{"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":"<div style=\"background:#4d79ff   ;border-radius:5px; font-family:'Times';font-size:35px;color:  #f2f2f2\" ><center>&ensp; \nDog Breed Classification 🐶🐕‍🐩</center></div>\n\n\n![image.png](attachment:b25c444f-487f-49a5-baa6-c28d3054c482.png)\n\nWelcome to Dog breed classification Problem.\n\nIn this we have given various dog breed images we need to clasify the breed of the dog based on the provided images. So its a multiclass classification problem\n\n\n* In this notebook I'm implementing a convolutional neural network which is capable of classifing pictures of dogs by their breeds.\n\n### Let's get started with a detailed EDA:\n","metadata":{},"attachments":{"b25c444f-487f-49a5-baa6-c28d3054c482.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"<div style=\";font-family:'Times';font-size:30px;color:  #4d79ff\" >📌 <b>Importing Libraries</b></div>","metadata":{}},{"cell_type":"code","source":"import glob\nimport random\nimport base64\nimport seaborn as sn\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nfrom PIL import Image\nfrom io import BytesIO\nfrom IPython.display import HTML\n\n\nfrom sklearn import preprocessing\nfrom sklearn.model_selection import train_test_split\n\n\nimport numpy as np\nimport os,cv2,random,time,shutil,csv\nimport tensorflow as tf\nfrom matplotlib import pyplot as plt\nfrom matplotlib import image as mpimg\nfrom tqdm import tqdm\nimport json,os,cv2,keras\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.models import Model\nfrom keras.layers import BatchNormalization,Dense,GlobalAveragePooling2D,Lambda,Dropout,InputLayer,Input\nfrom tensorflow.keras.utils import to_categorical,plot_model\nfrom tensorflow.keras.preprocessing.image import load_img\n\n\nfrom tensorflow.keras.utils import to_categorical\nfrom tensorflow.keras.models import Sequential, Model\nfrom tensorflow.keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D\nfrom tensorflow.keras.optimizers import RMSprop,Adam\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\nfrom tensorflow.keras.applications import VGG16","metadata":{"execution":{"iopub.status.busy":"2022-08-05T18:32:20.158689Z","iopub.execute_input":"2022-08-05T18:32:20.159029Z","iopub.status.idle":"2022-08-05T18:32:20.168416Z","shell.execute_reply.started":"2022-08-05T18:32:20.158970Z","shell.execute_reply":"2022-08-05T18:32:20.167618Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Constants\n\nThe constants mostly store paths such as path to the working folder (path), path to the csv storing the labels (labels_csv_path) and the path to the train folder (train_path) & test folder (test_path).\n\nThe number of epochs is also set here (number_of_epochs).","metadata":{}},{"cell_type":"code","source":"# paths\npath = r'../input/'\nlabels_csv_path = path + 'labels.csv'\nsample_submission_csv_path = path + 'sample_submission.csv'\nsubmission_csv_path =  '../input/sample_submission.csv'\ntrain_path = path + 'train'\ntest_path = path + 'test'\n\nnumber_of_epochs = 10\n\nprint(f'Constants are set. Fine tuning takes {number_of_epochs} epochs.')","metadata":{"execution":{"iopub.status.busy":"2022-08-05T18:32:20.319216Z","iopub.execute_input":"2022-08-05T18:32:20.319766Z","iopub.status.idle":"2022-08-05T18:32:20.325813Z","shell.execute_reply.started":"2022-08-05T18:32:20.319703Z","shell.execute_reply":"2022-08-05T18:32:20.324946Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n<div style=\"background:#4d79ff   ;border-radius:5px; font-family:'Times';font-size:35px;color:  #f2f2f2\" ><center>&ensp; \nExploratory Data Analysis 📉📊📋</center></div>\nThe aim of any exploratory data analysis is to have a better understanding of the data to which the model needs to be fit, e.g. understand the shape/size/amount/distribution etc. of the input. Obviously, the analysis depends on the task at hand (what we want to get out of the model i.e.: image classification in this case) and on the model which we plan to deploy.","metadata":{}},{"cell_type":"code","source":"labels_df = pd.read_csv(labels_csv_path)\nsamp_subm = pd.read_csv(submission_csv_path)\nprint(f'The shape of the labels: {labels_df.shape}')","metadata":{"execution":{"iopub.status.busy":"2022-08-05T18:32:20.474689Z","iopub.execute_input":"2022-08-05T18:32:20.475048Z","iopub.status.idle":"2022-08-05T18:32:20.869183Z","shell.execute_reply.started":"2022-08-05T18:32:20.474987Z","shell.execute_reply":"2022-08-05T18:32:20.868294Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Pandas' read_csv reads everything from the given file (labels.csv in this case) and stores the content of the csv file in a so called dataframe. Then, we check the shape (number of rows & number of columns) of the dataframe.","metadata":{}},{"cell_type":"code","source":"labels_df.head()","metadata":{"execution":{"iopub.status.busy":"2022-08-05T18:32:20.872070Z","iopub.execute_input":"2022-08-05T18:32:20.872550Z","iopub.status.idle":"2022-08-05T18:32:20.885712Z","shell.execute_reply.started":"2022-08-05T18:32:20.872494Z","shell.execute_reply":"2022-08-05T18:32:20.884850Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"The head prints the first & last five entries of the dataframe. The first column contains the unique ID, the other column stores the breed of the dog on the given picture. Next, I'm checking the number of pictures in the train folder...","metadata":{}},{"cell_type":"code","source":"print(len(os.listdir(train_path)))","metadata":{"execution":{"iopub.status.busy":"2022-08-05T18:32:20.887814Z","iopub.execute_input":"2022-08-05T18:32:20.888215Z","iopub.status.idle":"2022-08-05T18:32:20.905729Z","shell.execute_reply.started":"2022-08-05T18:32:20.888142Z","shell.execute_reply":"2022-08-05T18:32:20.904593Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"...which is (unsurprisingly) 10222 just like in the label.csv file.","metadata":{}},{"cell_type":"markdown","source":"The number of images in the test folder is...","metadata":{}},{"cell_type":"code","source":"print(len(os.listdir(test_path)))","metadata":{"execution":{"iopub.status.busy":"2022-08-05T18:32:20.941984Z","iopub.execute_input":"2022-08-05T18:32:20.942282Z","iopub.status.idle":"2022-08-05T18:32:20.952416Z","shell.execute_reply.started":"2022-08-05T18:32:20.942230Z","shell.execute_reply":"2022-08-05T18:32:20.951325Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"That's how many items needs to be predicted.","metadata":{}},{"cell_type":"markdown","source":"Next, I'm checking the number of breeds (the unique occurrences in the breed column):","metadata":{}},{"cell_type":"code","source":"unique_breeds = pd.unique(labels_df['breed'])\nprint(len(unique_breeds))","metadata":{"execution":{"iopub.status.busy":"2022-08-05T18:32:21.045958Z","iopub.execute_input":"2022-08-05T18:32:21.046275Z","iopub.status.idle":"2022-08-05T18:32:21.052681Z","shell.execute_reply.started":"2022-08-05T18:32:21.046222Z","shell.execute_reply":"2022-08-05T18:32:21.051692Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Count of images per each breed","metadata":{}},{"cell_type":"code","source":"each_label = labels_df.groupby(\"breed\").count()\neach_label = each_label.rename(columns = {\"id\" : \"count\"})\neach_label = each_label.sort_values(\"count\", ascending=False)\neach_label","metadata":{"execution":{"iopub.status.busy":"2022-08-05T18:32:21.147350Z","iopub.execute_input":"2022-08-05T18:32:21.147648Z","iopub.status.idle":"2022-08-05T18:32:21.172466Z","shell.execute_reply.started":"2022-08-05T18:32:21.147594Z","shell.execute_reply":"2022-08-05T18:32:21.171777Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Ploting the breed image count distribution","metadata":{}},{"cell_type":"code","source":"ax=pd.value_counts(labels_df['breed'],ascending=True).plot(kind='barh',\n                                                       fontsize=\"40\",\n                                                       title=\"Class Distribution\",\n                                                       figsize=(50,100))\nax.set(xlabel=\"Images per class\", ylabel=\"Classes\")\nax.xaxis.label.set_size(20)\nax.yaxis.label.set_size(20)\nax.title.set_size(30)","metadata":{"execution":{"iopub.status.busy":"2022-08-05T18:32:21.271140Z","iopub.execute_input":"2022-08-05T18:32:21.271502Z","iopub.status.idle":"2022-08-05T18:32:29.683336Z","shell.execute_reply.started":"2022-08-05T18:32:21.271438Z","shell.execute_reply":"2022-08-05T18:32:29.682470Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### From above distribution graph, we can observe that the reduction in the quantity of samples per class is continous.\n","metadata":{}},{"cell_type":"code","source":"pd.set_option('display.max_colwidth', -1)\n\ndef get_thumbnail(path):\n    i = Image.open(path)\n    i.thumbnail((150, 150), Image.LANCZOS)\n    return i\n\ndef image_base64(im):\n    if isinstance(im, str):\n        im = get_thumbnail(im)\n    with BytesIO() as buffer:\n        im.save(buffer, 'jpeg')\n        return base64.b64encode(buffer.getvalue()).decode()\n\ndef image_formatter(im):\n    return f'<img src=\"data:image/jpeg;base64,{image_base64(im)}\">'","metadata":{"execution":{"iopub.status.busy":"2022-08-05T18:32:29.684902Z","iopub.execute_input":"2022-08-05T18:32:29.685337Z","iopub.status.idle":"2022-08-05T18:32:29.694804Z","shell.execute_reply.started":"2022-08-05T18:32:29.685291Z","shell.execute_reply":"2022-08-05T18:32:29.693490Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dogs = pd.read_csv('../input/labels.csv')\ndogs = dogs.sample(20)\ndogs['file'] = dogs.id.map(lambda id: f'../input/train/{id}.jpg')\ndogs['image'] = dogs.file.map(lambda f: get_thumbnail(f))\ndogs.head()","metadata":{"execution":{"iopub.status.busy":"2022-08-05T18:32:29.697739Z","iopub.execute_input":"2022-08-05T18:32:29.698493Z","iopub.status.idle":"2022-08-05T18:32:29.805332Z","shell.execute_reply.started":"2022-08-05T18:32:29.698074Z","shell.execute_reply":"2022-08-05T18:32:29.804662Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# displaying PIL.Image objects embedded in dataframe\nHTML(dogs[['breed', 'image']].to_html(formatters={'image': image_formatter}, escape=False))","metadata":{"execution":{"iopub.status.busy":"2022-08-05T18:32:29.807750Z","iopub.execute_input":"2022-08-05T18:32:29.808066Z","iopub.status.idle":"2022-08-05T18:32:29.839663Z","shell.execute_reply.started":"2022-08-05T18:32:29.808019Z","shell.execute_reply":"2022-08-05T18:32:29.839011Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Load Single Image\n\nWe plot the first image of of the train data.\n","metadata":{}},{"cell_type":"code","source":"id_ = labels_df.loc[0, 'id']\nbreed = labels_df.loc[0, 'breed']\nfile = id_+'.jpg'\nimg = cv2.imread(path+'train/'+file)\nprint('Shape:', img.shape)\nfig, ax = plt.subplots(1, 1, figsize=(7, 7))\nax.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))\nax.set_xticklabels([])\nax.set_yticklabels([])\nax.set_title(breed)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-08-05T18:32:29.840526Z","iopub.execute_input":"2022-08-05T18:32:29.840833Z","iopub.status.idle":"2022-08-05T18:32:30.209927Z","shell.execute_reply.started":"2022-08-05T18:32:29.840772Z","shell.execute_reply":"2022-08-05T18:32:30.209152Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n### Plot Examples\n\nWe plot example images of the breed top 10.\n","metadata":{}},{"cell_type":"code","source":"def plot_examples(category = 'scottish_deerhound'):\n    \"\"\" Plot 5 images of a given category \"\"\"\n    \n    fig, axs = plt.subplots(1, 5, figsize=(25, 20))\n    fig.subplots_adjust(hspace = .1, wspace=.1)\n    axs = axs.ravel()\n    temp = labels_df[labels_df['breed']==category].copy()\n    temp.index = range(len(temp.index))\n    for i in range(5):\n        id_ = temp.loc[i, 'id']\n        breed = temp.loc[i, 'breed']\n        file = id_+'.jpg'\n        img = cv2.imread(path+'train/'+file)\n        axs[i].imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))\n        axs[i].set_title(breed)\n        axs[i].set_xticklabels([])\n        axs[i].set_yticklabels([])\n    plt.show()","metadata":{"execution":{"iopub.status.busy":"2022-08-05T18:32:30.211029Z","iopub.execute_input":"2022-08-05T18:32:30.211424Z","iopub.status.idle":"2022-08-05T18:32:30.221942Z","shell.execute_reply.started":"2022-08-05T18:32:30.211380Z","shell.execute_reply":"2022-08-05T18:32:30.220948Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plot_examples()","metadata":{"execution":{"iopub.status.busy":"2022-08-05T18:32:30.223248Z","iopub.execute_input":"2022-08-05T18:32:30.223767Z","iopub.status.idle":"2022-08-05T18:32:31.570645Z","shell.execute_reply.started":"2022-08-05T18:32:30.223690Z","shell.execute_reply":"2022-08-05T18:32:31.569953Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plot_examples('maltese_dog')","metadata":{"execution":{"iopub.status.busy":"2022-08-05T18:32:31.571703Z","iopub.execute_input":"2022-08-05T18:32:31.572114Z","iopub.status.idle":"2022-08-05T18:32:32.841341Z","shell.execute_reply.started":"2022-08-05T18:32:31.572059Z","shell.execute_reply":"2022-08-05T18:32:32.839979Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plot_examples('afghan_hound')","metadata":{"execution":{"iopub.status.busy":"2022-08-05T18:32:32.842521Z","iopub.execute_input":"2022-08-05T18:32:32.843047Z","iopub.status.idle":"2022-08-05T18:32:34.156871Z","shell.execute_reply.started":"2022-08-05T18:32:32.842994Z","shell.execute_reply":"2022-08-05T18:32:34.156192Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Label Encoding \nConverting string label to unique int values","metadata":{}},{"cell_type":"code","source":"label_encoder = preprocessing.LabelEncoder()\n  \n# Encode labels in column 'species'.\nlabels_df['breed_label']= label_encoder.fit_transform(labels_df['breed'])\n  \nlabels_df['breed_label'].unique()","metadata":{"execution":{"iopub.status.busy":"2022-08-05T18:32:34.157854Z","iopub.execute_input":"2022-08-05T18:32:34.158239Z","iopub.status.idle":"2022-08-05T18:32:34.178736Z","shell.execute_reply.started":"2022-08-05T18:32:34.158195Z","shell.execute_reply":"2022-08-05T18:32:34.177635Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"labels_df[labels_df['breed']=='dingo'].head()","metadata":{"execution":{"iopub.status.busy":"2022-08-05T18:32:34.183387Z","iopub.execute_input":"2022-08-05T18:32:34.183922Z","iopub.status.idle":"2022-08-05T18:32:34.205795Z","shell.execute_reply.started":"2022-08-05T18:32:34.183604Z","shell.execute_reply":"2022-08-05T18:32:34.205011Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n<div style=\"background:#4d79ff   ;border-radius:5px; font-family:'Times';font-size:35px;color:  #f2f2f2\" ><center>&ensp; \nImage Preprocessing </center></div>\n\nAs we can see the images have different format: landscape or portrait. For the neural network we need a standard size. So we have to prepare the data.\n","metadata":{}},{"cell_type":"code","source":"def image_preprocessing(image, image_size):\n    \"\"\" Image Preprocessing \"\"\"\n\n    # Load Image\n    readFlag=cv2.COLOR_BGR2GRAY\n    #image = np.asarray(bytearray(resp.read()), dtype=\"uint8\")\n    #image = cv2.imdecode(image, readFlag)\n    image_gray = cv2.cvtColor(image, readFlag)\n    \n    # Crop Image\n    mid_row = int(image_gray.shape[0]/2)\n    mid_col = int(image_gray.shape[1]/2)\n    if image_gray.shape[0]>image_gray.shape[1]:\n        image_cropped = image_gray[mid_row-mid_col:mid_row+mid_col,\n                                   0:image_gray.shape[1]]\n    else:\n        image_cropped = image_gray[0:image_gray.shape[0],\n                                   mid_col-mid_row:mid_col+mid_row]\n    \n    # Rescale Image\n    image_rescale = cv2.resize(image_cropped,\n                               dsize=(image_size, image_size),\n                               interpolation=cv2.INTER_AREA)\n    return image_rescale\n\ndef plot_befor_after(image):\n    \"\"\" Compare original and prepared image \"\"\"\n    \n    fig, axs = plt.subplots(1, 2, figsize=(15, 10))\n    fig.subplots_adjust(hspace = .1, wspace=.1)\n    axs = axs.ravel()\n    # Plot Original Image\n    axs[0].imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))\n    axs[0].set_title('original shape: '+str(image.shape))\n    # Image Preprocessing\n    image_rescale = image_preprocessing(image, image_size)\n    # Plot Prepared Image\n    axs[1].imshow(image_rescale, cmap='gray')\n    axs[1].set_title('rescaled shape: '+str(image_rescale.shape))\n    for i in range(2):\n        axs[i].set_xticklabels([])\n        axs[i].set_yticklabels([])\n    plt.show()","metadata":{"execution":{"iopub.status.busy":"2022-08-05T18:32:34.208527Z","iopub.execute_input":"2022-08-05T18:32:34.209012Z","iopub.status.idle":"2022-08-05T18:32:34.221736Z","shell.execute_reply.started":"2022-08-05T18:32:34.208962Z","shell.execute_reply":"2022-08-05T18:32:34.220870Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### We set the image size:\n","metadata":{}},{"cell_type":"code","source":"image_size = 128","metadata":{"execution":{"iopub.status.busy":"2022-08-05T18:32:34.222649Z","iopub.execute_input":"2022-08-05T18:32:34.222962Z","iopub.status.idle":"2022-08-05T18:32:34.234791Z","shell.execute_reply.started":"2022-08-05T18:32:34.222856Z","shell.execute_reply":"2022-08-05T18:32:34.233784Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Example Landscape:\n","metadata":{}},{"cell_type":"code","source":"row = 0\nid_ = labels_df.loc[row, 'id']\nbreed = labels_df.loc[row, 'breed']\nfile = id_+'.jpg'\nimage = cv2.imread(path+'train/'+file)\nprint('Shape:', image.shape)\nplot_befor_after(image)","metadata":{"execution":{"iopub.status.busy":"2022-08-05T18:32:34.235721Z","iopub.execute_input":"2022-08-05T18:32:34.236009Z","iopub.status.idle":"2022-08-05T18:32:34.828077Z","shell.execute_reply.started":"2022-08-05T18:32:34.235901Z","shell.execute_reply":"2022-08-05T18:32:34.827376Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Example Portrait:\n","metadata":{}},{"cell_type":"code","source":"row = 10\nid_ = labels_df.loc[row, 'id']\nbreed = labels_df.loc[row, 'breed']\nfile = id_+'.jpg'\nimage = cv2.imread(path+'train/'+file)\nprint('Shape:', image.shape)\nplot_befor_after(image)","metadata":{"execution":{"iopub.status.busy":"2022-08-05T18:32:34.829191Z","iopub.execute_input":"2022-08-05T18:32:34.829672Z","iopub.status.idle":"2022-08-05T18:32:35.511602Z","shell.execute_reply.started":"2022-08-05T18:32:34.829624Z","shell.execute_reply":"2022-08-05T18:32:35.510863Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n<div style=\"background:#4d79ff   ;border-radius:5px; font-family:'Times';font-size:35px;color:  #f2f2f2\" ><center>&ensp; Read All Images </center></div>\n","metadata":{}},{"cell_type":"code","source":"def prepare_data(path, data, image_size):\n    \"\"\" Read all images into a numpy array \"\"\"\n    \n    X = np.empty((len(data), image_size, image_size), dtype=np.uint8)\n    for row in data.index:\n        id_ = data.loc[row, 'id']\n        file = id_ + '.jpg'\n        image = cv2.imread(path+file)\n        image_rescaled = image_preprocessing(image, image_size)\n        X[row, :, :] = image_rescaled\n    X = X.astype('float32')/255\n    return X","metadata":{"execution":{"iopub.status.busy":"2022-08-05T18:32:35.512958Z","iopub.execute_input":"2022-08-05T18:32:35.513510Z","iopub.status.idle":"2022-08-05T18:32:35.521152Z","shell.execute_reply.started":"2022-08-05T18:32:35.513459Z","shell.execute_reply":"2022-08-05T18:32:35.520166Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_train = prepare_data(path+'train/', labels_df, image_size)\nX_test = prepare_data(path+'test/', samp_subm, image_size)","metadata":{"execution":{"iopub.status.busy":"2022-08-05T18:32:35.522603Z","iopub.execute_input":"2022-08-05T18:32:35.523177Z","iopub.status.idle":"2022-08-05T18:34:21.139336Z","shell.execute_reply.started":"2022-08-05T18:32:35.523115Z","shell.execute_reply":"2022-08-05T18:34:21.138431Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Encode the labels","metadata":{}},{"cell_type":"code","source":"y_train = labels_df['breed']\ny_train = pd.get_dummies(y_train)","metadata":{"execution":{"iopub.status.busy":"2022-08-05T18:34:54.852302Z","iopub.execute_input":"2022-08-05T18:34:54.852635Z","iopub.status.idle":"2022-08-05T18:34:54.858874Z","shell.execute_reply.started":"2022-08-05T18:34:54.852579Z","shell.execute_reply":"2022-08-05T18:34:54.858067Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Split Train Data\n","metadata":{}},{"cell_type":"code","source":"X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size = 0.2, random_state=2021)","metadata":{"execution":{"iopub.status.busy":"2022-08-05T18:34:57.650899Z","iopub.execute_input":"2022-08-05T18:34:57.651246Z","iopub.status.idle":"2022-08-05T18:34:58.168995Z","shell.execute_reply.started":"2022-08-05T18:34:57.651190Z","shell.execute_reply":"2022-08-05T18:34:58.168230Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print('Shape train data:', X_train.shape)\nprint('Shape val data:', X_val.shape)","metadata":{"execution":{"iopub.status.busy":"2022-08-05T18:34:58.170741Z","iopub.execute_input":"2022-08-05T18:34:58.171047Z","iopub.status.idle":"2022-08-05T18:34:58.177345Z","shell.execute_reply.started":"2022-08-05T18:34:58.170997Z","shell.execute_reply":"2022-08-05T18:34:58.175809Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Reshape Data","metadata":{}},{"cell_type":"code","source":"X_train = X_train.reshape(-1,image_size,image_size,1)\nX_val = X_val.reshape(-1,image_size,image_size,1)\nX_test = X_test.reshape(-1,image_size,image_size,1)","metadata":{"execution":{"iopub.status.busy":"2022-08-05T18:34:58.190673Z","iopub.execute_input":"2022-08-05T18:34:58.190981Z","iopub.status.idle":"2022-08-05T18:34:58.196026Z","shell.execute_reply.started":"2022-08-05T18:34:58.190905Z","shell.execute_reply":"2022-08-05T18:34:58.195045Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print('Shape train data:', X_train.shape)\nprint('Shape val data:', X_val.shape)","metadata":{"execution":{"iopub.status.busy":"2022-08-05T18:34:58.535288Z","iopub.execute_input":"2022-08-05T18:34:58.535591Z","iopub.status.idle":"2022-08-05T18:34:58.542258Z","shell.execute_reply.started":"2022-08-05T18:34:58.535537Z","shell.execute_reply":"2022-08-05T18:34:58.541178Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Building Model","metadata":{}},{"cell_type":"code","source":"CNN_model = Sequential()\nCNN_model.add(Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same', \n                 activation ='relu', input_shape = (image_size, image_size, 1)))\nCNN_model.add(Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same', \n                 activation ='relu'))\nCNN_model.add(MaxPool2D(pool_size=(2,2)))\nCNN_model.add(Dropout(0.15))\n\n\nCNN_model.add(Conv2D(filters = 64, kernel_size = (3,3),padding = 'Same', \n                 activation ='relu'))\nCNN_model.add(Conv2D(filters = 64, kernel_size = (3,3),padding = 'Same', \n                 activation ='relu'))\nCNN_model.add(MaxPool2D(pool_size=(2,2), strides=(2,2)))\nCNN_model.add(Dropout(0.15))\n\n\nCNN_model.add(Flatten())\n#model.add(Dense(256, activation = \"relu\"))\n#model.add(Dropout(0.3))\nCNN_model.add(Dense(120, activation = \"softmax\"))","metadata":{"execution":{"iopub.status.busy":"2022-08-05T18:34:58.799358Z","iopub.execute_input":"2022-08-05T18:34:58.799698Z","iopub.status.idle":"2022-08-05T18:34:59.002781Z","shell.execute_reply.started":"2022-08-05T18:34:58.799651Z","shell.execute_reply":"2022-08-05T18:34:59.001884Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Compile the model","metadata":{}},{"cell_type":"code","source":"CNN_model.compile(optimizer=Adam(lr=1e-3), loss='categorical_crossentropy', metrics=['accuracy'])","metadata":{"execution":{"iopub.status.busy":"2022-08-05T18:34:59.065032Z","iopub.execute_input":"2022-08-05T18:34:59.067290Z","iopub.status.idle":"2022-08-05T18:34:59.344155Z","shell.execute_reply.started":"2022-08-05T18:34:59.067226Z","shell.execute_reply":"2022-08-05T18:34:59.343299Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"CNN_model.summary()","metadata":{"execution":{"iopub.status.busy":"2022-08-05T18:34:59.462256Z","iopub.execute_input":"2022-08-05T18:34:59.464469Z","iopub.status.idle":"2022-08-05T18:34:59.478843Z","shell.execute_reply.started":"2022-08-05T18:34:59.464410Z","shell.execute_reply":"2022-08-05T18:34:59.478151Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Fit the model","metadata":{}},{"cell_type":"code","source":"epochs = 50\nbatch_size = 128\n\n\nCNN_history = CNN_model.fit(X_train, y_train,\n                    epochs=epochs,\n                    batch_size=batch_size,\n                    validation_data=(X_val, y_val))","metadata":{"execution":{"iopub.status.busy":"2022-08-05T18:40:54.667227Z","iopub.execute_input":"2022-08-05T18:40:54.667538Z","iopub.status.idle":"2022-08-05T18:46:38.654208Z","shell.execute_reply.started":"2022-08-05T18:40:54.667477Z","shell.execute_reply":"2022-08-05T18:46:38.653496Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<div style=\"background:#4d79ff   ;border-radius:5px; font-family:'Times';font-size:35px;color:  #f2f2f2\" ><center>&ensp; Analyse Training </center></div>\n","metadata":{}},{"cell_type":"code","source":"loss = CNN_history.history['loss']\nloss_val = CNN_history.history['val_loss']\nepochs = range(1, len(loss)+1)\nplt.plot(epochs, loss, label='loss_train')\nplt.plot(epochs, loss_val, label='loss_val')\nplt.title('value of the loss function')\nplt.xlabel('epochs')\nplt.ylabel('value of the loss function')\nplt.legend()\nplt.grid()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-08-05T18:35:43.720081Z","iopub.execute_input":"2022-08-05T18:35:43.720390Z","iopub.status.idle":"2022-08-05T18:35:44.009795Z","shell.execute_reply.started":"2022-08-05T18:35:43.720340Z","shell.execute_reply":"2022-08-05T18:35:44.008740Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"acc = CNN_history.history['acc']\nacc_val = CNN_history.history['val_acc']\nepochs = range(1, len(loss)+1)\nplt.plot(epochs, acc, label='accuracy_train')\nplt.plot(epochs, acc_val,  label='accuracy_val')\nplt.title('accuracy')\nplt.xlabel('epochs')\nplt.ylabel('value of accuracy')\nplt.legend()\nplt.grid()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-08-05T18:35:44.016435Z","iopub.execute_input":"2022-08-05T18:35:44.016945Z","iopub.status.idle":"2022-08-05T18:35:44.317389Z","shell.execute_reply.started":"2022-08-05T18:35:44.016767Z","shell.execute_reply":"2022-08-05T18:35:44.314277Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Predict Test Data","metadata":{}},{"cell_type":"code","source":"y_pred = CNN_model.predict_classes(X_val)","metadata":{"execution":{"iopub.status.busy":"2022-08-05T18:57:08.290046Z","iopub.execute_input":"2022-08-05T18:57:08.290346Z","iopub.status.idle":"2022-08-05T18:57:08.810812Z","shell.execute_reply.started":"2022-08-05T18:57:08.290293Z","shell.execute_reply":"2022-08-05T18:57:08.809998Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_real  = []\nfor i in np.array(y_val):\n    y_real.append(np.argmax(i))","metadata":{"execution":{"iopub.status.busy":"2022-08-05T19:05:19.352896Z","iopub.execute_input":"2022-08-05T19:05:19.353256Z","iopub.status.idle":"2022-08-05T19:05:19.364067Z","shell.execute_reply.started":"2022-08-05T19:05:19.353197Z","shell.execute_reply":"2022-08-05T19:05:19.363140Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"Y_val_text_label = list(y_val.columns)","metadata":{"execution":{"iopub.status.busy":"2022-08-05T19:28:04.972078Z","iopub.execute_input":"2022-08-05T19:28:04.972391Z","iopub.status.idle":"2022-08-05T19:28:04.976832Z","shell.execute_reply.started":"2022-08-05T19:28:04.972337Z","shell.execute_reply":"2022-08-05T19:28:04.975976Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import itertools\nfrom sklearn.metrics import confusion_matrix\n\n# Our function needs a different name to sklearn's plot_confusion_matrix\ndef make_confusion_matrix(y_true, y_pred, classes=None, figsize=(10, 10), text_size=15): \n  \"\"\"Makes a labelled confusion matrix comparing predictions and ground truth labels.\n\n  If classes is passed, confusion matrix will be labelled, if not, integer class values\n  will be used.\n\n  Args:\n    y_true: Array of truth labels (must be same shape as y_pred).\n    y_pred: Array of predicted labels (must be same shape as y_true).\n    classes: Array of class labels (e.g. string form). If `None`, integer labels are used.\n    figsize: Size of output figure (default=(10, 10)).\n    text_size: Size of output figure text (default=15).\n  \n  Returns:\n    A labelled confusion matrix plot comparing y_true and y_pred.\n\n  Example usage:\n    make_confusion_matrix(y_true=test_labels, # ground truth test labels\n                          y_pred=y_preds, # predicted labels\n                          classes=class_names, # array of class label names\n                          figsize=(15, 15),\n                          text_size=10)\n  \"\"\"  \n  # Create the confustion matrix\n  cm = confusion_matrix(y_true, y_pred)\n  cm_norm = cm.astype(\"float\") / cm.sum(axis=1)[:, np.newaxis] # normalize it\n  n_classes = cm.shape[0] # find the number of classes we're dealing with\n\n  # Plot the figure and make it pretty\n  fig, ax = plt.subplots(figsize=figsize)\n  cax = ax.matshow(cm, cmap=plt.cm.Blues) # colors will represent how 'correct' a class is, darker == better\n  fig.colorbar(cax)\n\n  # Are there a list of classes?\n  if classes:\n    labels = classes\n  else:\n    labels = np.arange(cm.shape[0])\n  \n  # Label the axes\n  ax.set(title=\"Confusion Matrix\",\n         xlabel=\"Predicted label\",\n         ylabel=\"True label\",\n         xticks=np.arange(n_classes), # create enough axis slots for each class\n         yticks=np.arange(n_classes), \n         xticklabels=labels, # axes will labeled with class names (if they exist) or ints\n         yticklabels=labels) # \n  ax.set_xticklabels(labels, rotation = 90)\n  \n  # Make x-axis labels appear on bottom\n  ax.xaxis.set_label_position(\"bottom\")\n  ax.xaxis.tick_bottom()\n\n  # Set the threshold for different colors\n  threshold = (cm.max() + cm.min()) / 2.\n\n  # Plot the text on each cell\n  for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n    plt.text(j, i, f\"{cm[i, j]}\",\n             horizontalalignment=\"center\",\n             color=\"white\" if cm[i, j] > threshold else \"black\",\n             size=text_size)","metadata":{"execution":{"iopub.status.busy":"2022-08-05T19:54:04.590950Z","iopub.execute_input":"2022-08-05T19:54:04.591287Z","iopub.status.idle":"2022-08-05T19:54:04.602226Z","shell.execute_reply.started":"2022-08-05T19:54:04.591232Z","shell.execute_reply":"2022-08-05T19:54:04.601348Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"make_confusion_matrix(y_true=list(y_real), \n                      y_pred=list(y_pred),\n                      classes=Y_val_text_label,\n                      figsize=(50, 50),\n                     text_size=10)","metadata":{"execution":{"iopub.status.busy":"2022-08-05T19:54:04.892152Z","iopub.execute_input":"2022-08-05T19:54:04.892472Z","iopub.status.idle":"2022-08-05T19:54:42.204296Z","shell.execute_reply.started":"2022-08-05T19:54:04.892418Z","shell.execute_reply":"2022-08-05T19:54:42.202937Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Observations:\n\n* Above CNN architecture model classifies very badly. so we need to move to more complex model","metadata":{}},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]}]}