{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.12.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceType":"competition","sourceId":3004,"databundleVersionId":861823}],"dockerImageVersionId":31328,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport tensorflow as tf\nfrom tensorflow.keras import layers, models\nimport os\n\ninput_path = '/kaggle/input/digit-recognizer/'\nif not os.path.exists(input_path):\n    for dirname, _, filenames in os.walk('/kaggle/input'):\n        if 'train.csv' in filenames:\n            input_path = dirname + '/'\n            break\n\ntrain = pd.read_csv(f\"{input_path}train.csv\")\ntest = pd.read_csv(f\"{input_path}test.csv\")\n\nX_train = train.drop(\"label\", axis=1).values\ny_train = train[\"label\"].values\nX_test = test.values\n\nX_train = X_train.reshape(-1, 28, 28, 1) / 255.0\nX_test = X_test.reshape(-1, 28, 28, 1) / 255.0\n\nmodel = models.Sequential([\n    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),\n    layers.BatchNormalization(),\n    layers.MaxPooling2D((2, 2)),\n    \n    layers.Conv2D(64, (3, 3), activation='relu'),\n    layers.BatchNormalization(),\n    layers.MaxPooling2D((2, 2)),\n    \n    layers.Flatten(),\n    layers.Dense(128, activation='relu'),\n    layers.Dropout(0.3),\n    layers.Dense(10, activation='softmax')\n])\n\nmodel.compile(optimizer='adam',\n              loss='sparse_categorical_crossentropy',\n              metrics=['accuracy'])\n\nprint(\"Начало обучения (это займет пару минут)...\")\nmodel.fit(X_train, y_train, epochs=10, batch_size=64, validation_split=0.1)\n\npredictions = model.predict(X_test)\nresults = np.argmax(predictions, axis=1)\n\nsubmission = pd.DataFrame({\n    \"ImageId\": range(1, len(results) + 1),\n    \"Label\": results\n})\n\nsubmission.to_csv('submission.csv', index=False)\nprint(\"Готово! Файл 'submission.csv' создан.\")","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2026-03-25T05:29:01.491499Z","iopub.execute_input":"2026-03-25T05:29:01.492739Z","iopub.status.idle":"2026-03-25T05:33:32.578302Z","shell.execute_reply.started":"2026-03-25T05:29:01.492683Z","shell.execute_reply":"2026-03-25T05:33:32.577275Z"}},"outputs":[],"execution_count":null}]}