{"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":"code","source":"import numpy as np\nimport pandas as pd\nfrom keras.preprocessing.image import ImageDataGenerator,load_img\nfrom tensorflow.keras.utils import to_categorical\nfrom sklearn.model_selection import train_test_split\nimport matplotlib.pyplot as plt\nimport random\nimport os","metadata":{"execution":{"iopub.status.busy":"2022-04-09T22:39:14.206076Z","iopub.execute_input":"2022-04-09T22:39:14.206673Z","iopub.status.idle":"2022-04-09T22:39:15.072199Z","shell.execute_reply.started":"2022-04-09T22:39:14.206635Z","shell.execute_reply":"2022-04-09T22:39:15.071448Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"Image_Width=128\nImage_Height=128\nImage_Size=(Image_Width,Image_Height)\nImage_Channels=3","metadata":{"execution":{"iopub.status.busy":"2022-04-09T22:39:15.980505Z","iopub.execute_input":"2022-04-09T22:39:15.981047Z","iopub.status.idle":"2022-04-09T22:39:15.986838Z","shell.execute_reply.started":"2022-04-09T22:39:15.981007Z","shell.execute_reply":"2022-04-09T22:39:15.986041Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import zipfile\nwith zipfile.ZipFile(\"../input/dogs-vs-cats/train.zip\", 'r') as zip_ref:\n    zip_ref.extractall(\"\")","metadata":{"execution":{"iopub.status.busy":"2022-04-09T22:39:16.600039Z","iopub.execute_input":"2022-04-09T22:39:16.600331Z","iopub.status.idle":"2022-04-09T22:39:27.411619Z","shell.execute_reply.started":"2022-04-09T22:39:16.600301Z","shell.execute_reply":"2022-04-09T22:39:27.410848Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"filenames=os.listdir(\"./train\")\ncategories=[]\nfor f_name in filenames:\n    category=f_name.split('.')[0]\n    if category=='dog':\n        categories.append(1)\n    else:\n        categories.append(0)\ndf=pd.DataFrame({\n    'filename':filenames,\n    'category':categories\n})","metadata":{"execution":{"iopub.status.busy":"2022-04-09T22:39:33.742327Z","iopub.execute_input":"2022-04-09T22:39:33.743116Z","iopub.status.idle":"2022-04-09T22:39:33.785080Z","shell.execute_reply.started":"2022-04-09T22:39:33.743061Z","shell.execute_reply":"2022-04-09T22:39:33.784418Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df.head()","metadata":{"execution":{"iopub.status.busy":"2022-04-09T22:39:34.810679Z","iopub.execute_input":"2022-04-09T22:39:34.810929Z","iopub.status.idle":"2022-04-09T22:39:34.827645Z","shell.execute_reply.started":"2022-04-09T22:39:34.810901Z","shell.execute_reply":"2022-04-09T22:39:34.826964Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import zipfile\nwith zipfile.ZipFile(\"../input/dogs-vs-cats/test1.zip\", 'r') as zip_ref:\n    zip_ref.extractall(\"\")","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_filenames = os.listdir(\"./test1\")\ntest_df = pd.DataFrame({\n    'filename': test_filenames\n})\nnb_samples = test_df.shape[0]","metadata":{"execution":{"iopub.status.busy":"2022-04-09T22:42:00.694718Z","iopub.execute_input":"2022-04-09T22:42:00.694975Z","iopub.status.idle":"2022-04-09T22:42:00.708904Z","shell.execute_reply.started":"2022-04-09T22:42:00.694947Z","shell.execute_reply":"2022-04-09T22:42:00.708267Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_df.head()","metadata":{"execution":{"iopub.status.busy":"2022-04-09T22:42:10.757107Z","iopub.execute_input":"2022-04-09T22:42:10.757785Z","iopub.status.idle":"2022-04-09T22:42:10.765827Z","shell.execute_reply.started":"2022-04-09T22:42:10.757747Z","shell.execute_reply":"2022-04-09T22:42:10.765077Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from keras.models import Sequential\nfrom keras.layers import Conv2D,MaxPooling2D,\\\n     Dropout,Flatten,Dense,Activation,\\\n     BatchNormalization\n\nmodel=Sequential()\n\nmodel.add(Conv2D(32,(3,3),activation='relu',input_shape=(Image_Width,Image_Height,Image_Channels)))\nmodel.add(BatchNormalization())\nmodel.add(MaxPooling2D(pool_size=(2,2)))\nmodel.add(Dropout(0.25))\n\nmodel.add(Conv2D(64,(3,3),activation='relu'))\nmodel.add(BatchNormalization())\nmodel.add(MaxPooling2D(pool_size=(2,2)))\nmodel.add(Dropout(0.25))\n\nmodel.add(Conv2D(128,(3,3),activation='relu'))\nmodel.add(BatchNormalization())\nmodel.add(MaxPooling2D(pool_size=(2,2)))\nmodel.add(Dropout(0.25))\n\nmodel.add(Flatten())\nmodel.add(Dense(512,activation='relu'))\nmodel.add(BatchNormalization())\nmodel.add(Dropout(0.5))\nmodel.add(Dense(2,activation='softmax'))\n\nmodel.compile(loss='categorical_crossentropy',\n  optimizer='rmsprop',metrics=['accuracy'])","metadata":{"execution":{"iopub.status.busy":"2022-04-09T22:39:35.785006Z","iopub.execute_input":"2022-04-09T22:39:35.785430Z","iopub.status.idle":"2022-04-09T22:39:35.977941Z","shell.execute_reply.started":"2022-04-09T22:39:35.785393Z","shell.execute_reply":"2022-04-09T22:39:35.977298Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.summary()","metadata":{"execution":{"iopub.status.busy":"2022-04-09T22:39:36.695174Z","iopub.execute_input":"2022-04-09T22:39:36.695593Z","iopub.status.idle":"2022-04-09T22:39:36.710046Z","shell.execute_reply.started":"2022-04-09T22:39:36.695559Z","shell.execute_reply":"2022-04-09T22:39:36.709330Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from keras.callbacks import EarlyStopping, ReduceLROnPlateau\nearlystop = EarlyStopping(patience = 10)\nlearning_rate_reduction = ReduceLROnPlateau(monitor = 'val_acc',patience = 2,verbose = 1,factor = 0.5,min_lr = 0.00001)\ncallbacks = [earlystop,learning_rate_reduction]","metadata":{"execution":{"iopub.status.busy":"2022-04-09T22:39:37.442720Z","iopub.execute_input":"2022-04-09T22:39:37.443375Z","iopub.status.idle":"2022-04-09T22:39:37.454447Z","shell.execute_reply.started":"2022-04-09T22:39:37.443335Z","shell.execute_reply":"2022-04-09T22:39:37.451168Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df[\"category\"] = df[\"category\"].replace({0:'cat',1:'dog'})\ntrain_df,validate_df = train_test_split(df,test_size=0.20,\n  random_state=42)\n\ntrain_df = train_df.reset_index(drop=True)\nvalidate_df = validate_df.reset_index(drop=True)\n\ntotal_train=train_df.shape[0]\ntotal_validate=validate_df.shape[0]\nbatch_size=15","metadata":{"execution":{"iopub.status.busy":"2022-04-09T22:39:38.991370Z","iopub.execute_input":"2022-04-09T22:39:38.991617Z","iopub.status.idle":"2022-04-09T22:39:39.008222Z","shell.execute_reply.started":"2022-04-09T22:39:38.991589Z","shell.execute_reply":"2022-04-09T22:39:39.007503Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_datagen = ImageDataGenerator(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(train_df,\n                                                 \"./train/\",x_col='filename',y_col='category',\n                                                 target_size=Image_Size,\n                                                 class_mode='categorical',\n                                                 batch_size=batch_size)\n\nvalidation_datagen = ImageDataGenerator(rescale=1./255)\nvalidation_generator = validation_datagen.flow_from_dataframe(\n    validate_df, \n    \"./train/\", \n    x_col='filename',\n    y_col='category',\n    target_size=Image_Size,\n    class_mode='categorical',\n    batch_size=batch_size\n)\n\ntest_datagen = ImageDataGenerator(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# test_generator = train_datagen.flow_from_dataframe(train_df,\n#                                                  \"./test1/\",x_col='filename',y_col='category',\n#                                                  target_size=Image_Size,\n#                                                  class_mode='categorical',\n#                                                  batch_size=batch_size)\n\n# I changed test_generator as follows and it functioned properly\ntest_generator = train_datagen.flow_from_dataframe(test_df,\n                                                   \"./test1/\",\n                                                   x_col=\"filename\",\n                                                   y_col=None,\n                                                   target_size=Image_Size,\n                                                   class_mode=None,\n                                                   batch_size=batch_size)","metadata":{"execution":{"iopub.status.busy":"2022-04-09T22:42:19.378412Z","iopub.execute_input":"2022-04-09T22:42:19.378672Z","iopub.status.idle":"2022-04-09T22:42:19.707860Z","shell.execute_reply.started":"2022-04-09T22:42:19.378643Z","shell.execute_reply":"2022-04-09T22:42:19.707164Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"epochs=10\nhistory = model.fit_generator(\n    train_generator, \n    epochs=epochs,\n    validation_data=validation_generator,\n    validation_steps=total_validate//batch_size,\n    steps_per_epoch=total_train//batch_size,\n    callbacks=callbacks\n)","metadata":{"execution":{"iopub.status.busy":"2022-04-09T22:42:23.019837Z","iopub.execute_input":"2022-04-09T22:42:23.020532Z","iopub.status.idle":"2022-04-09T22:44:40.386659Z","shell.execute_reply.started":"2022-04-09T22:42:23.020491Z","shell.execute_reply":"2022-04-09T22:44:40.385930Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.save(\"model1_catsVSdogs_10epoch.h5\")","metadata":{"execution":{"iopub.status.busy":"2022-04-09T22:44:40.388333Z","iopub.execute_input":"2022-04-09T22:44:40.388983Z","iopub.status.idle":"2022-04-09T22:44:40.632144Z","shell.execute_reply.started":"2022-04-09T22:44:40.388943Z","shell.execute_reply":"2022-04-09T22:44:40.631337Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"predict = model.predict_generator(test_generator, steps=np.ceil(nb_samples/batch_size))","metadata":{"execution":{"iopub.status.busy":"2022-04-09T22:45:50.323876Z","iopub.execute_input":"2022-04-09T22:45:50.324159Z","iopub.status.idle":"2022-04-09T22:46:59.198137Z","shell.execute_reply.started":"2022-04-09T22:45:50.324126Z","shell.execute_reply":"2022-04-09T22:46:59.197384Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_df['category'] = np.argmax(predict, axis=-1)\n\nlabel_map = dict((v,k) for k,v in train_generator.class_indices.items())\ntest_df['category'] = test_df['category'].replace(label_map)\n\ntest_df['category'] = test_df['category'].replace({ 'dog': 1, 'cat': 0 })","metadata":{"execution":{"iopub.status.busy":"2022-04-09T22:46:59.199842Z","iopub.execute_input":"2022-04-09T22:46:59.200080Z","iopub.status.idle":"2022-04-09T22:46:59.218481Z","shell.execute_reply.started":"2022-04-09T22:46:59.200047Z","shell.execute_reply":"2022-04-09T22:46:59.217845Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sample_test = test_df.head(18)\nsample_test.head()\nplt.figure(figsize=(12, 24))\nfor index, row in sample_test.iterrows():\n    filename = row['filename']\n    category = row['category']\n    img = load_img(\"./test1/\"+filename, target_size=Image_Size)\n    plt.subplot(6, 3, index+1)\n    plt.imshow(img)\n    plt.xlabel(filename + '(' + \"{}\".format(category) + ')' )\nplt.tight_layout()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-04-09T22:46:59.219591Z","iopub.execute_input":"2022-04-09T22:46:59.219811Z","iopub.status.idle":"2022-04-09T22:47:02.197947Z","shell.execute_reply.started":"2022-04-09T22:46:59.219781Z","shell.execute_reply":"2022-04-09T22:47:02.197159Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"results={\n    0:'cat',\n    1:'dog'\n}\nfrom PIL import Image\nimport numpy as np\nim=Image.open(\"./test1/4134.jpg\")\nim=im.resize(Image_Size)\nim=np.expand_dims(im,axis=0)\nim=np.array(im)\nim=im/255\npred=model.predict([im])[0]\nclasses_x=np.argmax(pred,axis=0)\nprint(classes_x,results[classes_x])","metadata":{"execution":{"iopub.status.busy":"2022-04-09T22:47:02.199628Z","iopub.execute_input":"2022-04-09T22:47:02.200018Z","iopub.status.idle":"2022-04-09T22:47:02.386520Z","shell.execute_reply.started":"2022-04-09T22:47:02.199981Z","shell.execute_reply":"2022-04-09T22:47:02.385166Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}