{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.14","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":3362,"databundleVersionId":31148,"sourceType":"competition"}],"dockerImageVersionId":30762,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# 1- Import Libraries","metadata":{}},{"cell_type":"code","source":"import os\nimport numpy as np\nimport pandas as pd\nimport random as rnd\nimport matplotlib.pyplot as plt\n%matplotlib inline\n\nfrom sklearn.model_selection import train_test_split\n\nimport keras\nfrom keras.models import Sequential\nfrom keras.utils import to_categorical\nfrom keras.preprocessing.image import load_img\nfrom keras.layers import Flatten, Dense, Dropout\nfrom keras.layers import Conv2D, MaxPool2D, BatchNormalization, Activation\n\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\n\nprint(os.listdir(\"/kaggle/input/dogs-vs-cats\"))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-10T11:19:05.225021Z","iopub.execute_input":"2025-07-10T11:19:05.225294Z","iopub.status.idle":"2025-07-10T11:19:19.852893Z","shell.execute_reply.started":"2025-07-10T11:19:05.225265Z","shell.execute_reply":"2025-07-10T11:19:19.851921Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# 2- Data-Constants","metadata":{}},{"cell_type":"code","source":"FAST_RUN = False\nIMAGE_WIDTH=128\nIMAGE_HEIGHT=128\nIMAGE_CHANNELS=3\nIMAGE_SIZE=(IMAGE_WIDTH, IMAGE_HEIGHT)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-10T11:20:27.185817Z","iopub.execute_input":"2025-07-10T11:20:27.186517Z","iopub.status.idle":"2025-07-10T11:20:27.190705Z","shell.execute_reply.started":"2025-07-10T11:20:27.186485Z","shell.execute_reply":"2025-07-10T11:20:27.189702Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# 3- Load DataSet","metadata":{}},{"cell_type":"code","source":"# import zipfile\n\n# zip_files = ['test1', 'train']\n\n# for zip_file in zip_files:\n#     with zipfile.ZipFile(f\"../input/dogs-vs-cats/{zip_file}.zip\", \"r\") as z:\n#         z.extractall(\".\")\n#         print(f\"{zip_file} unzipped\")","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"> ## Extract Compressed Files","metadata":{}},{"cell_type":"code","source":"import zipfile\n\npath = \"/kaggle/input/dogs-vs-cats\"\nfor zip_file in os.listdir(path)[:2]:\n    zipfile.ZipFile(f\"{path}/{zip_file}\", 'r').extractall(\".\")\n#     print(f\"{path}/{zip_file}\")\n    print(f\"{zip_file} unzipped\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-10T11:20:40.645821Z","iopub.execute_input":"2025-07-10T11:20:40.646435Z","iopub.status.idle":"2025-07-10T11:20:53.198381Z","shell.execute_reply.started":"2025-07-10T11:20:40.646401Z","shell.execute_reply":"2025-07-10T11:20:53.197373Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# 4- Data Preprocessing","metadata":{}},{"cell_type":"markdown","source":"> ## Label Encoding","metadata":{}},{"cell_type":"code","source":"train_images = os.listdir(\"/kaggle/working/train\")\nCATEGORIES = []\n\nfor image_name in train_images:\n    class_name = image_name.split('.')[0]  # cat.588.jpg\n    CATEGORIES.append(0 if class_name=='cat' else 1)\n\ndf = pd.DataFrame({\"train_images\": train_images, \"CATEGORIES\": CATEGORIES})","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-10T11:23:06.136451Z","iopub.execute_input":"2025-07-10T11:23:06.137144Z","iopub.status.idle":"2025-07-10T11:23:06.171434Z","shell.execute_reply.started":"2025-07-10T11:23:06.137113Z","shell.execute_reply":"2025-07-10T11:23:06.170726Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"> ## Show DataFrame","metadata":{}},{"cell_type":"code","source":"df.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-10T11:23:14.767761Z","iopub.execute_input":"2025-07-10T11:23:14.7681Z","iopub.status.idle":"2025-07-10T11:23:14.786211Z","shell.execute_reply.started":"2025-07-10T11:23:14.768069Z","shell.execute_reply":"2025-07-10T11:23:14.785241Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"df.tail()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-10T11:23:17.959392Z","iopub.execute_input":"2025-07-10T11:23:17.959729Z","iopub.status.idle":"2025-07-10T11:23:17.968939Z","shell.execute_reply.started":"2025-07-10T11:23:17.9597Z","shell.execute_reply":"2025-07-10T11:23:17.967977Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"df.describe()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-10T11:23:20.535643Z","iopub.execute_input":"2025-07-10T11:23:20.535974Z","iopub.status.idle":"2025-07-10T11:23:20.578879Z","shell.execute_reply.started":"2025-07-10T11:23:20.535945Z","shell.execute_reply":"2025-07-10T11:23:20.577921Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"df[\"CATEGORIES\"].value_counts()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-10T11:23:25.212298Z","iopub.execute_input":"2025-07-10T11:23:25.212649Z","iopub.status.idle":"2025-07-10T11:23:25.226308Z","shell.execute_reply.started":"2025-07-10T11:23:25.21262Z","shell.execute_reply":"2025-07-10T11:23:25.225265Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"> ## Visualize The Data","metadata":{}},{"cell_type":"code","source":"df[\"CATEGORIES\"].value_counts().plot.bar()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-10T11:23:27.693704Z","iopub.execute_input":"2025-07-10T11:23:27.694096Z","iopub.status.idle":"2025-07-10T11:23:27.945579Z","shell.execute_reply.started":"2025-07-10T11:23:27.694065Z","shell.execute_reply":"2025-07-10T11:23:27.94443Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"> ## *From our data we have 12500 cats and 12500 dogs*","metadata":{}},{"cell_type":"markdown","source":"> ## Show Dimensions Of Images","metadata":{}},{"cell_type":"code","source":"shapes = []\n\nfor image in df[\"train_images\"]:\n    path = os.path.join(\"/kaggle/working/train\",image)\n    image_array = plt.imread(path)\n    shapes.append(image_array.shape)\n    \nprint(pd.Series(shapes).value_counts())\n\nindex = np.argmin(shapes)\nprint(f\"\\nThe Minimum Dimension is ==> {shapes[index]}\\n\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-10T11:23:36.876417Z","iopub.execute_input":"2025-07-10T11:23:36.876779Z","iopub.status.idle":"2025-07-10T11:24:05.966097Z","shell.execute_reply.started":"2025-07-10T11:23:36.876747Z","shell.execute_reply":"2025-07-10T11:24:05.96511Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"> ## **See sample image**\n","metadata":{}},{"cell_type":"code","source":"sample = rnd.choice(train_images)\nimage = load_img(os.path.join(\"/kaggle/working/train\", sample))\nprint(type(image))\nplt.imshow(image)\nplt.axis(\"off\")\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-10T11:30:26.77363Z","iopub.execute_input":"2025-07-10T11:30:26.774535Z","iopub.status.idle":"2025-07-10T11:30:27.047116Z","shell.execute_reply.started":"2025-07-10T11:30:26.774486Z","shell.execute_reply":"2025-07-10T11:30:27.046238Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"> # Prepare data\n> Because we will use image genaretor with class_mode=\"categorical\". We need to convert column category into string. Then imagenerator will convert it one-hot encoding which is good for our classification.\n> So we will convert 1 to dog and 0 to cat","metadata":{}},{"cell_type":"code","source":"df[\"CATEGORIES\"] = df[\"CATEGORIES\"].replace({0: 'cat', 1: 'dog'})","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-10T11:30:44.883723Z","iopub.execute_input":"2025-07-10T11:30:44.884454Z","iopub.status.idle":"2025-07-10T11:30:44.893094Z","shell.execute_reply.started":"2025-07-10T11:30:44.88442Z","shell.execute_reply":"2025-07-10T11:30:44.892229Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"> # Split Data","metadata":{}},{"cell_type":"code","source":"train_df, valid_df = train_test_split(df, test_size=0.20, random_state=42)\n\ntrain_df = train_df.reset_index(drop=True)\nvalid_df = valid_df.reset_index(drop=True)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-10T11:30:48.422189Z","iopub.execute_input":"2025-07-10T11:30:48.422645Z","iopub.status.idle":"2025-07-10T11:30:48.439035Z","shell.execute_reply.started":"2025-07-10T11:30:48.422596Z","shell.execute_reply":"2025-07-10T11:30:48.438043Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"> # Show tarin DataFrame","metadata":{}},{"cell_type":"code","source":"train_df['CATEGORIES'].value_counts()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-10T11:30:51.263875Z","iopub.execute_input":"2025-07-10T11:30:51.264353Z","iopub.status.idle":"2025-07-10T11:30:51.274326Z","shell.execute_reply.started":"2025-07-10T11:30:51.264311Z","shell.execute_reply":"2025-07-10T11:30:51.273239Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_df['CATEGORIES'].value_counts().plot.bar()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-10T11:30:55.44224Z","iopub.execute_input":"2025-07-10T11:30:55.442619Z","iopub.status.idle":"2025-07-10T11:30:55.654024Z","shell.execute_reply.started":"2025-07-10T11:30:55.442587Z","shell.execute_reply":"2025-07-10T11:30:55.653126Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"> # Show valid DataFrame","metadata":{}},{"cell_type":"code","source":"valid_df['CATEGORIES'].value_counts()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-10T11:30:58.890118Z","iopub.execute_input":"2025-07-10T11:30:58.890494Z","iopub.status.idle":"2025-07-10T11:30:58.898738Z","shell.execute_reply.started":"2025-07-10T11:30:58.890461Z","shell.execute_reply":"2025-07-10T11:30:58.897708Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"valid_df['CATEGORIES'].value_counts().plot.bar()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-10T11:31:01.091473Z","iopub.execute_input":"2025-07-10T11:31:01.092298Z","iopub.status.idle":"2025-07-10T11:31:01.302936Z","shell.execute_reply.started":"2025-07-10T11:31:01.092262Z","shell.execute_reply":"2025-07-10T11:31:01.301774Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"> # Data Augmentation","metadata":{}},{"cell_type":"code","source":"total_train = train_df.shape[0]\ntotal_validate = valid_df.shape[0]\nbatch_size=15","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-10T11:31:03.851955Z","iopub.execute_input":"2025-07-10T11:31:03.852759Z","iopub.status.idle":"2025-07-10T11:31:03.856826Z","shell.execute_reply.started":"2025-07-10T11:31:03.852727Z","shell.execute_reply":"2025-07-10T11:31:03.85583Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"> ## Train Generator","metadata":{}},{"cell_type":"code","source":"df.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-10T11:31:06.798606Z","iopub.execute_input":"2025-07-10T11:31:06.799603Z","iopub.status.idle":"2025-07-10T11:31:06.811205Z","shell.execute_reply.started":"2025-07-10T11:31:06.799553Z","shell.execute_reply":"2025-07-10T11:31:06.809993Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_datagen = ImageDataGenerator(\n    rotation_range=15,\n    rescale=1./255,\n    shear_range=0.1,\n    zoom_range=0.2,\n    horizontal_flip=True,\n    width_shift_range=0.1,\n    height_shift_range=0.1\n)\n\ntrain_generator = train_datagen.flow_from_dataframe(\n    train_df, \n    \"/kaggle/working/train\", \n    x_col='train_images',\n    y_col='CATEGORIES',\n    target_size=IMAGE_SIZE,\n    class_mode='categorical',\n    batch_size=batch_size\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-10T11:31:09.617891Z","iopub.execute_input":"2025-07-10T11:31:09.618602Z","iopub.status.idle":"2025-07-10T11:31:09.803145Z","shell.execute_reply.started":"2025-07-10T11:31:09.618555Z","shell.execute_reply":"2025-07-10T11:31:09.802224Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"> # Valid Generator","metadata":{}},{"cell_type":"code","source":"valid_datagen = ImageDataGenerator(rescale=1./255)\n\nvalid_generator = valid_datagen.flow_from_dataframe(\n    valid_df, \n    \"/kaggle/working/train\", \n    x_col='train_images',\n    y_col='CATEGORIES',\n    target_size=IMAGE_SIZE,\n    class_mode='categorical',\n    batch_size=batch_size\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-10T11:31:12.542058Z","iopub.execute_input":"2025-07-10T11:31:12.542739Z","iopub.status.idle":"2025-07-10T11:31:12.595439Z","shell.execute_reply.started":"2025-07-10T11:31:12.542693Z","shell.execute_reply":"2025-07-10T11:31:12.594434Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"> # See how our generator work","metadata":{}},{"cell_type":"code","source":"example_df = train_df.sample(n=1) # take 1 image-sample\nprint(example_df, \"\\n\")\n\nexample_df= example_df.reset_index(drop=True)\nprint(example_df, \"\\n\")\n\nexample_generator = train_datagen.flow_from_dataframe(\n    example_df, \n    \"/kaggle/working/train\", \n    x_col='train_images',\n    y_col='CATEGORIES',\n    target_size=IMAGE_SIZE,\n    class_mode='categorical'\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-10T11:31:14.960242Z","iopub.execute_input":"2025-07-10T11:31:14.960665Z","iopub.status.idle":"2025-07-10T11:31:14.973546Z","shell.execute_reply.started":"2025-07-10T11:31:14.960631Z","shell.execute_reply":"2025-07-10T11:31:14.972536Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"> # Show Generated Images from One-Image","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize=(12, 12))\nfor i in range(0, 15):\n    plt.subplot(5, 3, i+1)\n    for X_batch, Y_batch in example_generator:\n        image = X_batch[0]\n        plt.imshow(image)\n        plt.axis('off')\n        break\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-10T11:31:18.448519Z","iopub.execute_input":"2025-07-10T11:31:18.449168Z","iopub.status.idle":"2025-07-10T11:31:19.630907Z","shell.execute_reply.started":"2025-07-10T11:31:18.449134Z","shell.execute_reply":"2025-07-10T11:31:19.629771Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# 5- Build The Model","metadata":{}},{"cell_type":"markdown","source":"![image](https://i.imgur.com/ebkMGGu.jpg)","metadata":{}},{"cell_type":"markdown","source":"> * **Input Layer**: It represent input image data. It will reshape image into single diminsion array. Example your image is 64x64 = 4096, it will convert to (4096,1) array.\n> * **Conv Layer**: This layer will extract features from image.\n> * **Pooling Layer**: This layerreduce the spatial volume of input image after convolution.\n> * **Fully Connected Layer**: It connect the network from a layer to another layer\n> * **Output Layer**: It is the predicted values layer.","metadata":{}},{"cell_type":"markdown","source":"> # Fit Model","metadata":{}},{"cell_type":"code","source":"model = Sequential([\n    Conv2D(32, (3, 3), activation='relu', input_shape=(IMAGE_WIDTH, IMAGE_HEIGHT, IMAGE_CHANNELS)),\n#     BatchNormalization(),\n    MaxPool2D(pool_size=(4,4)),\n    Dropout(rate=0.25),\n    \n    Conv2D(64, (3, 3), activation='relu'),\n    BatchNormalization(),\n    MaxPool2D(pool_size=(4,4)),\n    Dropout(rate=0.25),\n    \n    Conv2D(128, (3, 3), activation='relu'),\n    BatchNormalization(),\n    MaxPool2D(pool_size=(4,4)),\n    Dropout(rate=0.25),\n    \n    Flatten(),\n    \n    Dense(512, activation='relu'),\n    Dropout(rate=0.5),\n\n    Dense(2, activation='softmax') # 2 because we have cat and dog classes\n\n])\n\n# Compile\nmodel.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n\n# Summary of Model\nmodel.summary()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-10T11:32:45.796121Z","iopub.execute_input":"2025-07-10T11:32:45.796832Z","iopub.status.idle":"2025-07-10T11:32:46.669552Z","shell.execute_reply.started":"2025-07-10T11:32:45.796794Z","shell.execute_reply":"2025-07-10T11:32:46.668662Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"> ## Callbacks","metadata":{}},{"cell_type":"code","source":"from keras.callbacks import EarlyStopping, ReduceLROnPlateau","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-10T11:33:01.773662Z","iopub.execute_input":"2025-07-10T11:33:01.774023Z","iopub.status.idle":"2025-07-10T11:33:01.779938Z","shell.execute_reply.started":"2025-07-10T11:33:01.773993Z","shell.execute_reply":"2025-07-10T11:33:01.778891Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"> ### Early Stop\n> To prevent over fitting we will stop the learning after 10 epochs and val_loss value not decreased","metadata":{}},{"cell_type":"code","source":"earlystop = EarlyStopping(patience=5)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-10T11:33:04.272071Z","iopub.execute_input":"2025-07-10T11:33:04.272478Z","iopub.status.idle":"2025-07-10T11:33:04.276853Z","shell.execute_reply.started":"2025-07-10T11:33:04.272446Z","shell.execute_reply":"2025-07-10T11:33:04.275855Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"> ### Learning Rate Reduction\n> We will reduce the learning rate when then accuracy not increase for 2 steps","metadata":{}},{"cell_type":"code","source":"learning_rate_reduction = ReduceLROnPlateau(monitor='val_acc', \n                                            patience=2, \n                                            verbose=1, \n                                            factor=0.5, \n                                            min_lr=0.00001)\n\ncallbacks = [earlystop, learning_rate_reduction]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-10T11:33:07.272281Z","iopub.execute_input":"2025-07-10T11:33:07.273029Z","iopub.status.idle":"2025-07-10T11:33:07.277723Z","shell.execute_reply.started":"2025-07-10T11:33:07.272988Z","shell.execute_reply":"2025-07-10T11:33:07.276613Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"epochs = 3 if FAST_RUN else 10\n\nhistory = model.fit(train_generator,\n                    epochs=epochs,\n                    validation_data=valid_generator,\n#                     validation_steps=total_validate//batch_size, # 5000//15  = 333\n#                     steps_per_epoch=total_train//batch_size,     # 20000//15 = 1333\n                    callbacks=callbacks)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-10T11:33:10.144283Z","iopub.execute_input":"2025-07-10T11:33:10.144941Z","iopub.status.idle":"2025-07-10T11:51:35.934494Z","shell.execute_reply.started":"2025-07-10T11:33:10.144907Z","shell.execute_reply":"2025-07-10T11:51:35.933526Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"> # Visualize The Performance","metadata":{}},{"cell_type":"code","source":"accuracy = history.history[\"accuracy\"]\nval_accuracy = history.history[\"val_accuracy\"]\n\nloss = history.history[\"loss\"]\nval_loss = history.history[\"val_loss\"]\n\nepochs = range(len(accuracy))\n\nplt.figure(figsize=(12,6))\n\nplt.subplot(1,2,1)\nplt.plot(epochs, accuracy, 'bo', label=\"Train_Acc\")\nplt.plot(epochs, val_accuracy, 'r', label=\"val_Acc\")\nplt.legend(loc='best', shadow=True)\n\nplt.subplot(1,2,2)\nplt.plot(epochs, loss, 'bo', label=\"Train_Loss\")\nplt.plot(epochs, val_loss, 'r', label=\"val_Loss\")\nplt.legend(loc='best', shadow=True)\n\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-10T11:51:44.579826Z","iopub.execute_input":"2025-07-10T11:51:44.580186Z","iopub.status.idle":"2025-07-10T11:51:44.901144Z","shell.execute_reply.started":"2025-07-10T11:51:44.580141Z","shell.execute_reply":"2025-07-10T11:51:44.900226Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"> # The Epochs = 10 Only. We Can Increase The number of Epochs to Reach Suitable Accuracy.","metadata":{}},{"cell_type":"markdown","source":"> # Prepare Testing Data","metadata":{}},{"cell_type":"code","source":"test_file_names = os.listdir(\"/kaggle/working/test1\")\ntest_df = pd.DataFrame({'file_name': test_file_names})\n\ntotal_testing = test_df.shape[0]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-10T11:51:49.135547Z","iopub.execute_input":"2025-07-10T11:51:49.136636Z","iopub.status.idle":"2025-07-10T11:51:49.149655Z","shell.execute_reply.started":"2025-07-10T11:51:49.136591Z","shell.execute_reply":"2025-07-10T11:51:49.1489Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"> # Test The Model","metadata":{}},{"cell_type":"markdown","source":"> # Create Testing Generator","metadata":{}},{"cell_type":"code","source":"test_gen = ImageDataGenerator(rescale=1./255)\ntest_generator = test_gen.flow_from_dataframe(\n    test_df, \n    \"/kaggle/working/test1\", \n    x_col='file_name',\n    y_col=None,\n    class_mode=None,\n    target_size=IMAGE_SIZE,\n    batch_size=batch_size,\n    shuffle=False\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-10T11:51:59.214049Z","iopub.execute_input":"2025-07-10T11:51:59.214404Z","iopub.status.idle":"2025-07-10T11:51:59.309338Z","shell.execute_reply.started":"2025-07-10T11:51:59.214373Z","shell.execute_reply":"2025-07-10T11:51:59.30852Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"> # Predict","metadata":{}},{"cell_type":"code","source":"predictions = model.predict(test_generator)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-10T11:52:02.739798Z","iopub.execute_input":"2025-07-10T11:52:02.740436Z","iopub.status.idle":"2025-07-10T11:52:21.661043Z","shell.execute_reply.started":"2025-07-10T11:52:02.740404Z","shell.execute_reply":"2025-07-10T11:52:21.660128Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"> For categoral classication the prediction will come with probability of each category. So we will pick the category that have the highest probability with numpy average max","metadata":{}},{"cell_type":"code","source":"test_df[\"CATEGORIES\"] = np.argmax(predictions, axis=1)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-10T11:52:28.006929Z","iopub.execute_input":"2025-07-10T11:52:28.007556Z","iopub.status.idle":"2025-07-10T11:52:28.013302Z","shell.execute_reply.started":"2025-07-10T11:52:28.007519Z","shell.execute_reply":"2025-07-10T11:52:28.012351Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"> We will convert the predict category back into our generator classes by using train_generator.class_indices. It is the classes that image generator map while converting data into computer vision","metadata":{}},{"cell_type":"code","source":"label_map = dict((v,k) for k,v in train_generator.class_indices.items())\ntest_df['CATEGORIES'] = test_df['CATEGORIES'].replace(label_map)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-10T11:52:33.92268Z","iopub.execute_input":"2025-07-10T11:52:33.923533Z","iopub.status.idle":"2025-07-10T11:52:33.930379Z","shell.execute_reply.started":"2025-07-10T11:52:33.923487Z","shell.execute_reply":"2025-07-10T11:52:33.929355Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"> From our prepare data part. We map data with {1: 'dog', 0: 'cat'}. Now we will map the result back to dog is 1 and cat is 0\n\n","metadata":{}},{"cell_type":"code","source":"test_df['CATEGORIES'] = test_df['CATEGORIES'].replace({ 'dog': 1, 'cat': 0 })","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-10T11:52:40.110716Z","iopub.execute_input":"2025-07-10T11:52:40.111087Z","iopub.status.idle":"2025-07-10T11:52:40.124879Z","shell.execute_reply.started":"2025-07-10T11:52:40.111054Z","shell.execute_reply":"2025-07-10T11:52:40.123626Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# 6- Visualize The Result","metadata":{}},{"cell_type":"code","source":"test_df[\"CATEGORIES\"].value_counts().plot.bar()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-10T11:52:51.864928Z","iopub.execute_input":"2025-07-10T11:52:51.865834Z","iopub.status.idle":"2025-07-10T11:52:52.084364Z","shell.execute_reply.started":"2025-07-10T11:52:51.865777Z","shell.execute_reply":"2025-07-10T11:52:52.083159Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"> # Show Predicted Images","metadata":{}},{"cell_type":"code","source":"sample_test = test_df.head(36)\n\nplt.figure(figsize=(15,15))\n\nfor index, row in sample_test.iterrows():\n    file_name = row['file_name']\n    category = row['CATEGORIES']\n    path = os.path.join(\"/kaggle/working/test1\", file_name)\n    img = load_img(path, target_size=IMAGE_SIZE)\n    plt.subplot(6,6,index+1)\n    plt.imshow(img)\n    plt.title(f\"{file_name}\\n is ==> {'cat' if category==0 else 'dog'}\")\n    plt.axis('off')\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-10T11:52:55.277148Z","iopub.execute_input":"2025-07-10T11:52:55.278113Z","iopub.status.idle":"2025-07-10T11:52:58.991954Z","shell.execute_reply.started":"2025-07-10T11:52:55.278058Z","shell.execute_reply":"2025-07-10T11:52:58.990854Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# 7- Save The Model","metadata":{}},{"cell_type":"code","source":"model.save(\"CNN.h5\")\nmodel.save_weights(\"CNN.weights.h5\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-10T11:53:19.415514Z","iopub.execute_input":"2025-07-10T11:53:19.416458Z","iopub.status.idle":"2025-07-10T11:53:19.522646Z","shell.execute_reply.started":"2025-07-10T11:53:19.416424Z","shell.execute_reply":"2025-07-10T11:53:19.521511Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# 8- Submission","metadata":{}},{"cell_type":"code","source":"submission_df = test_df.copy()\nsubmission_df['id'] = submission_df['file_name'].str.split('.').str[0]\nsubmission_df['label'] = submission_df['CATEGORIES']\nsubmission_df.drop(['file_name', 'CATEGORIES'], axis=1, inplace=True)\nsubmission_df.to_csv(\"submission.csv\", index=False)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-10T11:53:29.869369Z","iopub.execute_input":"2025-07-10T11:53:29.870264Z","iopub.status.idle":"2025-07-10T11:53:29.92269Z","shell.execute_reply.started":"2025-07-10T11:53:29.870172Z","shell.execute_reply":"2025-07-10T11:53:29.92163Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"!wget https://wl-genial.cf.tsp.li/resize/728x/jpg/27e/3ac/d457dd54cdbe4443445792ccb8.jpg\nimagen = load_img('d457dd54cdbe4443445792ccb8.jpg', target_size=IMAGE_SIZE)\nimg = np.expand_dims(imagen, 0) # make 'batch' of 1\npred = model.predict(img)\n#pred = labels[\"label_names\"][np.argmax(pred)]\ncategory = np.argmax(pred)\nplt.imshow(imagen)\nplt.title(f\"esa imagen es \\n ==> {'cat' if category==0 else 'dog'}\")\nplt.axis('off')\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-10T11:53:47.080286Z","iopub.execute_input":"2025-07-10T11:53:47.080683Z","iopub.status.idle":"2025-07-10T11:53:49.621334Z","shell.execute_reply.started":"2025-07-10T11:53:47.080649Z","shell.execute_reply":"2025-07-10T11:53:49.620162Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"!wget https://oglebay.com/wp-content/uploads/2022/06/IMG_5670_M-e1654806092422.jpg\nimagen = load_img('IMG_5670_M-e1654806092422.jpg', target_size=IMAGE_SIZE)\nimg = np.expand_dims(imagen, 0) # make 'batch' of 1\npred = model.predict(img)\n#pred = labels[\"label_names\"][np.argmax(pred)]\ncategory = np.argmax(pred)\nplt.imshow(imagen)\nplt.title(f\"esa imagen es \\n ==> {'cat' if category==0 else 'dog'}\")\nplt.axis('off')\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-10T11:53:58.494897Z","iopub.execute_input":"2025-07-10T11:53:58.495932Z","iopub.status.idle":"2025-07-10T11:54:01.333664Z","shell.execute_reply.started":"2025-07-10T11:53:58.495885Z","shell.execute_reply":"2025-07-10T11:54:01.332142Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{},"outputs":[],"execution_count":null}]}