{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.11.11","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":477177,"sourceType":"datasetVersion","datasetId":216167}],"dockerImageVersionId":31041,"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)\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},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import keras\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom keras.models import *\nfrom keras.layers import *\nimport tensorflow as tf","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-24T16:46:22.236464Z","iopub.execute_input":"2025-06-24T16:46:22.236997Z","iopub.status.idle":"2025-06-24T16:46:36.414268Z","shell.execute_reply.started":"2025-06-24T16:46:22.236975Z","shell.execute_reply":"2025-06-24T16:46:36.41367Z"}},"outputs":[{"name":"stderr","text":"2025-06-24 16:46:23.736040: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nE0000 00:00:1750783583.928620 35 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nE0000 00:00:1750783583.982959 35 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n","output_type":"stream"}],"execution_count":15},{"cell_type":"code","source":"# 加载数据集\ndata = pd.read_csv(\"/kaggle/input/heart-disease-dataset/heart.csv\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-24T16:46:36.621119Z","iopub.execute_input":"2025-06-24T16:46:36.62206Z","iopub.status.idle":"2025-06-24T16:46:36.658806Z","shell.execute_reply.started":"2025-06-24T16:46:36.622033Z","shell.execute_reply":"2025-06-24T16:46:36.658254Z"}},"outputs":[],"execution_count":16},{"cell_type":"code","source":"# 划分特征X和目标值Y（二分类问题，目标值为'target'）\nX = data.drop(\"target\", axis=1) # 特征：所有列除了'target'\nY = data[\"target\"] # 目标变量：患病状态（0/1）","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-24T16:46:40.256594Z","iopub.execute_input":"2025-06-24T16:46:40.256867Z","iopub.status.idle":"2025-06-24T16:46:40.26924Z","shell.execute_reply.started":"2025-06-24T16:46:40.256842Z","shell.execute_reply":"2025-06-24T16:46:40.268247Z"}},"outputs":[],"execution_count":17},{"cell_type":"code","source":"print(\"X 的形状:\", X.shape) # 应为 (样本数, 13)\nprint(\"y 的形状:\", Y.shape) # 应为 (样本数,)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-24T16:46:43.916124Z","iopub.execute_input":"2025-06-24T16:46:43.916832Z","iopub.status.idle":"2025-06-24T16:46:43.921128Z","shell.execute_reply.started":"2025-06-24T16:46:43.916808Z","shell.execute_reply":"2025-06-24T16:46:43.920226Z"}},"outputs":[{"name":"stdout","text":"X 的形状: (1025, 13)\ny 的形状: (1025,)\n","output_type":"stream"}],"execution_count":18},{"cell_type":"code","source":"if not isinstance(X, np.ndarray):\n X = np.array(X) # 将X转换为Numpy数组\nif not isinstance(Y, np.ndarray):\n Y = np.array(Y) # 将Y转换为Numpy数组","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-24T16:46:47.505419Z","iopub.execute_input":"2025-06-24T16:46:47.505667Z","iopub.status.idle":"2025-06-24T16:46:47.510438Z","shell.execute_reply.started":"2025-06-24T16:46:47.50565Z","shell.execute_reply":"2025-06-24T16:46:47.509794Z"}},"outputs":[],"execution_count":19},{"cell_type":"code","source":"# 标准化特征（处理不同尺度的特征）\nscaler = StandardScaler()\nX_scaled = scaler.fit_transform(X)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-24T16:46:52.039482Z","iopub.execute_input":"2025-06-24T16:46:52.040133Z","iopub.status.idle":"2025-06-24T16:46:52.053085Z","shell.execute_reply.started":"2025-06-24T16:46:52.040112Z","shell.execute_reply":"2025-06-24T16:46:52.052372Z"}},"outputs":[],"execution_count":20},{"cell_type":"code","source":"# 划分训练集与测试集（8:2比例）\ntrain_data, test_data, train_label, test_label = train_test_split(\n X_scaled, Y, test_size=0.2, random_state=42\n)\n\nprint(\"\\n训练集特征形状:\", train_data.shape)\nprint(\"测试集特征形状:\", test_data.shape)\nprint(\"训练集标签形状:\", train_label.shape)\nprint(\"测试集标签形状:\", test_label.shape)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-24T16:46:59.389959Z","iopub.execute_input":"2025-06-24T16:46:59.390554Z","iopub.status.idle":"2025-06-24T16:46:59.396436Z","shell.execute_reply.started":"2025-06-24T16:46:59.390529Z","shell.execute_reply":"2025-06-24T16:46:59.395614Z"}},"outputs":[{"name":"stdout","text":"\n训练集特征形状: (820, 13)\n测试集特征形状: (205, 13)\n训练集标签形状: (820,)\n测试集标签形状: (205,)\n","output_type":"stream"}],"execution_count":22},{"cell_type":"code","source":"def linear_regression(features):\n inputs = Input(shape=(features,))\n outputs = Dense(1, activation='sigmoid') # 二分类用sigmoid（0-1输出）\n return Model(inputs=inputs, outputs=outputs)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-24T16:47:04.084865Z","iopub.execute_input":"2025-06-24T16:47:04.085174Z","iopub.status.idle":"2025-06-24T16:47:04.089478Z","shell.execute_reply.started":"2025-06-24T16:47:04.085153Z","shell.execute_reply":"2025-06-24T16:47:04.088714Z"}},"outputs":[],"execution_count":23},{"cell_type":"code","source":"from tensorflow.keras.models import Model\nfrom tensorflow.keras.layers import Input, Dense\n\ndef linear_regression(features):\n inputs = Input(shape=(features,))\n outputs = Dense(1, activation='sigmoid')(inputs) # 将inputs传入Dense层运算\n return Model(inputs=inputs, outputs=outputs)\n\nmodel = linear_regression(features = 13)\nmodel.summary()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-24T16:47:46.218174Z","iopub.execute_input":"2025-06-24T16:47:46.218791Z","iopub.status.idle":"2025-06-24T16:47:46.240545Z","shell.execute_reply.started":"2025-06-24T16:47:46.218767Z","shell.execute_reply":"2025-06-24T16:47:46.239961Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"\u001b[1mModel: \"functional_2\"\u001b[0m\n","text/html":"
Model: \"functional_2\"\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n│ input_layer_2 (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ dense_2 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m14\u001b[0m │\n└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n","text/html":"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃\n┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n│ input_layer_2 (InputLayer)           │ (None, 13)                  │               0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ dense_2 (Dense)                      │ (None, 1)                   │              14 │\n└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m14\u001b[0m (56.00 B)\n","text/html":"
 Total params: 14 (56.00 B)\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m14\u001b[0m (56.00 B)\n","text/html":"
 Trainable params: 14 (56.00 B)\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n","text/html":"
 Non-trainable params: 0 (0.00 B)\n
\n"},"metadata":{}}],"execution_count":26},{"cell_type":"code","source":"optimizer = keras.optimizers.Adam(learning_rate=0.01)\n\nmodel.compile(\n optimizer=optimizer,\n loss='binary_crossentropy', # 二分类损失函数\n metrics=['mae'] # 平均绝对误差\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-24T16:47:50.35184Z","iopub.execute_input":"2025-06-24T16:47:50.352115Z","iopub.status.idle":"2025-06-24T16:47:50.367042Z","shell.execute_reply.started":"2025-06-24T16:47:50.352093Z","shell.execute_reply":"2025-06-24T16:47:50.36618Z"}},"outputs":[],"execution_count":27},{"cell_type":"code","source":"# 训练模型\nhistory = model.fit(\n train_data, train_label,\n validation_data=(test_data, test_label),\n epochs=200,\n batch_size=16,\n verbose=1\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-24T16:47:54.014817Z","iopub.execute_input":"2025-06-24T16:47:54.015297Z","iopub.status.idle":"2025-06-24T16:48:35.802079Z","shell.execute_reply.started":"2025-06-24T16:47:54.015273Z","shell.execute_reply":"2025-06-24T16:48:35.801482Z"}},"outputs":[{"name":"stdout","text":"Epoch 1/200\n","output_type":"stream"},{"name":"stderr","text":"WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nI0000 00:00:1750783674.843502 118 service.cc:148] XLA service 0x7f50d0004ef0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\nI0000 00:00:1750783674.844027 118 service.cc:156] StreamExecutor device (0): Tesla P100-PCIE-16GB, Compute Capability 6.0\nI0000 00:00:1750783675.007396 118 cuda_dnn.cc:529] Loaded cuDNN version 90300\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m27/52\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.8166 - mae: 0.4858 ","output_type":"stream"},{"name":"stderr","text":"I0000 00:00:1750783675.286796 118 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 17ms/step - loss: 0.7578 - mae: 0.4587 - val_loss: 0.4933 - val_mae: 0.3271\nEpoch 2/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.4293 - mae: 0.2928 - val_loss: 0.4325 - val_mae: 0.2786\nEpoch 3/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3601 - mae: 0.2437 - val_loss: 0.4243 - val_mae: 0.2626\nEpoch 4/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3400 - mae: 0.2258 - val_loss: 0.4228 - val_mae: 0.2564\nEpoch 5/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3351 - mae: 0.2223 - val_loss: 0.4278 - val_mae: 0.2540\nEpoch 6/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3320 - mae: 0.2097 - val_loss: 0.4312 - val_mae: 0.2519\nEpoch 7/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3029 - mae: 0.2004 - val_loss: 0.4385 - val_mae: 0.2519\nEpoch 8/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3372 - mae: 0.2161 - val_loss: 0.4419 - val_mae: 0.2518\nEpoch 9/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3217 - mae: 0.2051 - val_loss: 0.4464 - val_mae: 0.2516\nEpoch 10/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3273 - mae: 0.2076 - val_loss: 0.4426 - val_mae: 0.2496\nEpoch 11/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3444 - mae: 0.2150 - val_loss: 0.4474 - val_mae: 0.2519\nEpoch 12/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3242 - mae: 0.2063 - val_loss: 0.4507 - val_mae: 0.2515\nEpoch 13/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3322 - mae: 0.2108 - val_loss: 0.4501 - val_mae: 0.2503\nEpoch 14/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3100 - mae: 0.2001 - val_loss: 0.4527 - val_mae: 0.2507\nEpoch 15/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3257 - mae: 0.2008 - val_loss: 0.4544 - val_mae: 0.2496\nEpoch 16/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.2960 - mae: 0.1893 - val_loss: 0.4511 - val_mae: 0.2508\nEpoch 17/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3161 - mae: 0.2006 - val_loss: 0.4553 - val_mae: 0.2501\nEpoch 18/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3170 - mae: 0.1978 - val_loss: 0.4568 - val_mae: 0.2496\nEpoch 19/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3337 - mae: 0.2065 - val_loss: 0.4547 - val_mae: 0.2492\nEpoch 20/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3253 - mae: 0.2079 - val_loss: 0.4564 - val_mae: 0.2505\nEpoch 21/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3120 - mae: 0.1984 - val_loss: 0.4577 - val_mae: 0.2521\nEpoch 22/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3175 - mae: 0.1971 - val_loss: 0.4590 - val_mae: 0.2517\nEpoch 23/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3205 - mae: 0.2017 - val_loss: 0.4537 - val_mae: 0.2511\nEpoch 24/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3123 - mae: 0.1979 - val_loss: 0.4559 - val_mae: 0.2515\nEpoch 25/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3864 - mae: 0.2327 - val_loss: 0.4576 - val_mae: 0.2509\nEpoch 26/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3231 - mae: 0.2013 - val_loss: 0.4597 - val_mae: 0.2515\nEpoch 27/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3315 - mae: 0.2036 - val_loss: 0.4580 - val_mae: 0.2518\nEpoch 28/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3158 - mae: 0.1983 - val_loss: 0.4544 - val_mae: 0.2496\nEpoch 29/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3646 - mae: 0.2168 - val_loss: 0.4564 - val_mae: 0.2521\nEpoch 30/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3294 - mae: 0.2031 - val_loss: 0.4574 - val_mae: 0.2495\nEpoch 31/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3428 - mae: 0.2044 - val_loss: 0.4558 - val_mae: 0.2515\nEpoch 32/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3282 - mae: 0.2036 - val_loss: 0.4614 - val_mae: 0.2513\nEpoch 33/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.3371 - mae: 0.2059 - val_loss: 0.4564 - val_mae: 0.2495\nEpoch 34/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3229 - mae: 0.1943 - val_loss: 0.4533 - val_mae: 0.2487\nEpoch 35/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3247 - mae: 0.2039 - val_loss: 0.4601 - val_mae: 0.2489\nEpoch 36/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3270 - mae: 0.2003 - val_loss: 0.4585 - val_mae: 0.2523\nEpoch 37/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.3367 - mae: 0.2059 - val_loss: 0.4532 - val_mae: 0.2513\nEpoch 38/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3336 - mae: 0.2051 - val_loss: 0.4540 - val_mae: 0.2499\nEpoch 39/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3275 - mae: 0.2026 - val_loss: 0.4577 - val_mae: 0.2500\nEpoch 40/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3292 - mae: 0.2037 - val_loss: 0.4592 - val_mae: 0.2513\nEpoch 41/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3357 - mae: 0.2092 - val_loss: 0.4567 - val_mae: 0.2498\nEpoch 42/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.3363 - mae: 0.2004 - val_loss: 0.4568 - val_mae: 0.2496\nEpoch 43/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3326 - mae: 0.2060 - val_loss: 0.4503 - val_mae: 0.2496\nEpoch 44/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3304 - mae: 0.2029 - val_loss: 0.4548 - val_mae: 0.2494\nEpoch 45/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3440 - mae: 0.2091 - val_loss: 0.4599 - val_mae: 0.2499\nEpoch 46/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3229 - mae: 0.1971 - val_loss: 0.4577 - val_mae: 0.2494\nEpoch 47/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3053 - mae: 0.1915 - val_loss: 0.4528 - val_mae: 0.2485\nEpoch 48/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3329 - mae: 0.2085 - val_loss: 0.4585 - val_mae: 0.2524\nEpoch 49/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3459 - mae: 0.2080 - val_loss: 0.4562 - val_mae: 0.2514\nEpoch 50/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3252 - mae: 0.2029 - val_loss: 0.4590 - val_mae: 0.2500\nEpoch 51/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3147 - mae: 0.2013 - val_loss: 0.4526 - val_mae: 0.2506\nEpoch 52/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3559 - mae: 0.2166 - val_loss: 0.4563 - val_mae: 0.2514\nEpoch 53/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.3390 - mae: 0.2082 - val_loss: 0.4556 - val_mae: 0.2494\nEpoch 54/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.3345 - mae: 0.2022 - val_loss: 0.4531 - val_mae: 0.2482\nEpoch 55/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3280 - mae: 0.2001 - val_loss: 0.4624 - val_mae: 0.2510\nEpoch 56/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3016 - mae: 0.1897 - val_loss: 0.4542 - val_mae: 0.2502\nEpoch 57/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3478 - mae: 0.2152 - val_loss: 0.4519 - val_mae: 0.2500\nEpoch 58/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3363 - mae: 0.2047 - val_loss: 0.4563 - val_mae: 0.2506\nEpoch 59/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.3482 - mae: 0.2140 - val_loss: 0.4568 - val_mae: 0.2499\nEpoch 60/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.3074 - mae: 0.1988 - val_loss: 0.4606 - val_mae: 0.2501\nEpoch 61/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3315 - mae: 0.2051 - val_loss: 0.4526 - val_mae: 0.2520\nEpoch 62/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3245 - mae: 0.2052 - val_loss: 0.4565 - val_mae: 0.2520\nEpoch 63/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3301 - mae: 0.2059 - val_loss: 0.4555 - val_mae: 0.2500\nEpoch 64/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3619 - mae: 0.2168 - val_loss: 0.4567 - val_mae: 0.2502\nEpoch 65/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.3107 - mae: 0.1959 - val_loss: 0.4568 - val_mae: 0.2487\nEpoch 66/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.3089 - mae: 0.1995 - val_loss: 0.4589 - val_mae: 0.2508\nEpoch 67/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.3223 - mae: 0.2004 - val_loss: 0.4536 - val_mae: 0.2516\nEpoch 68/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.3370 - mae: 0.2055 - val_loss: 0.4555 - val_mae: 0.2502\nEpoch 69/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.3280 - mae: 0.2047 - val_loss: 0.4542 - val_mae: 0.2493\nEpoch 70/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.2886 - mae: 0.1863 - val_loss: 0.4583 - val_mae: 0.2501\nEpoch 71/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.3407 - mae: 0.2079 - val_loss: 0.4566 - val_mae: 0.2506\nEpoch 72/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.3239 - mae: 0.1977 - val_loss: 0.4574 - val_mae: 0.2501\nEpoch 73/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3470 - mae: 0.2115 - val_loss: 0.4531 - val_mae: 0.2482\nEpoch 74/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3440 - mae: 0.2074 - val_loss: 0.4586 - val_mae: 0.2511\nEpoch 75/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3530 - mae: 0.2140 - val_loss: 0.4589 - val_mae: 0.2511\nEpoch 76/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3114 - mae: 0.2022 - val_loss: 0.4577 - val_mae: 0.2495\nEpoch 77/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3740 - mae: 0.2202 - val_loss: 0.4557 - val_mae: 0.2515\nEpoch 78/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3595 - mae: 0.2127 - val_loss: 0.4587 - val_mae: 0.2495\nEpoch 79/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3358 - mae: 0.2075 - val_loss: 0.4585 - val_mae: 0.2507\nEpoch 80/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3339 - mae: 0.2078 - val_loss: 0.4623 - val_mae: 0.2509\nEpoch 81/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3474 - mae: 0.2066 - val_loss: 0.4565 - val_mae: 0.2498\nEpoch 82/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.2975 - mae: 0.1868 - val_loss: 0.4596 - val_mae: 0.2505\nEpoch 83/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3328 - mae: 0.2072 - val_loss: 0.4602 - val_mae: 0.2522\nEpoch 84/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3575 - mae: 0.2127 - val_loss: 0.4559 - val_mae: 0.2503\nEpoch 85/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3584 - mae: 0.2132 - val_loss: 0.4565 - val_mae: 0.2490\nEpoch 86/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.3295 - mae: 0.2111 - val_loss: 0.4609 - val_mae: 0.2505\nEpoch 87/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3377 - mae: 0.2074 - val_loss: 0.4607 - val_mae: 0.2498\nEpoch 88/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3363 - mae: 0.2006 - val_loss: 0.4590 - val_mae: 0.2509\nEpoch 89/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.2975 - mae: 0.1843 - val_loss: 0.4584 - val_mae: 0.2510\nEpoch 90/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3412 - mae: 0.2059 - val_loss: 0.4607 - val_mae: 0.2512\nEpoch 91/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3392 - mae: 0.2099 - val_loss: 0.4595 - val_mae: 0.2489\nEpoch 92/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3241 - mae: 0.1998 - val_loss: 0.4533 - val_mae: 0.2485\nEpoch 93/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3280 - mae: 0.1984 - val_loss: 0.4600 - val_mae: 0.2530\nEpoch 94/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3022 - mae: 0.1909 - val_loss: 0.4590 - val_mae: 0.2505\nEpoch 95/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3316 - mae: 0.2082 - val_loss: 0.4631 - val_mae: 0.2509\nEpoch 96/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3412 - mae: 0.2056 - val_loss: 0.4580 - val_mae: 0.2513\nEpoch 97/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3411 - mae: 0.2020 - val_loss: 0.4587 - val_mae: 0.2502\nEpoch 98/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3119 - mae: 0.1995 - val_loss: 0.4566 - val_mae: 0.2488\nEpoch 99/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3610 - mae: 0.2144 - val_loss: 0.4566 - val_mae: 0.2505\nEpoch 100/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3429 - mae: 0.2091 - val_loss: 0.4574 - val_mae: 0.2496\nEpoch 101/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3855 - mae: 0.2272 - val_loss: 0.4623 - val_mae: 0.2508\nEpoch 102/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3222 - mae: 0.1994 - val_loss: 0.4607 - val_mae: 0.2487\nEpoch 103/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.3550 - mae: 0.2133 - val_loss: 0.4594 - val_mae: 0.2508\nEpoch 104/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3378 - mae: 0.2077 - val_loss: 0.4546 - val_mae: 0.2493\nEpoch 105/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3219 - mae: 0.1976 - val_loss: 0.4589 - val_mae: 0.2494\nEpoch 106/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3181 - mae: 0.1964 - val_loss: 0.4514 - val_mae: 0.2491\nEpoch 107/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3386 - mae: 0.2103 - val_loss: 0.4579 - val_mae: 0.2505\nEpoch 108/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3526 - mae: 0.2103 - val_loss: 0.4579 - val_mae: 0.2499\nEpoch 109/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3294 - mae: 0.2058 - val_loss: 0.4563 - val_mae: 0.2504\nEpoch 110/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.3302 - mae: 0.2020 - val_loss: 0.4565 - val_mae: 0.2500\nEpoch 111/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3138 - mae: 0.1908 - val_loss: 0.4570 - val_mae: 0.2508\nEpoch 112/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.3046 - mae: 0.1907 - val_loss: 0.4556 - val_mae: 0.2491\nEpoch 113/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3296 - mae: 0.2027 - val_loss: 0.4545 - val_mae: 0.2497\nEpoch 114/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3440 - mae: 0.2104 - val_loss: 0.4525 - val_mae: 0.2497\nEpoch 115/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.3466 - mae: 0.2100 - val_loss: 0.4574 - val_mae: 0.2508\nEpoch 116/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3397 - mae: 0.2062 - val_loss: 0.4572 - val_mae: 0.2499\nEpoch 117/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3367 - mae: 0.2049 - val_loss: 0.4603 - val_mae: 0.2503\nEpoch 118/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3256 - mae: 0.1983 - val_loss: 0.4566 - val_mae: 0.2501\nEpoch 119/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3555 - mae: 0.2172 - val_loss: 0.4520 - val_mae: 0.2498\nEpoch 120/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3303 - mae: 0.2071 - val_loss: 0.4608 - val_mae: 0.2515\nEpoch 121/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3292 - mae: 0.2012 - val_loss: 0.4603 - val_mae: 0.2516\nEpoch 122/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3669 - mae: 0.2188 - val_loss: 0.4529 - val_mae: 0.2517\nEpoch 123/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3507 - mae: 0.2101 - val_loss: 0.4551 - val_mae: 0.2519\nEpoch 124/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3351 - mae: 0.2060 - val_loss: 0.4602 - val_mae: 0.2519\nEpoch 125/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.2912 - mae: 0.1844 - val_loss: 0.4642 - val_mae: 0.2497\nEpoch 126/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.3510 - mae: 0.2114 - val_loss: 0.4581 - val_mae: 0.2515\nEpoch 127/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3154 - mae: 0.1918 - val_loss: 0.4502 - val_mae: 0.2493\nEpoch 128/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3058 - mae: 0.1948 - val_loss: 0.4577 - val_mae: 0.2476\nEpoch 129/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3074 - mae: 0.1945 - val_loss: 0.4570 - val_mae: 0.2521\nEpoch 130/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3348 - mae: 0.2063 - val_loss: 0.4550 - val_mae: 0.2503\nEpoch 131/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3202 - mae: 0.1997 - val_loss: 0.4532 - val_mae: 0.2505\nEpoch 132/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3572 - mae: 0.2179 - val_loss: 0.4569 - val_mae: 0.2508\nEpoch 133/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3364 - mae: 0.2023 - val_loss: 0.4566 - val_mae: 0.2496\nEpoch 134/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3493 - mae: 0.2119 - val_loss: 0.4569 - val_mae: 0.2488\nEpoch 135/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3314 - mae: 0.2054 - val_loss: 0.4572 - val_mae: 0.2505\nEpoch 136/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.3409 - mae: 0.2128 - val_loss: 0.4560 - val_mae: 0.2497\nEpoch 137/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.3413 - mae: 0.2072 - val_loss: 0.4623 - val_mae: 0.2511\nEpoch 138/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3262 - mae: 0.1999 - val_loss: 0.4611 - val_mae: 0.2505\nEpoch 139/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.2903 - mae: 0.1841 - val_loss: 0.4560 - val_mae: 0.2503\nEpoch 140/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3080 - mae: 0.1977 - val_loss: 0.4596 - val_mae: 0.2491\nEpoch 141/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3359 - mae: 0.2063 - val_loss: 0.4559 - val_mae: 0.2511\nEpoch 142/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3180 - mae: 0.1967 - val_loss: 0.4573 - val_mae: 0.2501\nEpoch 143/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3555 - mae: 0.2131 - val_loss: 0.4563 - val_mae: 0.2502\nEpoch 144/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3396 - mae: 0.2082 - val_loss: 0.4619 - val_mae: 0.2496\nEpoch 145/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3157 - mae: 0.1915 - val_loss: 0.4586 - val_mae: 0.2495\nEpoch 146/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3328 - mae: 0.2058 - val_loss: 0.4589 - val_mae: 0.2507\nEpoch 147/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3552 - mae: 0.2145 - val_loss: 0.4565 - val_mae: 0.2500\nEpoch 148/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3243 - mae: 0.2015 - val_loss: 0.4575 - val_mae: 0.2501\nEpoch 149/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3372 - mae: 0.2059 - val_loss: 0.4600 - val_mae: 0.2504\nEpoch 150/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.3332 - mae: 0.2019 - val_loss: 0.4547 - val_mae: 0.2493\nEpoch 151/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.3242 - mae: 0.2031 - val_loss: 0.4587 - val_mae: 0.2507\nEpoch 152/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.3361 - mae: 0.1990 - val_loss: 0.4557 - val_mae: 0.2493\nEpoch 153/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.3621 - mae: 0.2182 - val_loss: 0.4564 - val_mae: 0.2491\nEpoch 154/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.3336 - mae: 0.2054 - val_loss: 0.4572 - val_mae: 0.2489\nEpoch 155/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.3737 - mae: 0.2217 - val_loss: 0.4519 - val_mae: 0.2511\nEpoch 156/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.3280 - mae: 0.2027 - val_loss: 0.4575 - val_mae: 0.2512\nEpoch 157/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.3313 - mae: 0.2089 - val_loss: 0.4595 - val_mae: 0.2508\nEpoch 158/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.3402 - mae: 0.2040 - val_loss: 0.4599 - val_mae: 0.2512\nEpoch 159/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3431 - mae: 0.2094 - val_loss: 0.4581 - val_mae: 0.2493\nEpoch 160/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3413 - mae: 0.2080 - val_loss: 0.4565 - val_mae: 0.2516\nEpoch 161/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3411 - mae: 0.2138 - val_loss: 0.4602 - val_mae: 0.2514\nEpoch 162/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3136 - mae: 0.1957 - val_loss: 0.4573 - val_mae: 0.2492\nEpoch 163/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3379 - mae: 0.2082 - val_loss: 0.4600 - val_mae: 0.2519\nEpoch 164/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3499 - mae: 0.2087 - val_loss: 0.4590 - val_mae: 0.2503\nEpoch 165/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3250 - mae: 0.1994 - val_loss: 0.4598 - val_mae: 0.2503\nEpoch 166/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3518 - mae: 0.2153 - val_loss: 0.4584 - val_mae: 0.2499\nEpoch 167/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3528 - mae: 0.2094 - val_loss: 0.4566 - val_mae: 0.2497\nEpoch 168/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3254 - mae: 0.1987 - val_loss: 0.4585 - val_mae: 0.2501\nEpoch 169/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3286 - mae: 0.2013 - val_loss: 0.4568 - val_mae: 0.2509\nEpoch 170/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3409 - mae: 0.2065 - val_loss: 0.4599 - val_mae: 0.2500\nEpoch 171/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3176 - mae: 0.1985 - val_loss: 0.4572 - val_mae: 0.2496\nEpoch 172/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3597 - mae: 0.2164 - val_loss: 0.4553 - val_mae: 0.2500\nEpoch 173/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3259 - mae: 0.2030 - val_loss: 0.4567 - val_mae: 0.2496\nEpoch 174/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3478 - mae: 0.2065 - val_loss: 0.4573 - val_mae: 0.2502\nEpoch 175/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3268 - mae: 0.2101 - val_loss: 0.4571 - val_mae: 0.2506\nEpoch 176/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3162 - mae: 0.2011 - val_loss: 0.4574 - val_mae: 0.2498\nEpoch 177/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3183 - mae: 0.2003 - val_loss: 0.4573 - val_mae: 0.2512\nEpoch 178/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3520 - mae: 0.2088 - val_loss: 0.4586 - val_mae: 0.2498\nEpoch 179/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3339 - mae: 0.2016 - val_loss: 0.4567 - val_mae: 0.2489\nEpoch 180/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3085 - mae: 0.1978 - val_loss: 0.4573 - val_mae: 0.2489\nEpoch 181/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.2968 - mae: 0.1927 - val_loss: 0.4589 - val_mae: 0.2517\nEpoch 182/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3200 - mae: 0.1971 - val_loss: 0.4585 - val_mae: 0.2512\nEpoch 183/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3124 - mae: 0.1962 - val_loss: 0.4558 - val_mae: 0.2517\nEpoch 184/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3417 - mae: 0.2133 - val_loss: 0.4579 - val_mae: 0.2505\nEpoch 185/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3043 - mae: 0.1959 - val_loss: 0.4600 - val_mae: 0.2500\nEpoch 186/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.3487 - mae: 0.2113 - val_loss: 0.4570 - val_mae: 0.2496\nEpoch 187/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.3677 - mae: 0.2160 - val_loss: 0.4549 - val_mae: 0.2483\nEpoch 188/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3360 - mae: 0.2072 - val_loss: 0.4575 - val_mae: 0.2493\nEpoch 189/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3395 - mae: 0.2122 - val_loss: 0.4555 - val_mae: 0.2489\nEpoch 190/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3242 - mae: 0.2023 - val_loss: 0.4581 - val_mae: 0.2510\nEpoch 191/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3465 - mae: 0.2051 - val_loss: 0.4565 - val_mae: 0.2494\nEpoch 192/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3311 - mae: 0.2032 - val_loss: 0.4566 - val_mae: 0.2495\nEpoch 193/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3617 - mae: 0.2142 - val_loss: 0.4547 - val_mae: 0.2501\nEpoch 194/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3560 - mae: 0.2161 - val_loss: 0.4561 - val_mae: 0.2496\nEpoch 195/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3300 - mae: 0.2063 - val_loss: 0.4636 - val_mae: 0.2500\nEpoch 196/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3435 - mae: 0.2108 - val_loss: 0.4570 - val_mae: 0.2499\nEpoch 197/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3278 - mae: 0.2079 - val_loss: 0.4533 - val_mae: 0.2501\nEpoch 198/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.2900 - mae: 0.1889 - val_loss: 0.4506 - val_mae: 0.2518\nEpoch 199/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3418 - mae: 0.2100 - val_loss: 0.4529 - val_mae: 0.2516\nEpoch 200/200\n\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3304 - mae: 0.2059 - val_loss: 0.4525 - val_mae: 0.2501\n","output_type":"stream"}],"execution_count":28},{"cell_type":"code","source":"def plot_history(history):\n plt.figure(figsize=(12, 4))\n \n # 损失曲线\n plt.subplot(1, 2, 1)\n plt.plot(history.history['loss'], label='Train Loss')\n plt.plot(history.history['val_loss'], label='Validation Loss')\n plt.xlabel('Epoch')\n plt.ylabel('Loss (Binary Crossentropy)')\n plt.legend()\n \n # 平均绝对误差曲线\n plt.subplot(1, 2, 2)\n plt.plot(history.history['mae'], label='Train MAE')\n plt.plot(history.history['val_mae'], label='Validation MAE')\n plt.xlabel('Epoch')\n plt.ylabel('MAE')\n plt.legend()\n \n plt.tight_layout()\n plt.show()\n\nplot_history(history)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-24T16:48:39.827018Z","iopub.execute_input":"2025-06-24T16:48:39.827687Z","iopub.status.idle":"2025-06-24T16:48:40.218029Z","shell.execute_reply.started":"2025-06-24T16:48:39.827661Z","shell.execute_reply":"2025-06-24T16:48:40.217263Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{}}],"execution_count":29},{"cell_type":"code","source":"test_loss, test_mae = model.evaluate(test_data, test_label, verbose=0)\nprint(f\"\\nTest Loss: {test_loss:.4f}\")\nprint(f\"Test MAE: {test_mae:.4f}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-24T16:48:46.743773Z","iopub.execute_input":"2025-06-24T16:48:46.744467Z","iopub.status.idle":"2025-06-24T16:48:46.973435Z","shell.execute_reply.started":"2025-06-24T16:48:46.744443Z","shell.execute_reply":"2025-06-24T16:48:46.972696Z"}},"outputs":[{"name":"stdout","text":"\nTest Loss: 0.4525\nTest MAE: 0.2501\n","output_type":"stream"}],"execution_count":30},{"cell_type":"code","source":"# 预测测试集\ny_pred = model.predict(test_data).flatten() # 输出为概率值（0-1）","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-24T16:49:11.152084Z","iopub.execute_input":"2025-06-24T16:49:11.153006Z","iopub.status.idle":"2025-06-24T16:49:11.453235Z","shell.execute_reply.started":"2025-06-24T16:49:11.152978Z","shell.execute_reply":"2025-06-24T16:49:11.452619Z"}},"outputs":[{"name":"stdout","text":"\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n","output_type":"stream"}],"execution_count":31},{"cell_type":"code","source":"from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n\n# 假设 test_label 和 y_pred 已经有合适的值\ntest_label = [1, 2, 3]\ny_pred = [1.2, 2.1, 2.9]\n\nmse = mean_squared_error(test_label, y_pred)\nrmse = np.sqrt(mse)\nmae = mean_absolute_error(test_label, y_pred)\nr2 = r2_score(test_label, y_pred)\n\nprint(f\"\\nRMSE: {rmse:.4f}\")\nprint(f\"R² Score: {r2:.4f}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-24T16:49:14.159773Z","iopub.execute_input":"2025-06-24T16:49:14.160453Z","iopub.status.idle":"2025-06-24T16:49:14.166885Z","shell.execute_reply.started":"2025-06-24T16:49:14.160432Z","shell.execute_reply":"2025-06-24T16:49:14.166117Z"}},"outputs":[{"name":"stdout","text":"\nRMSE: 0.1414\nR² Score: 0.9700\n","output_type":"stream"}],"execution_count":32},{"cell_type":"code","source":"# 预测值与真实值对比图\nplt.figure(figsize=(8, 6))\nplt.scatter(test_label, y_pred, alpha=0.5, label='Predictions')\nplt.plot([0, 1], [0, 1], 'k--', lw=2, label='Perfect Prediction') # 对角线参考线\nplt.xlabel('True Values (Heart Disease Status)')\nplt.ylabel('Predicted Probability')\nplt.title('True vs Predicted Probabilities')\nplt.legend()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-24T16:49:17.766762Z","iopub.execute_input":"2025-06-24T16:49:17.767401Z","iopub.status.idle":"2025-06-24T16:49:17.967383Z","shell.execute_reply.started":"2025-06-24T16:49:17.76738Z","shell.execute_reply":"2025-06-24T16:49:17.966595Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{}}],"execution_count":33},{"cell_type":"code","source":"# 假设这里已经有了test_label和y_pred这两个列表\ntest_label = [1, 2, 3] # 示例数据，实际应替换为真实数据\ny_pred = [0.5, 1.5, 2.5] # 示例数据，实际应替换为真实数据\n\n# 将列表转换为numpy数组\ntest_label = np.array(test_label)\ny_pred = np.array(y_pred)\n\nresiduals = test_label - y_pred \nplt.figure(figsize=(8, 6))\nplt.scatter(y_pred, residuals, alpha=0.5)\nplt.axhline(y = 0, color='r', linestyle='--')\nplt.xlabel('Predicted Probability')\nplt.ylabel('Residuals (True - Predicted)')\nplt.title('Residual Plot')\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-24T16:49:22.595528Z","iopub.execute_input":"2025-06-24T16:49:22.595824Z","iopub.status.idle":"2025-06-24T16:49:22.75676Z","shell.execute_reply.started":"2025-06-24T16:49:22.595806Z","shell.execute_reply":"2025-06-24T16:49:22.756042Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{}}],"execution_count":34},{"cell_type":"code","source":"train_data= np.array([[63, 1, 3, 145, 233, 1, 2, 150, 0, 2.3, 0, 3, 6]]) \nscaler.fit(train_data) \nnew_data = np.array([[63, 1, 3, 145, 233, 1, 2, 150, 0, 2.3, 0, 3, 6]]) \nnew_data_scaled = scaler.transform(new_data) ","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-24T17:17:20.764989Z","iopub.execute_input":"2025-06-24T17:17:20.765728Z","iopub.status.idle":"2025-06-24T17:17:20.770906Z","shell.execute_reply.started":"2025-06-24T17:17:20.765704Z","shell.execute_reply":"2025-06-24T17:17:20.77008Z"}},"outputs":[],"execution_count":68},{"cell_type":"code","source":"# 预测患病概率\npredicted_prob = model.predict(new_data_scaled)[0][0]\nprint(f\"\\nPredicted Heart Disease Probability: {predicted_prob*100:.2f}%\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-24T17:17:23.39376Z","iopub.execute_input":"2025-06-24T17:17:23.394482Z","iopub.status.idle":"2025-06-24T17:17:23.471522Z","shell.execute_reply.started":"2025-06-24T17:17:23.394459Z","shell.execute_reply":"2025-06-24T17:17:23.470762Z"}},"outputs":[{"name":"stdout","text":"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n\nPredicted Heart Disease Probability: 45.20%\n","output_type":"stream"}],"execution_count":69}]}