{"cells":[{"metadata":{},"cell_type":"markdown","source":"### Please check this video for better understanding :)"},{"metadata":{"trusted":true},"cell_type":"code","source":"from IPython.display import IFrame, YouTubeVideo\nYouTubeVideo('JnskIHjAxkc',width=600, height=400)","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"#Importing required libraries\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nimport os\nimport gc\n\nfrom sklearn.model_selection import train_test_split\n\n\nimport tensorflow as tf\nfrom tqdm.autonotebook import tqdm\n\nimport numpy as np #\nimport pandas as pd \n\nfrom keras import Sequential\nfrom keras.callbacks import EarlyStopping\n\nfrom keras.optimizers import Adam, SGD\nfrom keras.callbacks import ReduceLROnPlateau\nfrom keras.layers import Flatten,Dense,BatchNormalization,Activation,Dropout\nfrom keras.layers import Lambda, Input, GlobalAveragePooling2D,BatchNormalization\nfrom keras.utils import to_categorical\n# from keras import regularizers\nfrom tensorflow.keras.models import Model\n\n\nfrom keras.preprocessing.image import load_img\n# from keras.preprocessing.image import img_to_array\n# from keras.applications.imagenet_utils import decode_predictions\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\n# import os\n# for dirname, _, filenames in os.walk('/kaggle/input'):\n#     for filename in filenames:\n#         print(os.path.join(dirname, filename))\n\n# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## End to End Dog Breed Multi Class Classification\n### 1.Problem\nThere are 120 breeds of dog in the data. We have to identify according to their breeds.\n### 2.Data\nThere are training set and a test set of images of dogs. Each image has a filename that is its unique id. The dataset comprises 120 breeds of dogs.\n* train.zip - the training set, you are provided the breed for these dogs\n* test.zip - the test set, you must predict the probability of each breed for each image\n* sample_submission.csv - a sample submission file in the correct format\n* labels.csv - the breeds for the images in the train set\n\n### Evaluation\nCross entropy loss.\n### Features\n* we are dealing with the unstructure data set.\n* There are about 10000+ images in the training data.\n* There are about 10000+ images in the test data.\n\nNow Since we have unstructured data we have to work with the library like tensorflow. so lets import the necessary libraries and directly jump into the project"},{"metadata":{"trusted":true},"cell_type":"code","source":"\n# Check for GPU\nprint(\"GPU\", \"available (YESS!!!!)\" if tf.config.list_physical_devices(\"GPU\") else \"not available :(\")\ntf.config.list_physical_devices(\"GPU\")","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"#reading labels csv file\n\nlabels = pd.read_csv('/kaggle/input/dog-breed-identification/labels.csv')\nlabels.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#describe\nlabels.describe()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#function to show bar length\ndef barw(ax): \n    \n    for p in ax.patches:\n        val = p.get_width() #height of the bar\n        x = p.get_x()+ p.get_width() # x- position \n        y = p.get_y() + p.get_height()/2 #y-position\n        ax.annotate(round(val,2),(x,y))\n        \n#finding top dog brands\n\nplt.figure(figsize = (15,30))\nax0 =sns.countplot(y=labels['breed'],order=labels['breed'].value_counts().index)\nbarw(ax0)\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# #total unique breeds\n\n# labels['breed'].nunique()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**Observation:**\n1. We have total 120 unique breeds\n2. We have atleast 60 images per category of breed\n3. Google recommends at least 10 images per class to bet a better model\n4. More sample available ----- > More efficeint the model will be"},{"metadata":{"trusted":true},"cell_type":"code","source":"# Lets check one image\nfrom IPython.display import display, Image\nImage(\"../input/dog-breed-identification/train/43572ba7edf772a95f539e57afd9eb43.jpg\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import os\nif len(os.listdir('/kaggle/input/dog-breed-identification/train/')) == len(labels['id']):\n    print('Number of file matches number of actual images!')\nelse:\n    print('Number of file doesnot matches number of actual images!!')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"\n#Create list of alphabetically sorted labels.\nclasses = sorted(list(set(labels['breed'])))\nn_classes = len(classes)\nprint('Total unique breed {}'.format(n_classes))\n\n\n\n#Map each label string to an integer label.\nclass_to_num = dict(zip(classes, range(n_classes)))\nclass_to_num\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**One-hot Encoding:**\nSince the output of our predictor for each input is a vector of probabilities for each class we must convert out label dataset to be the same format. That is for each input a row vector of length num_classes with a 1 at the index of the label and 0's everywhere else."},{"metadata":{"trusted":true},"cell_type":"code","source":"\ninput_shape = (331,331,3)\n\n\ndef images_to_array(directory, label_dataframe, target_size = input_shape):\n    \n    image_labels = label_dataframe['breed']\n    images = np.zeros([len(label_dataframe), target_size[0], target_size[1], target_size[2]],dtype=np.uint8) #as we have huge data and limited ram memory. uint8 takes less memory\n    y = np.zeros([len(label_dataframe),1],dtype = np.uint8)\n    \n    for ix, image_name in enumerate(tqdm(label_dataframe['id'].values)):\n        img_dir = os.path.join(directory, image_name + '.jpg')\n        img = load_img(img_dir, target_size = target_size)\n#         img = np.expand_dims(img, axis=0)\n#         img = processed_image_resnet(img)\n#         img = img/255\n        images[ix]=img\n#         images[ix] = img_to_array(img)\n        del img\n        \n        dog_breed = image_labels[ix]\n        y[ix] = class_to_num[dog_breed]\n    \n    y = to_categorical(y)\n    \n    return images,y","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import time \nt = time.time()\n\nX,y = images_to_array('/kaggle/input/dog-breed-identification/train', labels[:])\n\nprint('runtime in seconds: {}'.format(time.time() - t))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# y[0]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# X[0]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"## Another way to create one hot encoded vectors\n# dummy = pd.get_dummies(df_50['breed'])\n\n# classes = dummy.columns \n# print('we have total {} number of unique dog breeds'.format(len(classes)))\n\n#convert this into np.array\n\n# y = np.array(dummy)\n# # we can delete the dummy because we dont need it anymore ----- > We are saving RAM\n\n# del dummy\n\n# y[0:2]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# np.where(y[5]==1)[0][0]\n\n# lets check some dogs and their breeds\nn=25\n\n# setup the figure \nplt.figure(figsize=(20,20))\n\nfor i in range(n):\n#     print(i)\n    ax = plt.subplot(5, 5, i+1)\n    plt.title(classes[np.where(y[i] ==1)[0][0]])\n    plt.imshow(X[i].astype('int32')) # .astype('int32') ---> as imshow() needs integer data to read the image\n    ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### Creating callbacks:\n\n(things to help our model)\n\nCallbacks 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\nReduceLROnPlateau: Reduce learning rate when a metric has stopped improving."},{"metadata":{"trusted":true},"cell_type":"code","source":"#Learning Rate Annealer\nlrr= ReduceLROnPlateau(monitor='val_acc', factor=.01, patience=3, min_lr=1e-5,verbose = 1)\n\n#Prepare call backs\nEarlyStop = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Hyperparameters\nbatch_size= 128\nepochs=50\nlearn_rate=.001\nsgd=SGD(lr=learn_rate,momentum=.9,nesterov=False)\nadam=Adam(lr=learn_rate, beta_1=0.9, beta_2=0.999, epsilon=None,  amsgrad=False)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Model Building:\n\n* The basic idea here is to extract features from the data set from pretrained models and create a simple deep net by using all those features combined.\n* So we will GlobalAveragePooling2D to extract a pooled output from our selected models\n* img_size = You can change this shape according to the documentation of the pretrained model\n* Keras documentation for all models: https://keras.io/api/applications/\n\n"},{"metadata":{},"cell_type":"markdown","source":"![dog%20breed.png](attachment:dog%20breed.png)","attachments":{"dog%20breed.png":{"image/png":"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"}}},{"metadata":{"trusted":true},"cell_type":"code","source":"#function to extract features from the dataset by a given pretrained model\nimg_size = (331,331,3)\n\ndef get_features(model_name, model_preprocessor, input_size, data):\n\n    input_layer = Input(input_size)\n    preprocessor = Lambda(model_preprocessor)(input_layer)\n    base_model = model_name(weights='imagenet', include_top=False,\n                            input_shape=input_size)(preprocessor)\n    avg = GlobalAveragePooling2D()(base_model)\n    feature_extractor = Model(inputs = input_layer, outputs = avg)\n    \n    #Extract feature.\n    feature_maps = feature_extractor.predict(data, verbose=1)\n    print('Feature maps shape: ', feature_maps.shape)\n    return feature_maps","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Extract features using InceptionV3 \nfrom keras.applications.inception_v3 import InceptionV3, preprocess_input\ninception_preprocessor = preprocess_input\ninception_features = get_features(InceptionV3,\n                                  inception_preprocessor,\n                                  img_size, X)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Extract features using Xception \nfrom keras.applications.xception import Xception, preprocess_input\nxception_preprocessor = preprocess_input\nxception_features = get_features(Xception,\n                                 xception_preprocessor,\n                                 img_size, X)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Extract features using InceptionResNetV2 \nfrom keras.applications.inception_resnet_v2 import InceptionResNetV2, preprocess_input\ninc_resnet_preprocessor = preprocess_input\ninc_resnet_features = get_features(InceptionResNetV2,\n                                   inc_resnet_preprocessor,\n                                   img_size, X)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Extract features using NASNetLarge \nfrom keras.applications.nasnet import NASNetLarge, preprocess_input\nnasnet_preprocessor = preprocess_input\nnasnet_features = get_features(NASNetLarge,\n                               nasnet_preprocessor,\n                               img_size, X)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"del X #to free up some ram memory\ngc.collect()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Creating final featuremap by combining all extracted features\n\nfinal_features = np.concatenate([inception_features,\n                                 xception_features,\n                                 nasnet_features,\n                                 inc_resnet_features,], axis=-1) #axis=-1 to concatinate horizontally\n\nprint('Final feature maps shape', final_features.shape)\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Prepare Deep net\n\nmodel = Sequential()\n# model.add(Dense(1028,input_shape=(final_features.shape[1],)))\nmodel.add(Dropout(0.7,input_shape=(final_features.shape[1],)))\nmodel.add(Dense(n_classes,activation= 'softmax'))\n\nmodel.compile(optimizer=adam,\n              loss='categorical_crossentropy',\n              metrics=['accuracy'])\n\n#Training the model. \nhistory = model.fit(final_features, y,\n            batch_size=batch_size,\n            epochs=epochs,\n            validation_split=0.2,\n            callbacks=[lrr,EarlyStop])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#deleting to free up ram memory\n\ndel inception_features\ndel xception_features\ndel nasnet_features\ndel inc_resnet_features\ndel final_features\ngc.collect()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# sample_df = pd.read_csv('/kaggle/input/dog-breed-identification/sample_submission.csv')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# sample_df.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Function to read images from test directory\n\ndef images_to_array_test(test_path, img_size = (331,331,3)):\n    test_filenames = [test_path + fname for fname in os.listdir(test_path)]\n\n    data_size = len(test_filenames)\n    images = np.zeros([data_size, img_size[0], img_size[1], 3], dtype=np.uint8)\n    \n    \n    for ix,img_dir in enumerate(tqdm(test_filenames)):\n#         img_dir = os.path.join(directory, image_name + '.jpg')\n        img = load_img(img_dir, target_size = img_size)\n#         img = np.expand_dims(img, axis=0)\n#         img = processed_image_resnet(img)\n#         img = img/255\n        images[ix]=img\n#         images[ix] = img_to_array(img)\n        del img\n    print('Ouptut Data Size: ', images.shape)\n    return images\n\ntest_data = images_to_array_test('/kaggle/input/dog-breed-identification/test/', img_size)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Extract test data features.\ndef extact_features(data):\n    inception_features = get_features(InceptionV3, inception_preprocessor, img_size, data)\n    xception_features = get_features(Xception, xception_preprocessor, img_size, data)\n    nasnet_features = get_features(NASNetLarge, nasnet_preprocessor, img_size, data)\n    inc_resnet_features = get_features(InceptionResNetV2, inc_resnet_preprocessor, img_size, data)\n\n    final_features = np.concatenate([inception_features,\n                                     xception_features,\n                                     nasnet_features,\n                                     inc_resnet_features],axis=-1)\n    \n    print('Final feature maps shape', final_features.shape)\n    \n    #deleting to free up ram memory\n    del inception_features\n    del xception_features\n    del nasnet_features\n    del inc_resnet_features\n    gc.collect()\n    \n    \n    return final_features\n\ntest_features = extact_features(test_data)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Free up some space.\ndel test_data\ngc.collect()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Predict test labels given test data features.\n\npred = model.predict(test_features)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# First prediction\nprint(pred[0])\nprint(f\"Max value (probability of prediction): {np.max(pred[0])}\") # the max probability value predicted by the model\nprint(f\"Sum: {np.sum(pred[0])}\") # because we used softmax activation in our model, this will be close to 1\nprint(f\"Max index: {np.argmax(pred[0])}\") # the index of where the max value in predictions[0] occurs\nprint(f\"Predicted label: {classes[np.argmax(pred[0])]}\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Create pandas DataFrame with empty columns\npreds_df = pd.DataFrame(columns=[\"id\"] + list(classes))\npreds_df.head()\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Append test image ID's to predictions DataFrame\ntest_path = \"/kaggle/input/dog-breed-identification/test/\"\npreds_df[\"id\"] = [os.path.splitext(path)[0] for path in os.listdir(test_path)]\npreds_df.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"preds_df.loc[:,list(classes)]= pred\n\npreds_df.to_csv('submission.csv',index=None)\npreds_df.head()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Testing with custom input:"},{"metadata":{"trusted":true},"cell_type":"code","source":"#Custom input\n\nImage('../input/goldend/DBS_GoldRetriever_1280.jpg')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#reading the image and converting it into an np array\n\nimg_g = load_img('../input/goldend/DBS_GoldRetriever_1280.jpg',target_size = img_size)\nimg_g = np.expand_dims(img_g, axis=0) # as we trained our model in (row, img_height, img_width, img_rgb) format, np.expand_dims convert the image into this format\n# img_g","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"img_g.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# #Predict test labels given test data features.\ntest_features = extact_features(img_g)\npredg = model.predict(test_features)\nprint(f\"Predicted label: {classes[np.argmax(predg[0])]}\")\nprint(f\"Probability of prediction): {round(np.max(predg[0])) * 100} %\")","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Please upvote this notebook :)\n\n#### Special Thanks to :\n1. https://www.kaggle.com/phylake1337/0-18-loss-simple-feature-extractors"},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"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":4,"nbformat_minor":4}