From d0f54932c30b48e27c14b06666a461c8f8c2e1e9 Mon Sep 17 00:00:00 2001 From: "Ronnie.Ma" Date: Tue, 12 Jan 2021 15:48:45 +0800 Subject: [PATCH] Created using Colaboratory --- neural_networks_tutorial.ipynb | 5433 ++++++++++++++++++++++++++++++++ 1 file changed, 5433 insertions(+) create mode 100644 neural_networks_tutorial.ipynb diff --git a/neural_networks_tutorial.ipynb b/neural_networks_tutorial.ipynb new file mode 100644 index 0000000..60c89e3 --- /dev/null +++ b/neural_networks_tutorial.ipynb @@ -0,0 +1,5433 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "neural-networks-tutorial.ipynb", + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ScvnRKjsV0zS" + }, + "source": [ + "# Neural Networks from Scratch with Python Code and Math in Detail\n", + "\n", + "https://towardsai.net/neural-networks-with-python" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "TfbSw80eVNCS", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "4d206787-8bf3-4c5d-aeba-925c7fe5abde" + }, + "source": [ + "# Import required libraries:\n", + "import numpy as np\n", + "# Define input features:\n", + "input_features = np.array([[0,0],[0,1],[1,0],[1,1]])\n", + "print (\"input features shape: \", input_features.shape)\n", + "print (\"input features: \\n\",input_features)\n", + "\n", + "# Define target output:\n", + "target_output = np.array([[0,1,1,1]])\n", + "\n", + "# Reshaping our target output into vector:\n", + "target_output = target_output.reshape(4,1)\n", + "print(\"target output shape: \", target_output.shape)\n", + "print (\"target output: \\n\",target_output)\n", + "\n", + "# Define weights:\n", + "weights = np.array([[0.1],[0.2]])\n", + "print(\"initial weights' shape: \", weights.shape)\n", + "print (\"initial weights: \\n\",weights)\n", + "\n", + "# initial Bias:\n", + "bias = 0.3\n", + "# Learning Rate:\n", + "lr = 0.05\n" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "text": [ + "input features shape: (4, 2)\n", + "input features: \n", + " [[0 0]\n", + " [0 1]\n", + " [1 0]\n", + " [1 1]]\n", + "target output shape: (4, 1)\n", + "target output: \n", + " [[0]\n", + " [1]\n", + " [1]\n", + " [1]]\n", + "initial weights' shape: (2, 1)\n", + "initial weights: \n", + " [[0.1]\n", + " [0.2]]\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Aw80MdneZmVx", + "outputId": "7bc09686-45e0-4583-8b3d-bbbd099ba37f", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "source": [ + "# Sigmoid function(classical method):\n", + "def sigmoid(x):\n", + " return 1/(1+np.exp(-x))\n", + "\n", + "# Derivative of sigmoid function (for back propagation):\n", + "def sigmoid_der(x):\n", + " return sigmoid(x)*(1-sigmoid(x))# Main logic for neural network:\n", + "\n", + "# ReLU function(Morden method):\n", + "def ReLU(x):\n", + " x = np.maximum(0, x)\n", + " return x\n", + "# Derivative of ReLU function (for back propagation):\n", + "def RuLU_der(x):\n", + " return ReLU(x) * (1 - ReLU(x))\n", + "\n", + "History_or_loss = []\n", + "# Running our code 10000 times:\n", + "for epoch in range(10000):\n", + " inputs = input_features\n", + "# Feedforward input:\n", + " in_o = np.dot(inputs, weights) + bias\n", + "# Feedforward output:\n", + " out_o = sigmoid(in_o)\n", + "# out_o = ReLU(in_o)\n", + "\n", + " print(\"output of feedforward: \", out_o)\n", + "\n", + "# Backpropogation \n", + "# Calculating error\n", + " error = out_o - target_output\n", + " \n", + " #Going with the formula:\n", + " x = error.sum()\n", + " print(\"loss of calculation: \",x)\n", + " \n", + " # Calculating derivative:\n", + " derror_douto = error\n", + " douto_dino = sigmoid_der(out_o)\n", + "# douto_dino = RuLU_der(out_o)\n", + " \n", + " #Multiplying individual derivatives:\n", + " deriv = derror_douto * douto_dino \n", + " \n", + " #Multiplying with the 3rd individual derivative:\n", + " #Finding the transpose of input_features:\n", + " inputs = input_features.T\n", + " deriv_final = np.dot(inputs,deriv)\n", + " \n", + " #Updating the weights values:\n", + " weights -= lr * deriv_final \n", + " #Updating the bias weight value:\n", + " for i in deriv:\n", + " bias -= lr * i #Check the final values for weight and biasprint (weights)\n", + "\n", + "print(\"weights after back-propagation ->\", weights) \n", + "print (\"bias after backpropagation ->\", bias)\n", + "\n" + ], + "execution_count": 39, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n", + " [0.9999997 ]]\n", + "loss of calculation: -0.00014433630985088465\n", 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back-propagation -> [[9.84310642]\n", + " [9.84312231]]\n", + "bias after backpropagation -> [-4.57936786]\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "yDI1jRcCdTcy", + "outputId": "c7389ee6-f364-4e8a-83bf-6d976bb6b8bc", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "source": [ + "# Making Prediction from new inputs\n", + "#Taking inputs:\n", + "single_point = np.array([1,0])\n", + "#1st step:\n", + "result1 = np.dot(single_point, weights) + bias \n", + "#2nd step:\n", + "result2 = sigmoid(result1) \n", + "#Print final result\n", + "print(\"1st prediction of [1,1], a or b\",np.round(result2,4))\n", + "\n", + "\n", + "#Taking inputs:\n", + "single_point = np.array([1,1]) \n", + "#1st step:\n", + "result1 = np.dot(single_point, weights) + bias \n", + "#2nd step:\n", + "result2 = sigmoid(result1) \n", + "#Print final result\n", + "print(\"2nd prediction of [1,1], a or b\",np.round(result2,4))\n", + "\n", + "#Taking inputs:\n", + "single_point = np.array([0,0]) \n", + "#1st step:\n", + "result1 = np.dot(single_point, weights) + bias \n", + "#2nd step:\n", + "result2 = sigmoid(result1) \n", + "#Print final result\n", + "print(\"3rd prediction of [0,0], a or b\",np.round(result2,4))\n" + ], + "execution_count": 40, + "outputs": [ + { + "output_type": "stream", + "text": [ + "1st prediction of [1,1], a or b [0.9949]\n", + "2nd prediction of [1,1], a or b [1.]\n", + "3rd prediction of [0,0], a or b [0.0102]\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "YrRCooGFcb0f", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 295 + }, + "outputId": "275a1ab6-cd9b-475f-9775-f198e161a9e3" + }, + "source": [ + "# Import required libraries:\n", + "import numpy as np# Define input features:\n", + "input_features = np.array([[0,0],[0,1],[1,0],[1,1]])\n", + "print (input_features.shape)\n", + "print (input_features)# Define target output:\n", + "target_output = np.array([[0,1,1,1]])# Reshaping our target output into vector:\n", + "target_output = target_output.reshape(4,1)\n", + "print(target_output.shape)\n", + "print (target_output)# Define weights:\n", + "weights = np.array([[0.1],[0.2]])\n", + "print(weights.shape)\n", + "print (weights)# Define learning rate:\n", + "lr = 0.05# Sigmoid function:\n", + "def sigmoid(x):\n", + " return 1/(1+np.exp(-x))# Derivative of sigmoid function:\n", + "def sigmoid_der(x):\n", + " return sigmoid(x)*(1-sigmoid(x))# Main logic for neural network:\n", + "# Running our code 10000 times:for epoch in range(10000):\n", + " inputs = input_features#Feedforward input:\n", + " pred_in = np.dot(inputs, weights)#Feedforward output:\n", + " pred_out = sigmoid(pred_in)#Backpropogation \n", + " #Calculating error\n", + " error = pred_out - target_output\n", + " x = error.sum()\n", + " \n", + " #Going with the formula:\n", + " print(x)\n", + " \n", + " #Calculating derivative:\n", + " dcost_dpred = error\n", + " dpred_dz = sigmoid_der(pred_out)\n", + " \n", + " #Multiplying individual derivatives:\n", + " z_delta = dcost_dpred * dpred_dz#Multiplying with the 3rd individual derivative:\n", + " inputs = input_features.T\n", + " weights -= lr * np.dot(inputs, z_delta)\n", + " \n", + " \n", + "#Taking inputs:\n", + "single_point = np.array([1,0])#1st step:\n", + "result1 = np.dot(single_point, weights)#2nd step:\n", + "result2 = sigmoid(result1)#Print final result\n", + "print(result2)#Taking inputs:\n", + "single_point = np.array([0,0])#1st step:\n", + "result1 = np.dot(single_point, weights)#2nd step:\n", + "result2 = sigmoid(result1)#Print final result\n", + "print(result2)#Taking inputs:\n", + "single_point = np.array([1,1])#1st step:\n", + "result1 = np.dot(single_point, weights)#2nd step:\n", + "result2 = sigmoid(result1)#Print final result\n", + "print(result2)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "(4, 2)\n", + "[[0 0]\n", + " [0 1]\n", + " [1 0]\n", + " [1 1]]\n", + "(4, 1)\n", + "[[0]\n", + " [1]\n", + " [1]\n", + " [1]]\n", + "(2, 1)\n", + "[[0.1]\n", + " [0.2]]\n", + "[0.52497919]\n", + "[0.5]\n", + "[0.57444252]\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "ES5UHf2ufWXc", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 607 + }, + "outputId": "508da63f-9aaa-4bb0-8c56-294ab7fc0ce6" + }, + "source": [ + "# Import required libraries:\n", + "import numpy as np# Define input features:\n", + "input_features = np.array([[1,0,0,1],[1,0,0,0],[0,0,1,1],\n", + " [0,1,0,0],[1,1,0,0],[0,0,1,1],\n", + " [0,0,0,1],[0,0,1,0]])\n", + "print (input_features.shape)\n", + "print (input_features)# Define target output:\n", + "target_output = np.array([[1,1,0,0,1,1,0,0]])# Reshaping our target output into vector:\n", + "target_output = target_output.reshape(8,1)\n", + "print(target_output.shape)\n", + "print (target_output)# Define weights:\n", + "weights = np.array([[0.1],[0.2],[0.3],[0.4]])\n", + "print(weights.shape)\n", + "print (weights)# Bias weight:\n", + "bias = 0.3# Learning Rate:\n", + "lr = 0.05# Sigmoid function:\n", + "def sigmoid(x):\n", + " return 1/(1+np.exp(-x))# Derivative of sigmoid function:\n", + "def sigmoid_der(x):\n", + " return sigmoid(x)*(1-sigmoid(x))# Main logic for neural network:\n", + "# Running our code 10000 times:for epoch in range(10000):\n", + " inputs = input_features#Feedforward input:\n", + " pred_in = np.dot(inputs, weights) + bias#Feedforward output:\n", + " pred_out = sigmoid(pred_in)#Backpropogation \n", + " #Calculating error\n", + " error = pred_out - target_output\n", + " \n", + " #Going with the formula:\n", + " x = error.sum()\n", + " print(x)\n", + " \n", + " #Calculating derivative:\n", + " dcost_dpred = error\n", + " dpred_dz = sigmoid_der(pred_out)\n", + " \n", + " #Multiplying individual derivatives:\n", + " z_delta = dcost_dpred * dpred_dz#Multiplying with the 3rd individual derivative:\n", + " inputs = input_features.T\n", + " weights -= lr * np.dot(inputs, z_delta)#Updating the bias weight value:\n", + " for i in z_delta:\n", + " bias -= lr * i#Printing final weights: \n", + "\n", + "print (weights)\n", + "print (\"\\n\\n\")\n", + "print (bias)#Taking inputs:\n", + "single_point = np.array([1,0,0,1])#1st step:\n", + "result1 = np.dot(single_point, weights) + bias#2nd step:\n", + "result2 = sigmoid(result1)#Print final result\n", + "print(result2)#Taking inputs:\n", + "single_point = np.array([0,0,1,0])#1st step:\n", + "result1 = np.dot(single_point, weights) + bias#2nd step:\n", + "result2 = sigmoid(result1)#Print final result\n", + "print(result2)#Taking inputs:\n", + "single_point = np.array([1,0,1,0])#1st step:\n", + "result1 = np.dot(single_point, weights) + bias#2nd step:\n", + "result2 = sigmoid(result1)#Print final result\n", + "print(result2)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "(8, 4)\n", + "[[1 0 0 1]\n", + " [1 0 0 0]\n", + " [0 0 1 1]\n", + " [0 1 0 0]\n", + " [1 1 0 0]\n", + " [0 0 1 1]\n", + " [0 0 0 1]\n", + " [0 0 1 0]]\n", + "(8, 1)\n", + "[[1]\n", + " [1]\n", + " [0]\n", + " [0]\n", + " [1]\n", + " [1]\n", + " [0]\n", + " [0]]\n", + "(4, 1)\n", + "[[0.1]\n", + " [0.2]\n", + " [0.3]\n", + " [0.4]]\n", + "[[0.1]\n", + " [0.2]\n", + " [0.3]\n", + " [0.4]]\n", + "\n", + "\n", + "\n", + "0.3\n", + "[0.68997448]\n", + "[0.64565631]\n", + "[0.66818777]\n" + ], + "name": "stdout" + } + ] + } + ] +} \ No newline at end of file