{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# 🐶**End-to-end Multiclass Dog Breed Classification** 🐕\n\n\n---\n\n\n## `Workflow`\n\n**Multi-class image classifier using TensorFlow 2.0 and TensorFlow Hub.**\n\n## `Problem`\n\n**Identifying the breed of a dog given an image of a dog.**\n\n> when I take a photo of a dog, wanna know what breed of dog it is.\n\n## `Data`\n\n**Using Kaggle's dog breed identification competition data**\n\n**`PS`**: provided Data `ID, Dog type` to learn pattern, show image of provided `ID` of Data, try to know what kind of `dog type`.\n\n[go to data](https://www.kaggle.com/competitions/dog-breed-identification/data)\n\n## `Evaluation`\n\n**Prediction probabilities for each dog breed of each test image.**\n\n[go to evaluation](https://www.kaggle.com/competitions/dog-breed-identification/overview/evaluation)\n\n## `Features`\n\n*   Unstructered data, can be used deep learning/transfer learning.\n*   120 breeds of dogs: \"120 different classes\"\n*   Around 10.000+ images in the training/test set\n*   Training set have labels, Test set has no labels \"to predict them\"\n\n","metadata":{"id":"3aNUjd9Btbiw"}},{"cell_type":"code","source":"#@title unzip file\n# !unzip \"drive/MyDrive/Colab Notebooks/Dog Breed Identification/dog-breed-identification.zip\" -d \"drive/MyDrive/Colab Notebooks/Dog Breed Identification/\"","metadata":{"id":"Jn2sY4auB3uj","cellView":"form"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### **Get our workspace ready**\n\n* Import TensorFlow 2.x 👌\n* Import TensorFlow Hub 👌\n* Using GPU 👌","metadata":{"id":"_uULLxZzx1og"}},{"cell_type":"code","source":"import os\nimport pandas as pd\nimport matplotlib.pyplot as plt\n# import TensorFlow and Hub\nimport tensorflow as tf\nimport tensorflow_hub as hub\nprint(\"TF version:\", tf.__version__)\nprint(\"TF Hub version:\", hub.__version__)\n\n# check for GPU availability\nprint(\"GPU\", \"available\" if tf.config.list_physical_devices else \"not available\")","metadata":{"id":"HF5SQdk1tgIH","outputId":"60e85451-5820-48a8-9499-59fd91b515ed"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Getting our data ready \"turning into Tensors\"\n\n**Data has to be in numerical format.**\n\n> Turning images into Tensors `numerical representations`\n\n* Accessing data and checking out the labels.","metadata":{"id":"421hUvtDY9qN"}},{"cell_type":"code","source":"# Checking the labels of data\nlabels_csv = pd.read_csv(\"drive/MyDrive/Colab Notebooks/Dog Breed Identification/labels.csv\")\nprint(labels_csv.describe())\nprint(labels_csv.head())","metadata":{"id":"PNpliAJ2ZLPl","outputId":"519a46b0-1083-47f1-8d85-a05efe99d7b2"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"labels_csv.head()","metadata":{"id":"Dl9V0S5dc8QF","outputId":"c0196aab-9348-4012-eaaf-22b421c887c8"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# How many images are there of each bread?\nlabels_csv[\"breed\"].value_counts()","metadata":{"id":"9TbK7_x8kpUq","outputId":"96c5cd8b-fbac-43bb-dd19-735996d3d27b"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Visualize counted values of each bread\nlabels_csv[\"breed\"].value_counts().plot.bar(figsize=(20, 10));","metadata":{"id":"bfrhS4wRlMUX","outputId":"f230f8c5-3578-4b3d-d6fa-9bbee9cf39f1"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"labels_csv[\"breed\"].value_counts().mean()","metadata":{"id":"axWRf6MjmIKw","outputId":"a5e0d552-5231-4d44-f944-f73784d6dd0e"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# View image on notebook\nfrom IPython.display import Image\nImage(\"drive/MyDrive/Colab Notebooks/Dog Breed Identification/train/0021f9ceb3235effd7fcde7f7538ed62.jpg\")","metadata":{"id":"xn9XI-qBmfQE","outputId":"ad9b0a24-8795-45fc-8155-73491e51d5bc"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Getting images and labels\n**get list of all images file pathnames**","metadata":{"id":"q2y5ZS7kprdC"}},{"cell_type":"code","source":"# pathnames from image ID\nfilenames = [\"drive/MyDrive/Colab Notebooks/Dog Breed Identification/train/\" + fname + \".jpg\" for fname in labels_csv[\"id\"]]\n\nfilenames[:5]","metadata":{"id":"XD2GP2XQqDaL","outputId":"1f6bd228-7c40-4cf6-9905-11158f006ed7"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":" # crosscheck number of filenames if matches number of actual image files\n if (len(os.listdir(\"drive/MyDrive/Colab Notebooks/Dog Breed Identification/train/\")) == len(filenames)):\n   print(\"Filenames match actual amount of files, so Proceed.\")\n else:\n   print(\"Does not match !!!\")","metadata":{"id":"mAKEp0efqtbC","outputId":"42ab2b68-9f62-4f46-eee5-b6d5d18601e8"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# check image randomly\nImage(filenames[10000])","metadata":{"id":"_I1W3ubWtvhu","outputId":"bc363547-a51a-4098-cd56-4acaa179a101"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"labels_csv[\"breed\"][10000]","metadata":{"id":"TUfxMl-2wJ33","outputId":"577b83f7-db9e-4943-86e0-f2abb7734ad3"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"The list of training image filepaths are done, `prepare labels`","metadata":{"id":"7_9i3pva6VDb"}},{"cell_type":"code","source":"import numpy as np\nlabels = labels_csv[\"breed\"].to_numpy()\nlabels","metadata":{"id":"Vj4hk2uC6rU5","outputId":"7a073b51-5ca8-4fb0-abaa-ec2669342896"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"len(labels)","metadata":{"id":"7NoC9pXJ61lQ","outputId":"a2960309-acf5-4139-f7e9-bdf5e6e848cd"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# crosscheck if number of labels matches the number of filenames\nif (len(labels) == len(filenames)):\n  print(\"Number of labels matches number of filenames.\")\nelse:\n  print(\"Number of labels does not match number of filenames !!!\")","metadata":{"id":"2scbqknv65_s","outputId":"059d4d51-0292-4a86-d097-fff013da4333"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":" # find unique label values\n unique_breeds = np.unique(labels)\n unique_breeds","metadata":{"id":"MLl3S9227nsE","outputId":"b9acc1bf-8c1a-4533-b1f0-6d5ec3cc80b8"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"len(unique_breeds)","metadata":{"id":"a8O7XRvJ8HWc","outputId":"6cb5b19e-ffe5-4e02-8ce6-9515b5d85441"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Turn a single label into an array of booleans\nprint(labels[0])\nlabels[0] == unique_breeds","metadata":{"id":"MReOqppe8am-","outputId":"b244de68-ea26-4cee-8cb5-2de7ceae1bca"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Turn every label into a boolean array\nboolean_labels = [label == unique_breeds for label in labels]\nboolean_labels[:2]","metadata":{"id":"oI5GHsnLAFAt","outputId":"671261ba-e16e-4706-93b2-25cbdd4b6fdd"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"len(boolean_labels)","metadata":{"id":"TZrkbnSeAi78","outputId":"066ee999-4553-4757-fd90-45fd56694a08"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#@title SHOW `Turning boolean array into integers`\n# # original label\n# print(labels[0])\n# # index where label occurs\n# print(np.where(unique_breeds == labels[0]))\n# # index where label occurs in boolean array\n# print(boolean_labels[0].argmax())\n# # check number 1 where sample label occurs\n# print(boolean_labels[0].astype(int))","metadata":{"cellView":"form","id":"hWWXgejWAoaX"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Creating our own validation set\n\n**There is no validation set are given, so let's create own**","metadata":{"id":"Bv6Hrtp-Scbk"}},{"cell_type":"code","source":"# Setup X and y variables\nX = filenames\ny = boolean_labels","metadata":{"id":"BJ22gSC5S3ZJ"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Experimenting with ~1000 images to figure out quality of modeling**","metadata":{"id":"TNK4ibB6TTIB"}},{"cell_type":"code","source":"# Set number of images to use for experimenting\nNUM_IMAGES = 1000 #@param {type: \"slider\", min:1000, max:10000, step:1000}","metadata":{"id":"g-5rPZ3VTR3o"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Split data into train and validation sets\nfrom sklearn.model_selection import train_test_split\n\n# Splt them into training and validation of total size NUM_IMAGES\nX_train, X_val, y_train, y_val = train_test_split(X[:NUM_IMAGES],\n                                                  y[:NUM_IMAGES],\n                                                  test_size=0.2,\n                                                  random_state=42)\nlen(X_train), len(y_train), len(X_val), len(y_val)","metadata":{"id":"M_krXS9tUGMS","outputId":"7b4198a4-0fc1-40f9-b5af-d2bde04834d2"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# check training data\nX_train[:5], y_train[:2]","metadata":{"id":"tHtuk1ldcfez","outputId":"1c5fb33c-d522-42d8-e51d-540935afcf60"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Preprocessing Images `turning images into Tensors`\n\n* take an image filepath as input\n* use tensorflow to read the file and save it to a vaiable, `image`\n* turn `image` into tensors\n* normalize image `convert color channel values from 0-255 to 0-1`\n* resize the `image` to be a shape of (`224, 224`)\n* return the modified type","metadata":{"id":"ieeyPs3VpCup"}},{"cell_type":"code","source":"#@title What importing image looks like\n# Convert image to Numpy array\nfrom matplotlib.pyplot import imread\nimage = imread(filenames[102])\nimage.shape","metadata":{"id":"InvCGvmwc1V2","outputId":"592ee7e0-eb9f-484c-fd2e-a8f20723f009"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# images are combination of RGB color '0-255'\nimage.max(), image.min()","metadata":{"id":"AQxCgrz2PVLQ","outputId":"d1336bfc-ed35-4182-974d-d6e29d4de7f5"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# convert to tensor in order to run in GPU\ntf.constant(image)","metadata":{"id":"p1Gz5DaBRjMp","outputId":"4f84af33-b593-496a-b050-c08ea630fc97"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Image is a Tensor in above so proceed all of them as using function:\n\n* take an image filepath as input\n* use tensorflow to read the file and save it to a vaiable, `image`\n* turn `image` into tensors\n* normalize image `convert color channel values from 0-255 to 0-1`\n* resize the `image` to be a shape of (`224, 224`)\n* return the modified type","metadata":{"id":"isWIkUFEBPCB"}},{"cell_type":"code","source":"# define image size\nIMG_SIZE = 224\n\n# create a function for preprocessing images\ndef process_image(image_path, img_size=IMG_SIZE):\n  \"\"\"\n  Input an image file path and turns the image into a Tensor.\n  \"\"\"\n  # read in an image file\n  image = tf.io.read_file(image_path)\n  # turn the jpg image into numerical tensor with 3 colour channels \"red, green, blue\"\n  image = tf.image.decode_jpeg(image, channels=3)\n  # convert the colour channel values from 0-255 to 0-1 values\n  image = tf.image.convert_image_dtype(image, tf.float32)\n  # resize the image to desired value \"224x224\"\n  image = tf.image.resize(image, size=[img_size, img_size])\n\n  return image","metadata":{"id":"4CS1SOZXR63C"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Turning data into batches\n\n**take in account, only some simples of images, the best number is `32`**\n\n> In order to use TensorFlow effectively, Tuples of Tensor Data must look like;\n`(image, label)`","metadata":{"id":"CstaAehrF00n"}},{"cell_type":"code","source":"# simple function to return a tuple (image, label)\ndef get_image_label(image_path, label):\n  \"\"\"\n  Input an image file path name and the associated label,\n  processes the image and returns a tuple of (image, label)\n  \"\"\"\n  image = process_image(image_path)\n  return image, label","metadata":{"id":"XbO62Kw9CE8Q"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# tuple of tensor data\n(process_image(X[42]), tf.constant(y[42]))","metadata":{"id":"LNIu0-PYLyUu","outputId":"539cf6e6-b691-464f-9b4e-be08eef9ae01"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Turned data into tuples of tensors in the form: `image, label`**\n\nProceed another function to turn all of data `X & y` into batches;\n* **handling 3 different sets of data**;\n  * training\n  * validation\n  * test\n\n***best optimized batch size is `32` according to `Yann Lecun`***","metadata":{"id":"gQ6iDmoWuGpu"}},{"cell_type":"code","source":"# define the batch size 32\nBATCH_SIZE = 32\n\n# function for turning data into batches\ndef create_data_batches(X, y=None, batch_size=BATCH_SIZE, valid_data=False, test_data=False):\n  \"\"\"\n  Creates batches of data out of image (X) and label (y) pairs.\n  Shuffles the data if it's training data but doesn't shuffle if it's validation data.\n  Also accepts test data as input (no labels).\n  \"\"\"\n  # if the data is a test dataset, there is no labels\n  if test_data:\n    print(\"Creating test data batches...\")\n    data = tf.data.Dataset.from_tensor_slices((tf.constant(X))) # filepaths\n    data_batch = data.map(process_image).batch(BATCH_SIZE)\n    return data_batch\n  \n  # if the data is a valida dataset, does not require shuffle it\n  elif valid_data:\n    print(\"Creating validation data batches...\")\n    data = tf.data.Dataset.from_tensor_slices((tf.constant(X), # filepaths\n                                               tf.constant(y))) # labels\n    data_batch = data.map(get_image_label).batch(BATCH_SIZE)\n    return data_batch\n  \n  else:\n    # if the data is a training dataset, shuffle it\n    print(\"Creating training data batches...\")\n    # turn filepaths and labels into tensors\n    data = tf.data.Dataset.from_tensor_slices((tf.constant(X), # filepaths\n                                               tf.constant(y))) # labels\n    \n    # shuffling pathnames and labels before mapping image processor function is faster than shuffling images\n    data = data.shuffle(buffer_size=len(X))\n\n    # creating (image, label) tuples (this also turns the image path into a preprocessed image)\n    data = data.map(get_image_label)\n\n    # turn the data into batches\n    data_batch = data.batch(BATCH_SIZE)\n  return data_batch","metadata":{"id":"RZBdRzIfMBOq"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# create training and validation data batches\ntrain_data = create_data_batches(X_train, y_train)\nval_data = create_data_batches(X_val, y_val, valid_data=True)","metadata":{"id":"pD2eGe9w1k9U","outputId":"632685bf-928c-4c61-c703-7d5f06c1c6a7"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# check out the different attributes of our data batches\ntrain_data.element_spec, val_data.element_spec","metadata":{"id":"JbYHSpSH2JBN","outputId":"c93f354c-4758-4912-ab2a-2b5c1bd9c98c"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Data in batches, The mean is in Tensor pairs of (`images, labels`) ready for use on a GPU.**\n\n## Visualizing data batches\n\n***Check batches data in visual way in order to figure out more effectify***","metadata":{"id":"S4mSSorJG0C6"}},{"cell_type":"code","source":"# create function for viewing images in a data batch\ndef show_images(images, label):\n  \"\"\"\n  Display images from Data batch\n  \"\"\"\n  # setup the figure\n  plt.figure(figsize=(20, 20))\n  # loop through images for desired amount\n  for i in range(30):\n    # create subplots, rows x columns\n    ax = plt.subplot(6, 5, i+1)\n    # display an image\n    plt.imshow(images[i])\n    # add the image label as the title\n    plt.title(unique_breeds[label[i].argmax()])\n    # turn grid lines off\n    plt.axis(\"off\")","metadata":{"id":"5gx79yBHGDB9"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"> Using `as_numpy_iterator()` and `next()` method, to iterate values inside data batch","metadata":{"id":"rmbZy2K-JaEd"}},{"cell_type":"code","source":"# visualize training images fom the training data batch\ntrain_images, train_labels = next(train_data.as_numpy_iterator())\nshow_images(train_images, train_labels)","metadata":{"id":"trYqyPQKJVb4","outputId":"e8780195-cab2-4415-f73e-bb9487b4f08a"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Visualize validation images from the validation data batch\nval_images, val_labels = next(val_data.as_numpy_iterator())\nshow_images(val_images, val_labels)","metadata":{"id":"_8fZxCNPss4g","outputId":"be1e52c3-312c-4cfb-a4ae-d37b32bf0275"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## CREATING AND TRAINING A MODEL\n\n* **Using `TensorFlowHub`, to find pretrained model about your problem and then use it, It is als called `Transfer Learning`**\n\n* **`Choosing Model`: Problem in above, It is about image classification, go to  \n[navigate page of TensorFlow Hub](https://tfhub.dev/s?module-type=image-augmentation,image-classification,image-feature-vector,image-generator,image-object-detection,image-others,image-style-transfer,image-rnn-agent)**\n\n  * navigate `image classification`: [go to only image-classification](https://tfhub.dev/s?module-type=image-classification)\n  * finding exactly matched model `mobilenet_v2_130_224/classification`: [go to mobilenet](https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5)\n  * `Check Info in navigate regarding your desired model`: Above shape of images are 224,224, finding pretraining model according to this information.","metadata":{"id":"OzrGCym3vF9E"}},{"cell_type":"markdown","source":"### Building a Model\n***Before starting a building, There are someparts need to be done***\n\n* The input shape `images, in the form of Tensors` to our model.\n* The output shape `image labels, in the form of Tensors` of our model.\n* The URL of the model that need to be used. [go to URL](https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5)\n\n> According to Form of Tensors, This is stardard practice for using machine learning model.","metadata":{"id":"mKWiAlWkm3Iw"}},{"cell_type":"code","source":" # setup input shape to the model\n INPUT_SHAPE = [None, IMG_SIZE, IMG_SIZE, 3] # batch, height, width, colour channels\n\n # setup output shape of the model\n OUTPUT_SHAPE = len(unique_breeds) # number of unique labels\n\n # setup model URL from TensorFlow Hub\n MODEL_URL = \"https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\"","metadata":{"id":"R8yqToz0svuW"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Inputs, outputs and model are done.\n\nBuilding a model in TensorFlow,\n\n> Best way to use [The Keras API](https://www.tensorflow.org/guide/keras/sequential_model)\n\nThe Function with below requirment:\n* Takes the input shape, output shape and the model chosen URL as parameters.\n* Defines the layers in Keras model in a sequential fashion `step by step`\n* Compiles the model `evaluated and improved`\n* Builds the model `getting input shape`\n* Returns the model.","metadata":{"id":"k0sbO8gzlVgn"}},{"cell_type":"code","source":"# create a function to builds a Keras model\ndef create_model(input_shape=INPUT_SHAPE, output_shape=OUTPUT_SHAPE, model_url=MODEL_URL):\n  print(\"Building model with:\", MODEL_URL)\n  # Setup the model layers\n\n  model = tf.keras.Sequential([  \n      hub.KerasLayer(MODEL_URL), # Layer 1 \"input layer\"    \n      tf.keras.layers.Dense(units=OUTPUT_SHAPE,\n                            activation=\"softmax\"), # Layer 2 \"output layer\"                       \n  ])\n\n  # compile the model\n  model.compile(\n      loss=tf.keras.losses.CategoricalCrossentropy(), # Model wants to reduce this \"how wrong its guesses are\"\n      optimizer=tf.keras.optimizers.Adam(), # Model try to figure out for improving its guesses\n      metrics=[\"accuracy\"] # show score\n  )\n\n  # build the model\n  model.build(INPUT_SHAPE) # exists what type of inputs\n\n  return model","metadata":{"id":"w9xAx4_LmbFd"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### Setting up the model layers\nThere are two ways to do this in Keras, the [functional](https://www.tensorflow.org/guide/keras/functional) and [sequential](https://www.tensorflow.org/guide/keras/sequential_model#build_a_simple_model)\n\n> The Keras documentation states, `Functional` is for complex models. `Sequential` is for a linear stack of layers.\n\n* The Layer 1 of TensorFlow Hub `hub.KerasLayer(MODEL_URL)`: input layer takes images and finds patter in them based on the patterns [ mobilenet_v2_130_224](https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5)\n\n* The Layer 2 `tf.keras.layers.Dense()`: ouput layer of model, all information discovered in the input layer, it is in shape for the number of unique labes `120`\n\n* The `Activation=\"softmax\"`: Assign probability value to each value of 120 labels `between 0 and 1`. The higher value mean is model should input label for image, In addition, If this was `binary classification` problem activation will be `sigmoid`.\n\n* How choose `Activation Function`\n![activation.png](data:image/png;base64,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)","metadata":{"id":"JHBTs_XzrQIj"}},{"cell_type":"markdown","source":"#### Compiling the model\n**`model.compile()` terminology:**\n* `loss`: the models goal is to minimize this, getting to 0 means the model is learning perfectly.\n* `optimizer`: Adam is the optimizer, to figure out for lower loss function. there are others exist such as [RMSprop](https://ruder.io/optimizing-gradient-descent/index.html#rmsprop) and [Stochastic gradient descent](https://en.wikipedia.org/wiki/Stochastic_gradient_descent)\n* `metrics`: Accuracy of how well our model is prediction the correct image label.\n\n#### Build\nUsing `model.build()`: what input shape it can be expected.\n\n* The input shape is `[None, IMG_SIZE, IMG_SIZE, 3]` or `[None, 224, 224, 3]` or `[batch_size, img_height, img_width, color_channels]`.\n* `None` is batch size, we already setup it as 32.\n* `summary()`: idea of model how it is looks like.","metadata":{"id":"khxR-0FsvI7t"}},{"cell_type":"code","source":"# create a model and check its details\nmodel = create_model()\nmodel.summary()","metadata":{"id":"MCa0SkVkpE-L","outputId":"d23ebfda-4444-4891-d33d-f2e273b62ea8"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"> * ***The non-trainable parameters are the patterns learned by `mobilenet_v2_130_224` belong to source of [ImageNet](https://image-net.org/index.php), It is about ~`14M` images.***\n* ***The trainable parameters are the ones in the added dense layer***\n\n**The main bulk of the information in model has already been learned, go to adapt it.**\n","metadata":{"id":"amECevwWNYrL"}},{"cell_type":"markdown","source":"#### Creating callbacks\n\n> ***`Callbacks` are helper functions a model can use during `training` to do things such as `save` a models progress, check a models `progress` or `stop` `training` early if a model `stops improving`.***\n\n* TensorBoard callback\n* Early Stopping callback","metadata":{"id":"BoKT89DDOjdU"}},{"cell_type":"markdown","source":"#### TensorBoard Callback\n[TensorBoard](https://www.tensorflow.org/tensorboard/get_started) provide a visual way to monitor the progress of model during and after training.\n\n> `TensorBoard` can be used [directly in a notebook](https://www.tensorflow.org/tensorboard/tensorboard_in_notebooks) to track performance measures of a model such as loss and accuracy.\n\n***Three things need to be completed to use TensorBoard callback and view TensorBoard in notebook;***\n\n* Load the TensorBoard notebook extension.\n* Create a TensorBoard callback which is able to save logs to a directory and pass it to model's `fit()` function.\n* Visualize the models training logs using the `%tensorboard` function.","metadata":{"id":"aTavVbzMPuMV"}},{"cell_type":"code","source":"# load the TensorBoard notebook extension\n%load_ext tensorboard","metadata":{"id":"2Xl0VZb5qkej","outputId":"d41103cc-3cb2-4d21-efee-9f00adabc24c"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import datetime\n\n# create a function to build a TensorBoard callback\ndef create_tensorboard_callback():\n  # create a log directory for storing TensorBoard logs\n  logdir = os.path.join(\"drive/MyDrive/Colab Notebooks/Dog Breed Identification/logs\",\n                        datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n  return tf.keras.callbacks.TensorBoard(logdir)","metadata":{"id":"Um69jnQdgPeb"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### Early Stopping Callback\n[Early stopping](https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/EarlyStopping) helps prevent overfitting by stopping a model when a certain evaluation metric stops improving.\n\n> ***Early stopping: Keep finding patterns until the quality of those patterns starts to go down.***","metadata":{"id":"r8-xl6gbhq9I"}},{"cell_type":"code","source":"# create early stopping \"once model stops improving, stop training\"\nearly_stopping = tf.keras.callbacks.EarlyStopping(monitor=\"val_accuracy\",\n                                                  patience=3) # stops after 3 rounds of no improvements","metadata":{"id":"m0NVIa2QhoPl"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Training a model `on a subset of data`\n\nIt already setup, to be trained on 1000 images, trained on 800 images, validated on 200 images.\n\n> * `NUM_EPOCHS`: defines how many passes of the data to find patterns in each do image and see which patterns relate to each label.\n* `NUM_EPOCHS=1`: does not enough for model to figure out pattern of data.\n","metadata":{"id":"HMoD8z3vjmpo"}},{"cell_type":"code","source":"# check again if GPU is available\nprint(\"GPU\", \"available...\" if tf.config.list_physical_devices(\"GPU\") else \"not available\")","metadata":{"id":"2oDwLD3Zjdx1","outputId":"89eed97d-abdb-4464-ac22-153f610966c4"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# how many rounds should we get the model to look through the data\nNUM_EPOCHS = 100 #@param {type:\"slider\", min:10, max:1000, step:10}","metadata":{"id":"9m1Ey_EalhA8"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"***GPU is running and NUM_EPOCHS setup is done.***\n\n**Functon to trains a model;**\n* Create a model using `create_model()`\n* Setup a TensorBoard callback using `create_tensorboard_callback()`\n* Call the `fit()` function on model passing it the training data, validation data, number of epochs to train.\n* return the fitted model.","metadata":{"id":"7SCbpdfvmC6W"}},{"cell_type":"code","source":"# build a function to train and return a trained model\ndef train_model():\n  \"\"\"\n  Trains a given model and returns the trained version.\n  \"\"\"\n  # create a model\n  model = create_model()\n\n  # create new TensorBoard session everytime, train a model\n  tensorboard = create_tensorboard_callback()\n\n  # fit the model to the data passing it the callbacks\n  model.fit(x=train_data,\n            epochs=NUM_EPOCHS,\n            validation_data=val_data,\n            validation_freq=1, # check validation metrics every epoch\n            callbacks=[tensorboard, early_stopping])\n  return model","metadata":{"id":"bQpK7ukyl9x4"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# fit the model to the data\nmodel = train_model()","metadata":{"id":"FrKNkvDZrmQb","outputId":"073b4405-76af-4e5a-e842-b3cc28d12086"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"> * trained images are 800, batch_size=32.\n* 25 X 32 = 800 \n  * Epoch steps are `25` \n* validated images are 200, batch_size=32.\n* 32 X 6.25 = 200\n  * Epoch steps are ~7","metadata":{"id":"Q4UBbPFYtMnE"}},{"cell_type":"markdown","source":"#### Checking the TensorBoard Logs\n***Checking trained model for showing performance in visual way***","metadata":{"id":"bZkh9ZPYgSSc"}},{"cell_type":"code","source":"%tensorboard --logdir drive/My\\ Drive/Colab\\ Notebooks/Dog\\ Breed\\ Identification/logs","metadata":{"id":"rYJNwk5qrzr0","outputId":"ebba5592-c898-4308-8bae-89e6d39a6063"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Making and evaluating predictions using a trained model\n> before increase amount of images for scale-up model, check any other way to make better model.\n\n* Using `predic()`, do prediction with given same format that model trained on.","metadata":{"id":"GDYRGcDPlmq2"}},{"cell_type":"code","source":"# make predictions on the validation data\npredictions = model.predict(val_data, verbose=1) # verbose1 - progress bar\npredictions\n","metadata":{"id":"GjBveVmgg2UY","outputId":"fd0fed84-e61d-4a7a-a007-cc390f4170ac"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# check the shape of predictions\npredictions.shape","metadata":{"id":"Lg1FdNjvmyXL","outputId":"6c4040b9-ab40-40c2-950b-d766fcb09e28"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"> ***Making predictions with model returns an array with a different value for each label.***\n* Making predictions on the validation data `200 images` returns an array `predictions` of arrays, each containing `120` different values `one for each unique dog breed`\n\n* That different values are the probabilities for the model has predicted a certain image being a certain breed of dog. The higher value, choosen label for the dog.","metadata":{"id":"bIDBG5rlo_ih"}},{"cell_type":"code","source":"#@title Convert an array of probabilities into an actual label and show it in visual way with predicted and actual label.\nval_img = 13\nprint(predictions[val_img]) # First prediction\nprint(f\"Max value (probability of prediction): {np.max(predictions[val_img])}\") # the max probability value predicted by the model\nprint(f\"Sum: {np.sum(predictions[val_img])}\") # Value will be close to 1 because of using softmax\nprint(f\"Max index: {np.argmax(predictions[val_img])}\") # The index of where the max value in predictions[val_img] accurs\nprint(f\"Predicted label: {unique_breeds[np.argmax(predictions[val_img])]}\") # the predicted label\nprint(f\"Actual label: {unique_breeds[np.argmax(val_labels[val_img])]}\") # the actual label\n# display an image\nplt.imshow(val_images[val_img])\n# add the image label as the title\nplt.title(unique_breeds[np.argmax(val_labels[val_img])])","metadata":{"id":"ZUTI-V1xnaou","outputId":"d4e7cf1b-2445-48e9-cbed-792079c970c5"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### Prediction probabilities aka confidence levels","metadata":{"id":"4YNlQTAItgRa"}},{"cell_type":"code","source":"# turn prediction probabilities into respective label\ndef get_pred_label(prediction_probabilities):\n  \"\"\"\n  Turns an array of prediction probabilities into a label.\n  \"\"\"\n  return unique_breeds[np.argmax(prediction_probabilities)]","metadata":{"id":"BS6mHHrhrZO4"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# get a predicted label based on an array of prediction probabilities\npred_label = get_pred_label(predictions[val_img])\npred_label","metadata":{"id":"FYlL4M3HUY1j"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"***The model never seen validation images and labels***\n\n> `val_data` is in batch form, to get a list of validation images and labels, it need to be unbatched using `unbatch()`, later on turn it into a iterator using `as_numpy_iterator()`.","metadata":{"id":"RuAYeUnXf5lD"}},{"cell_type":"code","source":"# create a function to unbatch a batched dataset\ndef unbatchify(data):\n  \"\"\"\n  Takes a batched dataset of (image, label) tensors and \n  returns separate arrays of images and labels\n  \"\"\"\n  images = []\n  labels = []\n  # loop through unbatched data\n  for image, label in data.unbatch().as_numpy_iterator():\n    images.append(image)\n    labels.append(unique_breeds[np.argmax(label)])\n  return images, labels\n\n# unbatchify the validation data\nval_images, val_labels = unbatchify(val_data)\nval_images[val_img], val_labels[val_img]","metadata":{"id":"TdWvmDCzfkkG","outputId":"4127b2c6-2e51-4215-8985-1547ccf5c8ff"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"`The Results`:\n> * Prediction labels\n* Validation labels `truth labels`\n* Validation iamges\n\nThe visualize the result of model:\n* Take an array of prediction probabilities, an array of truth labels, and array of images and an integer.\n* Convert the prediction probabilities to an predicted label.\n* Plot the predicted label, its predicted probability, the truth label and target image on a single plot.","metadata":{"id":"KoEn4ZhOjzKF"}},{"cell_type":"code","source":"def plot_pred(prediction_probabilities, labels, images, n=1):\n  \"\"\"\n  View the prediction, ground truth label and image for sample n.\n  \"\"\"\n  pred_prob, true_label, image = prediction_probabilities[n], labels[n], images[n]\n\n  # get the pred label\n  pred_label = get_pred_label(pred_prob)\n\n  # plot image and remove ticks\n  plt.imshow(image)\n  plt.xticks([])\n  plt.yticks([])\n\n  # change the color of the title depending on if the prediction is right or wrong\n  if pred_label == true_label:\n    color = \"green\"\n  else:\n    color = \"red\"\n  \n  plt.title(\"{} {:2.0f}% ({})\".format(pred_label,\n                                      np.max(pred_prob)*100,\n                                      true_label),\n                                      color=color)","metadata":{"id":"4i3AhflNhodD"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# view an example prediction, original image and truth label\nplot_pred(prediction_probabilities=predictions,\n          labels=val_labels,\n          images=val_images, n=5)","metadata":{"id":"Uyg_lOGSl_KI","outputId":"4483f7b7-4e05-4da3-e2bf-bbf11c1cc4cf"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"***A multi-class `120 different dog breeds`, it will be nice to see other guesses, what if the model predicts a certain label with less than %20 probability, what will be other options for that image.***\n\n`create function to demonstrate above issue`:\n* take an input of a prediction probabilities array, a ground truth labels array and an integer.\n* find the predicted label using `get_pred_label()`\n* find the top 10:\n  * prediction probabilities indexes\n  * prediction probabilities values\n  * prediction labels\n* plot the top 10 prediction probabilitiy values and labels, coloring the true label green.","metadata":{"id":"V9yU4OUurYNC"}},{"cell_type":"code","source":"def plot_pred_conf(prediction_probabilities, labels, n=1):\n  \"\"\"\n  Plots the top 10 highest prediction confidences along with\n  the truth label for sample n.\n  \"\"\"\n  pred_prob, true_label = prediction_probabilities[n], labels[n]\n\n  # get the predicted label\n  pred_label = get_pred_label(pred_prob)\n\n  # find the top 10 prediction confidence indexes\n  top_10_pred_indexes = pred_prob.argsort()[-10:][::-1]\n  # find the top 10 prediction confidence values\n  top_10_pred_values = pred_prob[top_10_pred_indexes]\n  # find the top 10 prediction labels\n  top_10_pred_labels = unique_breeds[top_10_pred_indexes]\n\n  # setup plot\n  top_plot = plt.bar(np.arange(len(top_10_pred_labels)),\n                      top_10_pred_values,\n                      color=\"grey\")\n  plt.xticks(np.arange(len(top_10_pred_labels)),\n             labels=top_10_pred_labels,\n             rotation=\"vertical\")\n  \n  # change color of true label\n  if np.isin(true_label, top_10_pred_labels):\n    top_plot[np.argmax(top_10_pred_labels == true_label)].set_color(\"green\")\n  else:\n    pass","metadata":{"id":"GrL8-4xCmMfU"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plot_pred_conf(prediction_probabilities=predictions,\n               labels=val_labels,\n               n=11)","metadata":{"id":"HiKt4oYWu4r-","outputId":"f75a6136-c418-421f-827f-4ec708c0c509"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"***Using functions about visualize predictions and evaluate the model for more of val_data.***","metadata":{"id":"ZA1xEqzO9lYx"}},{"cell_type":"code","source":"i_multiplier = 50\nnum_rows = 5\nnum_cols = 3\nnum_images = num_rows*num_cols\nplt.figure(figsize=(10*num_cols, 5*num_rows))\nfor i in range(num_images):\n  plt.subplot(num_rows, 2*num_cols, 2*i+1)\n  plot_pred(prediction_probabilities=predictions,\n            labels=val_labels,\n            images=val_images,\n            n=i+i_multiplier)\n  plt.subplot(num_rows, 2*num_cols, 2*i+2)\n  plot_pred_conf(prediction_probabilities=predictions,\n                 labels=val_labels,\n                 n=i+i_multiplier)\nplt.tight_layout(h_pad=1.0)\nplt.show()","metadata":{"id":"zzvpmlNv9VwJ","outputId":"cd5a00dd-ac3c-4b33-831e-5044e93f8dbf"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Saving and reloading a model\n***Using [Keras model in h5](https://www.tensorflow.org/tutorials/keras/save_and_load), create a function while model takes input and utilise the `save()` in order to save it as h5 file to created models directory.***","metadata":{"id":"jEwAQbfBE6OD"}},{"cell_type":"code","source":"def save_model(model, suffix=None):\n  \"\"\"\n  Saves a given model in a models directory and appends a suffix (str)\n  for clarity and reuse.\n  \"\"\"\n  # create model directory with current time\n  modeldir = os.path.join(\"drive/MyDrive/Colab Notebooks/Dog Breed Identification/models\",\n                          datetime.datetime.now().strftime(\"%Y%m%d-%H%M%s\"))\n  model_path = modeldir + \"-\" + suffix + \".h5\" # save format of model\n  print(f\"Saving model to: {model_path}.\")\n  model.save(model_path)\n  return model_path","metadata":{"id":"jPBxNrneEPHt"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"> * create a function in order to load saved model using `tf.keras.models.load_model()`\n* Using a component from TensorFlow Hub `hub.KerasLayer`, It must pass it as parameter `custom_objects`.","metadata":{"id":"y1mzrZukMYdy"}},{"cell_type":"code","source":"def load_model(model_path):\n  \"\"\"\n  Loads a saved model from a created models directory.\n  \"\"\"\n  print(f\"Loading saved model from: {model_path}\")\n  model = tf.keras.models.load_model(model_path,\n                                     custom_objects={\"KerasLayer\":hub.KerasLayer})\n  return model","metadata":{"id":"cKlCn4PiMTf-"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# save trained model for 1000 images\nsave_model(model, suffix=\"Dog_Breed_1000_images_Adam\")","metadata":{"id":"KefIuccMNd-s","outputId":"93450a78-3f58-4c77-d5c0-6a77fd8a5af3"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# load model trained on 1000 images\nmodel_1000_images = load_model(\"drive/MyDrive/Colab Notebooks/Dog Breed Identification/models/20221219-20091671480546-Dog_Breed_1000_images_Adam.h5\")","metadata":{"id":"5atb-v3rNxw9","outputId":"434cc787-aae1-43aa-985b-7c6e4e098c25"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"> ***Compare the two models `unsaved one and loaded one`, using `evaluate()`***","metadata":{"id":"RkAt9B7mOqaw"}},{"cell_type":"code","source":"# evaluate the pre-saved model\nmodel.evaluate(val_data)","metadata":{"id":"xZZlraZSOSPO","outputId":"45fb7f1d-bd2f-4e1b-df10-7af841d5213b"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# evaluate the loaded model\nmodel_1000_images.evaluate(val_data)","metadata":{"id":"TPQ-DWysPBqK","outputId":"64623448-72d1-4a6a-f437-80ec53244f96"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Training a Model `on the full data`\n* Training filepaths: X\n* Training labels: y","metadata":{"id":"tS1ql8-_Dpyt"}},{"cell_type":"code","source":"# size of the full dataset\nlen(X), len(y)","metadata":{"id":"kBhPs_TBPOGl","outputId":"8cfc8993-8282-4e7a-c42d-ae984741cbd5"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Create Data Batches\n\n> ***before train a model for full data, turn them into a data batch using previously created `create_data_batches()` function.***","metadata":{"id":"oBciQAaKEFze"}},{"cell_type":"code","source":"# turn full training data in a data batch\nfull_data = create_data_batches(X, y)","metadata":{"id":"ZifjUpGdD-wn","outputId":"f60ce9fa-bdde-4655-9cc2-2f0396bce9d1"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Create Model\n\n> ***create model with batched data using previously created `create_model()` function.***","metadata":{"id":"QpPaaHMgEvwY"}},{"cell_type":"code","source":"# instantiate a new model for training on the full dataset\nfull_model = create_model()","metadata":{"id":"bBayfvXiEohw","outputId":"33a59408-8a31-4a30-e69d-3e3b6ae98580"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Use Callbacks\n\n> ***crated new model as `full_model` instance, continue with callbacks.***","metadata":{"id":"vzlGHIYlFKPa"}},{"cell_type":"code","source":"# create full model callbacks\n# tensorboard callback\nfull_model_tensorboard = create_tensorboard_callback()\n\n# early stopping callback\n# note: there is no validation set when training on all the data\nfull_model_early_stopping = tf.keras.callbacks.EarlyStopping(monitor=\"accuracy\",\n                                                             patience=3)","metadata":{"id":"c1RBw9NSFGks"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Reuse TensorBoard to Monitoring Model Performance\n\n> ***Monitoring trained model with TensorBoard.***","metadata":{"id":"Xchd2WrYGPFI"}},{"cell_type":"code","source":"%tensorboard --logdir drive/My\\ Drive/Colab\\ Notebooks/Dog\\ Breed\\ Identification/logs","metadata":{"id":"K10Ja3XnGCbL","outputId":"10f8aaaa-e577-4b6c-b212-f0c07de1f2d0"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Start to Train Model `on the full data`\n\n> ***Start training model on all of the data ~10000 images.***","metadata":{"id":"03_7_pZGG2FD"}},{"cell_type":"code","source":"# fit the full model to the full training data\nfull_model.fit(x=full_data,\n               epochs=NUM_EPOCHS,\n               callbacks=[full_model_tensorboard,\n                          full_model_early_stopping])","metadata":{"id":"sNuETlzrGV6d","outputId":"963d61ad-3bc4-41a1-d82e-14a48bfb4bf5"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Saving and reloading the full model\nUsing `save_model()`","metadata":{"id":"eKIvZqpETXff"}},{"cell_type":"code","source":"# save model\nsave_model(full_model, suffix=\"full-image-set-mobilenetv2-Adam\")","metadata":{"id":"OXXKi5d5RPzU","outputId":"6d08bd39-bf3e-4bb4-d440-3d12c86c98a5"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# load model\nloaded_full_model = load_model('drive/MyDrive/Colab Notebooks/Dog Breed Identification/models/20221219-21341671485681-full-image-set-mobilenetv2-Adam.h5')","metadata":{"id":"kySvpAIeT28v","outputId":"7851d1f2-1dd1-44c2-815c-170b93601ca6"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Making predictions on the test dataset\n\n***All need to be in the same format***\n\n> Using `create_data_batches()` function previously created.\n\n**Predictions on the test data**:\n* Get the test image filenames.\n* Convert te filenames into test data batches using `create_data_batches()` and setting the `test_data` parameter to `True`, because there is no labels for test images.\n* Make a predictions array by passing the test data batches to the `predict()` function.","metadata":{"id":"xjtTD-pwUdiw"}},{"cell_type":"code","source":"# load test image filenames\ntest_path = 'drive/MyDrive/Colab Notebooks/Dog Breed Identification/test/'\ntest_filenames = [test_path + fname for fname in os.listdir(test_path)]\n\ntest_filenames[:10]","metadata":{"id":"hg30Q9YbUSqe","outputId":"55b881ed-6f02-481a-ac6d-47d462388891"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"len(test_filenames)","metadata":{"id":"aqDSHzkyV7Yh","outputId":"d5936ac9-a565-4331-a252-ef68f17a9902"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# create data batch for test images\ntest_data = create_data_batches(test_filenames, test_data=True)","metadata":{"id":"9aOnlkDZWLVj","outputId":"01792e89-6481-436d-d960-b42f054ad254"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"***Start predict more then 10000 test images with `full_data_model`***","metadata":{"id":"3-00ntDNXKMe"}},{"cell_type":"code","source":"# predictions on test data batch\ntest_predictions = loaded_full_model.predict(test_data,\n                                             verbose=1)","metadata":{"id":"26r2OsscXAhg","outputId":"9902c1be-f99c-439d-c2aa-415ee2d3893d"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"***Predictions are done for `324` batches***","metadata":{"id":"LImGeu0CZl8T"}},{"cell_type":"code","source":"# check test predictions\ntest_predictions[0]","metadata":{"id":"0Vx84pqPXhOh","outputId":"8d95e5cf-e91f-4808-8a2c-a89b921d3f04"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Preparing test dataset predictions for `Kaggle`\n\nAs per [sample submission](https://www.kaggle.com/c/dog-breed-identification/overview/evaluation), Model output probabilities each for label along with the image ID's.\n\n**Proceed data in requested format**:\n* Create a pandas DataFrame with and ID column as well as a column for each dog breed.\n* Add data to the ID column by extracting the test image ID's from their filepaths.\n* Add data `prediction probabilities` to each of the dog breed columns using the `unique_breeds` list and the `test_predictions` list\n* Export the DataFrame as a CSV to submit it to Kaggle.","metadata":{"id":"Wdde6eNwZ6RT"}},{"cell_type":"code","source":"# Create pandas DataFrame with empty columns\npreds_df = pd.DataFrame(columns=[\"id\"] + list(unique_breeds))\npreds_df.head()","metadata":{"id":"OakmMKB8ZzDO","outputId":"13de87de-33d2-4c05-b5e7-afdf5bd1bd81"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import os\n# Append test image ID's to predictions DataFrame\ntest_path = 'drive/MyDrive/Colab Notebooks/Dog Breed Identification/test/'\npreds_df[\"id\"] = [os.path.splitext(path)[0] for path in os.listdir(test_path)]\npreds_df.head()","metadata":{"id":"TJXhhVAmbQKb","outputId":"5ec4ac21-c8b4-4465-d7b9-6c95922f5b06"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# add the prediction probabilities to each dog breed column\npreds_df[list(unique_breeds)] = test_predictions\npreds_df.head()","metadata":{"id":"HcY9KC0BkdqP","outputId":"782fdf42-43fc-4a2e-8bcb-252c49dfa58a"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Export to csv in order to submit it to Kaggle**","metadata":{"id":"Nr6lE0wiHmSl"}},{"cell_type":"code","source":"# save test predictions dataframe to csv\npreds_df.to_csv(\"drive/MyDrive/Colab Notebooks/Dog Breed Identification/submission/test_predictions_submission_mobilenetV2_1.csv\", index=False)","metadata":{"id":"Dol4MhkHG9Lh"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Making predictions on custom images\n\n***Custom images must have same format than the model was trained on.***\n\n**To Do List**:\n* Get the filepaths of own images.\n* Turn the filepaths into batches using `create_data_batches()`. There is no label in custom images, set is like `test_data`, parameter to `True`\n* Pass the custom image data batch to model's `predict()` method.\n* Convert the prediction output probabilities to prediction labels.\n* Compare the predicted labels to the custom images.","metadata":{"id":"KMM-Uk-eLinv"}},{"cell_type":"code","source":"# get custom image filepaths\ncustom_path = \"drive/MyDrive/Colab Notebooks/Dog Breed Identification/custom-images/\"\ncustom_image_paths = [custom_path + fname for fname in os.listdir(custom_path)]\ncustom_image_paths","metadata":{"id":"UO23W3g4IS4_","outputId":"df2c2520-ad93-4d3e-8616-db88e6c55af6"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# turn custom image into batch\ncustom_data = create_data_batches(custom_image_paths, test_data=True)","metadata":{"id":"gc9KaVLCPcUl","outputId":"9b0b649b-c348-4e85-90c4-9c312b0cbd9f"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# make predictions on the custom data\ncustom_preds = loaded_full_model.predict(custom_data)","metadata":{"id":"xBIHhtE0PyH1","outputId":"00ea6dd3-0679-48b3-999d-791210061bd8"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# get custom image prediction labels\ncustom_pred_labels = [get_pred_label(custom_preds[i]) for i in range(len(custom_preds))]\ncustom_pred_labels","metadata":{"id":"z9SpYLdyP5Gy","outputId":"edbd5b97-4cf2-42bb-a39a-d3f6ed47d9fd"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# get custom images\ncustom_images = []\n# loop through unbatched data\nfor image in custom_data.unbatch().as_numpy_iterator():\n  custom_images.append(image)","metadata":{"id":"PDk4cmr-T_hU"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# check custom image predictions\nplt.figure(figsize=(10, 10))\nfor i, image in enumerate(custom_images):\n  plt.subplot(2, 3, i+1)\n  plt.xticks([])\n  plt.yticks([])\n  plt.title(custom_pred_labels[i])\n  plt.imshow(image)","metadata":{"id":"7VFX5GqVYHK6","outputId":"94bd443e-e916-49e9-dad1-0b23b0a0db64"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"***It works***\n> It can be better with `Improving model` process;\n\n* Data augmentation\n* Fine-tuning\n* Using different model from TensorFlow Hub ","metadata":{"id":"9vH0SxdyaEi1"}},{"cell_type":"code","source":"","metadata":{"id":"bjvxQTQKlAKG"},"execution_count":null,"outputs":[]}]}