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1997 lines (1997 loc) · 259 KB
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{
"cells": [
{
"cell_type": "markdown",
"id": "4e97d7ce",
"metadata": {
"id": "x0uJW5xqqqlm",
"papermill": {
"duration": 0.006249,
"end_time": "2025-12-12T13:41:03.524580",
"exception": false,
"start_time": "2025-12-12T13:41:03.518331",
"status": "completed"
},
"tags": []
},
"source": [
"# Fifa 19 Player Classification with ANN"
]
},
{
"cell_type": "markdown",
"id": "6eda7264",
"metadata": {
"id": "rUtv2wc70nzi",
"papermill": {
"duration": 0.005179,
"end_time": "2025-12-12T13:41:03.535260",
"exception": false,
"start_time": "2025-12-12T13:41:03.530081",
"status": "completed"
},
"tags": []
},
"source": [
"## Import libraries"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "79a1d295",
"metadata": {
"execution": {
"iopub.execute_input": "2025-12-12T13:41:03.546563Z",
"iopub.status.busy": "2025-12-12T13:41:03.546342Z",
"iopub.status.idle": "2025-12-12T13:41:09.709608Z",
"shell.execute_reply": "2025-12-12T13:41:09.708898Z"
},
"papermill": {
"duration": 6.170584,
"end_time": "2025-12-12T13:41:09.711031",
"exception": false,
"start_time": "2025-12-12T13:41:03.540447",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting protobuf==3.20.3\r\n",
" Downloading protobuf-3.20.3-py2.py3-none-any.whl.metadata (720 bytes)\r\n",
"Downloading protobuf-3.20.3-py2.py3-none-any.whl (162 kB)\r\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m162.1/162.1 kB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
"\u001b[?25hInstalling collected packages: protobuf\r\n",
" Attempting uninstall: protobuf\r\n",
" Found existing installation: protobuf 6.33.0\r\n",
" Uninstalling protobuf-6.33.0:\r\n",
" Successfully uninstalled protobuf-6.33.0\r\n",
"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\r\n",
"bigframes 2.12.0 requires google-cloud-bigquery-storage<3.0.0,>=2.30.0, which is not installed.\r\n",
"opentelemetry-proto 1.37.0 requires protobuf<7.0,>=5.0, but you have protobuf 3.20.3 which is incompatible.\r\n",
"onnx 1.18.0 requires protobuf>=4.25.1, but you have protobuf 3.20.3 which is incompatible.\r\n",
"a2a-sdk 0.3.10 requires protobuf>=5.29.5, but you have protobuf 3.20.3 which is incompatible.\r\n",
"ray 2.51.1 requires click!=8.3.0,>=7.0, but you have click 8.3.0 which is incompatible.\r\n",
"bigframes 2.12.0 requires rich<14,>=12.4.4, but you have rich 14.2.0 which is incompatible.\r\n",
"tensorflow-metadata 1.17.2 requires protobuf>=4.25.2; python_version >= \"3.11\", but you have protobuf 3.20.3 which is incompatible.\r\n",
"pydrive2 1.21.3 requires cryptography<44, but you have cryptography 46.0.3 which is incompatible.\r\n",
"pydrive2 1.21.3 requires pyOpenSSL<=24.2.1,>=19.1.0, but you have pyopenssl 25.3.0 which is incompatible.\r\n",
"ydf 0.13.0 requires protobuf<7.0.0,>=5.29.1, but you have protobuf 3.20.3 which is incompatible.\r\n",
"grpcio-status 1.71.2 requires protobuf<6.0dev,>=5.26.1, but you have protobuf 3.20.3 which is incompatible.\r\n",
"gcsfs 2025.3.0 requires fsspec==2025.3.0, but you have fsspec 2025.10.0 which is incompatible.\u001b[0m\u001b[31m\r\n",
"\u001b[0mSuccessfully installed protobuf-3.20.3\r\n"
]
}
],
"source": [
"# Fix compatibility issue\n",
"!pip install protobuf==3.20.3 --break-system-packages"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "31b34a6d",
"metadata": {
"execution": {
"iopub.execute_input": "2025-12-12T13:41:09.723930Z",
"iopub.status.busy": "2025-12-12T13:41:09.723680Z",
"iopub.status.idle": "2025-12-12T13:41:33.711372Z",
"shell.execute_reply": "2025-12-12T13:41:33.710806Z"
},
"id": "V4Dwpm86q2jO",
"papermill": {
"duration": 23.995544,
"end_time": "2025-12-12T13:41:33.712642",
"exception": false,
"start_time": "2025-12-12T13:41:09.717098",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2025-12-12 13:41:12.448602: 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\n",
"WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
"E0000 00:00:1765546872.821113 20 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
"E0000 00:00:1765546872.919750 20 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tensorflow version: 2.18.0\n",
"Keras version: 3.8.0\n"
]
}
],
"source": [
"# Tensorflow and tf.keras\n",
"import tensorflow as tf\n",
"from tensorflow import keras\n",
"print(\"Tensorflow version:\",tf.__version__)\n",
"print(\"Keras version:\",keras.__version__)\n",
"\n",
"#Helper libraries\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.metrics import classification_report, confusion_matrix"
]
},
{
"cell_type": "markdown",
"id": "bee0b1f4",
"metadata": {
"id": "DO_C0SJq0pai",
"papermill": {
"duration": 0.005601,
"end_time": "2025-12-12T13:41:33.724780",
"exception": false,
"start_time": "2025-12-12T13:41:33.719179",
"status": "completed"
},
"tags": []
},
"source": [
"## Load data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "ed57af6f",
"metadata": {
"execution": {
"iopub.execute_input": "2025-12-12T13:41:33.737414Z",
"iopub.status.busy": "2025-12-12T13:41:33.736925Z",
"iopub.status.idle": "2025-12-12T13:41:34.019710Z",
"shell.execute_reply": "2025-12-12T13:41:34.018884Z"
},
"executionInfo": {
"elapsed": 8,
"status": "ok",
"timestamp": 1725271595046,
"user": {
"displayName": "Daniel Manrique",
"userId": "14181066300969909559"
},
"user_tz": -120
},
"id": "-SAQX4N2qqls",
"outputId": "3317c164-9ed2-4d8a-c1c7-e2d683e0ff52",
"papermill": {
"duration": 0.290557,
"end_time": "2025-12-12T13:41:34.020981",
"exception": false,
"start_time": "2025-12-12T13:41:33.730424",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"x_train: (10325, 22)\n",
"t_train: (10325, 4)\n",
"x_dev: (2582, 22)\n",
"t_dev: (2582, 4)\n",
"x_test: (3227, 22)\n",
"t_test: (3227, 4)\n",
"Training samples: 10325\n",
"Validation samples: 2582\n",
"Testing samples: 3227\n"
]
}
],
"source": [
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"ATT_FILE = \"/kaggle/input/footballplayers/FootballPlayerPreparedCleanAttributes.csv\"\n",
"LABEL_FILE = \"/kaggle/input/footballplayers/FootballPlayerOneHotEncodedClasses.csv\"\n",
"\n",
"scaler = StandardScaler()\n",
"\n",
"attributes = pd.read_csv(ATT_FILE)\n",
"label = pd.read_csv(LABEL_FILE)\n",
"\n",
"# Stratified split\n",
"y_labels = np.argmax(label.values, axis=1)\n",
"x_temp, x_final_test, y_temp, t_final_test = train_test_split(\n",
" attributes.values, label.values, \n",
" test_size=0.2, stratify=y_labels, random_state=8\n",
")\n",
"\n",
"y_temp_labels = np.argmax(y_temp, axis=1)\n",
"x_train, x_dev, t_train, t_dev = train_test_split(\n",
" x_temp, y_temp,\n",
" test_size=0.2, stratify=y_temp_labels, random_state=8\n",
")\n",
"\n",
"# Add normalization\n",
"x_train = scaler.fit_transform(x_train)\n",
"x_dev = scaler.transform(x_dev)\n",
"x_final_test = scaler.transform(x_final_test)\n",
"\n",
"print (\"x_train:\",x_train.shape)\n",
"print (\"t_train:\",t_train.shape)\n",
"\n",
"print (\"x_dev:\",x_dev.shape)\n",
"print (\"t_dev:\",t_dev.shape)\n",
"\n",
"print (\"x_test:\",x_final_test.shape)\n",
"print (\"t_test:\",t_final_test.shape)\n",
"\n",
"print(\"Training samples: \", len(x_train))\n",
"print(\"Validation samples: \", len(x_dev))\n",
"print(\"Testing samples: \", len(x_final_test))"
]
},
{
"cell_type": "markdown",
"id": "f215ce36",
"metadata": {
"papermill": {
"duration": 0.006097,
"end_time": "2025-12-12T13:41:34.034779",
"exception": false,
"start_time": "2025-12-12T13:41:34.028682",
"status": "completed"
},
"tags": []
},
"source": [
"## Class weights"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "bda03cc4",
"metadata": {
"execution": {
"iopub.execute_input": "2025-12-12T13:41:34.047360Z",
"iopub.status.busy": "2025-12-12T13:41:34.046736Z",
"iopub.status.idle": "2025-12-12T13:41:34.052914Z",
"shell.execute_reply": "2025-12-12T13:41:34.052170Z"
},
"papermill": {
"duration": 0.013616,
"end_time": "2025-12-12T13:41:34.054046",
"exception": false,
"start_time": "2025-12-12T13:41:34.040430",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"\n",
"from sklearn.utils.class_weight import compute_class_weight\n",
"\n",
"# Convert from one-hot to class indices\n",
"y_train_labels = np.argmax(t_train, axis=1)\n",
"\n",
"# Compute balanced class weights\n",
"class_weights = compute_class_weight(\n",
" 'balanced',\n",
" classes=np.unique(y_train_labels),\n",
" y=y_train_labels\n",
")\n",
"\n",
"# Dictionary mapping class index to weight\n",
"class_weight_dict = dict(enumerate(class_weights))"
]
},
{
"cell_type": "markdown",
"id": "d5b7b1c8",
"metadata": {
"id": "dawlzBekqql1",
"papermill": {
"duration": 0.005721,
"end_time": "2025-12-12T13:41:34.065597",
"exception": false,
"start_time": "2025-12-12T13:41:34.059876",
"status": "completed"
},
"tags": []
},
"source": [
"## Initialize variables"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "538c1777",
"metadata": {
"execution": {
"iopub.execute_input": "2025-12-12T13:41:34.078777Z",
"iopub.status.busy": "2025-12-12T13:41:34.078081Z",
"iopub.status.idle": "2025-12-12T13:41:34.081699Z",
"shell.execute_reply": "2025-12-12T13:41:34.081179Z"
},
"id": "kdnGXfsNqql2",
"papermill": {
"duration": 0.011225,
"end_time": "2025-12-12T13:41:34.082741",
"exception": false,
"start_time": "2025-12-12T13:41:34.071516",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"INPUTS = x_train.shape[1]\n",
"OUTPUTS = t_train.shape[1]\n",
"NUM_TRAINING_EXAMPLES = int(round(x_train.shape[0]/1))\n",
"NUM_DEV_EXAMPLES = int (round (x_dev.shape[0]/1))"
]
},
{
"cell_type": "markdown",
"id": "ed0aba0b",
"metadata": {
"id": "zzdGAahIqqmL",
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"tags": []
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"source": [
"## Set hyperparameters"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "faffd431",
"metadata": {
"execution": {
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"source": [
"n_epochs = 1000\n",
"lr = 0.0001\n",
"batch_size = 128\n",
"momentun = 0.9 \n",
"weight_decay = 0.001 # increase for regularization"
]
},
{
"cell_type": "markdown",
"id": "319b5d9d",
"metadata": {
"id": "Dl-AsL42qqmN",
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"tags": []
},
"source": [
"## Build the deep neural model"
]
},
{
"cell_type": "markdown",
"id": "6a5a373b",
"metadata": {
"id": "45IuRP1UvrSN",
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"source": [
"Using **the functional API**, the Input layer defines the shape of each data element to expect as a tuple.\n",
"* The Input layer defines the shape of each data element to expect as a tuple.\n",
"* The Dense layers contain a number of neurons that are densely connected to the previous layer (every unit in the layer is connected to every unit in the previous layer)."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "deb3e378",
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"executionInfo": {
"elapsed": 646,
"status": "ok",
"timestamp": 1725271596682,
"user": {
"displayName": "Daniel Manrique",
"userId": "14181066300969909559"
},
"user_tz": -120
},
"id": "OdL6pIl5xqPy",
"outputId": "fedae5cd-5e6a-4f95-909d-dd9caece4542",
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"status": "completed"
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"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"I0000 00:00:1765546895.205524 20 gpu_device.cc:2022] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 15513 MB memory: -> device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0\n",
"/usr/local/lib/python3.11/dist-packages/keras/src/layers/activations/leaky_relu.py:41: UserWarning: Argument `alpha` is deprecated. Use `negative_slope` instead.\n",
" warnings.warn(\n"
]
},
{
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
"</pre>\n"
],
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"\u001b[1mModel: \"sequential\"\u001b[0m\n"
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
"┃<span style=\"font-weight: bold\"> Layer (type) </span>┃<span style=\"font-weight: bold\"> Output Shape </span>┃<span style=\"font-weight: bold\"> Param # </span>┃\n",
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
"│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">22,528</span> │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ batch_normalization │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">4,096</span> │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>) │ │ │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ leaky_re_lu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LeakyReLU</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">524,288</span> │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ batch_normalization_1 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>) │ │ │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ leaky_re_lu_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LeakyReLU</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">131,072</span> │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ batch_normalization_2 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>) │ │ │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ leaky_re_lu_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LeakyReLU</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dropout_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32,768</span> │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ batch_normalization_3 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>) │ │ │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ leaky_re_lu_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LeakyReLU</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dropout_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dense_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">516</span> │\n",
"└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
"</pre>\n"
],
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"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\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",
"│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m22,528\u001b[0m │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ batch_normalization │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m4,096\u001b[0m │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ leaky_re_lu (\u001b[38;5;33mLeakyReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dropout (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dense_1 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m524,288\u001b[0m │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ batch_normalization_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ leaky_re_lu_1 (\u001b[38;5;33mLeakyReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\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;34m256\u001b[0m) │ \u001b[38;5;34m131,072\u001b[0m │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ batch_normalization_2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ leaky_re_lu_2 (\u001b[38;5;33mLeakyReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dense_3 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m32,768\u001b[0m │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ batch_normalization_3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ leaky_re_lu_3 (\u001b[38;5;33mLeakyReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dense_4 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m) │ \u001b[38;5;34m516\u001b[0m │\n",
"└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
]
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">718,852</span> (2.74 MB)\n",
"</pre>\n"
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"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m718,852\u001b[0m (2.74 MB)\n"
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">715,012</span> (2.73 MB)\n",
"</pre>\n"
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"text/plain": [
"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m715,012\u001b[0m (2.73 MB)\n"
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">3,840</span> (15.00 KB)\n",
"</pre>\n"
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"text/plain": [
"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m3,840\u001b[0m (15.00 KB)\n"
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],
"source": [
"from tensorflow.keras import layers, Sequential\n",
"from tensorflow.keras import regularizers\n",
"from tensorflow.keras import layers\n",
"from tensorflow.keras.layers import (\n",
" Dense, Activation, Flatten, BatchNormalization, \n",
" Dropout, Input, LeakyReLU\n",
")\n",
"\n",
"\n",
"# Modelo sequencial. Feed Forward. \n",
"model = Sequential([keras.layers.Input(shape=(INPUTS,))])\n",
"\n",
"# 1 capa oculta\n",
"model.add(Dense(1024, kernel_initializer='he_normal', use_bias=False))\n",
"model.add(BatchNormalization())\n",
"model.add(LeakyReLU(alpha=0.1))\n",
"model.add(Dropout(0.5))\n",
"\n",
"# 2 capa oculta\n",
"model.add(Dense(512, kernel_initializer='he_normal', use_bias=False))\n",
"model.add(BatchNormalization())\n",
"model.add(LeakyReLU(alpha=0.1))\n",
"model.add(Dropout(0.4))\n",
"\n",
"# 3 capa oculta\n",
"model.add(Dense(256, kernel_initializer='he_normal', use_bias=False))\n",
"model.add(BatchNormalization())\n",
"model.add(LeakyReLU(alpha=0.1))\n",
"model.add(Dropout(0.3))\n",
"\n",
"# 4 capa oculta\n",
"model.add(Dense(128, kernel_initializer='he_normal', use_bias=False))\n",
"model.add(BatchNormalization())\n",
"model.add(LeakyReLU(alpha=0.1))\n",
"model.add(Dropout(0.2))\n",
"\n",
"# Capa de clasificación\n",
"model.add(Dense(\n",
" 4,\n",
" activation='softmax',\n",
" kernel_initializer='glorot_uniform'\n",
"))\n",
"\n",
"\n",
"model.summary()"
]
},
{
"cell_type": "markdown",
"id": "efce4a9b",
"metadata": {
"id": "UHB1vBGXRmRe",
"papermill": {
"duration": 0.006213,
"end_time": "2025-12-12T13:41:36.908648",
"exception": false,
"start_time": "2025-12-12T13:41:36.902435",
"status": "completed"
},
"tags": []
},
"source": [
"For example, the last layer's kernel comprises 78 parameters to adjust: 25 neurons in the previous layer by 3 neurons in the output layer plus the 3 biases, one for each output neuron."
]
},
{
"cell_type": "markdown",
"id": "f29ba406",
"metadata": {
"id": "zeHjWFsrTg1j",
"papermill": {
"duration": 0.006145,
"end_time": "2025-12-12T13:41:36.921574",
"exception": false,
"start_time": "2025-12-12T13:41:36.915429",
"status": "completed"
},
"tags": []
},
"source": [
"## Compile the model"
]
},
{
"cell_type": "markdown",
"id": "1cd5c641",
"metadata": {
"id": "b2nH4ki3TlqB",
"papermill": {
"duration": 0.00611,
"end_time": "2025-12-12T13:41:36.933847",
"exception": false,
"start_time": "2025-12-12T13:41:36.927737",
"status": "completed"
},
"tags": []
},
"source": [
"Available loss functions, optimizers, and metrics at: https://keras.io/losses/, https://keras.io/optimizers/ and https://keras.io/metrics/."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "e2228d55",
"metadata": {
"execution": {
"iopub.execute_input": "2025-12-12T13:41:36.947515Z",
"iopub.status.busy": "2025-12-12T13:41:36.946929Z",
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"shell.execute_reply": "2025-12-12T13:41:36.961899Z"
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"papermill": {
"duration": 0.023454,
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"exception": false,
"start_time": "2025-12-12T13:41:36.940029",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"\n",
"from tensorflow.keras.optimizers import AdamW\n",
"\n",
"opt = AdamW(\n",
" learning_rate=lr,\n",
" beta_1=momentun,\n",
" beta_2=0.999,\n",
" epsilon=1e-8,\n",
" amsgrad=True,\n",
" clipnorm=1.0,\n",
" weight_decay=weight_decay\n",
")\n",
"\n",
"model.compile(optimizer=opt, loss=tf.keras.losses.CategoricalCrossentropy(label_smoothing=0.1), metrics=['categorical_accuracy'])"
]
},
{
"cell_type": "markdown",
"id": "1d79a9e2",
"metadata": {
"papermill": {
"duration": 0.006299,
"end_time": "2025-12-12T13:41:36.975977",
"exception": false,
"start_time": "2025-12-12T13:41:36.969678",
"status": "completed"
},
"tags": []
},
"source": [
"## Callbacks"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "0e0d2182",
"metadata": {
"execution": {
"iopub.execute_input": "2025-12-12T13:41:36.989942Z",
"iopub.status.busy": "2025-12-12T13:41:36.989697Z",
"iopub.status.idle": "2025-12-12T13:41:36.996406Z",
"shell.execute_reply": "2025-12-12T13:41:36.995875Z"
},
"papermill": {
"duration": 0.014973,
"end_time": "2025-12-12T13:41:36.997449",
"exception": false,
"start_time": "2025-12-12T13:41:36.982476",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"from tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau\n",
"\n",
"checkpoint = ModelCheckpoint(\n",
" 'mejores_pesos.weights.h5',\n",
" monitor='val_categorical_accuracy',\n",
" mode='max',\n",
" save_best_only=True,\n",
" save_weights_only=True,\n",
" verbose=0\n",
")\n",
"\n",
"reduce_lr = ReduceLROnPlateau(\n",
" 'val_loss',\n",
" factor=0.5,\n",
" patience=50,\n",
" verbose=1\n",
")"
]
},
{
"cell_type": "markdown",
"id": "c7412c9d",
"metadata": {
"id": "oQZsDnk8bCa8",
"papermill": {
"duration": 0.006168,
"end_time": "2025-12-12T13:41:37.009869",
"exception": false,
"start_time": "2025-12-12T13:41:37.003701",
"status": "completed"
},
"tags": []
},
"source": [
"## Train the model with M-BGD"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "bf09bd82",
"metadata": {
"execution": {
"iopub.execute_input": "2025-12-12T13:41:37.023100Z",
"iopub.status.busy": "2025-12-12T13:41:37.022896Z",
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"shell.execute_reply": "2025-12-12T13:47:18.557205Z"
},
"executionInfo": {
"elapsed": 73420,
"status": "ok",
"timestamp": 1725271670099,
"user": {
"displayName": "Daniel Manrique",
"userId": "14181066300969909559"
},
"user_tz": -120
},
"id": "7xiouC6pbG0m",
"outputId": "fcdb61f2-081c-4b65-a011-3e4c3ced7328",
"papermill": {
"duration": 341.543246,
"end_time": "2025-12-12T13:47:18.559260",
"exception": false,
"start_time": "2025-12-12T13:41:37.016014",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
"I0000 00:00:1765546901.825499 68 service.cc:148] XLA service 0x7bca98006350 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
"I0000 00:00:1765546901.826685 68 service.cc:156] StreamExecutor device (0): Tesla P100-PCIE-16GB, Compute Capability 6.0\n",
"I0000 00:00:1765546902.449468 68 cuda_dnn.cc:529] Loaded cuDNN version 90300\n",
"I0000 00:00:1765546905.676537 68 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Epoch 651: ReduceLROnPlateau reducing learning rate to 4.999999873689376e-05.\n",
"\n",
"Epoch 701: ReduceLROnPlateau reducing learning rate to 2.499999936844688e-05.\n",
"\n",
"Epoch 751: ReduceLROnPlateau reducing learning rate to 1.249999968422344e-05.\n",
"\n",
"Epoch 801: ReduceLROnPlateau reducing learning rate to 6.24999984211172e-06.\n",
"\n",
"Epoch 851: ReduceLROnPlateau reducing learning rate to 3.12499992105586e-06.\n",
"\n",
"Epoch 901: ReduceLROnPlateau reducing learning rate to 1.56249996052793e-06.\n",
"\n",
"Epoch 951: ReduceLROnPlateau reducing learning rate to 7.81249980263965e-07.\n",
"341.4425027059999\n"
]
}
],
"source": [
"import time\n",
"\n",
"start = time.perf_counter()\n",
"\n",
"history = model.fit(\n",
" x_train,\n",
" t_train,\n",
" batch_size=batch_size,\n",
" epochs=n_epochs,\n",
" verbose=0,\n",
" validation_data=(x_dev, t_dev),\n",
" callbacks=[checkpoint, reduce_lr],\n",
" class_weight=class_weight_dict\n",
")\n",
"\n",
"print (time.perf_counter() - start)\n",
"\n",
"model.load_weights('mejores_pesos.weights.h5')"
]
},
{
"cell_type": "markdown",
"id": "4eb0661d",
"metadata": {
"id": "3PAP6f8o1rBc",
"papermill": {
"duration": 0.006657,
"end_time": "2025-12-12T13:47:18.573085",
"exception": false,
"start_time": "2025-12-12T13:47:18.566428",
"status": "completed"
},
"tags": []
},
"source": [
"## Get the results"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "f26ab70c",
"metadata": {
"execution": {
"iopub.execute_input": "2025-12-12T13:47:18.587662Z",
"iopub.status.busy": "2025-12-12T13:47:18.587435Z",
"iopub.status.idle": "2025-12-12T13:47:18.592519Z",
"shell.execute_reply": "2025-12-12T13:47:18.591804Z"
},
"papermill": {
"duration": 0.013741,
"end_time": "2025-12-12T13:47:18.593671",
"exception": false,
"start_time": "2025-12-12T13:47:18.579930",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"results=pd.DataFrame(history.history)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "5254b63e",
"metadata": {
"execution": {
"iopub.execute_input": "2025-12-12T13:47:18.607998Z",
"iopub.status.busy": "2025-12-12T13:47:18.607794Z",
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"shell.execute_reply": "2025-12-12T13:47:18.876016Z"
},
"executionInfo": {
"elapsed": 13,
"status": "ok",
"timestamp": 1725271670099,
"user": {