{"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":"# 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","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import numpy as np\nimport tensorflow as tf\nfrom tensorflow.keras import layers\nfrom tensorflow.keras.models import Sequential\n\n# Load training data\nX_train = np.load(\"train_data.npy\")\ny_train = np.load(\"train_labels.npy\")\n\n# Build model\nmodel = Sequential([\n  layers.Conv3D(32, (3, 3, 3), activation='relu', input_shape=(64, 64, 64, 1)),\n  layers.MaxPooling3D((2, 2, 2)),\n  layers.Conv3D(64, (3, 3, 3), activation='relu'),\n  layers.MaxPooling3D((2, 2, 2)),\n  layers.Conv3D(128, (3, 3, 3), activation='relu'),\n  layers.MaxPooling3D((2, 2, 2)),\n  layers.Flatten(),\n  layers.Dense(64, activation='relu'),\n  layers.Dense(1, activation='sigmoid')\n])\n\n# Compile model\nmodel.compile(optimizer='adam',\n              loss='binary_crossentropy',\n              metrics=['accuracy'])\n\n# Train model\nmodel.fit(X_train, y_train, epochs=10, batch_size=32)\n\n# Load test data\nX_test = np.load(\"test_data.npy\")\ny_test = np.load(\"test_labels.npy\")\n\n# Evaluate model on test data\nloss, accuracy = model.evaluate(X_test, y_test)\nprint(f\"Test loss: {loss}\")\nprint(f\"Test accuracy: {accuracy}\")","metadata":{"execution":{"iopub.status.busy":"2023-04-13T17:18:49.275309Z","iopub.execute_input":"2023-04-13T17:18:49.275647Z","iopub.status.idle":"2023-04-13T17:18:52.433761Z","shell.execute_reply.started":"2023-04-13T17:18:49.275621Z","shell.execute_reply":"2023-04-13T17:18:52.431702Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import numpy as np\nimport tensorflow as tf\nfrom tensorflow.keras import layers\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.preprocessing.image import load_img, img_to_array\n\n# Load training data\nX_train = []\ny_train = []\n\nfor i in range(1, 5):\n    xray = np.load(f\"train_xray_{i}.npy\")\n    photo = img_to_array(load_img(f\"train_photo_{i}.jpg\", color_mode=\"grayscale\"))\n    mask = np.load(f\"train_mask_{i}.npy\")\n    \n    for j in range(xray.shape[0]):\n        X_train.append(np.expand_dims(xray[j], axis=-1))\n        y_train.append(mask[j])\n\nX_train = np.array(X_train)\ny_train = np.array(y_train)\n\n# Build model\nmodel = Sequential([\n  layers.Conv3D(32, (3, 3, 3), activation='relu', input_shape=(128, 128, 128, 1)),\n  layers.MaxPooling3D((2, 2, 2)),\n  layers.Conv3D(64, (3, 3, 3), activation='relu'),\n  layers.MaxPooling3D((2, 2, 2)),\n  layers.Conv3D(128, (3, 3, 3), activation='relu'),\n  layers.MaxPooling3D((2, 2, 2)),\n  layers.Flatten(),\n  layers.Dense(64, activation='relu'),\n  layers.Dense(1, activation='sigmoid')\n])\n\n# Compile model\nmodel.compile(optimizer='adam',\n              loss='binary_crossentropy',\n              metrics=['accuracy'])\n\n# Train model\nmodel.fit(X_train, y_train, epochs=10, batch_size=32)\n\n# Load test data\nX_test = []\ny_test = []\n\nfor i in range(5, 9):\n    xray = np.load(f\"test_xray_{i}.npy\")\n    photo = img_to_array(load_img(f\"test_photo_{i}.jpg\", color_mode=\"grayscale\"))\n    mask = np.load(f\"test_mask_{i}.npy\")\n    \n    for j in range(xray.shape[0]):\n        X_test.append(np.expand_dims(xray[j], axis=-1))\n        y_test.append(mask[j])\n\nX_test = np.array(X_test)\ny_test = np.array(y_test)\n\n# Evaluate model on test data\nloss, accuracy = model.evaluate(X_test, y_test)\nprint(f\"Test loss: {loss}\")\nprint(f\"Test accuracy: {accuracy}\")","metadata":{},"execution_count":null,"outputs":[]}]}