{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","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":30627,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\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\nimport os\nfor 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 20GB 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","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-01-27T04:25:31.836115Z","iopub.execute_input":"2024-01-27T04:25:31.836559Z","iopub.status.idle":"2024-01-27T04:25:32.187172Z","shell.execute_reply.started":"2024-01-27T04:25:31.836523Z","shell.execute_reply":"2024-01-27T04:25:32.186299Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import os\nprint(os.listdir(\"../input\"))\n\nimport zipfile\n\nwith zipfile.ZipFile(\"/kaggle/input/dogs-vs-cats/test1.zip\",\"r\") as z:\n    z.extractall(\".\")\n    \nwith zipfile.ZipFile(\"/kaggle/input/dogs-vs-cats/train.zip\",\"r\") as z:\n    z.extractall(\".\")","metadata":{"execution":{"iopub.status.busy":"2024-01-27T04:25:33.827659Z","iopub.execute_input":"2024-01-27T04:25:33.828125Z","iopub.status.idle":"2024-01-27T04:25:49.920748Z","shell.execute_reply.started":"2024-01-27T04:25:33.828093Z","shell.execute_reply":"2024-01-27T04:25:49.919956Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# plot dog photos from the dogs vs cats dataset\nfrom matplotlib import pyplot\nfrom matplotlib.image import imread\ntrain_folder = '/kaggle/working/train/'\n\n# define location of dataset\nfolder = train_folder\n# plot first few images\nfor i in range(9):\n\t# define subplot\n\tpyplot.subplot(330 + 1 + i)\n\t# define filename\n\tfilename = folder + 'dog.' + str(i) + '.jpg'\n\t# load image pixels\n\timage = imread(filename)\n\t# plot raw pixel data\n\tpyplot.imshow(image)\n# show the figure\npyplot.show()","metadata":{"execution":{"iopub.status.busy":"2024-01-27T04:25:52.852413Z","iopub.execute_input":"2024-01-27T04:25:52.852891Z","iopub.status.idle":"2024-01-27T04:25:54.086864Z","shell.execute_reply.started":"2024-01-27T04:25:52.852857Z","shell.execute_reply":"2024-01-27T04:25:54.085978Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# plot first few images\nfor i in range(9):\n\t# define subplot\n\tpyplot.subplot(330 + 1 + i)\n\t# define filename\n\tfilename = folder + 'cat.' + str(i) + '.jpg'\n\t# load image pixels\n\timage = imread(filename)\n\t# plot raw pixel data\n\tpyplot.imshow(image)\n# show the figure\npyplot.show()","metadata":{"execution":{"iopub.status.busy":"2024-01-27T04:25:54.420141Z","iopub.execute_input":"2024-01-27T04:25:54.420759Z","iopub.status.idle":"2024-01-27T04:25:55.506452Z","shell.execute_reply.started":"2024-01-27T04:25:54.420724Z","shell.execute_reply":"2024-01-27T04:25:55.505539Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# load dogs vs cats dataset, reshape and save to a new file\nfrom os import listdir\nfrom numpy import asarray\nfrom numpy import save\nfrom keras.preprocessing.image import load_img\nfrom keras.preprocessing.image import img_to_array","metadata":{"execution":{"iopub.status.busy":"2024-01-27T04:25:55.507903Z","iopub.execute_input":"2024-01-27T04:25:55.508183Z","iopub.status.idle":"2024-01-27T04:26:06.675536Z","shell.execute_reply.started":"2024-01-27T04:25:55.508158Z","shell.execute_reply":"2024-01-27T04:26:06.674754Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# define location of dataset\nfolder = 'train/'\nphotos, labels = list(), list()\n# enumerate files in the directory\nfor file in listdir(folder):\n#for file in listdir(folder[:10_000]): # select 10_000 images\n\t# determine class\n\toutput = 0.0\n\tif file.startswith('dog'):\n\t\toutput = 1.0\n\t# load image\n\tphoto = load_img(folder + file, target_size=(200, 200))\n\t# convert to numpy array\n\tphoto = img_to_array(photo)\n\t# store\n\tphotos.append(photo)\n\tlabels.append(output)\n# convert to a numpy arrays\nphotos = asarray(photos)\nlabels = asarray(labels)\nprint(photos.shape, labels.shape)\n# save the reshaped photos\nsave('dogs_vs_cats_photos.npy', photos)\nsave('dogs_vs_cats_labels.npy', labels)","metadata":{"execution":{"iopub.status.busy":"2024-01-27T04:26:06.677469Z","iopub.execute_input":"2024-01-27T04:26:06.678132Z","iopub.status.idle":"2024-01-27T04:27:32.845620Z","shell.execute_reply.started":"2024-01-27T04:26:06.678096Z","shell.execute_reply":"2024-01-27T04:27:32.843386Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!ls -l","metadata":{"execution":{"iopub.status.busy":"2024-01-27T04:27:32.848722Z","iopub.execute_input":"2024-01-27T04:27:32.849088Z","iopub.status.idle":"2024-01-27T04:27:34.203429Z","shell.execute_reply.started":"2024-01-27T04:27:32.849049Z","shell.execute_reply":"2024-01-27T04:27:34.202403Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# load and confirm the shape\nfrom numpy import load\nphotos = load('dogs_vs_cats_photos.npy')\nlabels = load('dogs_vs_cats_labels.npy')\nprint(photos.shape, labels.shape)","metadata":{"execution":{"iopub.status.busy":"2024-01-27T04:27:34.205933Z","iopub.execute_input":"2024-01-27T04:27:34.206249Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# create directories\nimport os\ndataset_home = 'dataset_dogs_vs_cats/'\nsubdirs = ['train/', 'test/']\nfor subdir in subdirs:\n\t# create label subdirectories\n\tlabeldirs = ['dogs/', 'cats/']\n\tfor labldir in labeldirs:\n\t\tnewdir = dataset_home + subdir + labldir\n\t\tos.makedirs(newdir, exist_ok=True)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# organize dataset into a useful structure\nfrom os import makedirs\nfrom os import listdir\nfrom shutil import copyfile\nfrom random import seed\nfrom random import random\n# create directories\ndataset_home = 'dataset_dogs_vs_cats/'\nsubdirs = ['train/', 'test/']\nfor subdir in subdirs:\n\t# create label subdirectories\n\tlabeldirs = ['dogs/', 'cats/']\n\tfor labldir in labeldirs:\n\t\tnewdir = dataset_home + subdir + labldir\n\t\tmakedirs(newdir, exist_ok=True)\n# seed random number generator\nseed(1)\n# define ratio of pictures to use for validation\nval_ratio = 0.25\n# copy training dataset images into subdirectories\nsrc_directory = 'train/'\nfor file in listdir(src_directory):\n\tsrc = src_directory + '/' + file\n\tdst_dir = 'train/'\n\tif random() < val_ratio:\n\t\tdst_dir = 'test/'\n\tif file.startswith('cat'):\n\t\tdst = dataset_home + dst_dir + 'cats/'  + file\n\t\tcopyfile(src, dst)\n\telif file.startswith('dog'):\n\t\tdst = dataset_home + dst_dir + 'dogs/'  + file\n\t\tcopyfile(src, dst)\n    ","metadata":{"execution":{"iopub.status.busy":"2024-01-27T04:29:36.276501Z","iopub.execute_input":"2024-01-27T04:29:36.277523Z","iopub.status.idle":"2024-01-27T04:29:51.290330Z","shell.execute_reply.started":"2024-01-27T04:29:36.277470Z","shell.execute_reply":"2024-01-27T04:29:51.289534Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!ls -l test1 | wc","metadata":{"execution":{"iopub.status.busy":"2024-01-27T04:29:51.291755Z","iopub.execute_input":"2024-01-27T04:29:51.292047Z","iopub.status.idle":"2024-01-27T04:29:52.568376Z","shell.execute_reply.started":"2024-01-27T04:29:51.292021Z","shell.execute_reply":"2024-01-27T04:29:52.567180Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<h1>CNN MODEL</h1>","metadata":{}},{"cell_type":"code","source":"# baseline model for the dogs vs cats dataset\nimport sys\nfrom matplotlib import pyplot\nfrom keras.utils import to_categorical\nfrom keras.models import Sequential\nfrom keras.layers import Conv2D\nfrom keras.layers import MaxPooling2D\nfrom keras.layers import Dense\nfrom keras.layers import Flatten\nfrom keras.optimizers import SGD\nfrom keras.preprocessing.image import ImageDataGenerator\n\n# define cnn model\ndef define_model1():\n\tmodel = Sequential()\n\tmodel.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same', input_shape=(200, 200, 3)))\n\tmodel.add(MaxPooling2D((2, 2)))\n\tmodel.add(Flatten())\n\tmodel.add(Dense(128, activation='relu', kernel_initializer='he_uniform'))\n\tmodel.add(Dense(1, activation='sigmoid'))\n\t# compile model\n\topt = SGD(lr=0.001, momentum=0.9)\n\tmodel.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])\n\treturn model\n\n# plot diagnostic learning curves\ndef summarize_diagnostics(history):\n\t# plot loss\n\tpyplot.subplot(211)\n\tpyplot.title('Cross Entropy Loss')\n\tpyplot.plot(history.history['loss'], color='blue', label='train')\n\tpyplot.plot(history.history['val_loss'], color='orange', label='test')\n\t# plot accuracy\n\tpyplot.subplot(212)\n\tpyplot.title('Classification Accuracy')\n\tpyplot.plot(history.history['accuracy'], color='blue', label='train')\n\tpyplot.plot(history.history['val_accuracy'], color='orange', label='test')\n\t# save plot to file\n\tfilename = sys.argv[0].split('/')[-1]\n\tpyplot.savefig(filename + '_plot.png')\n\tpyplot.close()\n\n# run the test harness for evaluating a model\ndef run_test_harness1():\n\t# define model\n\tmodel = define_model1()\n\t# create data generator\n\tdatagen = ImageDataGenerator(rescale=1.0/255.0)\n\t# prepare iterators\n\ttrain_it = datagen.flow_from_directory('dataset_dogs_vs_cats/train/',\n\t\tclass_mode='binary', batch_size=8, target_size=(200, 200))\n\ttest_it = datagen.flow_from_directory('dataset_dogs_vs_cats/test/',\n\t\tclass_mode='binary', batch_size=8, target_size=(200, 200))\n\t# fit model\n\thistory = model.fit_generator(train_it, steps_per_epoch=len(train_it),\n\t\tvalidation_data=test_it, validation_steps=len(test_it), epochs=20, verbose=1)\n\t# evaluate model\n\t_, acc = model.evaluate_generator(test_it, steps=len(test_it), verbose=0)\n\tprint('> %.3f' % (acc * 100.0))\n\t# learning curves\n\tsummarize_diagnostics(history)","metadata":{"execution":{"iopub.status.busy":"2024-01-27T04:31:47.886117Z","iopub.execute_input":"2024-01-27T04:31:47.886530Z","iopub.status.idle":"2024-01-27T04:31:47.902005Z","shell.execute_reply.started":"2024-01-27T04:31:47.886498Z","shell.execute_reply":"2024-01-27T04:31:47.900970Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model1 = define_model1()#difine model again\nmodel1.summary()","metadata":{"execution":{"iopub.status.busy":"2024-01-27T04:31:49.051273Z","iopub.execute_input":"2024-01-27T04:31:49.052147Z","iopub.status.idle":"2024-01-27T04:31:49.120252Z","shell.execute_reply.started":"2024-01-27T04:31:49.052111Z","shell.execute_reply":"2024-01-27T04:31:49.119391Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"run_test_harness1()","metadata":{"execution":{"iopub.status.busy":"2024-01-27T04:31:50.235804Z","iopub.execute_input":"2024-01-27T04:31:50.236717Z","iopub.status.idle":"2024-01-27T04:47:14.474678Z","shell.execute_reply.started":"2024-01-27T04:31:50.236678Z","shell.execute_reply":"2024-01-27T04:47:14.473407Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!pip install livelossplot -q\nfrom livelossplot import PlotLossesKeras","metadata":{"execution":{"iopub.status.busy":"2024-01-26T11:34:07.616761Z","iopub.execute_input":"2024-01-26T11:34:07.617677Z","iopub.status.idle":"2024-01-26T11:34:20.192187Z","shell.execute_reply.started":"2024-01-26T11:34:07.617640Z","shell.execute_reply":"2024-01-26T11:34:20.190530Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\nimport sys\nfrom matplotlib import pyplot\nfrom keras.utils import to_categorical\nfrom keras.applications.vgg16 import VGG16\nfrom keras.models import Model\nfrom keras.layers import Dense\nfrom keras.layers import Flatten\nfrom keras.optimizers import SGD\nfrom keras.preprocessing.image import ImageDataGenerator\n\n# define cnn model\ndef define_model():\n\t# load model\n\tmodel = VGG16(include_top=False, input_shape=(224, 224, 3))\n\t# mark loaded layers as not trainable\n\tfor layer in model.layers:\n\t\tlayer.trainable = False\n\t# add new classifier layers\n\tflat1 = Flatten()(model.layers[-1].output)\n\tclass1 = Dense(128, activation='relu', kernel_initializer='he_uniform')(flat1)\n\toutput = Dense(1, activation='sigmoid')(class1)#softmax\n\t# define new model\n\tmodel = Model(inputs=model.inputs, outputs=output)\n\t# compile model\n\topt = SGD(lr=0.001, momentum=0.9)\n\tmodel.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])\n\treturn model\n\n# plot diagnostic learning curves\ndef summarize_diagnostics(history):\n\t# plot loss\n\tpyplot.subplot(211)\n\tpyplot.title('Cross Entropy Loss')\n\tpyplot.plot(history.history['loss'], color='blue', label='train')\n\tpyplot.plot(history.history['val_loss'], color='orange', label='test')\n\t# plot accuracy\n\tpyplot.subplot(212)\n\tpyplot.title('Classification Accuracy')\n\tpyplot.plot(history.history['accuracy'], color='blue', label='train')\n\tpyplot.plot(history.history['val_accuracy'], color='orange', label='test')\n\t# save plot to file\n\tfilename = sys.argv[0].split('/')[-1]\n\tpyplot.savefig(filename + '_plot.png')\n\tpyplot.close()\n\n# run the test harness for evaluating a model\ndef run_test_harness():\n\t# define model\n\tmodel = define_model()\n\t# create data generator\n\tdatagen = ImageDataGenerator(featurewise_center=True)\n\t# specify imagenet mean values for centering\n\tdatagen.mean = [123.68, 116.779, 103.939]\n\t# prepare iterator\n\ttrain_it = datagen.flow_from_directory('dataset_dogs_vs_cats/train/',\n\t\tclass_mode='binary', batch_size=128, target_size=(224, 224))\n\ttest_it = datagen.flow_from_directory('dataset_dogs_vs_cats/test/',\n\t\tclass_mode='binary', batch_size=128, target_size=(224, 224))#change batch size to 128\n\t# fit model\n\thistory = model.fit(train_it, \n                        steps_per_epoch=len(train_it),\n                        validation_data=test_it, \n                        validation_steps=len(test_it), \n                        epochs=10, verbose=1,\n                        callbacks=[PlotLossesKeras()]\n                       )\n\t# evaluate model\n\t_, acc = model.evaluate_generator(test_it, steps=len(test_it), verbose=1)\n\tprint('> %.3f' % (acc * 100.0))","metadata":{"execution":{"iopub.status.busy":"2024-01-26T11:34:20.197409Z","iopub.execute_input":"2024-01-26T11:34:20.198120Z","iopub.status.idle":"2024-01-26T11:34:20.214881Z","shell.execute_reply.started":"2024-01-26T11:34:20.198074Z","shell.execute_reply":"2024-01-26T11:34:20.214045Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model = define_model()#difine model again\nmodel.summary()\n","metadata":{"execution":{"iopub.status.busy":"2024-01-26T11:34:20.216169Z","iopub.execute_input":"2024-01-26T11:34:20.216523Z","iopub.status.idle":"2024-01-26T11:34:20.679084Z","shell.execute_reply.started":"2024-01-26T11:34:20.216496Z","shell.execute_reply":"2024-01-26T11:34:20.678132Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"run_test_harness()","metadata":{"execution":{"iopub.status.busy":"2024-01-26T11:34:20.680948Z","iopub.execute_input":"2024-01-26T11:34:20.681245Z","iopub.status.idle":"2024-01-26T11:54:47.591555Z","shell.execute_reply.started":"2024-01-26T11:34:20.681220Z","shell.execute_reply":"2024-01-26T11:54:47.590600Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}