mirror of
https://github.com/mmaithani/data-science.git
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901 lines
66 KiB
Plaintext
901 lines
66 KiB
Plaintext
{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"name": "tensorflow loss functions.ipynb",
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"provenance": [],
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"authorship_tag": "ABX9TyOtGREuyh1fN4kwNeBepqXW",
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"include_colab_link": true
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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}
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},
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "view-in-github",
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"colab_type": "text"
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},
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"source": [
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"<a href=\"https://colab.research.google.com/github/mmaithani/data-science/blob/main/tensorflow_loss_functions.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "Pjo2jozZ61qT"
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},
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"source": [
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"# Tensorflow Keras Loss Functions\r\n",
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"``tf.keras.losses\r\n",
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"``\r\n",
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""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "bWnrTwy-2pjn"
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},
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"source": [
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"# Probabilistic Losses\r\n",
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"* BinaryCrossentropy \r\n",
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"* CategoricalCrossentropy \r\n",
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"* SparseCategoricalCrossentropy \r\n",
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"* Poisson \r\n",
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"* KLDivergence "
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "Pg20dnZYjxlp"
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},
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"source": [
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"## BinaryCrossentropy loss\r\n",
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"```\r\n",
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"tf.keras.losses.BinaryCrossentropy\r\n",
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"```\r\n",
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"\r\n"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "9YS__A9kkepM"
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},
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"source": [
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"import tensorflow as tf\r\n",
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"import numpy as np"
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],
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"execution_count": 56,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "eW83N_wxjmgw",
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"outputId": "60ff1293-05a3-48aa-a550-0a72e29e1bbc"
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},
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"source": [
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"y_true = [[0., 1.], [0., 0.]]\r\n",
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"y_pred = [[0.5, 0.4], [0.4, 0.5]]\r\n",
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"\r\n",
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"# Using 'auto'/'sum_over_batch_size' reduction type\r\n",
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"bce_loss = tf.keras.losses.BinaryCrossentropy()\r\n",
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"bce_loss(y_true, y_pred).numpy()"
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],
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"execution_count": 57,
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"0.70335245"
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]
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},
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"metadata": {
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"tags": []
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},
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"execution_count": 57
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}
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "dZL5sAiRlGXf",
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"outputId": "7c7e2591-e92b-402b-8cc3-f9dae60e96e5"
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},
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"source": [
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"# Calling with 'sample_weight'.\r\n",
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"bce_loss(y_true, y_pred, sample_weight=[1, 0]).numpy()"
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],
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"execution_count": 58,
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"0.40235937"
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]
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},
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"metadata": {
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"tags": []
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},
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"execution_count": 58
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "ilNm02z9lBkh"
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},
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"source": [
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"## CategoricalCrossentropy loss\r\n",
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"`tf.keras.losses.CategoricalCrossentropy`"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "yGwl2wyqkhtC",
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"outputId": "9f9d058c-8264-4d73-bc78-c4808a748f59"
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},
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"source": [
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"# inputs\r\n",
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"y_true = [[0, 1, 0], [0, 0, 1]]\r\n",
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"y_pred = [[0.05, 0.95, 0], [0.1, 0.8, 0.1]]\r\n",
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"\r\n",
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"# Using 'auto'/'sum_over_batch_size' reduction type.\r\n",
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"cce_loss = tf.keras.losses.CategoricalCrossentropy()\r\n",
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"cce_loss(y_true, y_pred).numpy()\r\n"
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],
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"execution_count": 59,
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"1.1769392"
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]
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},
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"metadata": {
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"tags": []
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},
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"execution_count": 59
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "sm8Bmm7Bt9Eo"
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},
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"source": [
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"## sparse_categorical_crossentropy\r\n",
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"```\r\n",
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"tf.keras.losses.sparse_categorical_crossentropy\r\n",
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"```"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "gfPX_yNMtOAI",
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"outputId": "7a0cbea5-3978-4f84-85d8-b550ce3d2f6f"
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},
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"source": [
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"y_true = [1, 2]\r\n",
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"y_pred = [[0.05, 0.95, 0], [0.1, 0.8, 0.1]]\r\n",
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"loss = tf.keras.losses.sparse_categorical_crossentropy(y_true, y_pred)\r\n",
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"assert loss.shape == (2,)\r\n",
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"loss.numpy()\r\n"
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],
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"execution_count": 60,
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"array([0.05129344, 2.3025851 ], dtype=float32)"
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]
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},
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"metadata": {
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"tags": []
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},
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"execution_count": 60
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "6dlnf5G-s4G_"
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},
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"source": [
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"## poisson loss"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "0lQ4ijw6svri",
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"outputId": "52cb35ef-20b8-4075-83df-3affcbb7064d"
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},
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"source": [
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"y_true = [[0., 1.], [0., 0.]]\r\n",
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"y_pred = [[1., 1.], [0., 0.]]\r\n",
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"# Using 'auto'/'sum_over_batch_size' reduction type.\r\n",
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"p = tf.keras.losses.Poisson()\r\n",
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"p(y_true, y_pred).numpy()\r\n"
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],
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"execution_count": 61,
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"0.49999997"
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]
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},
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"metadata": {
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"tags": []
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},
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"execution_count": 61
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "q5niJ1uJrdul"
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},
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"source": [
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"## KLDivergence loss\r\n"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "7iR4uKkmrb-5",
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"outputId": "65f010e8-245e-4014-8093-b75de7bdb3b4"
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},
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"source": [
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"y_true = [[0, 1], [0, 0]]\r\n",
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"y_pred = [[0.6, 0.4], [0.4, 0.6]]\r\n",
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"# Using 'auto'/'sum_over_batch_size' reduction type.\r\n",
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"kl = tf.keras.losses.KLDivergence()\r\n",
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"kl(y_true, y_pred).numpy()\r\n"
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],
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"execution_count": 62,
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"0.45814306"
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]
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},
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"metadata": {
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"tags": []
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},
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"execution_count": 62
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "yBpo_uNa3HCg"
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},
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"source": [
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"\r\n",
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"\r\n",
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"---\r\n",
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"\r\n",
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"---\r\n",
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"\r\n",
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"\r\n",
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"\r\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "KmdqbWfr0vgv"
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},
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"source": [
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"# Regression losses\r\n",
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"\r\n",
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"\r\n",
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"* Mean Squared error\r\n",
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"* Mean Absolute Error\r\n",
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"* Mean Absolute percentage error\r\n",
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"* MeanSquaredLogarithmicError \r\n",
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"* CosineSimilarity\r\n",
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"* Huber\r\n",
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"* LogCosh\r\n",
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"\r\n",
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"\r\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "pR0kzNoJsomp"
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},
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"source": [
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"## MeanSquaredError"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "u4bj32uX56An"
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},
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"source": [
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"### a.) Mean Square error class"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "kf8AwSF4smO6",
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"outputId": "a4eb6c1a-ebb5-4986-cf43-5bcd66cc86ed"
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},
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"source": [
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"y_true = [[0., 1.], [0., 0.]]\r\n",
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"y_pred = [[1., 1.], [1., 0.]]\r\n",
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"# Using 'auto'/'sum_over_batch_size' reduction type.\r\n",
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"mse = tf.keras.losses.MeanSquaredError()\r\n",
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"mse(y_true, y_pred).numpy()"
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],
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"execution_count": 63,
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"0.5"
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]
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},
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"metadata": {
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"tags": []
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},
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"execution_count": 63
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "SySj0geW5xTE"
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},
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"source": [
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"### b.) Mean square function"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "t7QdmC6V51FB",
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"outputId": "928b4bb0-8f01-4f0d-e402-5172ed1d5f24"
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},
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"source": [
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"tf.keras.losses.mean_squared_error"
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],
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"execution_count": 64,
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"<function tensorflow.python.keras.losses.mean_squared_error>"
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]
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},
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"metadata": {
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"tags": []
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},
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"execution_count": 64
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "uiNAvFCmsGx4"
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},
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"source": [
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"## MeanAbsoluteError"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "Ns5T_b6RsTY5",
|
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"outputId": "d653b9a8-b7b5-48d6-ef73-4f817f25d8ed"
|
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},
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"source": [
|
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"y_true = [[0., 1.], [0., 0.]]\r\n",
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"y_pred = [[1., 1.], [1., 0.]]\r\n",
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"# Using 'auto'/'sum_over_batch_size' reduction type.\r\n",
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"mae = tf.keras.losses.MeanAbsoluteError()\r\n",
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"mae(y_true, y_pred).numpy()"
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],
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"execution_count": 65,
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|
"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"0.5"
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]
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},
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"metadata": {
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"tags": []
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},
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"execution_count": 65
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "zuGLdWj6skb6"
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},
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"source": [
|
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"## MeanAbsolutePercentageError"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
|
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},
|
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"id": "A0hYJEdhsT7E",
|
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"outputId": "d6867e3b-fc06-4497-f585-0f6dc2972689"
|
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},
|
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"source": [
|
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"y_true = [[2., 1.], [2., 3.]]\r\n",
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"y_pred = [[1., 1.], [1., 0.]]\r\n",
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"# Using 'auto'/'sum_over_batch_size' reduction type.\r\n",
|
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"mape = tf.keras.losses.MeanAbsolutePercentageError()\r\n",
|
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"mape(y_true, y_pred).numpy()\r\n"
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],
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"execution_count": 66,
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|
"outputs": [
|
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{
|
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"output_type": "execute_result",
|
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"data": {
|
|
"text/plain": [
|
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"50.0"
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]
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},
|
|
"metadata": {
|
|
"tags": []
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|
},
|
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"execution_count": 66
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|
}
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]
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},
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{
|
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"cell_type": "markdown",
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"metadata": {
|
|
"id": "b4DxdB1Ust0m"
|
|
},
|
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"source": [
|
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"## MeanSquaredLogarithmicError"
|
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]
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},
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{
|
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"cell_type": "code",
|
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"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "ALRKo6c6sqgb",
|
|
"outputId": "4efd0cfb-7dbf-4a1d-883a-aebea89b45db"
|
|
},
|
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"source": [
|
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"y_true = [[0., 1.], [0., 0.]]\r\n",
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"y_pred = [[1., 1.], [1., 0.]]\r\n",
|
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"# Using 'auto'/'sum_over_batch_size' reduction type.\r\n",
|
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"msle = tf.keras.losses.MeanSquaredLogarithmicError()\r\n",
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"msle(y_true, y_pred).numpy()"
|
|
],
|
|
"execution_count": 67,
|
|
"outputs": [
|
|
{
|
|
"output_type": "execute_result",
|
|
"data": {
|
|
"text/plain": [
|
|
"0.24022643"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"execution_count": 67
|
|
}
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "RjPmk_C4mnWW"
|
|
},
|
|
"source": [
|
|
"## CosineSimilarity loss\r\n",
|
|
"```\r\n",
|
|
"tf.keras.losses.CosineSimilarity\r\n",
|
|
"```\r\n",
|
|
"\r\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "svq-ZnO4miIF",
|
|
"outputId": "dc3e4309-f993-4cb8-9b15-e74a4c56428a"
|
|
},
|
|
"source": [
|
|
"y_true = [[0., 1.], [1., 1.]]\r\n",
|
|
"y_pred = [[1., 0.], [1., 1.]]\r\n",
|
|
"\r\n",
|
|
"# Using 'auto'/'sum_over_batch_size' reduction type.\r\n",
|
|
"cosine_loss = tf.keras.losses.CosineSimilarity(axis=1)\r\n",
|
|
"cosine_loss(y_true, y_pred).numpy()"
|
|
],
|
|
"execution_count": 68,
|
|
"outputs": [
|
|
{
|
|
"output_type": "execute_result",
|
|
"data": {
|
|
"text/plain": [
|
|
"-0.49999997"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"execution_count": 68
|
|
}
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "cWtUns91nZm2"
|
|
},
|
|
"source": [
|
|
"## Huber loss\r\n",
|
|
"```\r\n",
|
|
"tf.keras.losses.Huber\r\n",
|
|
"```\r\n",
|
|
"\r\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "Pa_sP8BZnQ_E",
|
|
"outputId": "38176304-6de3-4e79-9e82-6f158afb24b4"
|
|
},
|
|
"source": [
|
|
"y_true = [[0, 1], [0, 0]]\r\n",
|
|
"y_pred = [[0.5, 0.4], [0.4, 0.5]]\r\n",
|
|
"\r\n",
|
|
"# Using 'auto'/'sum_over_batch_size' reduction type.\r\n",
|
|
"hub_loss = tf.keras.losses.Huber()\r\n",
|
|
"h_loss(y_true, y_pred).numpy()\r\n"
|
|
],
|
|
"execution_count": 69,
|
|
"outputs": [
|
|
{
|
|
"output_type": "execute_result",
|
|
"data": {
|
|
"text/plain": [
|
|
"1.25"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"execution_count": 69
|
|
}
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "50cj6TdiryNK"
|
|
},
|
|
"source": [
|
|
"## LogCosh loss"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "wPFg0Jpin42Y",
|
|
"outputId": "cd942ae8-c208-4e4d-869c-5451abbe24cb"
|
|
},
|
|
"source": [
|
|
"y_true = [[0., 1.], [0., 0.]]\r\n",
|
|
"y_pred = [[1., 1.], [0., 0.]]\r\n",
|
|
"\r\n",
|
|
"# Using 'auto'/'sum_over_batch_size' reduction type.\r\n",
|
|
"l = tf.keras.losses.LogCosh()\r\n",
|
|
"l(y_true, y_pred).numpy()\r\n"
|
|
],
|
|
"execution_count": 70,
|
|
"outputs": [
|
|
{
|
|
"output_type": "execute_result",
|
|
"data": {
|
|
"text/plain": [
|
|
"0.1084452"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"execution_count": 70
|
|
}
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "ygG8G4Xc3y9f"
|
|
},
|
|
"source": [
|
|
"\r\n",
|
|
"\r\n",
|
|
"---\r\n",
|
|
"\r\n",
|
|
"---\r\n",
|
|
"\r\n",
|
|
"---\r\n",
|
|
"\r\n",
|
|
"\r\n",
|
|
"\r\n",
|
|
"\r\n",
|
|
"\r\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "xTn0RllY30c_"
|
|
},
|
|
"source": [
|
|
"# Hinge losses for \"maximum-margin\" classification\r\n",
|
|
"* Hinge \r\n",
|
|
"* SquaredHinge \r\n",
|
|
"* CategoricalHinge \r\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "ix3NZRmfm5LJ"
|
|
},
|
|
"source": [
|
|
"## Hinge loss\r\n",
|
|
"```\r\n",
|
|
"tf.keras.losses.Hinge\r\n",
|
|
"```\r\n",
|
|
"\r\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "qd3FprohmvXg",
|
|
"outputId": "26f82e29-bb3e-48e3-8c58-c5c6fc5ab021"
|
|
},
|
|
"source": [
|
|
"y_true = [[0., 1.], [0., 0.]]\r\n",
|
|
"y_pred = [[0.5, 0.4], [0.4, 0.5]]\r\n",
|
|
"\r\n",
|
|
"# Using 'auto'/'sum_over_batch_size' reduction type.\r\n",
|
|
"h_loss = tf.keras.losses.Hinge()\r\n",
|
|
"h_loss(y_true, y_pred).numpy()"
|
|
],
|
|
"execution_count": 71,
|
|
"outputs": [
|
|
{
|
|
"output_type": "execute_result",
|
|
"data": {
|
|
"text/plain": [
|
|
"1.25"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"execution_count": 71
|
|
}
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "LQU4RKFj4Euv"
|
|
},
|
|
"source": [
|
|
"## Squared Hinge loss\r\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "NtYYekEt5ABB"
|
|
},
|
|
"source": [
|
|
"\r\n",
|
|
"\r\n",
|
|
"### a.) squared hinge Class\r\n",
|
|
"```\r\n",
|
|
"tf.keras.losses.SquaredHinge\r\n",
|
|
"```"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "KRRfX_yW5BXX",
|
|
"outputId": "7a762f57-edda-4a6b-ada5-844b8198a280"
|
|
},
|
|
"source": [
|
|
"y_true = [[0., 1.], [0., 0.]]\r\n",
|
|
"y_pred = [[0.6, 0.4], [0.4, 0.6]]\r\n",
|
|
"\r\n",
|
|
"# Using 'auto'/'sum_over_batch_size' reduction type. \r\n",
|
|
"h = tf.keras.losses.SquaredHinge()\r\n",
|
|
"h(y_true, y_pred).numpy()"
|
|
],
|
|
"execution_count": 72,
|
|
"outputs": [
|
|
{
|
|
"output_type": "execute_result",
|
|
"data": {
|
|
"text/plain": [
|
|
"1.86"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"execution_count": 72
|
|
}
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "NNGw-p4448YZ"
|
|
},
|
|
"source": [
|
|
"\r\n",
|
|
"### b.) squared hinge Function\r\n",
|
|
"```\r\n",
|
|
"tf.keras.losses.squared_hinge\r\n",
|
|
"```"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"metadata": {
|
|
"id": "lXLHdOuA5DXa"
|
|
},
|
|
"source": [
|
|
"y_true = np.random.choice([-1, 1], size=(2, 3))\r\n",
|
|
"y_pred = np.random.random(size=(2, 3))\r\n",
|
|
"loss = tf.keras.losses.squared_hinge(y_true, y_pred)\r\n",
|
|
"assert loss.shape == (2,)\r\n",
|
|
"assert np.array_equal(\r\n",
|
|
" loss.numpy(),\r\n",
|
|
" np.mean(np.square(np.maximum(1. - y_true * y_pred, 0.)), axis=-1))"
|
|
],
|
|
"execution_count": 73,
|
|
"outputs": []
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "aADWZmvzmPfk"
|
|
},
|
|
"source": [
|
|
"## CategoricalHinge loss\r\n",
|
|
"```\r\n",
|
|
"tf.keras.losses.CategoricalHinge\r\n",
|
|
"```\r\n",
|
|
"\r\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "0Q07huIFmD46",
|
|
"outputId": "6c3f4de2-b9d0-4bac-c630-78fd123540c9"
|
|
},
|
|
"source": [
|
|
"y_true = [[0, 1], [0, 0]]\r\n",
|
|
"y_pred = [[0.5, 0.4], [0.4, 0.5]]\r\n",
|
|
"\r\n",
|
|
"# Using 'auto'/'sum_over_batch_size' reduction type.\r\n",
|
|
"h = tf.keras.losses.CategoricalHinge()\r\n",
|
|
"h(y_true, y_pred).numpy()"
|
|
],
|
|
"execution_count": 74,
|
|
"outputs": [
|
|
{
|
|
"output_type": "execute_result",
|
|
"data": {
|
|
"text/plain": [
|
|
"1.3"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"execution_count": 74
|
|
}
|
|
]
|
|
}
|
|
]
|
|
} |