzD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ddu7bKYL1jbWuef/V9/wNe+9m/7kV59MkVcCPvtH/2I8WjKhe3zjHLHL/3a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\nRK5CYII=\n","text/plain":["<Figure size 720x720 with 8 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"fum8FA8nSIpI","colab_type":"text"},"source":["## Try the Example Image"]},{"cell_type":"code","metadata":{"id":"i-hFIl_6SMYz","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":170},"outputId":"d9c10973-615e-4b9b-e3a6-ad0ad985ab45","executionInfo":{"status":"ok","timestamp":1582281824211,"user_tz":-480,"elapsed":5656,"user":{"displayName":"王DevOps","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAzLB0C3whTHAdHpq24UrEWqGtbhJElQxTU5_b_4g=s64","userId":"04300517850278510646"}}},"source":["image_url = 'https://tensorflow.org/images/surf.jpg'\n","image_extension = image_url[-4:]\n","image_path = tf.keras.utils.get_file('image' + image_extension,\n","                                     origin=image_url)\n","\n","result, attention_plots = evaluate(image_path)\n","print('Prediction: {}'.format(result))\n","plot_attention(image_path, result, attention_plots)"],"execution_count":95,"outputs":[{"output_type":"stream","text":["Prediction: ['a', 'person', 'surfing', 'with', 'his', 'surfboard', 'on', 'as', 'she', 'stands', 'in', 'the', 'water', 'while', 'looking', 'over', 'the', 'wave', 'while', 'water', 'by', 'forested', 'past', '<end>']\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAtUAAAB0CAYAAABKbM4JAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n1dTXL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HVfAFbl2mcMrjfi7lg13+L0LepYDppvwGM5aL5FHctB8w14LAfNN+exbG9UNAzD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\nZ6M/rL3J/64AAAAASUVORK5CYII=\n","text/plain":["<Figure size 720x720 with 24 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"dtdQpwHIWFDb","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]}]}