{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceId":5441,"databundleVersionId":38425,"sourceType":"competition"}],"dockerImageVersionId":30673,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"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)\nimport os, shutil\n\nfrom keras import layers\nfrom keras import models\nfrom keras import optimizers\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\n# for dirname, _, filenames in os.walk('/kaggle/input'):\n#     for filename in filenames:\n#         print(os.path.join(dirname, filename))\n\n# You can write up to 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\n\n#unzip files into output\n#! unzip /kaggle/input/dogs-vs-cats-redux-kernels-edition/test.zip\n#! unzip /kaggle/input/dogs-vs-cats-redux-kernels-edition/train.zip\n\n\n# bellow several directories are made to place the test, validation, and train data into. \n# \nbase_dir = '/kaggle/working/'\ntrain_dir = os.path.join(base_dir, 'train1')\nvalidation_dir = os.path.join(base_dir, 'validation')\ntest_dir = os.path.join(base_dir, 'test1')\ntrain_cat_dir = os.path.join(train_dir, 'cats')\ntrain_dog_dir = os.path.join(train_dir, 'dogs')\nvalidation_cat_dir = os.path.join(validation_dir, 'cats')\nvalidation_dog_dir = os.path.join(validation_dir, 'dogs')\ntest_cat_dir = os.path.join(test_dir, 'cats')\ntest_dog_dir = os.path.join(test_dir, 'dogs')\noriginal_dataset_train_dir = '/kaggle/working/train'\n\n# os.mkdir(train_dir)\n# os.mkdir(validation_dir)\n# os.mkdir(test_dir)\n# os.mkdir(train_cat_dir)\n# os.mkdir(train_dog_dir)\n# os.mkdir(validation_cat_dir)\n# os.mkdir(validation_dog_dir)\n# os.mkdir(test_cat_dir)\n# os.mkdir(test_dog_dir)\n\n\n\n\n#input jpg data from the original unziped files test and train, into the new directories created from above\n\n\n# train validate test cat data\n\n# #train\n# fnames = ['cat.{}.jpg'.format(i) for i in range(1000)] # get cat pics 0-999\n# for fname in fnames:\n#     src = os.path.join(original_dataset_train_dir, fname)\n#     dst = os.path.join(train_cat_dir, fname)\n#     shutil.copyfile(src, dst)\n\n# #validation\n# fnames = ['cat.{}.jpg'.format(i) for i in range(1000, 1500)] # get cat pics 1000-1499\n# for fname in fnames:\n#     src = os.path.join(original_dataset_train_dir, fname)\n#     dst = os.path.join(validation_cat_dir, fname)\n#     shutil.copyfile(src, dst)\n    \n# #test\n# fnames = ['cat.{}.jpg'.format(i) for i in range(1500, 2000)] # get cat pics 1500-1999\n# for fname in fnames:\n#     src = os.path.join(original_dataset_train_dir, fname)\n#     dst = os.path.join(test_cat_dir, fname)\n#     shutil.copyfile(src, dst)    \n    \n    \n    \n# # train validate test dog data\n\n# #train\n# fnames = ['dog.{}.jpg'.format(i) for i in range(1000)] # get dog pics 0-999\n# for fname in fnames:\n#     src = os.path.join(original_dataset_train_dir, fname)\n#     dst = os.path.join(train_dog_dir, fname)\n#     shutil.copyfile(src, dst)\n\n# #validation\n# fnames = ['dog.{}.jpg'.format(i) for i in range(1000, 1500)] # get dog pics 1000-1499\n# for fname in fnames:\n#     src = os.path.join(original_dataset_train_dir, fname)\n#     dst = os.path.join(validation_dog_dir, fname)\n#     shutil.copyfile(src, dst)\n    \n# #test\n# fnames = ['dog.{}.jpg'.format(i) for i in range(1500, 2000)] # get dog pics 1500-1999\n# for fname in fnames:\n#     src = os.path.join(original_dataset_train_dir, fname)\n#     dst = os.path.join(test_dog_dir, fname)\n#     shutil.copyfile(src, dst)  \n\n\n\n# #check size of image in each directory\n# print('cat train: ', len(os.listdir(train_cat_dir)))\n# print('cat test: ', len(os.listdir(validation_cat_dir)))\n# print('cat validate: ', len(os.listdir(test_cat_dir)))\n\n# print('dog train: ', len(os.listdir(train_dog_dir)))\n# print('dog test: ', len(os.listdir(validation_dog_dir)))\n# print('dog validate: ', len(os.listdir(test_dog_dir)))\n\n\n\n# create model\nmodel = models.Sequential()\nmodel.add(layers.Conv2D(32, (3,3), activation = 'relu', input_shape = (150, 150, 3) ))\n\nmodel.add(layers.MaxPooling2D((2,2)))\nmodel.add(layers.Conv2D(64, (3,3), activation='relu'))\n\nmodel.add(layers.MaxPooling2D((2,2)))\nmodel.add(layers.Conv2D(128, (3,3), activation='relu'))\n\nmodel.add(layers.MaxPooling2D((2,2)))\nmodel.add(layers.Conv2D(128, (3,3), activation='relu'))\n\nmodel.add(layers.MaxPooling2D((2,2)))\n\nmodel.add(layers.Flatten())\n\nmodel.add(layers.Dense(512, activation = 'relu'))\nmodel.add(layers.Dense(1, activation = 'sigmoid'))\n\n\n# model.summary()\n\nmodel.compile(loss = 'binary_crossentropy', \n              optimizer=optimizers.RMSprop(learning_rate=1e-4), \n              metrics=['acc'])\n\n# filter, _ = model.layers[0].get_weights()\n\n# print(filter.shape)\n\n\n\ntrain_datagen = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1./255)\ntest_datagen = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1./255)\n\ntrain_generator = train_datagen.flow_from_directory(\n    train_dir, \n    target_size=(150,150),\n    batch_size = 20, \n    class_mode = 'binary'\n)\n\nvalidation_generator = test_datagen.flow_from_directory(\n    validation_dir, \n    target_size=(150,150),\n    batch_size = 20, \n    class_mode = 'binary'\n)\n\nhistory = model.fit(\n    train_generator, \n    steps_per_epoch=100, \n    epochs=30,\n    validation_data=validation_generator,\n    validation_steps=50)\n\n# model.save('cats_and_dogs_small_l.h5')\n\n\n#plot training and validation error: \n\nacc = history.history['acc']\nval_acc = history.history['val_acc']\nloss=history.history['loss']\nval_loss=history.history['val_loss']\n\nepochs = range(1, len(acc) +1 )\n\nplt.plot(epochs, acc, 'bo', label='Training acc')\nplt.plot(epochs, val_acc, 'b', label='Validation acc')\nplt.title('Training and validation accuracy')\nplt.legend()\n\nplt.figure()\n\nplt.plot(epochs, loss, 'bo', label='Training loss')\nplt.plot(epochs, val_loss, 'b', label='Validation loss')\nplt.title('Training and validation loss')\nplt.legend()\n\nplt.show()\nprint('Successful')\n\n\n\n\n\n# folder = validation_cat_dir\n# for filename in os.listdir(folder):\n#     file_path = os.path.join(folder, filename)\n#     try:\n#         if os.path.isfile(file_path) or os.path.islink(file_path):\n#             os.unlink(file_path)\n#         elif os.path.isdir(file_path):\n#             shutil.rmtree(file_path)\n#     except Exception as e:\n#         print('Failed to delete %s. Reason: %s' % (file_path, e))\n\n\n\n\n\n\n","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-05-12T18:30:27.672171Z","iopub.execute_input":"2024-05-12T18:30:27.672545Z","iopub.status.idle":"2024-05-12T18:31:35.438796Z","shell.execute_reply.started":"2024-05-12T18:30:27.672518Z","shell.execute_reply":"2024-05-12T18:31:35.437899Z"},"trusted":true},"outputs":[{"name":"stdout","text":"Found 2000 images belonging to 2 classes.\nFound 1000 images belonging to 2 classes.\nEpoch 1/30\n\u001b[1m  6/100\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 27ms/step - acc: 0.4147 - loss: 0.6999","output_type":"stream"},{"name":"stderr","text":"W0000 00:00:1715538630.016397      89 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m 99/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - acc: 0.4974 - loss: 0.6948","output_type":"stream"},{"name":"stderr","text":"W0000 00:00:1715538633.100594      89 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 43ms/step - acc: 0.4978 - loss: 0.6948 - val_acc: 0.5560 - val_loss: 0.6852\nEpoch 2/30\n\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116us/step - acc: 0.0000e+00 - loss: 0.0000e+00 - val_acc: 0.0000e+00 - val_loss: 0.0000e+00\nEpoch 3/30\n\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 41ms/step - acc: 0.5691 - loss: 0.6827 - val_acc: 0.6020 - val_loss: 0.6656\nEpoch 4/30\n\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94us/step - acc: 0.0000e+00 - loss: 0.0000e+00 - val_acc: 0.0000e+00 - val_loss: 0.0000e+00\nEpoch 5/30\n\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 40ms/step - acc: 0.5980 - loss: 0.6633 - val_acc: 0.6020 - val_loss: 0.6549\nEpoch 6/30\n\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111us/step - acc: 0.0000e+00 - loss: 0.0000e+00 - val_acc: 0.0000e+00 - val_loss: 0.0000e+00\nEpoch 7/30\n\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 40ms/step - acc: 0.6222 - loss: 0.6417 - val_acc: 0.6160 - val_loss: 0.6456\nEpoch 8/30\n\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87us/step - acc: 0.0000e+00 - loss: 0.0000e+00 - val_acc: 0.0000e+00 - val_loss: 0.0000e+00\nEpoch 9/30\n\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 41ms/step - acc: 0.6605 - loss: 0.6253 - val_acc: 0.6590 - val_loss: 0.6170\nEpoch 10/30\n\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91us/step - acc: 0.0000e+00 - loss: 0.0000e+00 - val_acc: 0.0000e+00 - val_loss: 0.0000e+00\nEpoch 11/30\n\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 42ms/step - acc: 0.6826 - loss: 0.5925 - val_acc: 0.6330 - val_loss: 0.6268\nEpoch 12/30\n\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89us/step - acc: 0.0000e+00 - loss: 0.0000e+00 - val_acc: 0.0000e+00 - val_loss: 0.0000e+00\nEpoch 13/30\n\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 40ms/step - acc: 0.7162 - loss: 0.5572 - val_acc: 0.6570 - val_loss: 0.6163\nEpoch 14/30\n\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94us/step - acc: 0.0000e+00 - loss: 0.0000e+00 - val_acc: 0.0000e+00 - val_loss: 0.0000e+00\nEpoch 15/30\n\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 40ms/step - acc: 0.7492 - loss: 0.5152 - val_acc: 0.6900 - val_loss: 0.5870\nEpoch 16/30\n\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108us/step - acc: 0.0000e+00 - loss: 0.0000e+00 - val_acc: 0.0000e+00 - val_loss: 0.0000e+00\nEpoch 17/30\n\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 41ms/step - acc: 0.7633 - loss: 0.5118 - val_acc: 0.6810 - val_loss: 0.5962\nEpoch 18/30\n\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112us/step - acc: 0.0000e+00 - loss: 0.0000e+00 - val_acc: 0.0000e+00 - val_loss: 0.0000e+00\nEpoch 19/30\n\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 41ms/step - acc: 0.7364 - loss: 0.5102 - val_acc: 0.6690 - val_loss: 0.5967\nEpoch 20/30\n\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89us/step - acc: 0.0000e+00 - loss: 0.0000e+00 - val_acc: 0.0000e+00 - val_loss: 0.0000e+00\nEpoch 21/30\n\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 41ms/step - acc: 0.7707 - loss: 0.4741 - val_acc: 0.6800 - val_loss: 0.6150\nEpoch 22/30\n\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94us/step - acc: 0.0000e+00 - loss: 0.0000e+00 - val_acc: 0.0000e+00 - val_loss: 0.0000e+00\nEpoch 23/30\n\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 41ms/step - acc: 0.7682 - loss: 0.4746 - val_acc: 0.6740 - val_loss: 0.6127\nEpoch 24/30\n\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113us/step - acc: 0.0000e+00 - loss: 0.0000e+00 - val_acc: 0.0000e+00 - val_loss: 0.0000e+00\nEpoch 25/30\n\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 42ms/step - acc: 0.7927 - loss: 0.4349 - val_acc: 0.7130 - val_loss: 0.5528\nEpoch 26/30\n\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106us/step - acc: 0.0000e+00 - loss: 0.0000e+00 - val_acc: 0.0000e+00 - val_loss: 0.0000e+00\nEpoch 27/30\n\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 42ms/step - acc: 0.8183 - loss: 0.3941 - val_acc: 0.7200 - val_loss: 0.5413\nEpoch 28/30\n\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109us/step - acc: 0.0000e+00 - loss: 0.0000e+00 - val_acc: 0.0000e+00 - val_loss: 0.0000e+00\nEpoch 29/30\n\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 40ms/step - acc: 0.8369 - loss: 0.3793 - val_acc: 0.7100 - val_loss: 0.5610\nEpoch 30/30\n\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105us/step - acc: 0.0000e+00 - loss: 0.0000e+00 - val_acc: 0.0000e+00 - val_loss: 0.0000e+00\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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"},"metadata":{}},{"name":"stdout","text":"Successful\n","output_type":"stream"}],"execution_count":14},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"markdown","source":"","metadata":{}}]}