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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# Data visualization\n",
"import numpy as np\n",
"import pandas as pd \n",
"\n",
"# Keras\n",
"from keras.models import Sequential\n",
"from keras.layers import Dense\n",
"from keras.layers import Dropout\n",
"from keras.optimizers import Adam\n",
"from keras.utils.np_utils import to_categorical\n",
"from keras.callbacks import EarlyStopping\n",
"import keras_tuner as kt\n",
"\n",
"# Train-Test\n",
"from sklearn.model_selection import train_test_split\n",
"# Classification Report\n",
"from sklearn.metrics import confusion_matrix, precision_recall_fscore_support\n",
"\n",
"import pickle\n",
"\n",
"import warnings\n",
"warnings.filterwarnings('ignore')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Describe Dataset"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Determine important landmarks for plank\n",
"IMPORTANT_LMS = [\n",
" \"NOSE\",\n",
" \"LEFT_SHOULDER\",\n",
" \"RIGHT_SHOULDER\",\n",
" \"LEFT_ELBOW\",\n",
" \"RIGHT_ELBOW\",\n",
" \"LEFT_WRIST\",\n",
" \"RIGHT_WRIST\",\n",
" \"LEFT_HIP\",\n",
" \"RIGHT_HIP\",\n",
" \"LEFT_KNEE\",\n",
" \"RIGHT_KNEE\",\n",
" \"LEFT_ANKLE\",\n",
" \"RIGHT_ANKLE\",\n",
" \"LEFT_HEEL\",\n",
" \"RIGHT_HEEL\",\n",
" \"LEFT_FOOT_INDEX\",\n",
" \"RIGHT_FOOT_INDEX\",\n",
"]\n",
"\n",
"# Generate all columns of the data frame\n",
"\n",
"HEADERS = [\"label\"] # Label column\n",
"\n",
"for lm in IMPORTANT_LMS:\n",
" HEADERS += [f\"{lm.lower()}_x\", f\"{lm.lower()}_y\", f\"{lm.lower()}_z\", f\"{lm.lower()}_v\"]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Headers: ['label', 'nose_x', 'nose_y', 'nose_z', 'nose_v', 'left_shoulder_x', 'left_shoulder_y', 'left_shoulder_z', 'left_shoulder_v', 'right_shoulder_x', 'right_shoulder_y', 'right_shoulder_z', 'right_shoulder_v', 'left_elbow_x', 'left_elbow_y', 'left_elbow_z', 'left_elbow_v', 'right_elbow_x', 'right_elbow_y', 'right_elbow_z', 'right_elbow_v', 'left_wrist_x', 'left_wrist_y', 'left_wrist_z', 'left_wrist_v', 'right_wrist_x', 'right_wrist_y', 'right_wrist_z', 'right_wrist_v', 'left_hip_x', 'left_hip_y', 'left_hip_z', 'left_hip_v', 'right_hip_x', 'right_hip_y', 'right_hip_z', 'right_hip_v', 'left_knee_x', 'left_knee_y', 'left_knee_z', 'left_knee_v', 'right_knee_x', 'right_knee_y', 'right_knee_z', 'right_knee_v', 'left_ankle_x', 'left_ankle_y', 'left_ankle_z', 'left_ankle_v', 'right_ankle_x', 'right_ankle_y', 'right_ankle_z', 'right_ankle_v', 'left_heel_x', 'left_heel_y', 'left_heel_z', 'left_heel_v', 'right_heel_x', 'right_heel_y', 'right_heel_z', 'right_heel_v', 'left_foot_index_x', 'left_foot_index_y', 'left_foot_index_z', 'left_foot_index_v', 'right_foot_index_x', 'right_foot_index_y', 'right_foot_index_z', 'right_foot_index_v']\n",
"Number of rows: 28520 \n",
"Number of columns: 69\n",
"\n",
"Labels: \n",
"C 9904\n",
"L 9546\n",
"H 9070\n",
"Name: label, dtype: int64\n",
"\n",
"Missing values: False\n",
"\n",
"Duplicate Rows : 0\n"
]
}
],
"source": [
"def describe_dataset(dataset_path: str):\n",
" '''\n",
" Describe dataset\n",
" '''\n",
"\n",
" data = pd.read_csv(dataset_path)\n",
" print(f\"Headers: {list(data.columns.values)}\")\n",
" print(f'Number of rows: {data.shape[0]} \\nNumber of columns: {data.shape[1]}\\n')\n",
" print(f\"Labels: \\n{data['label'].value_counts()}\\n\")\n",
" print(f\"Missing values: {data.isnull().values.any()}\\n\")\n",
" \n",
" duplicate = data[data.duplicated()]\n",
" print(f\"Duplicate Rows : {len(duplicate.sum(axis=1))}\")\n",
"\n",
" return data\n",
"\n",
"\n",
"# Remove duplicate rows (optional)\n",
"def remove_duplicate_rows(dataset_path: str):\n",
" '''\n",
" Remove duplicated data from the dataset then save it to another files\n",
" '''\n",
" \n",
" df = pd.read_csv(dataset_path)\n",
" df.drop_duplicates(keep=\"first\", inplace=True)\n",
" df.to_csv(f\"cleaned_train.csv\", sep=',', encoding='utf-8', index=False)\n",
"\n",
"\n",
"def round_up_metric_results(results) -> list:\n",
" '''Round up metrics results such as precision score, recall score, ...'''\n",
" return list(map(lambda el: round(el, 3), results))\n",
"\n",
"\n",
"df = describe_dataset(\"./train.csv\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Preprocess Dataset"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of rows: 28520 \n",
"Number of columns: 69\n",
"\n",
"Labels: \n",
"0 9904\n",
"2 9546\n",
"1 9070\n",
"Name: label, dtype: int64\n",
"\n"
]
}
],
"source": [
"# load dataset\n",
"df = pd.read_csv(\"./train.csv\")\n",
"\n",
"# Categorizing label\n",
"df.loc[df[\"label\"] == \"C\", \"label\"] = 0\n",
"df.loc[df[\"label\"] == \"H\", \"label\"] = 1\n",
"df.loc[df[\"label\"] == \"L\", \"label\"] = 2\n",
"\n",
"print(f'Number of rows: {df.shape[0]} \\nNumber of columns: {df.shape[1]}\\n')\n",
"print(f\"Labels: \\n{df['label'].value_counts()}\\n\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# Standard Scaling of features\n",
"# Dump input scaler\n",
"with open(\"./model/input_scaler.pkl\", \"rb\") as f2:\n",
" sc = pickle.load(f2)\n",
"\n",
"x = df.drop(\"label\", axis = 1)\n",
"x = pd.DataFrame(sc.transform(x))\n",
"\n",
"y = df[\"label\"]\n",
"\n",
"# # Converting prediction to categorical\n",
"y_cat = to_categorical(y)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"x_train, x_test, y_train, y_test = train_test_split(x.values, y_cat, test_size=0.2, random_state=1234)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Build Model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.1. Set up"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"stop_early = EarlyStopping(monitor='loss', patience=3)\n",
"\n",
"# Final Results\n",
"final_models = {}"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"def describe_model(model):\n",
" '''\n",
" Describe Model architecture\n",
" '''\n",
" print(f\"Describe models architecture\")\n",
" for i, layer in enumerate(model.layers):\n",
" number_of_units = layer.units if hasattr(layer, 'units') else 0\n",
"\n",
" if hasattr(layer, \"activation\"):\n",
" print(f\"Layer-{i + 1}: {number_of_units} units, func: \", layer.activation)\n",
" else:\n",
" print(f\"Layer-{i + 1}: {number_of_units} units, func: None\")\n",
" \n",
"\n",
"def get_best_model(tuner):\n",
" '''\n",
" Describe and return the best model found from keras tuner\n",
" '''\n",
" best_hps = tuner.get_best_hyperparameters(num_trials=1)[0]\n",
" best_model = tuner.hypermodel.build(best_hps)\n",
"\n",
" describe_model(best_model)\n",
"\n",
" print(\"\\nOther params:\")\n",
" ignore_params = [\"tuner\", \"activation\", \"layer\"]\n",
" for param, value in best_hps.values.items():\n",
" if not any(word in param for word in ignore_params):\n",
" print(f\"{param}: {value}\")\n",
"\n",
" return best_model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.2. Model with 3 layers"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"def model_3l_builder(hp):\n",
" model = Sequential()\n",
" model.add(Dense(68, input_dim = 68, activation = \"relu\"))\n",
"\n",
" hp_activation = hp.Choice('activation', values=['relu', 'tanh'])\n",
" hp_layer_1 = hp.Int('layer_1', min_value=32, max_value=512, step=32)\n",
" hp_learning_rate = hp.Choice('learning_rate', values=[1e-2, 1e-3, 1e-4])\n",
"\n",
" model.add(Dense(units=hp_layer_1, activation=hp_activation))\n",
" model.add(Dense(3, activation = \"softmax\"))\n",
"\n",
" model.compile(optimizer=Adam(learning_rate=hp_learning_rate), loss=\"categorical_crossentropy\", metrics = [\"accuracy\"])\n",
" \n",
" return model"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Trial 30 Complete [00h 00m 55s]\n",
"accuracy: 0.9989480972290039\n",
"\n",
"Best accuracy So Far: 0.9990357756614685\n",
"Total elapsed time: 00h 11m 35s\n",
"INFO:tensorflow:Oracle triggered exit\n"
]
}
],
"source": [
"tuner_3l = kt.Hyperband(\n",
" model_3l_builder,\n",
" objective='accuracy',\n",
" max_epochs=10,\n",
" directory='keras_tuner_dir',\n",
" project_name='keras_tuner_demo'\n",
")\n",
"tuner_3l.search(x_train, y_train, epochs=10, callbacks=[stop_early])"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Describe models architecture\n",
"Layer-1: 68 units, func: <function relu at 0x17f4a2310>\n",
"Layer-2: 32 units, func: <function tanh at 0x17f4a2670>\n",
"Layer-3: 3 units, func: <function softmax at 0x17f49d8b0>\n",
"learning_rate: 0.001\n",
"Epoch 1/100\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-11-22 22:34:40.154500: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"2282/2282 [==============================] - ETA: 0s - loss: 0.0489 - accuracy: 0.9877"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-11-22 22:35:00.478855: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"2282/2282 [==============================] - 24s 10ms/step - loss: 0.0489 - accuracy: 0.9877 - val_loss: 0.0067 - val_accuracy: 0.9986\n",
"Epoch 2/100\n",
"2282/2282 [==============================] - 22s 10ms/step - loss: 0.0096 - accuracy: 0.9975 - val_loss: 0.0045 - val_accuracy: 0.9989\n",
"Epoch 3/100\n",
"2282/2282 [==============================] - 22s 10ms/step - loss: 0.0100 - accuracy: 0.9976 - val_loss: 0.0076 - val_accuracy: 0.9979\n",
"Epoch 4/100\n",
"2282/2282 [==============================] - 22s 10ms/step - loss: 0.0063 - accuracy: 0.9982 - val_loss: 0.0038 - val_accuracy: 0.9986\n",
"Epoch 5/100\n",
"2282/2282 [==============================] - 22s 10ms/step - loss: 0.0066 - accuracy: 0.9981 - val_loss: 0.0012 - val_accuracy: 0.9996\n",
"Epoch 6/100\n",
"2282/2282 [==============================] - 22s 10ms/step - loss: 0.0058 - accuracy: 0.9985 - val_loss: 0.0014 - val_accuracy: 0.9993\n",
"Epoch 7/100\n",
"2282/2282 [==============================] - 22s 10ms/step - loss: 0.0073 - accuracy: 0.9981 - val_loss: 0.0034 - val_accuracy: 0.9989\n",
"Epoch 8/100\n",
"2282/2282 [==============================] - 22s 10ms/step - loss: 0.0045 - accuracy: 0.9987 - val_loss: 9.3191e-04 - val_accuracy: 0.9996\n",
"Epoch 9/100\n",
"2282/2282 [==============================] - 22s 10ms/step - loss: 0.0052 - accuracy: 0.9989 - val_loss: 0.0023 - val_accuracy: 0.9993\n",
"Epoch 10/100\n",
"2282/2282 [==============================] - 23s 10ms/step - loss: 0.0054 - accuracy: 0.9989 - val_loss: 0.0023 - val_accuracy: 0.9995\n",
"Epoch 11/100\n",
"2282/2282 [==============================] - 24s 10ms/step - loss: 0.0045 - accuracy: 0.9988 - val_loss: 0.0012 - val_accuracy: 0.9995\n",
"Epoch 12/100\n",
"2282/2282 [==============================] - 23s 10ms/step - loss: 0.0048 - accuracy: 0.9986 - val_loss: 0.0034 - val_accuracy: 0.9988\n",
"Epoch 13/100\n",
"2282/2282 [==============================] - 23s 10ms/step - loss: 0.0040 - accuracy: 0.9990 - val_loss: 0.0021 - val_accuracy: 0.9991\n",
"Epoch 14/100\n",
"2282/2282 [==============================] - 23s 10ms/step - loss: 0.0044 - accuracy: 0.9988 - val_loss: 0.0014 - val_accuracy: 0.9996\n",
"Epoch 15/100\n",
"2282/2282 [==============================] - 24s 11ms/step - loss: 0.0040 - accuracy: 0.9990 - val_loss: 0.0046 - val_accuracy: 0.9982\n",
"Epoch 16/100\n",
"2282/2282 [==============================] - 23s 10ms/step - loss: 0.0049 - accuracy: 0.9989 - val_loss: 9.1994e-04 - val_accuracy: 0.9998\n",
"Epoch 17/100\n",
"2282/2282 [==============================] - 23s 10ms/step - loss: 0.0037 - accuracy: 0.9992 - val_loss: 0.0042 - val_accuracy: 0.9984\n",
"Epoch 18/100\n",
"2282/2282 [==============================] - 23s 10ms/step - loss: 0.0035 - accuracy: 0.9991 - val_loss: 9.2398e-04 - val_accuracy: 0.9996\n",
"Epoch 19/100\n",
"2282/2282 [==============================] - 23s 10ms/step - loss: 0.0040 - accuracy: 0.9990 - val_loss: 0.0063 - val_accuracy: 0.9982\n",
"Epoch 20/100\n",
"2282/2282 [==============================] - 23s 10ms/step - loss: 0.0027 - accuracy: 0.9994 - val_loss: 0.0015 - val_accuracy: 0.9995\n",
"Epoch 21/100\n",
"2282/2282 [==============================] - 24s 10ms/step - loss: 0.0033 - accuracy: 0.9991 - val_loss: 0.0011 - val_accuracy: 0.9996\n",
"Epoch 22/100\n",
"2282/2282 [==============================] - 23s 10ms/step - loss: 0.0034 - accuracy: 0.9991 - val_loss: 0.0040 - val_accuracy: 0.9991\n",
"Epoch 23/100\n",
"2282/2282 [==============================] - 23s 10ms/step - loss: 0.0034 - accuracy: 0.9990 - val_loss: 0.0019 - val_accuracy: 0.9996\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x28cbaa700>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model_3l = get_best_model(tuner_3l)\n",
"model_3l.fit(x_train, y_train, epochs=100, batch_size=10, validation_data=(x_test, y_test), callbacks=[stop_early])"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"final_models[\"3_layers\"] = model_3l"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.3. Model with 5 layers"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"def model_5l_builder(hp):\n",
" model = Sequential()\n",
" model.add(Dense(68, input_dim = 68, activation = \"relu\"))\n",
"\n",
" hp_activation = hp.Choice('activation', values=['relu', 'tanh'])\n",
" hp_layer_1 = hp.Int('layer_1', min_value=32, max_value=512, step=32)\n",
" hp_layer_2 = hp.Int('layer_2', min_value=32, max_value=512, step=32)\n",
" hp_layer_3 = hp.Int('layer_3', min_value=32, max_value=512, step=32)\n",
" hp_learning_rate = hp.Choice('learning_rate', values=[1e-2, 1e-3, 1e-4])\n",
"\n",
" model.add(Dense(units=hp_layer_1, activation=hp_activation))\n",
" model.add(Dense(units=hp_layer_2, activation=hp_activation))\n",
" model.add(Dense(units=hp_layer_3, activation=hp_activation))\n",
" model.add(Dense(3, activation = \"softmax\"))\n",
"\n",
" model.compile(optimizer=Adam(learning_rate=hp_learning_rate), loss=\"categorical_crossentropy\", metrics = [\"accuracy\"])\n",
" \n",
" return model"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Trial 30 Complete [00h 01m 07s]\n",
"accuracy: 0.9977647066116333\n",
"\n",
"Best accuracy So Far: 0.9995178580284119\n",
"Total elapsed time: 00h 14m 00s\n",
"INFO:tensorflow:Oracle triggered exit\n"
]
}
],
"source": [
"tuner_5l = kt.Hyperband(\n",
" model_5l_builder,\n",
" objective='accuracy',\n",
" max_epochs=10,\n",
" directory='keras_tuner_dir',\n",
" project_name='keras_tuner_demo_1'\n",
")\n",
"tuner_5l.search(x_train, y_train, epochs=10, callbacks=[stop_early])"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Describe models architecture\n",
"Layer-1: 68 units, func: <function relu at 0x17f4a2310>\n",
"Layer-2: 96 units, func: <function relu at 0x17f4a2310>\n",
"Layer-3: 416 units, func: <function relu at 0x17f4a2310>\n",
"Layer-4: 320 units, func: <function relu at 0x17f4a2310>\n",
"Layer-5: 3 units, func: <function softmax at 0x17f49d8b0>\n",
"learning_rate: 0.0001\n",
"Epoch 1/100\n",
" 1/2282 [..............................] - ETA: 12:30 - loss: 1.1718 - accuracy: 0.3000"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-11-22 23:03:14.751713: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"2282/2282 [==============================] - ETA: 0s - loss: 0.0861 - accuracy: 0.9754"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-11-22 23:03:38.016530: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"2282/2282 [==============================] - 28s 12ms/step - loss: 0.0861 - accuracy: 0.9754 - val_loss: 0.0077 - val_accuracy: 0.9988\n",
"Epoch 2/100\n",
"2282/2282 [==============================] - 28s 12ms/step - loss: 0.0099 - accuracy: 0.9979 - val_loss: 0.0114 - val_accuracy: 0.9977\n",
"Epoch 3/100\n",
"2282/2282 [==============================] - 26s 12ms/step - loss: 0.0077 - accuracy: 0.9978 - val_loss: 0.0027 - val_accuracy: 0.9993\n",
"Epoch 4/100\n",
"2282/2282 [==============================] - 26s 11ms/step - loss: 0.0066 - accuracy: 0.9982 - val_loss: 0.0021 - val_accuracy: 0.9993\n",
"Epoch 5/100\n",
"2282/2282 [==============================] - 27s 12ms/step - loss: 0.0046 - accuracy: 0.9990 - val_loss: 9.3464e-04 - val_accuracy: 0.9996\n",
"Epoch 6/100\n",
"2282/2282 [==============================] - 27s 12ms/step - loss: 0.0051 - accuracy: 0.9986 - val_loss: 0.0013 - val_accuracy: 0.9995\n",
"Epoch 7/100\n",
"2282/2282 [==============================] - 27s 12ms/step - loss: 0.0045 - accuracy: 0.9989 - val_loss: 0.0021 - val_accuracy: 0.9991\n",
"Epoch 8/100\n",
"2282/2282 [==============================] - 27s 12ms/step - loss: 0.0034 - accuracy: 0.9992 - val_loss: 0.0031 - val_accuracy: 0.9986\n",
"Epoch 9/100\n",
"2282/2282 [==============================] - 27s 12ms/step - loss: 0.0038 - accuracy: 0.9990 - val_loss: 9.4086e-04 - val_accuracy: 0.9996\n",
"Epoch 10/100\n",
"2282/2282 [==============================] - 27s 12ms/step - loss: 0.0028 - accuracy: 0.9993 - val_loss: 7.7891e-04 - val_accuracy: 0.9996\n",
"Epoch 11/100\n",
"2282/2282 [==============================] - 27s 12ms/step - loss: 0.0029 - accuracy: 0.9995 - val_loss: 0.0013 - val_accuracy: 0.9995\n",
"Epoch 12/100\n",
"2282/2282 [==============================] - 27s 12ms/step - loss: 0.0030 - accuracy: 0.9993 - val_loss: 0.0017 - val_accuracy: 0.9993\n",
"Epoch 13/100\n",
"2282/2282 [==============================] - 28s 12ms/step - loss: 0.0019 - accuracy: 0.9995 - val_loss: 0.0016 - val_accuracy: 0.9995\n",
"Epoch 14/100\n",
"2282/2282 [==============================] - 27s 12ms/step - loss: 0.0021 - accuracy: 0.9995 - val_loss: 0.0015 - val_accuracy: 0.9995\n",
"Epoch 15/100\n",
"2282/2282 [==============================] - 27s 12ms/step - loss: 0.0031 - accuracy: 0.9992 - val_loss: 8.1616e-04 - val_accuracy: 0.9996\n",
"Epoch 16/100\n",
"2282/2282 [==============================] - 27s 12ms/step - loss: 0.0019 - accuracy: 0.9994 - val_loss: 6.3959e-04 - val_accuracy: 0.9998\n",
"Epoch 17/100\n",
"2282/2282 [==============================] - 27s 12ms/step - loss: 0.0022 - accuracy: 0.9994 - val_loss: 0.0017 - val_accuracy: 0.9995\n",
"Epoch 18/100\n",
"2282/2282 [==============================] - 27s 12ms/step - loss: 0.0014 - accuracy: 0.9996 - val_loss: 0.0015 - val_accuracy: 0.9993\n",
"Epoch 19/100\n",
"2282/2282 [==============================] - 27s 12ms/step - loss: 0.0021 - accuracy: 0.9995 - val_loss: 0.0013 - val_accuracy: 0.9996\n",
"Epoch 20/100\n",
"2282/2282 [==============================] - 27s 12ms/step - loss: 0.0018 - accuracy: 0.9996 - val_loss: 0.0025 - val_accuracy: 0.9988\n",
"Epoch 21/100\n",
"2282/2282 [==============================] - 27s 12ms/step - loss: 0.0017 - accuracy: 0.9995 - val_loss: 0.0017 - val_accuracy: 0.9996\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x307417f70>"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model_5l = get_best_model(tuner_5l)\n",
"model_5l.fit(x_train, y_train, epochs=100, batch_size=10, validation_data=(x_test, y_test), callbacks=[stop_early])"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"final_models[\"5_layers\"] = model_5l"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.4. Model with 7 layers (along with Dropout)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"def model_7lD_builder(hp):\n",
" model = Sequential()\n",
" model.add(Dense(68, input_dim = 68, activation = \"relu\"))\n",
"\n",
" hp_activation = hp.Choice('activation', values=['relu', 'tanh'])\n",
" hp_layer_1 = hp.Int('layer_1', min_value=32, max_value=512, step=32)\n",
" hp_layer_2 = hp.Int('layer_2', min_value=32, max_value=512, step=32)\n",
" hp_layer_3 = hp.Int('layer_3', min_value=32, max_value=512, step=32)\n",
" hp_dropout_1 = hp.Float('dropout_1', min_value=0.1, max_value=0.5, step=0.1)\n",
" hp_dropout_2 = hp.Float('dropout_2', min_value=0.1, max_value=0.5, step=0.1)\n",
" hp_learning_rate = hp.Choice('learning_rate', values=[1e-2, 1e-3, 1e-4])\n",
"\n",
" model.add(Dense(units=hp_layer_1, activation=hp_activation))\n",
" model.add(Dropout(rate=hp_dropout_1))\n",
" model.add(Dense(units=hp_layer_2, activation=hp_activation))\n",
" model.add(Dropout(rate=hp_dropout_2))\n",
" model.add(Dense(units=hp_layer_3, activation=hp_activation))\n",
" model.add(Dense(3, activation = \"softmax\"))\n",
"\n",
" model.compile(optimizer=Adam(learning_rate=hp_learning_rate), loss=\"categorical_crossentropy\", metrics = [\"accuracy\"])\n",
" \n",
" return model"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:tensorflow:Reloading Oracle from existing project keras_tuner_dir/keras_tuner_demo_4/oracle.json\n",
"INFO:tensorflow:Reloading Tuner from keras_tuner_dir/keras_tuner_demo_4/tuner0.json\n",
"INFO:tensorflow:Oracle triggered exit\n"
]
}
],
"source": [
"tuner_7lD = kt.Hyperband(\n",
" model_7lD_builder,\n",
" objective='accuracy',\n",
" max_epochs=10,\n",
" directory='keras_tuner_dir',\n",
" project_name='keras_tuner_demo_4'\n",
")\n",
"tuner_7lD.search(x_train, y_train, epochs=10, callbacks=[stop_early])"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Describe models architecture\n",
"Layer-1: 68 units, func: <function relu at 0x17f4a2310>\n",
"Layer-2: 480 units, func: <function relu at 0x17f4a2310>\n",
"Layer-3: 0 units, func: None\n",
"Layer-4: 256 units, func: <function relu at 0x17f4a2310>\n",
"Layer-5: 0 units, func: None\n",
"Layer-6: 480 units, func: <function relu at 0x17f4a2310>\n",
"Layer-7: 3 units, func: <function softmax at 0x17f49d8b0>\n",
"\n",
"Other params:\n",
"dropout_1: 0.30000000000000004\n",
"dropout_2: 0.4\n",
"learning_rate: 0.0001\n",
"Epoch 1/100\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-11-22 23:59:17.522675: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"2282/2282 [==============================] - ETA: 0s - loss: 0.0917 - accuracy: 0.9722"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-11-22 23:59:44.220298: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"2282/2282 [==============================] - 31s 13ms/step - loss: 0.0917 - accuracy: 0.9722 - val_loss: 0.0082 - val_accuracy: 0.9984\n",
"Epoch 2/100\n",
"2282/2282 [==============================] - 31s 14ms/step - loss: 0.0121 - accuracy: 0.9968 - val_loss: 0.0067 - val_accuracy: 0.9988\n",
"Epoch 3/100\n",
"2282/2282 [==============================] - 29s 13ms/step - loss: 0.0096 - accuracy: 0.9975 - val_loss: 0.0134 - val_accuracy: 0.9965\n",
"Epoch 4/100\n",
"2282/2282 [==============================] - 30s 13ms/step - loss: 0.0083 - accuracy: 0.9981 - val_loss: 0.0051 - val_accuracy: 0.9989\n",
"Epoch 5/100\n",
"2282/2282 [==============================] - 30s 13ms/step - loss: 0.0060 - accuracy: 0.9986 - val_loss: 0.0025 - val_accuracy: 0.9996\n",
"Epoch 6/100\n",
"2282/2282 [==============================] - 30s 13ms/step - loss: 0.0072 - accuracy: 0.9983 - val_loss: 0.0046 - val_accuracy: 0.9995\n",
"Epoch 7/100\n",
"2282/2282 [==============================] - 30s 13ms/step - loss: 0.0047 - accuracy: 0.9990 - val_loss: 0.0024 - val_accuracy: 0.9993\n",
"Epoch 8/100\n",
"2282/2282 [==============================] - 30s 13ms/step - loss: 0.0042 - accuracy: 0.9992 - val_loss: 0.0041 - val_accuracy: 0.9995\n",
"Epoch 9/100\n",
"2282/2282 [==============================] - 31s 13ms/step - loss: 0.0048 - accuracy: 0.9988 - val_loss: 0.0014 - val_accuracy: 0.9996\n",
"Epoch 10/100\n",
"2282/2282 [==============================] - 31s 14ms/step - loss: 0.0042 - accuracy: 0.9990 - val_loss: 0.0014 - val_accuracy: 0.9996\n",
"Epoch 11/100\n",
"2282/2282 [==============================] - 30s 13ms/step - loss: 0.0041 - accuracy: 0.9989 - val_loss: 0.0021 - val_accuracy: 0.9993\n",
"Epoch 12/100\n",
"2282/2282 [==============================] - 30s 13ms/step - loss: 0.0034 - accuracy: 0.9992 - val_loss: 0.0013 - val_accuracy: 0.9995\n",
"Epoch 13/100\n",
"2282/2282 [==============================] - 30s 13ms/step - loss: 0.0029 - accuracy: 0.9994 - val_loss: 0.0012 - val_accuracy: 0.9995\n",
"Epoch 14/100\n",
"2282/2282 [==============================] - 30s 13ms/step - loss: 0.0031 - accuracy: 0.9994 - val_loss: 0.0058 - val_accuracy: 0.9977\n",
"Epoch 15/100\n",
"2282/2282 [==============================] - 30s 13ms/step - loss: 0.0026 - accuracy: 0.9993 - val_loss: 0.0015 - val_accuracy: 0.9996\n",
"Epoch 16/100\n",
"2282/2282 [==============================] - 31s 13ms/step - loss: 0.0043 - accuracy: 0.9989 - val_loss: 0.0014 - val_accuracy: 0.9995\n",
"Epoch 17/100\n",
"2282/2282 [==============================] - 30s 13ms/step - loss: 0.0021 - accuracy: 0.9996 - val_loss: 6.3795e-04 - val_accuracy: 0.9996\n",
"Epoch 18/100\n",
"2282/2282 [==============================] - 30s 13ms/step - loss: 0.0021 - accuracy: 0.9995 - val_loss: 0.0015 - val_accuracy: 0.9996\n",
"Epoch 19/100\n",
"2282/2282 [==============================] - 30s 13ms/step - loss: 0.0030 - accuracy: 0.9993 - val_loss: 0.0028 - val_accuracy: 0.9993\n",
"Epoch 20/100\n",
"2282/2282 [==============================] - 30s 13ms/step - loss: 0.0020 - accuracy: 0.9995 - val_loss: 9.1208e-04 - val_accuracy: 0.9996\n",
"Epoch 21/100\n",
"2282/2282 [==============================] - 30s 13ms/step - loss: 0.0036 - accuracy: 0.9991 - val_loss: 2.6149e-04 - val_accuracy: 0.9998\n",
"Epoch 22/100\n",
"2282/2282 [==============================] - 30s 13ms/step - loss: 0.0025 - accuracy: 0.9993 - val_loss: 0.0011 - val_accuracy: 0.9996\n",
"Epoch 23/100\n",
"2282/2282 [==============================] - 30s 13ms/step - loss: 0.0021 - accuracy: 0.9994 - val_loss: 6.3920e-04 - val_accuracy: 0.9996\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x33a122970>"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model_7lD = get_best_model(tuner_7lD)\n",
"model_7lD.fit(x_train, y_train, epochs=100, batch_size=10, validation_data=(x_test, y_test), callbacks=[stop_early])"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"final_models[\"7_layers_with_dropout\"] = model_7lD"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.5. Model with 7 layers"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"def model_7l_builder(hp):\n",
" model = Sequential()\n",
" model.add(Dense(68, input_dim = 68, activation = \"relu\"))\n",
"\n",
" hp_activation = hp.Choice('activation', values=['relu', 'tanh'])\n",
" hp_layer_1 = hp.Int('layer_1', min_value=32, max_value=512, step=32)\n",
" hp_layer_2 = hp.Int('layer_2', min_value=32, max_value=512, step=32)\n",
" hp_layer_3 = hp.Int('layer_3', min_value=32, max_value=512, step=32)\n",
" hp_layer_4 = hp.Int('layer_4', min_value=32, max_value=512, step=32)\n",
" hp_layer_5 = hp.Int('layer_5', min_value=32, max_value=512, step=32)\n",
" hp_learning_rate = hp.Choice('learning_rate', values=[1e-2, 1e-3, 1e-4])\n",
"\n",
" model.add(Dense(units=hp_layer_1, activation=hp_activation))\n",
" model.add(Dense(units=hp_layer_2, activation=hp_activation))\n",
" model.add(Dense(units=hp_layer_3, activation=hp_activation))\n",
" model.add(Dense(units=hp_layer_4, activation=hp_activation))\n",
" model.add(Dense(units=hp_layer_5, activation=hp_activation))\n",
" model.add(Dense(3, activation = \"softmax\"))\n",
"\n",
" model.compile(optimizer=Adam(learning_rate=hp_learning_rate), loss=\"categorical_crossentropy\", metrics = [\"accuracy\"])\n",
" \n",
" return model"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Trial 30 Complete [00h 01m 26s]\n",
"accuracy: 0.9993863701820374\n",
"\n",
"Best accuracy So Far: 0.9993863701820374\n",
"Total elapsed time: 00h 17m 16s\n",
"INFO:tensorflow:Oracle triggered exit\n"
]
}
],
"source": [
"tuner_7l = kt.Hyperband(\n",
" model_7l_builder,\n",
" objective='accuracy',\n",
" max_epochs=10,\n",
" directory='keras_tuner_dir',\n",
" project_name='keras_tuner_demo_5'\n",
")\n",
"tuner_7l.search(x_train, y_train, epochs=10, callbacks=[stop_early])"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Describe models architecture\n",
"Layer-1: 68 units, func: <function relu at 0x17f4a2310>\n",
"Layer-2: 160 units, func: <function relu at 0x17f4a2310>\n",
"Layer-3: 416 units, func: <function relu at 0x17f4a2310>\n",
"Layer-4: 288 units, func: <function relu at 0x17f4a2310>\n",
"Layer-5: 512 units, func: <function relu at 0x17f4a2310>\n",
"Layer-6: 256 units, func: <function relu at 0x17f4a2310>\n",
"Layer-7: 3 units, func: <function softmax at 0x17f49d8b0>\n",
"\n",
"Other params:\n",
"learning_rate: 0.0001\n",
"Epoch 1/100\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-11-23 00:37:07.916975: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"2282/2282 [==============================] - ETA: 0s - loss: 0.0573 - accuracy: 0.9844"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-11-23 00:37:36.882232: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"2282/2282 [==============================] - 34s 15ms/step - loss: 0.0573 - accuracy: 0.9844 - val_loss: 0.0053 - val_accuracy: 0.9982\n",
"Epoch 2/100\n",
"2282/2282 [==============================] - 33s 15ms/step - loss: 0.0104 - accuracy: 0.9974 - val_loss: 0.0030 - val_accuracy: 0.9991\n",
"Epoch 3/100\n",
"2282/2282 [==============================] - 35s 15ms/step - loss: 0.0089 - accuracy: 0.9979 - val_loss: 0.0014 - val_accuracy: 0.9996\n",
"Epoch 4/100\n",
"2282/2282 [==============================] - 34s 15ms/step - loss: 0.0090 - accuracy: 0.9979 - val_loss: 0.0016 - val_accuracy: 0.9995\n",
"Epoch 5/100\n",
"2282/2282 [==============================] - 34s 15ms/step - loss: 0.0067 - accuracy: 0.9983 - val_loss: 0.0011 - val_accuracy: 0.9998\n",
"Epoch 6/100\n",
"2282/2282 [==============================] - 34s 15ms/step - loss: 0.0043 - accuracy: 0.9990 - val_loss: 0.0036 - val_accuracy: 0.9989\n",
"Epoch 7/100\n",
"2282/2282 [==============================] - 34s 15ms/step - loss: 0.0053 - accuracy: 0.9988 - val_loss: 0.0059 - val_accuracy: 0.9982\n",
"Epoch 8/100\n",
"2282/2282 [==============================] - 34s 15ms/step - loss: 0.0033 - accuracy: 0.9990 - val_loss: 0.0025 - val_accuracy: 0.9995\n",
"Epoch 9/100\n",
"2282/2282 [==============================] - 34s 15ms/step - loss: 0.0046 - accuracy: 0.9990 - val_loss: 0.0020 - val_accuracy: 0.9995\n",
"Epoch 10/100\n",
"2282/2282 [==============================] - 34s 15ms/step - loss: 0.0058 - accuracy: 0.9986 - val_loss: 0.0013 - val_accuracy: 0.9995\n",
"Epoch 11/100\n",
"2282/2282 [==============================] - 34s 15ms/step - loss: 0.0027 - accuracy: 0.9994 - val_loss: 0.0038 - val_accuracy: 0.9989\n",
"Epoch 12/100\n",
"2282/2282 [==============================] - 34s 15ms/step - loss: 0.0027 - accuracy: 0.9995 - val_loss: 0.0021 - val_accuracy: 0.9995\n",
"Epoch 13/100\n",
"2282/2282 [==============================] - 34s 15ms/step - loss: 0.0024 - accuracy: 0.9995 - val_loss: 0.0017 - val_accuracy: 0.9995\n",
"Epoch 14/100\n",
"2282/2282 [==============================] - 34s 15ms/step - loss: 0.0032 - accuracy: 0.9992 - val_loss: 6.6669e-04 - val_accuracy: 0.9998\n",
"Epoch 15/100\n",
"2282/2282 [==============================] - 34s 15ms/step - loss: 0.0038 - accuracy: 0.9992 - val_loss: 0.0014 - val_accuracy: 0.9996\n",
"Epoch 16/100\n",
"2282/2282 [==============================] - 34s 15ms/step - loss: 0.0028 - accuracy: 0.9993 - val_loss: 0.0019 - val_accuracy: 0.9996\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x3086633a0>"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model_7l = get_best_model(tuner_7l)\n",
"model_7l.fit(x_train, y_train, epochs=100, batch_size=10, validation_data=(x_test, y_test), callbacks=[stop_early])"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [],
"source": [
"final_models[\"7_layers\"] = model_7l"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.6. Describe Final Models"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3_layers: Describe models architecture\n",
"Layer-1: 68 units, func: <function relu at 0x179e05b80>\n",
"Layer-2: 32 units, func: <function tanh at 0x179e05ee0>\n",
"Layer-3: 3 units, func: <function softmax at 0x179e05160>\n",
"\n",
"5_layers: Describe models architecture\n",
"Layer-1: 68 units, func: <function relu at 0x179e05b80>\n",
"Layer-2: 96 units, func: <function relu at 0x179e05b80>\n",
"Layer-3: 416 units, func: <function relu at 0x179e05b80>\n",
"Layer-4: 320 units, func: <function relu at 0x179e05b80>\n",
"Layer-5: 3 units, func: <function softmax at 0x179e05160>\n",
"\n",
"7_layers_with_dropout: Describe models architecture\n",
"Layer-1: 68 units, func: <function relu at 0x179e05b80>\n",
"Layer-2: 480 units, func: <function relu at 0x179e05b80>\n",
"Layer-3: 0 units, func: None\n",
"Layer-4: 256 units, func: <function relu at 0x179e05b80>\n",
"Layer-5: 0 units, func: None\n",
"Layer-6: 480 units, func: <function relu at 0x179e05b80>\n",
"Layer-7: 3 units, func: <function softmax at 0x179e05160>\n",
"\n",
"7_layers: Describe models architecture\n",
"Layer-1: 68 units, func: <function relu at 0x179e05b80>\n",
"Layer-2: 160 units, func: <function relu at 0x179e05b80>\n",
"Layer-3: 416 units, func: <function relu at 0x179e05b80>\n",
"Layer-4: 288 units, func: <function relu at 0x179e05b80>\n",
"Layer-5: 512 units, func: <function relu at 0x179e05b80>\n",
"Layer-6: 256 units, func: <function relu at 0x179e05b80>\n",
"Layer-7: 3 units, func: <function softmax at 0x179e05160>\n",
"\n"
]
}
],
"source": [
"for name, model in final_models.items():\n",
" print(f\"{name}: \", end=\"\")\n",
" describe_model(model)\n",
" print()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Evaluate model with Train set"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Model</th>\n",
" <th>Precision Score</th>\n",
" <th>Recall Score</th>\n",
" <th>F1 score</th>\n",
" <th>Confusion Matrix</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>3_layers</td>\n",
" <td>[1.0, 1.0, 0.999]</td>\n",
" <td>[0.999, 1.0, 1.0]</td>\n",
" <td>[0.999, 1.0, 0.999]</td>\n",
" <td>[[1913, 0, 2], [0, 1844, 0], [0, 0, 1945]]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>5_layers</td>\n",
" <td>[0.999, 1.0, 0.999]</td>\n",
" <td>[0.999, 1.0, 0.999]</td>\n",
" <td>[0.999, 1.0, 0.999]</td>\n",
" <td>[[1914, 0, 1], [0, 1844, 0], [1, 0, 1944]]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>7_layers_with_dropout</td>\n",
" <td>[0.999, 1.0, 0.999]</td>\n",
" <td>[0.999, 1.0, 0.999]</td>\n",
" <td>[0.999, 1.0, 0.999]</td>\n",
" <td>[[1914, 0, 1], [0, 1844, 0], [1, 0, 1944]]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>7_layers</td>\n",
" <td>[0.999, 1.0, 0.999]</td>\n",
" <td>[0.999, 1.0, 0.999]</td>\n",
" <td>[0.999, 1.0, 0.999]</td>\n",
" <td>[[1914, 0, 1], [0, 1844, 0], [1, 0, 1944]]</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Model Precision Score Recall Score \\\n",
"0 3_layers [1.0, 1.0, 0.999] [0.999, 1.0, 1.0] \n",
"1 5_layers [0.999, 1.0, 0.999] [0.999, 1.0, 0.999] \n",
"2 7_layers_with_dropout [0.999, 1.0, 0.999] [0.999, 1.0, 0.999] \n",
"3 7_layers [0.999, 1.0, 0.999] [0.999, 1.0, 0.999] \n",
"\n",
" F1 score Confusion Matrix \n",
"0 [0.999, 1.0, 0.999] [[1913, 0, 2], [0, 1844, 0], [0, 0, 1945]] \n",
"1 [0.999, 1.0, 0.999] [[1914, 0, 1], [0, 1844, 0], [1, 0, 1944]] \n",
"2 [0.999, 1.0, 0.999] [[1914, 0, 1], [0, 1844, 0], [1, 0, 1944]] \n",
"3 [0.999, 1.0, 0.999] [[1914, 0, 1], [0, 1844, 0], [1, 0, 1944]] "
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_set_results = []\n",
"\n",
"for name, model in final_models.items():\n",
" # Evaluate model\n",
" predict_x = model.predict(x_test, verbose=False) \n",
" y_pred_class = np.argmax(predict_x, axis=1)\n",
" y_test_class = np.argmax(y_test, axis=1)\n",
"\n",
" cm = confusion_matrix(y_test_class, y_pred_class, labels=[0, 1, 2])\n",
" (p_score, r_score, f_score, _) = precision_recall_fscore_support(y_test_class, y_pred_class, labels=[0, 1, 2])\n",
" \n",
" train_set_results.append(( name, round_up_metric_results(p_score), round_up_metric_results(r_score), round_up_metric_results(f_score), cm ))\n",
"\n",
"train_set_results.sort(key=lambda k: sum(k[3]), reverse=True)\n",
"pd.DataFrame(train_set_results, columns=[\"Model\", \"Precision Score\", \"Recall Score\", \"F1 score\", \"Confusion Matrix\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. Evaluation with test set"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of rows: 710 \n",
"Number of columns: 69\n",
"\n",
"Labels: \n",
"1 241\n",
"2 235\n",
"0 234\n",
"Name: label, dtype: int64\n",
"\n"
]
}
],
"source": [
"# load dataset\n",
"test_df = pd.read_csv(\"./test.csv\")\n",
"\n",
"# Categorizing label\n",
"test_df.loc[test_df[\"label\"] == \"C\", \"label\"] = 0\n",
"test_df.loc[test_df[\"label\"] == \"H\", \"label\"] = 1\n",
"test_df.loc[test_df[\"label\"] == \"L\", \"label\"] = 2\n",
"\n",
"print(f'Number of rows: {test_df.shape[0]} \\nNumber of columns: {test_df.shape[1]}\\n')\n",
"print(f\"Labels: \\n{test_df['label'].value_counts()}\\n\")"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"# Standard Scaling of features\n",
"test_x = test_df.drop(\"label\", axis = 1)\n",
"test_x = pd.DataFrame(sc.transform(test_x))\n",
"\n",
"test_y = test_df[\"label\"]\n",
"\n",
"# # Converting prediction to categorical\n",
"test_y_cat = to_categorical(test_y)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Model</th>\n",
" <th>Precision Score</th>\n",
" <th>Recall Score</th>\n",
" <th>F1 score</th>\n",
" <th>Confusion Matrix</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>7_layers_with_dropout</td>\n",
" <td>[0.983, 1.0, 1.0]</td>\n",
" <td>[1.0, 0.992, 0.991]</td>\n",
" <td>[0.992, 0.996, 0.996]</td>\n",
" <td>[[234, 0, 0], [2, 239, 0], [2, 0, 233]]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>5_layers</td>\n",
" <td>[0.984, 0.937, 0.883]</td>\n",
" <td>[0.803, 0.992, 0.991]</td>\n",
" <td>[0.885, 0.964, 0.934]</td>\n",
" <td>[[188, 16, 30], [1, 239, 1], [2, 0, 233]]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>7_layers</td>\n",
" <td>[0.984, 0.825, 1.0]</td>\n",
" <td>[0.782, 0.996, 0.991]</td>\n",
" <td>[0.871, 0.902, 0.996]</td>\n",
" <td>[[183, 51, 0], [1, 240, 0], [2, 0, 233]]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3_layers</td>\n",
" <td>[0.88, 0.732, 1.0]</td>\n",
" <td>[0.624, 0.996, 0.919]</td>\n",
" <td>[0.73, 0.844, 0.958]</td>\n",
" <td>[[146, 88, 0], [1, 240, 0], [19, 0, 216]]</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Model Precision Score Recall Score \\\n",
"0 7_layers_with_dropout [0.983, 1.0, 1.0] [1.0, 0.992, 0.991] \n",
"1 5_layers [0.984, 0.937, 0.883] [0.803, 0.992, 0.991] \n",
"2 7_layers [0.984, 0.825, 1.0] [0.782, 0.996, 0.991] \n",
"3 3_layers [0.88, 0.732, 1.0] [0.624, 0.996, 0.919] \n",
"\n",
" F1 score Confusion Matrix \n",
"0 [0.992, 0.996, 0.996] [[234, 0, 0], [2, 239, 0], [2, 0, 233]] \n",
"1 [0.885, 0.964, 0.934] [[188, 16, 30], [1, 239, 1], [2, 0, 233]] \n",
"2 [0.871, 0.902, 0.996] [[183, 51, 0], [1, 240, 0], [2, 0, 233]] \n",
"3 [0.73, 0.844, 0.958] [[146, 88, 0], [1, 240, 0], [19, 0, 216]] "
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_set_results = []\n",
"\n",
"for name, model in final_models.items():\n",
" # Evaluate model\n",
" predict_x = model.predict(test_x, verbose=False) \n",
" y_pred_class = np.argmax(predict_x, axis=1)\n",
" y_test_class = np.argmax(test_y_cat, axis=1)\n",
"\n",
" cm = confusion_matrix(y_test_class, y_pred_class, labels=[0, 1, 2])\n",
" (p_score, r_score, f_score, _) = precision_recall_fscore_support(y_test_class, y_pred_class, labels=[0, 1, 2])\n",
" \n",
" test_set_results.append(( name, round_up_metric_results(p_score), round_up_metric_results(r_score), round_up_metric_results(f_score), cm ))\n",
"\n",
"test_set_results.sort(key=lambda k: sum(k[3]), reverse=True)\n",
"pd.DataFrame(test_set_results, columns=[\"Model\", \"Precision Score\", \"Recall Score\", \"F1 score\", \"Confusion Matrix\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 6. Conclusion & Dumped model\n",
"\n",
"Best model according to evaluation is model with 7 layers include some dropout layers"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:tensorflow:Assets written to: ram://72fd27ad-5e1d-4761-a5f0-b2b4682c6a65/assets\n"
]
}
],
"source": [
"# Dump the best model to a pickle file\n",
"with open(\"./model/plank_dp.pkl\", \"wb\") as f:\n",
" pickle.dump(final_models[\"7_layers_with_dropout\"], f)"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:tensorflow:Assets written to: ram://cc39563d-8dfe-4f23-9dc6-71462ea5ef5b/assets\n",
"INFO:tensorflow:Assets written to: ram://5855831e-e2d4-4bb1-9799-a65f926cefb3/assets\n",
"INFO:tensorflow:Assets written to: ram://bf30ae68-3e13-4624-8b29-c9c341693ed8/assets\n",
"INFO:tensorflow:Assets written to: ram://9b787241-d173-4542-88d3-5551c1cbb80d/assets\n"
]
}
],
"source": [
"# Dump final results\n",
"with open(\"./model/all_dp.pkl\", \"wb\") as f:\n",
" pickle.dump(final_models, f)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.8.13",
"language": "python",
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},
"language_info": {
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"version": 3
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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"vscode": {
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|