{"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"}},"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)\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":"2023-07-28T14:07:01.880628Z","iopub.execute_input":"2023-07-28T14:07:01.881247Z","iopub.status.idle":"2023-07-28T14:07:01.891247Z","shell.execute_reply.started":"2023-07-28T14:07:01.8812Z","shell.execute_reply":"2023-07-28T14:07:01.890299Z"},"trusted":true},"execution_count":51,"outputs":[{"name":"stdout","text":"/kaggle/input/dogs-vs-cats-redux-kernels-edition/sample_submission.csv\n/kaggle/input/dogs-vs-cats-redux-kernels-edition/train.zip\n/kaggle/input/dogs-vs-cats-redux-kernels-edition/test.zip\n","output_type":"stream"}]},{"cell_type":"code","source":"# zipファイルの解凍\n!unzip /kaggle/input/dogs-vs-cats-redux-kernels-edition/test.zip\n!unzip /kaggle/input/dogs-vs-cats-redux-kernels-edition/train.zip","metadata":{"execution":{"iopub.status.busy":"2023-07-28T14:07:05.714102Z","iopub.execute_input":"2023-07-28T14:07:05.714555Z","iopub.status.idle":"2023-07-28T14:08:38.36923Z","shell.execute_reply.started":"2023-07-28T14:07:05.714521Z","shell.execute_reply":"2023-07-28T14:08:38.367794Z"},"trusted":true},"execution_count":52,"outputs":[{"name":"stdout","text":"Archive: /kaggle/input/dogs-vs-cats-redux-kernels-edition/test.zip\nreplace test/1.jpg? [y]es, [n]o, [A]ll, [N]one, [r]ename: ^C\nArchive: /kaggle/input/dogs-vs-cats-redux-kernels-edition/train.zip\nreplace train/cat.0.jpg? [y]es, [n]o, [A]ll, [N]one, [r]ename: ^C\n","output_type":"stream"}]},{"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":{"execution":{"iopub.status.busy":"2023-07-28T14:08:43.217206Z","iopub.execute_input":"2023-07-28T14:08:43.217671Z","iopub.status.idle":"2023-07-28T14:08:43.228153Z","shell.execute_reply.started":"2023-07-28T14:08:43.217633Z","shell.execute_reply":"2023-07-28T14:08:43.226246Z"},"trusted":true},"execution_count":55,"outputs":[{"name":"stdout","text":"/kaggle/input/dogs-vs-cats-redux-kernels-edition/sample_submission.csv\n/kaggle/input/dogs-vs-cats-redux-kernels-edition/train.zip\n/kaggle/input/dogs-vs-cats-redux-kernels-edition/test.zip\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## フォルダの作製","metadata":{}},{"cell_type":"code","source":"!mkdir data\n!mkdir data/train\n!mkdir data/train/cats\n!mkdir data/train/dogs\n!mkdir data/vali\n!mkdir data/vali/cats\n!mkdir data/vali/dogs\n\nprint(\"finish\")","metadata":{"execution":{"iopub.status.busy":"2023-07-28T14:08:46.648539Z","iopub.execute_input":"2023-07-28T14:08:46.649009Z","iopub.status.idle":"2023-07-28T14:08:54.887177Z","shell.execute_reply.started":"2023-07-28T14:08:46.648972Z","shell.execute_reply":"2023-07-28T14:08:54.88558Z"},"trusted":true},"execution_count":56,"outputs":[{"name":"stdout","text":"mkdir: cannot create directory ‘data’: File exists\nmkdir: cannot create directory ‘data/train’: File exists\nmkdir: cannot create directory ‘data/train/cats’: File exists\nmkdir: cannot create directory ‘data/train/dogs’: File exists\nmkdir: cannot create directory ‘data/vali’: File exists\nmkdir: cannot create directory ‘data/vali/cats’: File exists\nmkdir: cannot create directory ‘data/vali/dogs’: File exists\nfinish\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## 画像の分別\ntrain内にdogとcatの画像が混合しているためフォルダに応じて振り分ける","metadata":{}},{"cell_type":"code","source":"# 学習用画像枚数（変更可。合計は12500以下にすること）\ntrain_img = 11000\nvali_img = 1500\n\nprint(\"finish\")","metadata":{"execution":{"iopub.status.busy":"2023-07-28T14:08:56.090767Z","iopub.execute_input":"2023-07-28T14:08:56.091399Z","iopub.status.idle":"2023-07-28T14:08:56.099714Z","shell.execute_reply.started":"2023-07-28T14:08:56.091354Z","shell.execute_reply":"2023-07-28T14:08:56.098218Z"},"trusted":true},"execution_count":57,"outputs":[{"name":"stdout","text":"finish\n","output_type":"stream"}]},{"cell_type":"code","source":"# 移動元のフォルダ\noriginal_dataset_dir = \"/kaggle/working/train\"\n# 移動先のtrain_catフォルダ\ntrain_cats_dir = \"/kaggle/working/data/train/cats\"\n# 移動先のtrain_dogフォルダ\ntrain_dogs_dir = \"/kaggle/working/data/train/dogs\"\n\nprint(\"finish\")","metadata":{"execution":{"iopub.status.busy":"2023-07-28T14:09:00.312698Z","iopub.execute_input":"2023-07-28T14:09:00.313169Z","iopub.status.idle":"2023-07-28T14:09:00.31959Z","shell.execute_reply.started":"2023-07-28T14:09:00.313128Z","shell.execute_reply":"2023-07-28T14:09:00.318605Z"},"trusted":true},"execution_count":58,"outputs":[{"name":"stdout","text":"finish\n","output_type":"stream"}]},{"cell_type":"code","source":"# 画像振り分け\nimport shutil\nfnames = [\"cat.{}.jpg\".format(i) for i in range(train_img)]\n\nfor fname in fnames:\n from_data = original_dataset_dir + \"/\" + fname\n to_data = train_cats_dir + \"/\" + fname\n shutil.copyfile(from_data, to_data)\n \nfnames = [\"dog.{}.jpg\".format(i) for i in range(train_img)]\n\nfor fname in fnames:\n from_data = original_dataset_dir + \"/\" + fname\n to_data = train_dogs_dir + \"/\" + fname\n shutil.copyfile(from_data, to_data)\n \nprint(\"finish\")","metadata":{"execution":{"iopub.status.busy":"2023-07-28T14:09:04.82585Z","iopub.execute_input":"2023-07-28T14:09:04.826313Z","iopub.status.idle":"2023-07-28T14:09:08.976485Z","shell.execute_reply.started":"2023-07-28T14:09:04.82628Z","shell.execute_reply":"2023-07-28T14:09:08.975007Z"},"trusted":true},"execution_count":59,"outputs":[{"name":"stdout","text":"finish\n","output_type":"stream"}]},{"cell_type":"code","source":"# 移動元のフォルダ\noriginal_dataset_dir = \"/kaggle/working/train\"\n# 移動先のvalidation_catフォルダ\nvali_cats_dir = \"/kaggle/working/data/vali/cats\"\n# 移動先のvalidation_catフォルダ\nvali_dogs_dir = \"/kaggle/working/data/vali/dogs\"\n\nprint(\"finish\")","metadata":{"execution":{"iopub.status.busy":"2023-07-28T14:09:12.305671Z","iopub.execute_input":"2023-07-28T14:09:12.306113Z","iopub.status.idle":"2023-07-28T14:09:12.313429Z","shell.execute_reply.started":"2023-07-28T14:09:12.30608Z","shell.execute_reply":"2023-07-28T14:09:12.311827Z"},"trusted":true},"execution_count":60,"outputs":[{"name":"stdout","text":"finish\n","output_type":"stream"}]},{"cell_type":"code","source":"fnames = [\"cat.{}.jpg\".format(i+train_img) for i in range(vali_img)]\n\nfor fname in fnames:\n from_data = original_dataset_dir + \"/\" + fname\n to_data = vali_cats_dir + \"/\" + fname\n shutil.copyfile(from_data, to_data)\n \nfnames = [\"dog.{}.jpg\".format(i+train_img) for i in range(vali_img)]\n\nfor fname in fnames:\n from_data = original_dataset_dir + \"/\" + fname\n to_data = vali_dogs_dir + \"/\" + fname\n shutil.copyfile(from_data, to_data)\n \nprint(\"finish\")","metadata":{"execution":{"iopub.status.busy":"2023-07-28T14:09:14.627724Z","iopub.execute_input":"2023-07-28T14:09:14.628168Z","iopub.status.idle":"2023-07-28T14:09:15.2434Z","shell.execute_reply.started":"2023-07-28T14:09:14.628125Z","shell.execute_reply":"2023-07-28T14:09:15.241138Z"},"trusted":true},"execution_count":61,"outputs":[{"name":"stdout","text":"finish\n","output_type":"stream"}]},{"cell_type":"code","source":"# 画像枚数の確認\nprint(\"train cat:{}\".format(len(os.listdir(train_cats_dir))))\nprint(\"train dog:{}\".format(len(os.listdir(train_dogs_dir))))\n\nprint(\"vali cat:{}\".format(len(os.listdir(vali_cats_dir))))\nprint(\"vali dog:{}\".format(len(os.listdir(vali_dogs_dir))))","metadata":{"execution":{"iopub.status.busy":"2023-07-28T14:09:22.476052Z","iopub.execute_input":"2023-07-28T14:09:22.476495Z","iopub.status.idle":"2023-07-28T14:09:22.505703Z","shell.execute_reply.started":"2023-07-28T14:09:22.476447Z","shell.execute_reply":"2023-07-28T14:09:22.50432Z"},"trusted":true},"execution_count":62,"outputs":[{"name":"stdout","text":"train cat:11000\ntrain dog:11000\nvali cat:1500\nvali dog:1500\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## ライブラリのインポート","metadata":{}},{"cell_type":"code","source":"import numpy as np\nimport matplotlib.pyplot as plt\n%matplotlib inline\n\nimport os\nimport math\nimport urllib.request\nimport zipfile\n\nfrom keras.models import Sequential, load_model, Model\nfrom keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense\nfrom keras.layers import Activation, BatchNormalization, Input\nfrom tensorflow.keras.optimizers import Adam\n# from keras.optimizers import adam_v2\n# from keras.optimizers import adam_v2, sgd_experimental\nfrom keras.utils import np_utils\nfrom keras.callbacks import EarlyStopping\nfrom keras.applications.vgg16 import VGG16\nfrom keras.preprocessing.image import ImageDataGenerator\nimport tensorflow as tf","metadata":{"execution":{"iopub.status.busy":"2023-07-28T14:09:26.394847Z","iopub.execute_input":"2023-07-28T14:09:26.395387Z","iopub.status.idle":"2023-07-28T14:09:26.411001Z","shell.execute_reply.started":"2023-07-28T14:09:26.395342Z","shell.execute_reply":"2023-07-28T14:09:26.409313Z"},"trusted":true},"execution_count":64,"outputs":[]},{"cell_type":"markdown","source":"## VGG16モデルの取得","metadata":{}},{"cell_type":"code","source":"# モデルの取得\nmodel = VGG16(include_top=False, weights=\"imagenet\")\n# モデル構造の確認\nmodel.summary()\n","metadata":{"execution":{"iopub.status.busy":"2023-07-28T14:09:29.090107Z","iopub.execute_input":"2023-07-28T14:09:29.091272Z","iopub.status.idle":"2023-07-28T14:09:29.710519Z","shell.execute_reply.started":"2023-07-28T14:09:29.091219Z","shell.execute_reply":"2023-07-28T14:09:29.709105Z"},"trusted":true},"execution_count":65,"outputs":[{"name":"stdout","text":"Model: \"vgg16\"\n_________________________________________________________________\n Layer (type) Output Shape Param # \n=================================================================\n input_8 (InputLayer) [(None, None, None, 3)] 0 \n \n block1_conv1 (Conv2D) (None, None, None, 64) 1792 \n \n block1_conv2 (Conv2D) (None, None, None, 64) 36928 \n \n block1_pool (MaxPooling2D) (None, None, None, 64) 0 \n \n block2_conv1 (Conv2D) (None, None, None, 128) 73856 \n \n block2_conv2 (Conv2D) (None, None, None, 128) 147584 \n \n block2_pool (MaxPooling2D) (None, None, None, 128) 0 \n \n block3_conv1 (Conv2D) (None, None, None, 256) 295168 \n \n block3_conv2 (Conv2D) (None, None, None, 256) 590080 \n \n block3_conv3 (Conv2D) (None, None, None, 256) 590080 \n \n block3_pool (MaxPooling2D) (None, None, None, 256) 0 \n \n block4_conv1 (Conv2D) (None, None, None, 512) 1180160 \n \n block4_conv2 (Conv2D) (None, None, None, 512) 2359808 \n \n block4_conv3 (Conv2D) (None, None, None, 512) 2359808 \n \n block4_pool (MaxPooling2D) (None, None, None, 512) 0 \n \n block5_conv1 (Conv2D) (None, None, None, 512) 2359808 \n \n block5_conv2 (Conv2D) (None, None, None, 512) 2359808 \n \n block5_conv3 (Conv2D) (None, None, None, 512) 2359808 \n \n block5_pool (MaxPooling2D) (None, None, None, 512) 0 \n \n=================================================================\nTotal params: 14,714,688\nTrainable params: 14,714,688\nNon-trainable params: 0\n_________________________________________________________________\n","output_type":"stream"}]},{"cell_type":"code","source":"model_vgg16 = VGG16(include_top=False, weights=\"imagenet\", input_tensor=Input(shape=(224,224,3)))\nmodel_vgg16.layers","metadata":{"execution":{"iopub.status.busy":"2023-07-28T14:09:32.259857Z","iopub.execute_input":"2023-07-28T14:09:32.260354Z","iopub.status.idle":"2023-07-28T14:09:32.79082Z","shell.execute_reply.started":"2023-07-28T14:09:32.260315Z","shell.execute_reply":"2023-07-28T14:09:32.788884Z"},"trusted":true},"execution_count":66,"outputs":[{"execution_count":66,"output_type":"execute_result","data":{"text/plain":"[,\n ,\n ,\n ,\n ,\n ,\n ,\n ,\n ,\n ,\n ,\n ,\n ,\n ,\n ,\n ,\n ,\n ,\n ]"},"metadata":{}}]},{"cell_type":"markdown","source":"## 学習データの作成","metadata":{}},{"cell_type":"code","source":"num_slass = 2\nbatch_size = 128\n\n# 学習データのパス\ntrain_dir = \"/kaggle/working/data/train\"\nvalid_dir = \"/kaggle/working/data/vali\"\n\n# データ拡張しながら，テンソル画像データのバッチを生成\n# データ拡張\ntrain_data_generator = ImageDataGenerator(rescale=1.0/255,\n rotation_range=45,\n horizontal_flip=True,\n vertical_flip=True,\n width_shift_range=0.2,\n height_shift_range=0.2,\n shear_range=math.pi/4,\n zoom_range=0.5)\n\nvalid_data_generator = ImageDataGenerator(rescale=1.0/255)\n\n# ディレクトリからデータを生成\n# 画像サイズはVGG16モデルにならい、(224.224)\ntrain_generator = train_data_generator.flow_from_directory(\n train_dir,\n target_size=(224,224),\n batch_size=batch_size,\n class_mode=\"binary\",\n shuffle=False\n)\n\nvalid_generator = valid_data_generator.flow_from_directory(\n valid_dir,\n target_size=(224,224),\n batch_size=batch_size,\n class_mode=\"binary\",\n shuffle=False\n)\n","metadata":{"execution":{"iopub.status.busy":"2023-07-28T14:09:34.761721Z","iopub.execute_input":"2023-07-28T14:09:34.762185Z","iopub.status.idle":"2023-07-28T14:09:35.596934Z","shell.execute_reply.started":"2023-07-28T14:09:34.762148Z","shell.execute_reply":"2023-07-28T14:09:35.595412Z"},"trusted":true},"execution_count":67,"outputs":[{"name":"stdout","text":"Found 22000 images belonging to 2 classes.\nFound 3000 images belonging to 2 classes.\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## モデル構築\n新しく全結合層を作成し、VGG16と合体させる","metadata":{}},{"cell_type":"code","source":"# VGG16をインポート\nmodel_vgg16 = VGG16(include_top=False, weights=\"imagenet\", input_tensor=Input(shape=(224,224,3)))\n\n# 全結合層を作成\nx = model_vgg16.output\nx = Flatten()(x)\nx = Dense(256)(x)\nx = Activation(\"relu\")(x)\nx = Dropout(0.5)(x)\nprediction = Dense(1, activation=\"sigmoid\")(x)\n\n# モデル合体\nmodel = Model(inputs=model_vgg16.input, outputs=prediction)\n","metadata":{"execution":{"iopub.status.busy":"2023-07-28T14:09:39.639141Z","iopub.execute_input":"2023-07-28T14:09:39.640081Z","iopub.status.idle":"2023-07-28T14:09:40.208539Z","shell.execute_reply.started":"2023-07-28T14:09:39.640038Z","shell.execute_reply":"2023-07-28T14:09:40.207031Z"},"trusted":true},"execution_count":68,"outputs":[]},{"cell_type":"markdown","source":"## ファインチューニング","metadata":{}},{"cell_type":"code","source":"# block4_pool(14層目)までの重みの更新をキャンセルし、浅い層の重みはそのまま使用する\nfor layer in model_vgg16.layers[:15]:\n layer.trainable=False\n\n# 学習の設定\n# 損失はコンペのスコアと同様にクロスエントロピー損失を使用\nmodel.compile(loss=\"binary_crossentropy\",\n optimizer=Adam(learning_rate=0.0001),\n metrics=[\"accuracy\"])","metadata":{"execution":{"iopub.status.busy":"2023-07-28T14:09:42.871745Z","iopub.execute_input":"2023-07-28T14:09:42.873048Z","iopub.status.idle":"2023-07-28T14:09:42.889891Z","shell.execute_reply.started":"2023-07-28T14:09:42.872993Z","shell.execute_reply":"2023-07-28T14:09:42.888404Z"},"trusted":true},"execution_count":69,"outputs":[]},{"cell_type":"markdown","source":"## 学習","metadata":{}},{"cell_type":"code","source":"# 学習\nhistory = model.fit(train_generator,\n epochs=10,\n verbose=1,\n validation_data=valid_generator,\n callbacks=[EarlyStopping(patience=10)])\n\n# モデル保存\nmodel.save(\"/kaggle/working/data/CNN_VGG16.h5\")","metadata":{"execution":{"iopub.status.busy":"2023-07-28T14:09:47.344602Z","iopub.execute_input":"2023-07-28T14:09:47.345049Z","iopub.status.idle":"2023-07-28T14:10:40.865613Z","shell.execute_reply.started":"2023-07-28T14:09:47.345016Z","shell.execute_reply":"2023-07-28T14:10:40.863579Z"},"trusted":true},"execution_count":70,"outputs":[{"name":"stdout","text":"Epoch 1/10\n","output_type":"stream"},{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)","Cell \u001b[0;32mIn[70], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# 学習\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m history \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_generator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4\u001b[0m \u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 5\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalid_generator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 6\u001b[0m \u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mEarlyStopping\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpatience\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;66;03m# モデル保存\u001b[39;00m\n\u001b[1;32m 9\u001b[0m model\u001b[38;5;241m.\u001b[39msave(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/kaggle/working/data/CNN_VGG16.h5\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/keras/utils/traceback_utils.py:65\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 63\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 64\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 65\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 66\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 67\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/keras/engine/training.py:1685\u001b[0m, in \u001b[0;36mModel.fit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m 1677\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m tf\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mexperimental\u001b[38;5;241m.\u001b[39mTrace(\n\u001b[1;32m 1678\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrain\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 1679\u001b[0m epoch_num\u001b[38;5;241m=\u001b[39mepoch,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1682\u001b[0m _r\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m 1683\u001b[0m ):\n\u001b[1;32m 1684\u001b[0m callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_begin(step)\n\u001b[0;32m-> 1685\u001b[0m tmp_logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1686\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data_handler\u001b[38;5;241m.\u001b[39mshould_sync:\n\u001b[1;32m 1687\u001b[0m context\u001b[38;5;241m.\u001b[39masync_wait()\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/tensorflow/python/util/traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 148\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 150\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 152\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:894\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 891\u001b[0m compiler \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxla\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnonXla\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 893\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m OptionalXlaContext(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile):\n\u001b[0;32m--> 894\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 896\u001b[0m new_tracing_count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexperimental_get_tracing_count()\n\u001b[1;32m 897\u001b[0m without_tracing \u001b[38;5;241m=\u001b[39m (tracing_count \u001b[38;5;241m==\u001b[39m new_tracing_count)\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:959\u001b[0m, in \u001b[0;36mFunction._call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 955\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m \u001b[38;5;66;03m# Fall through to cond-based initialization.\u001b[39;00m\n\u001b[1;32m 956\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 957\u001b[0m \u001b[38;5;66;03m# Lifting succeeded, so variables are initialized and we can run the\u001b[39;00m\n\u001b[1;32m 958\u001b[0m \u001b[38;5;66;03m# no_variable_creation function.\u001b[39;00m\n\u001b[0;32m--> 959\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_no_variable_creation_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 960\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 961\u001b[0m _, _, filtered_flat_args \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 962\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_variable_creation_fn\u001b[38;5;241m.\u001b[39m_function_spec \u001b[38;5;66;03m# pylint: disable=protected-access\u001b[39;00m\n\u001b[1;32m 963\u001b[0m \u001b[38;5;241m.\u001b[39mcanonicalize_function_inputs(\n\u001b[1;32m 964\u001b[0m args, kwds))\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/tracing_compiler.py:143\u001b[0m, in \u001b[0;36mTracingCompiler.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 140\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n\u001b[1;32m 141\u001b[0m (concrete_function,\n\u001b[1;32m 142\u001b[0m filtered_flat_args) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_maybe_define_function(args, kwargs)\n\u001b[0;32m--> 143\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mconcrete_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_flat\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 144\u001b[0m \u001b[43m \u001b[49m\u001b[43mfiltered_flat_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcaptured_inputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconcrete_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcaptured_inputs\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/monomorphic_function.py:1757\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m 1753\u001b[0m possible_gradient_type \u001b[38;5;241m=\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[1;32m 1754\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (possible_gradient_type \u001b[38;5;241m==\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[1;32m 1755\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m executing_eagerly):\n\u001b[1;32m 1756\u001b[0m \u001b[38;5;66;03m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[0;32m-> 1757\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_build_call_outputs(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_inference_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1758\u001b[0m \u001b[43m \u001b[49m\u001b[43mctx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcancellation_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcancellation_manager\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 1759\u001b[0m forward_backward \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[1;32m 1760\u001b[0m args,\n\u001b[1;32m 1761\u001b[0m possible_gradient_type,\n\u001b[1;32m 1762\u001b[0m executing_eagerly)\n\u001b[1;32m 1763\u001b[0m forward_function, args_with_tangents \u001b[38;5;241m=\u001b[39m forward_backward\u001b[38;5;241m.\u001b[39mforward()\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/monomorphic_function.py:381\u001b[0m, in \u001b[0;36m_EagerDefinedFunction.call\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m 379\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _InterpolateFunctionError(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 380\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cancellation_manager \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 381\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[43mexecute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 382\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msignature\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 383\u001b[0m \u001b[43m \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_num_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 384\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 385\u001b[0m \u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 386\u001b[0m \u001b[43m \u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mctx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 387\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 388\u001b[0m outputs \u001b[38;5;241m=\u001b[39m execute\u001b[38;5;241m.\u001b[39mexecute_with_cancellation(\n\u001b[1;32m 389\u001b[0m \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msignature\u001b[38;5;241m.\u001b[39mname),\n\u001b[1;32m 390\u001b[0m num_outputs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_outputs,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 393\u001b[0m ctx\u001b[38;5;241m=\u001b[39mctx,\n\u001b[1;32m 394\u001b[0m cancellation_manager\u001b[38;5;241m=\u001b[39mcancellation_manager)\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/tensorflow/python/eager/execute.py:52\u001b[0m, in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 51\u001b[0m ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[0;32m---> 52\u001b[0m tensors \u001b[38;5;241m=\u001b[39m \u001b[43mpywrap_tfe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTFE_Py_Execute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_handle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 53\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 54\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 55\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "],"ename":"KeyboardInterrupt","evalue":"","output_type":"error"}]},{"cell_type":"markdown","source":"## lossとaccuracyを可視化","metadata":{}},{"cell_type":"code","source":"# lossとaccuracyを可視化\nprint(\"-\"*100)\nplt.figure(1, figsize=(13,4))\nplt.subplots_adjust(wspace=0.5)\n\n# 学習曲線\nplt.subplot(1, 2, 1)\nplt.plot(history.history[\"loss\"], label=\"train\")\nplt.plot(history.history[\"val_loss\"], label=\"valid\")\nplt.title(\"train and valid loss\")\nplt.xlabel(\"epoch\")\nplt.ylabel(\"loss\")\nplt.legend()\nplt.grid()\n\n# 精度表示\nplt.subplot(1, 2, 2)\nplt.plot(history.history[\"accuracy\"], label=\"train\")\nplt.plot(history.history[\"val_accuracy\"], label=\"valid\")\nplt.title(\"train and valid accuracy\")\nplt.xlabel(\"epoch\")\nplt.ylabel(\"accuracy\")\nplt.legend()\nplt.grid()\n\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2023-07-28T12:46:18.425347Z","iopub.status.idle":"2023-07-28T12:46:18.426051Z","shell.execute_reply.started":"2023-07-28T12:46:18.425843Z","shell.execute_reply":"2023-07-28T12:46:18.425865Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## submission.csvの書き出し","metadata":{}},{"cell_type":"code","source":"import keras.utils as image\nfrom keras.utils import load_img, img_to_array\nimport re\nimport csv\nimport glob\nfrom keras import models\n# テストデータのパスを取得\np = re.compile(r'\\d+')\nimcount = 1\ntest_list = glob.glob('/kaggle/working/test/*.jpg')\n# 画像パスをソートする\ntest_list = sorted(test_list,key=lambda s: int(p.search(s).group()))\n\n#モデルの読み込み\n# model_vgg = models.load_model('/kaggle/working/data/CNN_VGG16.h5')\n# print(\"load model\")\n\n#csv書き込み（テストデータ1～テストデータ6250）\nwith open('/kaggle/working/submission.csv', 'w') as f:\n writer= csv.writer(f, lineterminator='\\n')\n writer.writerow(['id', 'label'])\n for i in range(6250):\n # 画像パスの取得\n j = test_list[i]\n # 画像読み込み\n img = image.load_img(j, target_size=(224, 224))\n # PIL 形式から ndarray に変換する\n x = img_to_array(img)\n # 次元の追加\n x = np.expand_dims(x, axis=0)\n # 正規化\n x = x / 255.0\n # 学習したモデルを用いて予測\n result_predict = model.predict(x)\n # csvファイルに書き込み\n writer.writerow([imcount, result_predict[0][0]])\n imcount += 1\n print(result_predict[0][0])\n\nprint(\"finish\")","metadata":{"execution":{"iopub.status.busy":"2023-07-28T12:46:18.427593Z","iopub.status.idle":"2023-07-28T12:46:18.428299Z","shell.execute_reply.started":"2023-07-28T12:46:18.428087Z","shell.execute_reply":"2023-07-28T12:46:18.428109Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#csv書き込み（テストデータ6250～テストデータ12500）\nwith open('/kaggle/working/submission.csv', 'a') as f:\n writer= csv.writer(f, lineterminator='\\n')\n for i in range(6250, 12500):\n j = test_list[i]\n img = image.load_img(j, target_size=(224, 224))\n x = img_to_array(img)\n x = np.expand_dims(x, axis=0)\n x = x / 255.0\n result_predict = model.predict(x)\n writer.writerow([imcount, result_predict[0][0]])\n imcount += 1\n print(i)\n\nprint(\"finish\")","metadata":{"execution":{"iopub.status.busy":"2023-07-28T12:46:18.429532Z","iopub.status.idle":"2023-07-28T12:46:18.429952Z","shell.execute_reply.started":"2023-07-28T12:46:18.42975Z","shell.execute_reply":"2023-07-28T12:46:18.429769Z"},"trusted":true},"execution_count":null,"outputs":[]}]}