From d636583a2b45ff8cfadc74ceb1aede3af7e29e39 Mon Sep 17 00:00:00 2001 From: Lala Date: Tue, 30 May 2023 22:55:47 +0700 Subject: [PATCH] Update model --- Data/drop_activity.csv | 10727 +++++++++++++++++++++++++++++++++++++ Data/drop_calorie.csv | 10727 +++++++++++++++++++++++++++++++++++++ Model/BMR_notebook.ipynb | 0 Model/Model_4.h5 | Bin 0 -> 75824 bytes Model/Model_5.h5 | Bin 0 -> 75792 bytes Model/Model_6.h5 | Bin 0 -> 47040 bytes Model/Model_7_BMR.h5 | Bin 0 -> 40720 bytes Model/Notebook.ipynb | 2 +- 8 files changed, 21455 insertions(+), 1 deletion(-) create mode 100644 Data/drop_activity.csv create mode 100644 Data/drop_calorie.csv create mode 100644 Model/BMR_notebook.ipynb create mode 100644 Model/Model_4.h5 create mode 100644 Model/Model_5.h5 create mode 100644 Model/Model_6.h5 create mode 100644 Model/Model_7_BMR.h5 diff --git a/Data/drop_activity.csv b/Data/drop_activity.csv new file mode 100644 index 0000000..563bd84 --- /dev/null +++ b/Data/drop_activity.csv @@ -0,0 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38.9988\n"]},{"data":{"text/plain":["38.99882125854492"]},"execution_count":18,"metadata":{},"output_type":"execute_result"}],"source":["model.evaluate(x_test,y_test)"]},{"cell_type":"code","execution_count":12,"metadata":{},"outputs":[{"data":{"text/plain":["array([1529.3469, 1838.1575, 1466.7944, ..., 2104.2021, 2083.9375,\n"," 1460.2761], dtype=float32)"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["predict=model.predict(x_test)\n","predict.squeeze()"]},{"cell_type":"code","execution_count":13,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["import matplotlib.pyplot as plt\n","import numpy as np\n","\n","# Assuming you have your true labels in a NumPy array called 'y_true'\n","# Assuming you have your predicted values in a NumPy array called 'y_pred'\n","\n","# Calculate the difference between true labels and predicted values\n","diff = y_test - predict.squeeze()\n","\n","# Plotting the difference\n","plt.plot(np.arange(len(diff)), diff)\n","plt.xlabel('Sample Index')\n","plt.ylabel('Difference')\n","plt.title('Regression: Difference between True Labels and Predicted Values')\n","plt.show()\n"]},{"cell_type":"code","execution_count":14,"metadata":{},"outputs":[{"data":{"text/plain":["count 2146.000000\n","mean -1.783319\n","std 4.940824\n","min -35.693971\n","25% -3.892978\n","50% -1.259424\n","75% 1.049507\n","max 16.058657\n","Name: calories_to_maintain_weight, dtype: float64"]},"execution_count":14,"metadata":{},"output_type":"execute_result"}],"source":["diff.describe()"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["hasil= y true - predict\n","\n","* y true= data asli -> y_test\n","* predict= data prediksi -> hasil predict model terhadap x_test\n","\n","if hasil<0 -> kalori yg diprediksi berlebihan\n","\n","if hasil >0 -> kalori yg diprediksi kurang"]},{"cell_type":"code","execution_count":15,"metadata":{},"outputs":[{"data":{"text/plain":["[-35.69397070312493,\n"," -32.451091992187685,\n"," -30.242751757812584,\n"," -27.14088554687487,\n"," -24.654923828124993,\n"," -24.456376953125073,\n"," -24.414593750000222,\n"," -23.643120117187436,\n"," -23.43681874999993,\n"," -23.136517578125222,\n"," -22.7832824218749,\n"," -22.20804492187517,\n"," -22.073666015624894,\n"," -21.534599609375164,\n"," -21.0693359375,\n"," -20.670703124999818,\n"," -20.556289062500127,\n"," -20.193855468749916,\n"," -20.059734375000062,\n"," 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kalori"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]}],"metadata":{"colab":{"authorship_tag":"ABX9TyN+sEJvIt1tB8r+RgCql4zA","mount_file_id":"1cqNVc_y8XxWEfpGv_pAUZytE7c6bUG0O","provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.0"}},"nbformat":4,"nbformat_minor":0} +{"cells":[{"attachments":{},"cell_type":"markdown","metadata":{"id":"ctnReOmIejpg"},"source":["# Import all needed Libraries/Module"]},{"cell_type":"code","execution_count":1,"metadata":{"executionInfo":{"elapsed":380,"status":"ok","timestamp":1684556416638,"user":{"displayName":"Laurensia Simanihuruk M335DSY2820","userId":"16934850312217312293"},"user_tz":-420},"id":"WSz8ryjEClCd"},"outputs":[],"source":["import pandas as pd\n","import seaborn as sns\n","import tensorflow as tf\n","from tensorflow.keras import layers\n","import numpy as np"]},{"cell_type":"code","execution_count":2,"metadata":{},"outputs":[{"data":{"text/html":["
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ageweight(kg)height(m)genderBMIBMRactivity_levelcalories_to_maintain_weight
0216.0978620.932025118.531612958.5841.21150.3008
1414.6193740.916687117.397496932.3831.71585.0511
2417.8999180.997288117.997414977.5781.91857.3982
3313.5328801.022786112.936609944.6891.91794.9091
4417.0394841.053977015.338909799.2291.91518.5351
...........................
107217295.4442361.631113135.8741691510.1541.31963.2002
107228597.9768991.631409136.8127241473.4181.72504.8106
107235097.7612591.631864136.7112171635.9251.72781.0725
107245796.8293241.631290136.3868711593.9811.32072.1753
107256397.6151861.633168036.5978501791.5121.22149.8144
\n","

10726 rows × 8 columns

\n","
"],"text/plain":[" age weight(kg) height(m) gender BMI BMR \n","0 2 16.097862 0.932025 1 18.531612 958.584 \\\n","1 4 14.619374 0.916687 1 17.397496 932.383 \n","2 4 17.899918 0.997288 1 17.997414 977.578 \n","3 3 13.532880 1.022786 1 12.936609 944.689 \n","4 4 17.039484 1.053977 0 15.338909 799.229 \n","... ... ... ... ... ... ... \n","10721 72 95.444236 1.631113 1 35.874169 1510.154 \n","10722 85 97.976899 1.631409 1 36.812724 1473.418 \n","10723 50 97.761259 1.631864 1 36.711217 1635.925 \n","10724 57 96.829324 1.631290 1 36.386871 1593.981 \n","10725 63 97.615186 1.633168 0 36.597850 1791.512 \n","\n"," activity_level calories_to_maintain_weight \n","0 1.2 1150.3008 \n","1 1.7 1585.0511 \n","2 1.9 1857.3982 \n","3 1.9 1794.9091 \n","4 1.9 1518.5351 \n","... ... ... \n","10721 1.3 1963.2002 \n","10722 1.7 2504.8106 \n","10723 1.7 2781.0725 \n","10724 1.3 2072.1753 \n","10725 1.2 2149.8144 \n","\n","[10726 rows x 8 columns]"]},"execution_count":2,"metadata":{},"output_type":"execute_result"}],"source":["data = pd.read_csv('../Data/after_preprocessing.csv')\n","data"]},{"cell_type":"code","execution_count":3,"metadata":{},"outputs":[],"source":["x=data.drop('calories_to_maintain_weight',axis=1)\n","y=data['calories_to_maintain_weight']"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["Normalization dapat mengontrol proses normalisasi secara fleksibel, seperti menentukan nilai rata-rata dan deviasi standar yang diinginkan, atau menggunakan statistik empiris dari data pelatihan. Normalisasi ini membantu dalam menjaga skala data yang konsisten, mempercepat konvergensi model, dan menghindari masalah gradien yang tidak stabil."]},{"cell_type":"code","execution_count":4,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["[2.6778576e+01 6.1647728e+01 1.5402758e+00 5.1948535e-01 2.4788317e+01\n"," 1.4420499e+03 1.4874511e+00]\n"]}],"source":["normalizer = tf.keras.layers.experimental.preprocessing.Normalization(input_shape=(x.shape[1],),axis=-1)\n","normalizer.adapt(np.array(x))\n","print(normalizer.mean.numpy())"]},{"cell_type":"code","execution_count":5,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":396,"status":"ok","timestamp":1684557068519,"user":{"displayName":"Laurensia Simanihuruk M335DSY2820","userId":"16934850312217312293"},"user_tz":-420},"id":"JJzceLndi8Wd","outputId":"95efe73f-7b2d-420d-9808-9caadc101ecf"},"outputs":[{"data":{"text/plain":["((8580, 7), (8580,))"]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["from sklearn.model_selection import train_test_split\n","\n","x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)\n","x_train.shape,y_train.shape"]},{"cell_type":"code","execution_count":6,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":85673,"status":"ok","timestamp":1684551947248,"user":{"displayName":"Laurensia Simanihuruk M335DSY2820","userId":"16934850312217312293"},"user_tz":-420},"id":"JtMEJng2RnIH","outputId":"9f6457ce-95b0-4783-81bd-fc3b9de44955"},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch 1/200\n","1716/1716 [==============================] - 2s 1ms/step - loss: 531.5947 - val_loss: 73.8925\n","Epoch 2/200\n","1716/1716 [==============================] - 1s 760us/step - loss: 54.3241 - val_loss: 42.6609\n","Epoch 3/200\n","1716/1716 [==============================] - 1s 773us/step - loss: 36.8800 - val_loss: 32.5663\n","Epoch 4/200\n","1716/1716 [==============================] - 1s 778us/step - loss: 28.2371 - val_loss: 26.3209\n","Epoch 5/200\n","1716/1716 [==============================] - 1s 776us/step - loss: 23.1769 - val_loss: 21.0337\n","Epoch 6/200\n","1716/1716 [==============================] - 1s 791us/step - loss: 19.5102 - val_loss: 17.5161\n","Epoch 7/200\n","1716/1716 [==============================] - 1s 768us/step - loss: 17.2982 - val_loss: 15.3438\n","Epoch 8/200\n","1716/1716 [==============================] - 1s 752us/step - loss: 14.9474 - val_loss: 13.9336\n","Epoch 9/200\n","1716/1716 [==============================] - 1s 736us/step - loss: 13.1746 - val_loss: 11.3816\n","Epoch 10/200\n","1716/1716 [==============================] - 1s 737us/step - loss: 11.7521 - val_loss: 13.2441\n","Epoch 11/200\n","1716/1716 [==============================] - 1s 736us/step - loss: 10.9950 - val_loss: 9.4659\n","Epoch 12/200\n","1716/1716 [==============================] - 1s 735us/step - loss: 10.3183 - val_loss: 10.6939\n","Epoch 13/200\n","1716/1716 [==============================] - 1s 733us/step - loss: 9.8831 - val_loss: 7.5602\n","Epoch 14/200\n","1716/1716 [==============================] - 1s 744us/step - loss: 9.4346 - val_loss: 10.1937\n","Epoch 15/200\n","1716/1716 [==============================] - 1s 746us/step - loss: 9.1067 - val_loss: 7.6069\n","Epoch 16/200\n","1716/1716 [==============================] - 1s 748us/step - loss: 8.9428 - val_loss: 10.5473\n","Epoch 17/200\n","1716/1716 [==============================] - 1s 729us/step - loss: 8.8194 - val_loss: 8.5155\n","Epoch 18/200\n","1716/1716 [==============================] - 1s 734us/step - loss: 8.6483 - val_loss: 7.8653\n","Epoch 19/200\n","1716/1716 [==============================] - 1s 748us/step - loss: 8.5672 - val_loss: 13.6216\n","Epoch 20/200\n","1716/1716 [==============================] - 1s 775us/step - loss: 8.4011 - val_loss: 9.3342\n","Epoch 21/200\n","1716/1716 [==============================] - 1s 804us/step - loss: 8.2285 - val_loss: 7.9524\n","Epoch 22/200\n","1716/1716 [==============================] - 1s 730us/step - loss: 8.3433 - val_loss: 8.9628\n","Epoch 23/200\n","1716/1716 [==============================] - 1s 716us/step - loss: 8.2259 - val_loss: 7.5242\n","Epoch 24/200\n","1716/1716 [==============================] - 1s 744us/step - loss: 7.9638 - val_loss: 10.0107\n","Epoch 25/200\n","1716/1716 [==============================] - 1s 734us/step - loss: 7.8177 - val_loss: 13.6886\n","Epoch 26/200\n","1716/1716 [==============================] - 1s 726us/step - loss: 7.5133 - val_loss: 6.6711\n","Epoch 27/200\n","1716/1716 [==============================] - 1s 746us/step - loss: 7.9110 - val_loss: 6.6450\n","Epoch 28/200\n","1716/1716 [==============================] - 1s 736us/step - loss: 8.0272 - val_loss: 7.7196\n","Epoch 29/200\n","1716/1716 [==============================] - 1s 726us/step - loss: 7.5528 - val_loss: 8.5609\n","Epoch 30/200\n","1716/1716 [==============================] - 1s 735us/step - loss: 7.6266 - val_loss: 6.2141\n","Epoch 31/200\n","1716/1716 [==============================] - 1s 761us/step - loss: 7.3883 - val_loss: 8.5132\n","Epoch 32/200\n","1716/1716 [==============================] - 1s 853us/step - loss: 7.2389 - val_loss: 5.7850\n","Epoch 33/200\n","1716/1716 [==============================] - 1s 734us/step - loss: 7.3696 - val_loss: 6.7543\n","Epoch 34/200\n","1716/1716 [==============================] - 1s 756us/step - loss: 7.3951 - val_loss: 8.7921\n","Epoch 35/200\n","1716/1716 [==============================] - 1s 782us/step - loss: 7.1196 - val_loss: 7.0700\n","Epoch 36/200\n","1716/1716 [==============================] - 1s 745us/step - loss: 7.0562 - val_loss: 8.1157\n","Epoch 37/200\n","1716/1716 [==============================] - 1s 739us/step - loss: 7.0974 - val_loss: 6.4067\n","Epoch 38/200\n","1716/1716 [==============================] - 1s 737us/step - loss: 7.2806 - val_loss: 5.9388\n","Epoch 39/200\n","1716/1716 [==============================] - 1s 723us/step - loss: 7.3855 - val_loss: 6.0203\n","Epoch 40/200\n","1716/1716 [==============================] - 1s 751us/step - loss: 7.0830 - val_loss: 5.0072\n","Epoch 41/200\n","1716/1716 [==============================] - 1s 729us/step - loss: 7.0721 - val_loss: 7.3251\n","Epoch 42/200\n","1716/1716 [==============================] - 1s 728us/step - loss: 7.0870 - val_loss: 6.9149\n","Epoch 43/200\n","1716/1716 [==============================] - 1s 768us/step - loss: 6.8901 - val_loss: 5.6030\n","Epoch 44/200\n","1716/1716 [==============================] - 1s 743us/step - loss: 7.0251 - val_loss: 9.3260\n","Epoch 45/200\n","1716/1716 [==============================] - 1s 732us/step - loss: 6.9046 - val_loss: 4.3906\n","Epoch 46/200\n","1716/1716 [==============================] - 1s 739us/step - loss: 6.6270 - val_loss: 4.6910\n","Epoch 47/200\n","1716/1716 [==============================] - 1s 742us/step - loss: 6.8264 - val_loss: 4.6531\n","Epoch 48/200\n","1716/1716 [==============================] - 1s 753us/step - loss: 6.4314 - val_loss: 4.5964\n","Epoch 49/200\n","1716/1716 [==============================] - 1s 785us/step - loss: 6.6293 - val_loss: 4.7984\n","Epoch 50/200\n","1716/1716 [==============================] - 1s 742us/step - loss: 6.5532 - val_loss: 5.1899\n","Epoch 51/200\n","1716/1716 [==============================] - 1s 731us/step - loss: 6.1657 - val_loss: 8.5815\n","Epoch 52/200\n","1716/1716 [==============================] - 1s 734us/step - loss: 6.5360 - val_loss: 7.5747\n","Epoch 53/200\n","1716/1716 [==============================] - 1s 751us/step - loss: 6.3131 - val_loss: 5.7839\n","Epoch 54/200\n","1716/1716 [==============================] - 1s 733us/step - loss: 6.0525 - val_loss: 6.5788\n","Epoch 55/200\n","1716/1716 [==============================] - 1s 741us/step - loss: 5.9600 - val_loss: 4.8296\n","Epoch 56/200\n","1716/1716 [==============================] - 1s 735us/step - loss: 5.8225 - val_loss: 4.6612\n","Epoch 57/200\n","1716/1716 [==============================] - 1s 729us/step - loss: 6.0722 - val_loss: 5.4625\n","Epoch 58/200\n","1716/1716 [==============================] - 1s 777us/step - loss: 5.7509 - val_loss: 5.7610\n","Epoch 59/200\n","1716/1716 [==============================] - 1s 733us/step - loss: 6.2097 - val_loss: 10.2176\n","Epoch 60/200\n","1716/1716 [==============================] - 1s 731us/step - loss: 6.0563 - val_loss: 8.0941\n","Epoch 61/200\n","1716/1716 [==============================] - 1s 735us/step - loss: 6.5176 - val_loss: 9.1346\n","Epoch 62/200\n","1716/1716 [==============================] - 1s 737us/step - loss: 5.7506 - val_loss: 4.7103\n","Epoch 63/200\n","1716/1716 [==============================] - 1s 742us/step - loss: 5.8188 - val_loss: 9.8232\n","Epoch 64/200\n","1716/1716 [==============================] - 2s 884us/step - loss: 5.8911 - val_loss: 5.2641\n","Epoch 65/200\n","1716/1716 [==============================] - 2s 1ms/step - loss: 6.3486 - val_loss: 6.9399\n","Epoch 66/200\n","1716/1716 [==============================] - 1s 805us/step - loss: 5.4418 - val_loss: 5.3164\n","Epoch 67/200\n","1716/1716 [==============================] - 1s 763us/step - loss: 5.6416 - val_loss: 8.4578\n","Epoch 68/200\n","1716/1716 [==============================] - 1s 776us/step - loss: 5.5403 - val_loss: 4.9290\n","Epoch 69/200\n","1716/1716 [==============================] - 1s 796us/step - loss: 5.9701 - val_loss: 3.6903\n","Epoch 70/200\n","1716/1716 [==============================] - 1s 800us/step - loss: 5.7487 - val_loss: 5.5732\n","Epoch 71/200\n","1716/1716 [==============================] - 1s 762us/step - loss: 5.5596 - val_loss: 3.7524\n","Epoch 72/200\n","1716/1716 [==============================] - 1s 799us/step - loss: 5.5345 - val_loss: 7.4584\n","Epoch 73/200\n","1716/1716 [==============================] - 1s 753us/step - loss: 5.3889 - val_loss: 4.0436\n","Epoch 74/200\n","1716/1716 [==============================] - 1s 775us/step - loss: 5.4372 - val_loss: 4.5620\n","Epoch 75/200\n","1716/1716 [==============================] - 1s 751us/step - loss: 5.7318 - val_loss: 4.7635\n","Epoch 76/200\n","1716/1716 [==============================] - 1s 726us/step - loss: 5.3547 - val_loss: 4.0109\n","Epoch 77/200\n","1716/1716 [==============================] - 2s 991us/step - loss: 5.3929 - val_loss: 4.9414\n","Epoch 78/200\n","1716/1716 [==============================] - 1s 763us/step - loss: 5.1973 - val_loss: 5.1381\n","Epoch 79/200\n","1716/1716 [==============================] - 1s 726us/step - loss: 5.0961 - val_loss: 5.0689\n","Epoch 80/200\n","1716/1716 [==============================] - 1s 736us/step - loss: 5.4024 - val_loss: 6.1689\n","Epoch 81/200\n","1716/1716 [==============================] - 1s 735us/step - loss: 5.4784 - val_loss: 10.1474\n","Epoch 82/200\n","1716/1716 [==============================] - 1s 759us/step - loss: 5.1939 - val_loss: 5.8478\n","Epoch 83/200\n","1716/1716 [==============================] - 1s 740us/step - loss: 5.2780 - val_loss: 6.8239\n","Epoch 84/200\n","1716/1716 [==============================] - 1s 744us/step - loss: 5.0423 - val_loss: 7.2692\n","Epoch 85/200\n","1716/1716 [==============================] - 1s 756us/step - loss: 5.2584 - val_loss: 5.8268\n","Epoch 86/200\n","1716/1716 [==============================] - 1s 763us/step - loss: 4.7400 - val_loss: 6.8170\n","Epoch 87/200\n","1716/1716 [==============================] - 1s 734us/step - loss: 4.8296 - val_loss: 3.3215\n","Epoch 88/200\n","1716/1716 [==============================] - 2s 999us/step - loss: 4.9758 - val_loss: 3.8283\n","Epoch 89/200\n","1716/1716 [==============================] - 1s 809us/step - loss: 4.8113 - val_loss: 5.4090\n","Epoch 90/200\n","1716/1716 [==============================] - 1s 846us/step - loss: 5.0724 - val_loss: 5.3601\n","Epoch 91/200\n","1716/1716 [==============================] - 1s 787us/step - loss: 5.3619 - val_loss: 4.0161\n","Epoch 92/200\n","1716/1716 [==============================] - 1s 788us/step - loss: 5.1165 - val_loss: 3.1666\n","Epoch 93/200\n","1716/1716 [==============================] - 1s 857us/step - loss: 4.7380 - val_loss: 3.0207\n","Epoch 94/200\n","1716/1716 [==============================] - 1s 780us/step - loss: 5.0259 - val_loss: 2.6049\n","Epoch 95/200\n","1716/1716 [==============================] - 1s 790us/step - loss: 4.7627 - val_loss: 4.3388\n","Epoch 96/200\n","1716/1716 [==============================] - 1s 740us/step - loss: 4.7435 - val_loss: 4.1900\n","Epoch 97/200\n","1716/1716 [==============================] - 1s 777us/step - loss: 4.6427 - val_loss: 11.8405\n","Epoch 98/200\n","1716/1716 [==============================] - 1s 758us/step - loss: 4.7527 - val_loss: 3.5919\n","Epoch 99/200\n","1716/1716 [==============================] - 1s 754us/step - loss: 4.9057 - val_loss: 3.1588\n","Epoch 100/200\n","1716/1716 [==============================] - 1s 734us/step - loss: 4.6200 - val_loss: 3.2256\n","Epoch 101/200\n","1716/1716 [==============================] - 1s 749us/step - loss: 4.5256 - val_loss: 10.3281\n","Epoch 102/200\n","1716/1716 [==============================] - 1s 749us/step - loss: 4.9034 - val_loss: 3.5582\n","Epoch 103/200\n","1716/1716 [==============================] - 1s 740us/step - loss: 4.7583 - val_loss: 3.1510\n","Epoch 104/200\n","1716/1716 [==============================] - 2s 1ms/step - loss: 5.1355 - val_loss: 5.4593\n","Epoch 105/200\n","1716/1716 [==============================] - 1s 720us/step - loss: 4.8721 - val_loss: 8.0232\n","Epoch 106/200\n","1716/1716 [==============================] - 1s 859us/step - loss: 4.8147 - val_loss: 2.5058\n","Epoch 107/200\n","1716/1716 [==============================] - 1s 763us/step - loss: 4.9023 - val_loss: 3.3795\n","Epoch 108/200\n","1716/1716 [==============================] - 1s 749us/step - loss: 4.4431 - val_loss: 4.7696\n","Epoch 109/200\n","1716/1716 [==============================] - 1s 740us/step - loss: 4.6902 - val_loss: 3.5004\n","Epoch 110/200\n","1716/1716 [==============================] - 1s 745us/step - loss: 4.6287 - val_loss: 4.7724\n","Epoch 111/200\n","1716/1716 [==============================] - 1s 813us/step - loss: 4.5653 - val_loss: 7.6315\n","Epoch 112/200\n","1716/1716 [==============================] - 1s 741us/step - loss: 4.6123 - val_loss: 5.1910\n","Epoch 113/200\n","1716/1716 [==============================] - 1s 742us/step - loss: 4.4489 - val_loss: 3.8643\n","Epoch 114/200\n","1716/1716 [==============================] - 1s 761us/step - loss: 4.6664 - val_loss: 3.1798\n","Epoch 115/200\n","1716/1716 [==============================] - 1s 794us/step - loss: 4.6672 - val_loss: 2.7517\n","Epoch 116/200\n","1716/1716 [==============================] - 1s 734us/step - loss: 4.3739 - val_loss: 4.9171\n","Epoch 117/200\n","1716/1716 [==============================] - 1s 748us/step - loss: 4.4300 - val_loss: 4.1081\n","Epoch 118/200\n","1716/1716 [==============================] - 1s 743us/step - loss: 4.3382 - val_loss: 5.8105\n","Epoch 119/200\n","1716/1716 [==============================] - 1s 741us/step - loss: 4.3704 - val_loss: 6.3109\n","Epoch 120/200\n","1716/1716 [==============================] - 1s 755us/step - loss: 4.7287 - val_loss: 7.8101\n","Epoch 121/200\n","1716/1716 [==============================] - 1s 732us/step - loss: 4.4559 - val_loss: 5.5539\n","Epoch 122/200\n","1716/1716 [==============================] - 1s 763us/step - loss: 4.6236 - val_loss: 5.6262\n","Epoch 123/200\n","1716/1716 [==============================] - 1s 798us/step - loss: 4.3619 - val_loss: 3.1274\n","Epoch 124/200\n","1716/1716 [==============================] - 1s 737us/step - loss: 4.4316 - val_loss: 6.3813\n","Epoch 125/200\n","1716/1716 [==============================] - 1s 755us/step - loss: 4.3907 - val_loss: 2.5207\n","Epoch 126/200\n","1716/1716 [==============================] - 1s 745us/step - loss: 4.5437 - val_loss: 2.7151\n","Epoch 127/200\n","1716/1716 [==============================] - 1s 739us/step - loss: 4.3514 - val_loss: 3.7521\n","Epoch 128/200\n","1716/1716 [==============================] - 1s 732us/step - loss: 4.2451 - val_loss: 3.5597\n","Epoch 129/200\n","1716/1716 [==============================] - 1s 750us/step - loss: 4.1695 - val_loss: 4.7195\n","Epoch 130/200\n","1716/1716 [==============================] - 1s 737us/step - loss: 4.2931 - val_loss: 4.3691\n","Epoch 131/200\n","1716/1716 [==============================] - 1s 740us/step - loss: 4.2017 - val_loss: 3.5236\n","Epoch 132/200\n","1716/1716 [==============================] - 1s 754us/step - loss: 4.4469 - val_loss: 4.9532\n","Epoch 133/200\n","1716/1716 [==============================] - 1s 732us/step - loss: 4.2486 - val_loss: 5.8323\n","Epoch 134/200\n","1716/1716 [==============================] - 1s 742us/step - loss: 4.6749 - val_loss: 7.7539\n","Epoch 135/200\n","1716/1716 [==============================] - 1s 780us/step - loss: 4.2592 - val_loss: 4.0423\n","Epoch 136/200\n","1716/1716 [==============================] - 1s 748us/step - loss: 4.4304 - val_loss: 8.4794\n","Epoch 137/200\n","1716/1716 [==============================] - 1s 767us/step - loss: 4.1925 - val_loss: 4.1104\n","Epoch 138/200\n","1716/1716 [==============================] - 1s 799us/step - loss: 4.3835 - val_loss: 3.7110\n","Epoch 139/200\n","1716/1716 [==============================] - 1s 781us/step - loss: 4.2698 - val_loss: 2.9003\n","Epoch 140/200\n","1716/1716 [==============================] - 2s 1ms/step - loss: 3.9086 - val_loss: 4.9140\n","Epoch 141/200\n","1716/1716 [==============================] - 2s 1ms/step - loss: 4.2372 - val_loss: 2.6745\n","Epoch 142/200\n","1716/1716 [==============================] - 2s 1ms/step - loss: 4.0728 - val_loss: 3.2298\n","Epoch 143/200\n","1716/1716 [==============================] - 1s 778us/step - loss: 4.5820 - val_loss: 2.1988\n","Epoch 144/200\n","1716/1716 [==============================] - 1s 742us/step - loss: 4.2907 - val_loss: 3.5902\n","Epoch 145/200\n","1716/1716 [==============================] - 1s 734us/step - loss: 4.6117 - val_loss: 2.4716\n","Epoch 146/200\n","1716/1716 [==============================] - 1s 745us/step - loss: 4.2695 - val_loss: 5.1383\n","Epoch 147/200\n","1716/1716 [==============================] - 1s 752us/step - loss: 4.0678 - val_loss: 4.0242\n","Epoch 148/200\n","1716/1716 [==============================] - 1s 732us/step - loss: 4.1824 - val_loss: 2.5604\n","Epoch 149/200\n","1716/1716 [==============================] - 1s 749us/step - loss: 4.3493 - val_loss: 4.4310\n","Epoch 150/200\n","1716/1716 [==============================] - 1s 766us/step - loss: 4.8707 - val_loss: 2.5975\n","Epoch 151/200\n","1716/1716 [==============================] - 2s 875us/step - loss: 4.2386 - val_loss: 2.7713\n","Epoch 152/200\n","1716/1716 [==============================] - 1s 768us/step - loss: 4.4223 - val_loss: 5.5571\n","Epoch 153/200\n","1716/1716 [==============================] - 1s 752us/step - loss: 4.1709 - val_loss: 2.3259\n","Epoch 154/200\n","1716/1716 [==============================] - 1s 740us/step - loss: 4.3038 - val_loss: 3.0529\n","Epoch 155/200\n","1716/1716 [==============================] - 1s 768us/step - loss: 4.2339 - val_loss: 2.7555\n","Epoch 156/200\n","1716/1716 [==============================] - 1s 756us/step - loss: 4.1202 - val_loss: 3.7130\n","Epoch 157/200\n","1716/1716 [==============================] - 1s 734us/step - loss: 4.1316 - val_loss: 5.6784\n","Epoch 158/200\n","1716/1716 [==============================] - 1s 772us/step - loss: 4.2749 - val_loss: 4.5463\n","Epoch 159/200\n","1716/1716 [==============================] - 1s 766us/step - loss: 4.3499 - val_loss: 2.8394\n","Epoch 160/200\n","1716/1716 [==============================] - 1s 739us/step - loss: 4.1073 - val_loss: 4.8084\n","Epoch 161/200\n","1716/1716 [==============================] - 1s 770us/step - loss: 4.2006 - val_loss: 3.0387\n","Epoch 162/200\n","1716/1716 [==============================] - 1s 756us/step - loss: 4.2280 - val_loss: 3.7193\n","Epoch 163/200\n","1716/1716 [==============================] - 1s 781us/step - loss: 4.3225 - val_loss: 4.4936\n","Epoch 164/200\n","1716/1716 [==============================] - 2s 899us/step - loss: 4.2717 - val_loss: 3.0821\n","Epoch 165/200\n","1716/1716 [==============================] - 4s 2ms/step - loss: 3.7815 - val_loss: 3.0347\n","Epoch 166/200\n","1716/1716 [==============================] - 2s 1ms/step - loss: 4.2545 - val_loss: 3.4271\n","Epoch 167/200\n","1716/1716 [==============================] - 2s 1ms/step - loss: 4.2599 - val_loss: 8.4848\n","Epoch 168/200\n","1716/1716 [==============================] - 2s 1ms/step - loss: 4.2730 - val_loss: 4.4136\n","Epoch 169/200\n","1716/1716 [==============================] - 1s 773us/step - loss: 4.0928 - val_loss: 3.9184\n","Epoch 170/200\n","1716/1716 [==============================] - 1s 802us/step - loss: 4.3283 - val_loss: 3.1615\n","Epoch 171/200\n","1716/1716 [==============================] - 1s 748us/step - loss: 3.9076 - val_loss: 3.3042\n","Epoch 172/200\n","1716/1716 [==============================] - 1s 767us/step - loss: 3.9860 - val_loss: 3.2600\n","Epoch 173/200\n","1716/1716 [==============================] - 1s 748us/step - loss: 3.9732 - val_loss: 1.9075\n","Epoch 174/200\n","1716/1716 [==============================] - 1s 759us/step - loss: 4.1428 - val_loss: 5.0316\n","Epoch 175/200\n","1716/1716 [==============================] - 1s 761us/step - loss: 4.4572 - val_loss: 2.7431\n","Epoch 176/200\n","1716/1716 [==============================] - 1s 725us/step - loss: 3.9420 - val_loss: 3.4751\n","Epoch 177/200\n","1716/1716 [==============================] - 1s 717us/step - loss: 4.2130 - val_loss: 1.4947\n","Epoch 178/200\n","1716/1716 [==============================] - 1s 721us/step - loss: 4.1016 - val_loss: 3.0489\n","Epoch 179/200\n","1716/1716 [==============================] - 1s 709us/step - loss: 3.9854 - val_loss: 6.0241\n","Epoch 180/200\n","1716/1716 [==============================] - 1s 730us/step - loss: 4.0637 - val_loss: 3.2790\n","Epoch 181/200\n","1716/1716 [==============================] - 1s 724us/step - loss: 4.1019 - val_loss: 2.8276\n","Epoch 182/200\n","1716/1716 [==============================] - 1s 734us/step - loss: 3.8796 - val_loss: 2.7354\n","Epoch 183/200\n","1716/1716 [==============================] - 1s 719us/step - loss: 4.2199 - val_loss: 7.2777\n","Epoch 184/200\n","1716/1716 [==============================] - 1s 730us/step - loss: 3.8062 - val_loss: 4.1273\n","Epoch 185/200\n","1716/1716 [==============================] - 1s 710us/step - loss: 4.2200 - val_loss: 4.1267\n","Epoch 186/200\n","1716/1716 [==============================] - 1s 718us/step - loss: 3.9456 - val_loss: 1.9569\n","Epoch 187/200\n","1716/1716 [==============================] - 1s 725us/step - loss: 4.1528 - val_loss: 4.5642\n","Epoch 188/200\n","1716/1716 [==============================] - 1s 726us/step - loss: 3.8395 - val_loss: 2.4754\n","Epoch 189/200\n","1716/1716 [==============================] - 1s 730us/step - loss: 3.8966 - val_loss: 3.7772\n","Epoch 190/200\n","1716/1716 [==============================] - 1s 721us/step - loss: 4.1428 - val_loss: 4.2892\n","Epoch 191/200\n","1716/1716 [==============================] - 1s 719us/step - loss: 4.1536 - val_loss: 2.9630\n","Epoch 192/200\n","1716/1716 [==============================] - 1s 741us/step - loss: 3.9673 - val_loss: 5.5799\n","Epoch 193/200\n","1716/1716 [==============================] - 1s 722us/step - loss: 4.0416 - val_loss: 3.2660\n","Epoch 194/200\n","1716/1716 [==============================] - 1s 714us/step - loss: 4.0562 - val_loss: 2.8134\n","Epoch 195/200\n","1716/1716 [==============================] - 1s 725us/step - loss: 3.9024 - val_loss: 2.2673\n","Epoch 196/200\n","1716/1716 [==============================] - 1s 716us/step - loss: 4.0112 - val_loss: 2.9822\n","Epoch 197/200\n","1716/1716 [==============================] - 1s 724us/step - loss: 4.1937 - val_loss: 3.4282\n","Epoch 198/200\n","1716/1716 [==============================] - 1s 710us/step - loss: 4.0424 - val_loss: 6.3179\n","Epoch 199/200\n","1716/1716 [==============================] - 1s 721us/step - loss: 3.9297 - val_loss: 2.5737\n","Epoch 200/200\n","1716/1716 [==============================] - 1s 725us/step - loss: 4.0933 - val_loss: 7.5477\n"]}],"source":["model = tf.keras.Sequential([\n"," normalizer,\n"," tf.keras.layers.Dense(64, activation='relu'),\n"," tf.keras.layers.Dense(32, activation='relu'),\n"," tf.keras.layers.Dense(10,activation='relu'),\n"," tf.keras.layers.Dense(1),\n","])\n","model.compile(loss='mean_absolute_error', optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),)\n","history=model.fit(x_train, y_train, epochs=200, batch_size=4,\n"," validation_split=0.2)"]},{"cell_type":"code","execution_count":7,"metadata":{"executionInfo":{"elapsed":10,"status":"ok","timestamp":1684552854447,"user":{"displayName":"Laurensia Simanihuruk M335DSY2820","userId":"16934850312217312293"},"user_tz":-420},"id":"7_HyUsp1veFY"},"outputs":[],"source":["import numpy as np\n","import matplotlib.pyplot as plt\n","import tensorflow as tf\n","\n","def plot_loss(history,start,end):\n"," x_index=[x for x in range (start,end)]\n"," plt.plot(x_index,history.history['loss'][start-1:end-1], label='loss')\n"," plt.plot(x_index,history.history['val_loss'][start-1:end-1], label='val_loss')\n"," plt.xlabel('Epoch')\n"," plt.ylabel('Loss MAE')\n"," plt.title(f'Loss betweeh epoch {start} and {end}')\n"," plt.legend()\n"," plt.grid(True)\n"," plt.show()"]},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":455},"executionInfo":{"elapsed":2141,"status":"ok","timestamp":1684552856581,"user":{"displayName":"Laurensia Simanihuruk M335DSY2820","userId":"16934850312217312293"},"user_tz":-420},"id":"EnR7iQbbvhwa","outputId":"f8b784b6-dbc6-4206-a3ec-f348b2c47c11"},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["plot_loss(history,100,200)"]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["68/68 [==============================] - 0s 862us/step - loss: 7.4995\n"]},{"data":{"text/plain":["7.499527454376221"]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["model.evaluate(x_test,y_test)"]},{"cell_type":"code","execution_count":10,"metadata":{},"outputs":[{"data":{"text/plain":["array([1527.4813, 1827.5636, 1472.6307, ..., 2110.8088, 2089.8167,\n"," 1450.4471], dtype=float32)"]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":["predict=model.predict(x_test)\n","predict.squeeze()"]},{"cell_type":"code","execution_count":11,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["import matplotlib.pyplot as plt\n","import numpy as np\n","\n","# Assuming you have your true labels in a NumPy array called 'y_true'\n","# Assuming you have your predicted values in a NumPy array called 'y_pred'\n","\n","# Calculate the difference between true labels and predicted values\n","diff = y_test - predict.squeeze()\n","\n","# Plotting the difference\n","plt.plot(np.arange(len(diff)), diff)\n","plt.xlabel('Sample Index')\n","plt.ylabel('Difference')\n","plt.title('Regression: Difference between True Labels and Predicted Values')\n","plt.show()\n"]},{"cell_type":"code","execution_count":12,"metadata":{},"outputs":[{"data":{"text/plain":["count 2146.000000\n","mean -7.324989\n","std 4.119444\n","min -25.100099\n","25% -10.053047\n","50% -7.556084\n","75% -4.716586\n","max 13.150942\n","Name: calories_to_maintain_weight, dtype: float64"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["diff.describe()"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["hasil= y true - predict\n","\n","* y true= data asli -> y_test\n","* predict= data prediksi -> hasil predict model terhadap x_test\n","\n","if hasil<0 -> kalori yg diprediksi berlebihan\n","\n","if hasil >0 -> kalori yg diprediksi kurang"]},{"cell_type":"code","execution_count":13,"metadata":{},"outputs":[{"data":{"text/plain":["[-25.100098632812433,\n"," -21.901653515625185,\n"," -21.5766455078126,\n"," -20.149245898437584,\n"," -18.98778710937495,\n"," -18.67938281250008,\n"," -18.643880859374804,\n"," -18.622725000000173,\n"," -18.20300253906271,\n"," -17.88315820312482,\n"," -17.880408203124716,\n"," -17.825994921875008,\n"," -17.770502343750195,\n"," -17.613047265625028,\n"," -17.506821484374996,\n"," -17.388177343749703,\n"," -17.303502734374888,\n"," -17.284010546874924,\n"," -17.238617187499585,\n"," 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...]"]},"execution_count":14,"metadata":{},"output_type":"execute_result"}],"source":["sorted(diff, reverse=True) #jumlah kalori yang diprediksi kekurangan kalori"]},{"cell_type":"code","execution_count":15,"metadata":{},"outputs":[],"source":["model.save('Model_4.h5') #evaluate loss: 1.8123"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]}],"metadata":{"colab":{"authorship_tag":"ABX9TyN+sEJvIt1tB8r+RgCql4zA","mount_file_id":"1cqNVc_y8XxWEfpGv_pAUZytE7c6bUG0O","provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.0"}},"nbformat":4,"nbformat_minor":0}