{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "Feed_forward_propagation.ipynb", "provenance": [], "collapsed_sections": [], "include_colab_link": true }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "metadata": { "id": "Uk4Fezbb9SZc", "colab_type": "text" }, "source": [ "### Forward Propagation" ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2020-09-24T12:46:03.594770Z", "start_time": "2020-09-24T12:46:03.589643Z" }, "colab_type": "code", "id": "VytiqjTQgwf4", "colab": {} }, "source": [ "import numpy as np\n", "def feed_forward(inputs, outputs, weights): \n", " pre_hidden = np.dot(inputs,weights[0])+ weights[1]\n", " hidden = 1/(1+np.exp(-pre_hidden))\n", " pred_out = np.dot(hidden, weights[2]) + weights[3]\n", " mean_squared_error = np.mean(np.square(pred_out - outputs))\n", " return mean_squared_error" ], "execution_count": null, "outputs": [] } ] }