{"action":"created","comment":{"url":"https://api.github.com/repos/tensorflow/docs/pulls/comments/237572277","pull_request_review_id":179904531,"id":237572277,"node_id":"MDI0OlB1bGxSZXF1ZXN0UmV2aWV3Q29tbWVudDIzNzU3MjI3Nw==","diff_hunk":"@@ -0,0 +1,1356 @@\n+{\n+  \"nbformat\": 4,\n+  \"nbformat_minor\": 0,\n+  \"metadata\": {\n+    \"colab\": {\n+      \"name\": \"Loading Images.ipynb\",\n+      \"version\": \"0.3.2\",\n+      \"provenance\": [],\n+      \"private_outputs\": true,\n+      \"collapsed_sections\": [],\n+      \"toc_visible\": true\n+    },\n+    \"kernelspec\": {\n+      \"name\": \"python3\",\n+      \"display_name\": \"Python 3\"\n+    }\n+  },\n+  \"cells\": [\n+    {\n+      \"metadata\": {\n+        \"deletable\": true,\n+        \"editable\": true,\n+        \"id\": \"ucMoYase6URl\",\n+        \"colab_type\": \"text\"\n+      },\n+      \"cell_type\": \"markdown\",\n+      \"source\": [\n+        \"# Load images with `tf.data`\"\n+      ]\n+    },\n+    {\n+      \"metadata\": {\n+        \"id\": \"_Wwu5SXZmEkB\",\n+        \"colab_type\": \"text\"\n+      },\n+      \"cell_type\": \"markdown\",\n+      \"source\": [\n+        \"<table class=\\\"tfo-notebook-buttons\\\" align=\\\"left\\\">\\n\",\n+        \"  <td>\\n\",\n+        \"    <a target=\\\"_blank\\\" href=\\\"https://www.tensorflow.org/tutorials/load_data/images\\\"><img src=\\\"https://www.tensorflow.org/images/tf_logo_32px.png\\\" />View on TensorFlow.org</a>\\n\",\n+        \"  </td>\\n\",\n+        \"  <td>\\n\",\n+        \"    <a target=\\\"_blank\\\" href=\\\"https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/load_data/images.ipynb\\\"><img src=\\\"https://www.tensorflow.org/images/colab_logo_32px.png\\\" />Run in Google Colab</a>\\n\",\n+        \"  </td>\\n\",\n+        \"  <td>\\n\",\n+        \"    <a target=\\\"_blank\\\" href=\\\"https://github.com/tensorflow/docs/blob/master/site/en/tutorials/load_data/images.ipynb\\\"><img src=\\\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\\\" />View source on GitHub</a>\\n\",\n+        \"  </td>\\n\",\n+        \"</table>\"\n+      ]\n+    },\n+    {\n+      \"metadata\": {\n+        \"id\": \"Oxw4WahM7DU9\",\n+        \"colab_type\": \"text\"\n+      },\n+      \"cell_type\": \"markdown\",\n+      \"source\": [\n+        \"This tutorial provides a simple example of how to load an image dataset using `tf.data`.\\n\",\n+        \"\\n\",\n+        \"The dataset used in this example is distributed as directories of images, with one class of image per directory.\"\n+      ]\n+    },\n+    {\n+      \"metadata\": {\n+        \"deletable\": true,\n+        \"editable\": true,\n+        \"id\": \"hoQQiZDB6URn\",\n+        \"colab_type\": \"text\"\n+      },\n+      \"cell_type\": \"markdown\",\n+      \"source\": [\n+        \"## Setup\"\n+      ]\n+    },\n+    {\n+      \"metadata\": {\n+        \"id\": \"DHz3JONNEHlj\",\n+        \"colab_type\": \"code\",\n+        \"colab\": {}\n+      },\n+      \"cell_type\": \"code\",\n+      \"source\": [\n+        \"import tensorflow as tf\\n\",\n+        \"tf.enable_eager_execution()\\n\",\n+        \"tf.VERSION\"\n+      ],\n+      \"execution_count\": 0,\n+      \"outputs\": []\n+    },\n+    {\n+      \"metadata\": {\n+        \"id\": \"rxndJHNC8YPM\",\n+        \"colab_type\": \"text\"\n+      },\n+      \"cell_type\": \"markdown\",\n+      \"source\": [\n+        \"## Download and inspect the dataset\"\n+      ]\n+    },\n+    {\n+      \"metadata\": {\n+        \"deletable\": true,\n+        \"editable\": true,\n+        \"id\": \"wO0InzL66URu\",\n+        \"colab_type\": \"text\"\n+      },\n+      \"cell_type\": \"markdown\",\n+      \"source\": [\n+        \"### Retrieve the images\\n\",\n+        \"\\n\",\n+        \"Before you start any training, you'll need a set of images to teach the network about the new classes you want to recognize. We've created an archive of creative-commons licensed flower photos to use initially. \"\n+      ]\n+    },\n+    {\n+      \"metadata\": {\n+        \"id\": \"rN-Pc6Zd6awg\",\n+        \"colab_type\": \"code\",\n+        \"colab\": {}\n+      },\n+      \"cell_type\": \"code\",\n+      \"source\": [\n+        \"import pathlib\\n\",\n+        \"data_root = tf.keras.utils.get_file('flower_photos','https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz', untar=True)\\n\",\n+        \"data_root = pathlib.Path(data_root)\\n\",\n+        \"print(data_root)\"\n+      ],\n+      \"execution_count\": 0,\n+      \"outputs\": []\n+    },\n+    {\n+      \"metadata\": {\n+        \"id\": \"rFkFK74oO--g\",\n+        \"colab_type\": \"text\"\n+      },\n+      \"cell_type\": \"markdown\",\n+      \"source\": [\n+        \"After downloading 218MB, you should now have a copy of the flower photos available:\"\n+      ]\n+    },\n+    {\n+      \"metadata\": {\n+        \"id\": \"7onR_lWE7Njj\",\n+        \"colab_type\": \"code\",\n+        \"colab\": {}\n+      },\n+      \"cell_type\": \"code\",\n+      \"source\": [\n+        \"for item in data_root.iterdir():\\n\",\n+        \"  print(item)\"\n+      ],\n+      \"execution_count\": 0,\n+      \"outputs\": []\n+    },\n+    {\n+      \"metadata\": {\n+        \"id\": \"4yYX3ZRqGOuq\",\n+        \"colab_type\": \"code\",\n+        \"colab\": {}\n+      },\n+      \"cell_type\": \"code\",\n+      \"source\": [\n+        \"import random\\n\",\n+        \"all_image_paths = list(data_root.glob('*/*'))\\n\",\n+        \"all_image_paths = [str(path) for path in all_image_paths]\\n\",\n+        \"random.shuffle(all_image_paths)\\n\",\n+        \"\\n\",\n+        \"len(all_image_paths)\"\n+      ],\n+      \"execution_count\": 0,\n+      \"outputs\": []\n+    },\n+    {\n+      \"metadata\": {\n+        \"id\": \"vkM-IpB-6URx\",\n+        \"colab_type\": \"text\"\n+      },\n+      \"cell_type\": \"markdown\",\n+      \"source\": [\n+        \"### Inspect the images\\n\",\n+        \"Now let's have a quick look at a couple of the images, so we know what we're dealing with:\"\n+      ]\n+    },\n+    {\n+      \"metadata\": {\n+        \"id\": \"wNGateQJ6UR1\",\n+        \"colab_type\": \"code\",\n+        \"colab\": {}\n+      },\n+      \"cell_type\": \"code\",\n+      \"source\": [\n+        \"attributions = (data_root/\\\"LICENSE.txt\\\").read_text().splitlines()[4:]\\n\",\n+        \"attributions = [line.split(' CC-BY') for line in attributions]\\n\",\n+        \"attributions = dict(attributions)\"\n+      ],\n+      \"execution_count\": 0,\n+      \"outputs\": []\n+    },\n+    {\n+      \"metadata\": {\n+        \"id\": \"jgowG2xu88Io\",\n+        \"colab_type\": \"code\",\n+        \"colab\": {}\n+      },\n+      \"cell_type\": \"code\",\n+      \"source\": [\n+        \"import IPython.display as display\\n\",\n+        \"\\n\",\n+        \"def caption_image(image_path):\\n\",\n+        \"    image_rel = pathlib.Path(image_path).relative_to(data_root)\\n\",\n+        \"    return \\\"Image (CC BY 2.0) \\\" + ' - '.join(attributions[str(image_rel)].split(' - ')[:-1])\\n\",\n+        \"    \"\n+      ],\n+      \"execution_count\": 0,\n+      \"outputs\": []\n+    },\n+    {\n+      \"metadata\": {\n+        \"id\": \"YIjLi-nX0txI\",\n+        \"colab_type\": \"code\",\n+        \"colab\": {}\n+      },\n+      \"cell_type\": \"code\",\n+      \"source\": [\n+        \"for n in range(3):\\n\",\n+        \"  image_path = random.choice(all_image_paths)\\n\",\n+        \"  display.display(display.Image(image_path))\\n\",\n+        \"  print(caption_image(image_path))\\n\",\n+        \"  print()\"\n+      ],\n+      \"execution_count\": 0,\n+      \"outputs\": []\n+    },\n+    {\n+      \"metadata\": {\n+        \"id\": \"OaNOr-co3WKk\",\n+        \"colab_type\": \"text\"\n+      },\n+      \"cell_type\": \"markdown\",\n+      \"source\": [\n+        \"### Determine the label for each image\"\n+      ]\n+    },\n+    {\n+      \"metadata\": {\n+        \"id\": \"-weOQpDw2Jnu\",\n+        \"colab_type\": \"text\"\n+      },\n+      \"cell_type\": \"markdown\",\n+      \"source\": [\n+        \"List the available labels:\"\n+      ]\n+    },\n+    {\n+      \"metadata\": {\n+        \"id\": \"ssUZ7Qh96UR3\",\n+        \"colab_type\": \"code\",\n+        \"colab\": {}\n+      },\n+      \"cell_type\": \"code\",\n+      \"source\": [\n+        \"label_names = sorted(item.name for item in data_root.glob('*/') if item.is_dir())\\n\",\n+        \"label_names\"\n+      ],\n+      \"execution_count\": 0,\n+      \"outputs\": []\n+    },\n+    {\n+      \"metadata\": {\n+        \"id\": \"9l_JEBql2OzS\",\n+        \"colab_type\": \"text\"\n+      },\n+      \"cell_type\": \"markdown\",\n+      \"source\": [\n+        \"Assign an index to each label:\"\n+      ]\n+    },\n+    {\n+      \"metadata\": {\n+        \"id\": \"Y8pCV46CzPlp\",\n+        \"colab_type\": \"code\",\n+        \"colab\": {}\n+      },\n+      \"cell_type\": \"code\",\n+      \"source\": [\n+        \"label_to_index = dict((name, index) for index,name in enumerate(label_names))\\n\",\n+        \"label_to_index\"\n+      ],\n+      \"execution_count\": 0,\n+      \"outputs\": []\n+    },\n+    {\n+      \"metadata\": {\n+        \"id\": \"VkXsHg162T9F\",\n+        \"colab_type\": \"text\"\n+      },\n+      \"cell_type\": \"markdown\",\n+      \"source\": [\n+        \"Create a list of every file, and its label index\"\n+      ]\n+    },\n+    {\n+      \"metadata\": {\n+        \"id\": \"q62i1RBP4Q02\",\n+        \"colab_type\": \"code\",\n+        \"colab\": {}\n+      },\n+      \"cell_type\": \"code\",\n+      \"source\": [\n+        \"all_image_labels = [label_to_index[pathlib.Path(path).parent.name]\\n\",\n+        \"                    for path in all_image_paths]\\n\",\n+        \"\\n\",\n+        \"print(\\\"First 10 labels indices: \\\", all_image_labels[:10])\"\n+      ],\n+      \"execution_count\": 0,\n+      \"outputs\": []\n+    },\n+    {\n+      \"metadata\": {\n+        \"id\": \"i5L09icm9iph\",\n+        \"colab_type\": \"text\"\n+      },\n+      \"cell_type\": \"markdown\",\n+      \"source\": [\n+        \"### Load and format the images\"\n+      ]\n+    },\n+    {\n+      \"metadata\": {\n+        \"id\": \"SbqqRUS79ooq\",\n+        \"colab_type\": \"text\"\n+      },\n+      \"cell_type\": \"markdown\",\n+      \"source\": [\n+        \"TensorFlow includes all the tools you need to load and process images:\"\n+      ]\n+    },\n+    {\n+      \"metadata\": {\n+        \"id\": \"jQZdySHvksOu\",\n+        \"colab_type\": \"code\",\n+        \"colab\": {}\n+      },\n+      \"cell_type\": \"code\",\n+      \"source\": [\n+        \"img_path = all_image_paths[0]\\n\",\n+        \"img_path\"\n+      ],\n+      \"execution_count\": 0,\n+      \"outputs\": []\n+    },\n+    {\n+      \"metadata\": {\n+        \"id\": \"2t2h2XCcmK1Y\",\n+        \"colab_type\": \"text\"\n+      },\n+      \"cell_type\": \"markdown\",\n+      \"source\": [\n+        \"here is the raw data:\"\n+      ]\n+    },\n+    {\n+      \"metadata\": {\n+        \"id\": \"LJfkyC_Qkt7A\",\n+        \"colab_type\": \"code\",\n+        \"colab\": {}\n+      },\n+      \"cell_type\": \"code\",\n+      \"source\": [\n+        \"img_raw = tf.read_file(img_path)\\n\",\n+        \"img_raw\"\n+      ],\n+      \"execution_count\": 0,\n+      \"outputs\": []\n+    },\n+    {\n+      \"metadata\": {\n+        \"id\": \"opN8AVc8mSbz\",\n+        \"colab_type\": \"text\"\n+      },\n+      \"cell_type\": \"markdown\",\n+      \"source\": [\n+        \"Decode it into an image tensor:\"\n+      ]\n+    },\n+    {\n+      \"metadata\": {\n+        \"id\": \"Tm0tdrlfk0Bb\",\n+        \"colab_type\": \"code\",\n+        \"colab\": {}\n+      },\n+      \"cell_type\": \"code\",\n+      \"source\": [\n+        \"img_tensor = tf.image.decode_image(img_raw)\\n\",\n+        \"\\n\",\n+        \"print(img_tensor.shape)\\n\",\n+        \"print(img_tensor.dtype)\"\n+      ],\n+      \"execution_count\": 0,\n+      \"outputs\": []\n+    },\n+    {\n+      \"metadata\": {\n+        \"id\": \"3k-Of2Tfmbeq\",\n+        \"colab_type\": \"text\"\n+      },\n+      \"cell_type\": \"markdown\",\n+      \"source\": [\n+        \"Resize it for your model:\"\n+      ]\n+    },\n+    {\n+      \"metadata\": {\n+        \"id\": \"XFpz-3_vlJgp\",\n+        \"colab_type\": \"code\",\n+        \"colab\": {}\n+      },\n+      \"cell_type\": \"code\",\n+      \"source\": [\n+        \"img_final = tf.image.resize_images(img_tensor, [192, 192])\\n\",\n+        \"img_final = img_final/255.0\\n\",\n+        \"print(img_final.shape)\\n\",\n+        \"print(img_final.numpy().min())\\n\",\n+        \"print(img_final.numpy().max())\\n\"\n+      ],\n+      \"execution_count\": 0,\n+      \"outputs\": []\n+    },\n+    {\n+      \"metadata\": {\n+        \"id\": \"aCsAa4Psl4AQ\",\n+        \"colab_type\": \"text\"\n+      },\n+      \"cell_type\": \"markdown\",\n+      \"source\": [\n+        \"Wrap up these up in simple functions for later.\"\n+      ]\n+    },\n+    {\n+      \"metadata\": {\n+        \"id\": \"HmUiZJNU73vA\",\n+        \"colab_type\": \"code\",\n+        \"colab\": {}\n+      },\n+      \"cell_type\": \"code\",\n+      \"source\": [\n+        \"def preprocess_image(image):\\n\",\n+        \"  image = tf.image.decode_jpeg(image, channels=3)\\n\",\n+        \"  image = tf.image.resize_images(image, [192, 192])\\n\",\n+        \"  image /= 255.0  # normalize to [0,1] range\\n\",\n+        \"\\n\",\n+        \"  return image\"\n+      ],\n+      \"execution_count\": 0,\n+      \"outputs\": []\n+    },\n+    {\n+      \"metadata\": {\n+        \"id\": \"einETrJnO-em\",\n+        \"colab_type\": \"code\",\n+        \"colab\": {}\n+      },\n+      \"cell_type\": \"code\",\n+      \"source\": [\n+        \"def load_and_preprocess_image(path):\\n\",\n+        \"  image = tf.read_file(path)\\n\",\n+        \"  return preprocess_image(image)\"\n+      ],\n+      \"execution_count\": 0,\n+      \"outputs\": []\n+    },\n+    {\n+      \"metadata\": {\n+        \"id\": \"3brWQcdtz78y\",\n+        \"colab_type\": \"code\",\n+        \"colab\": {}\n+      },\n+      \"cell_type\": \"code\",\n+      \"source\": [\n+        \"import matplotlib.pyplot as plt\\n\",\n+        \"\\n\",\n+        \"image_path = all_image_paths[0]\\n\",\n+        \"label = all_image_labels[0]\\n\",\n+        \"\\n\",\n+        \"plt.imshow(load_and_preprocess_image(img_path))\\n\",\n+        \"plt.grid(False)\\n\",\n+        \"plt.xlabel(caption_image(img_path))\\n\",\n+        \"plt.title(label_names[label].title())\\n\",\n+        \"print()\"\n+      ],\n+      \"execution_count\": 0,\n+      \"outputs\": []\n+    },\n+    {\n+      \"metadata\": {\n+        \"id\": \"n2TCr1TQ8pA3\",\n+        \"colab_type\": \"text\"\n+      },\n+      \"cell_type\": \"markdown\",\n+      \"source\": [\n+        \"## Build a `tf.data.Dataset`\"\n+      ]\n+    },\n+    {\n+      \"metadata\": {\n+        \"id\": \"6H9Z5Mq63nSH\",\n+        \"colab_type\": \"text\"\n+      },\n+      \"cell_type\": \"markdown\",\n+      \"source\": [\n+        \"### A dataset of images\"\n+      ]\n+    },\n+    {\n+      \"metadata\": {\n+        \"id\": \"GN-s04s-6Luq\",\n+        \"colab_type\": \"text\"\n+      },\n+      \"cell_type\": \"markdown\",\n+      \"source\": [\n+        \"The easiest way to build a `tf.data.Dataset` is using the `from_tensor_slices` method.\\n\",\n+        \"\\n\",\n+        \"Slicing the array of strings, results in a dataset of strings:\"\n+      ]\n+    },\n+    {\n+      \"metadata\": {\n+        \"id\": \"6oRPG3Jz3ie_\",\n+        \"colab_type\": \"code\",\n+        \"colab\": {}\n+      },\n+      \"cell_type\": \"code\",\n+      \"source\": [\n+        \"path_ds = tf.data.Dataset.from_tensor_slices(all_image_paths)\"\n+      ],\n+      \"execution_count\": 0,\n+      \"outputs\": []\n+    },\n+    {\n+      \"metadata\": {\n+        \"id\": \"uML4JeMmIAvO\",\n+        \"colab_type\": \"text\"\n+      },\n+      \"cell_type\": \"markdown\",\n+      \"source\": [\n+        \"The `output_shapes` and `output_types` fields describe the content of each item in the dataset. In this case it is a set of scalar binary-strings\"\n+      ]\n+    },\n+    {\n+      \"metadata\": {\n+        \"id\": \"mIsNflFbIK34\",\n+        \"colab_type\": \"code\",\n+        \"colab\": {}\n+      },\n+      \"cell_type\": \"code\",\n+      \"source\": [\n+        \"print('shape: ', repr(path_ds.output_shapes))\\n\",\n+        \"print('type: ', path_ds.output_types)\\n\",\n+        \"print()\\n\",\n+        \"print(path_ds)\"\n+      ],\n+      \"execution_count\": 0,\n+      \"outputs\": []\n+    },\n+    {\n+      \"metadata\": {\n+        \"id\": \"ZjyGcM8OwBJ2\",\n+        \"colab_type\": \"text\"\n+      },\n+      \"cell_type\": \"markdown\",\n+      \"source\": [\n+        \"Now create a new dataset that loads and formats images on the fly by mapping `preprocess_image` over the dataset of paths.\"\n+      ]\n+    },\n+    {\n+      \"metadata\": {\n+        \"id\": \"D1iba6f4khu-\",\n+        \"colab_type\": \"code\",\n+        \"colab\": {}\n+      },\n+      \"cell_type\": \"code\",\n+      \"source\": [\n+        \"image_ds = path_ds.map(load_and_preprocess_image, num_parallel_calls=8)\"","path":"site/en/tutorials/load_data/images.ipynb","position":582,"original_position":582,"commit_id":"9ef13a4a9a6014fc3abc0aadf75dbc7ed2d96873","original_commit_id":"9ef13a4a9a6014fc3abc0aadf75dbc7ed2d96873","user":{"login":"jsimsa","id":1072079,"node_id":"MDQ6VXNlcjEwNzIwNzk=","avatar_url":"https://avatars2.githubusercontent.com/u/1072079?v=4","gravatar_id":"","url":"https://api.github.com/users/jsimsa","html_url":"https://github.com/jsimsa","followers_url":"https://api.github.com/users/jsimsa/followers","following_url":"https://api.github.com/users/jsimsa/following{/other_user}","gists_url":"https://api.github.com/users/jsimsa/gists{/gist_id}","starred_url":"https://api.github.com/users/jsimsa/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/jsimsa/subscriptions","organizations_url":"https://api.github.com/users/jsimsa/orgs","repos_url":"https://api.github.com/users/jsimsa/repos","events_url":"https://api.github.com/users/jsimsa/events{/privacy}","received_events_url":"https://api.github.com/users/jsimsa/received_events","type":"User","site_admin":false},"body":"replace `num_parallel_calls=8` with `num_parallel_calls=tf.data.experimental.AUTOTUNE`","created_at":"2018-11-29T16:55:26Z","updated_at":"2018-11-29T16:55:26Z","html_url":"https://github.com/tensorflow/docs/pull/128#discussion_r237572277","pull_request_url":"https://api.github.com/repos/tensorflow/docs/pulls/128","author_association":"MEMBER","_links":{"self":{"href":"https://api.github.com/repos/tensorflow/docs/pulls/comments/237572277"},"html":{"href":"https://github.com/tensorflow/docs/pull/128#discussion_r237572277"},"pull_request":{"href":"https://api.github.com/repos/tensorflow/docs/pulls/128"}}},"pull_request":{"url":"https://api.github.com/repos/tensorflow/docs/pulls/128","id":223737458,"node_id":"MDExOlB1bGxSZXF1ZXN0MjIzNzM3NDU4","html_url":"https://github.com/tensorflow/docs/pull/128","diff_url":"https://github.com/tensorflow/docs/pull/128.diff","patch_url":"https://github.com/tensorflow/docs/pull/128.patch","issue_url":"https://api.github.com/repos/tensorflow/docs/issues/128","number":128,"state":"open","locked":false,"title":"A 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