From 209eebf19f5769b27a81e86fb4c95c5590139b62 Mon Sep 17 00:00:00 2001 From: Eduardo Cueto Mendoza Date: Tue, 28 Apr 2020 12:56:54 -0600 Subject: [PATCH] [MOD] finished the training of the NN --- Numpy_NN.ipynb | 276 +++++++++++++++++++++++++++++-------------------- 1 file changed, 164 insertions(+), 112 deletions(-) diff --git a/Numpy_NN.ipynb b/Numpy_NN.ipynb index 2b9ab5a..e63b333 100644 --- a/Numpy_NN.ipynb +++ b/Numpy_NN.ipynb @@ -1,43 +1,43 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import sklearn.datasets\n" + ] + }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import sklearn.datasets" + "X,y = sklearn.datasets.make_moons(200, noise = 0.15)\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, - "outputs": [], - "source": [ - "X,y = sklearn.datasets.make_moons(200,noise=0.15)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -49,33 +49,30 @@ } ], "source": [ - "plt.scatter(X[:,0],X[:,1],c=y)" + "plt.scatter(X[:,0],X[:,1], c=y)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(200, 2) (200,)\n" + ] + } + ], + "source": [ + "print(X.shape, y.shape)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(200, 2)" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, "outputs": [], "source": [ "# Hyperparameters\n", @@ -88,74 +85,72 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'W1' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel_dic\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'W1'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mW1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'b1'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mb1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'W2'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mW2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'b2'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mb2\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mNameError\u001b[0m: name 'W1' is not defined" - ] - } - ], - "source": [ - "model_dic = {'W1': W1, 'b1': b1,'W2': W2, 'b2': b2}" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ - "def retrieve(model_dict):\n", - " W1 = model_dic['W1']\n", - " b1 = model_dic['b1']\n", - " W2 = model_dic['W2']\n", - " b2 = model_dic['b2']\n", - " return W1,b1,W2,b2\n" + "def retreive(model_dict):\n", + " W1 = model_dict['W1']\n", + " b1 = model_dict['b1']\n", + " W2 = model_dict['W2']\n", + " b2 = model_dict['b2']\n", + " \n", + " return W1, b1, W2, b2\n" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "def forward(x, model_dict):\n", - " W1,b1,W2,b2 = retrieve(model_dict)\n", - " z1 = x.dot(W1) + b1\n", + " W1, b1, W2, b2 = retreive(model_dict)\n", + " z1 = X.dot(W1) + b1\n", " a1 = np.tanh(z1)\n", " z2 = a1.dot(W2) + b2\n", - " a2 = np.tanh(z2)\n", - " exp_scores = np.exp(a2)\n", - " softmax = exp_scores / np.sum(exp_scores, dim=1, keepdims=True)\n", - " return z1,a1,softmax\n", + " exp_scores = np.exp(z2)\n", + " softmax = exp_scores / np.sum(exp_scores, axis = 1, keepdims = True) \n", + " \n", + " return z1, a1, softmax\n", " " ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ - "def loss(softmax, y):\n", - " W1,b1,W2,b2 = retrieve(model_dict)\n", + "def loss(softmax, y, model_dict):\n", + " W1, b1, W2, b2 = retreive(model_dict)\n", " m = np.zeros(200)\n", " for i,correct_index in enumerate(y):\n", " predicted = softmax[i][correct_index]\n", " m[i] = predicted\n", - " log_prob = -np.log(predicted)\n", - " softmax_loss = np.sum(log_prob)\n", - " reg_loss = lambda_reg / 2*(np.sum(np.square(W1)) + np.sum(np.square(W2)))\n", - " loss = softmax_loss + reg_loss\n", - " return float(loss/y.shape[0])" + " log_prob = -np.log(m)\n", + " loss = np.sum(log_prob)\n", + " reg_loss = lambda_reg / 2 * (np.sum(np.square(W1)) + np.sum(np.square(W2)))\n", + " loss+= reg_loss\n", + " \n", + " return float(loss / y.shape[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def predict(model_dict, x):\n", + " W1, b1, W2, b2 = retreive(model_dict)\n", + " z1 = x.dot(W1) + b1\n", + " a1 = np.tanh(z1)\n", + " z2 = a1.dot(W2) + b2\n", + " exp_scores = np.exp(z2)\n", + " softmax = exp_scores / np.sum(exp_scores, axis = 1, keepdims = True) # (200,2)\n", + " \n", + " return np.argmax(softmax, axis = 1) # (200,)\n" ] }, { @@ -164,52 +159,109 @@ "metadata": {}, "outputs": [], "source": [ - "def predict(x, model_dict):\n", - " W1,b1,W2,b2 = retrieve(model_dict)\n", - " z1 = x.dot(W1) + b1\n", - " a1 = np.tanh(z1)\n", - " z2 = a1.dot(W2) + b2\n", - " a2 = np.tanh(z2)\n", - " exp_scores = np.exp(a2)\n", - " softmax = exp_scores / np.sum(exp_scores, dim=1, keepdims=True)\n", - " return np.argmax(softmax, axis=1)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "def backpropagation(x,y,model_dict,epochs):\n", + "def backpropagation(x, y, model_dict, epochs):\n", " for i in range(epochs):\n", - " W1,b1,W2,b2 = retrieve(model_dict)\n", - " z1,a1,probs = forward(x,model_dict)\n", + " W1, b1, W2, b2 = retreive(model_dict)\n", + " z1, a1, probs = forward(x, model_dict) # a1: (200,3), probs: (200,2)\n", " delta3 = np.copy(probs)\n", - " delta3[range(x.shaape[0]),y] -= 1\n", - " dW2 = (a1.T).dot(delta3)\n", - " db2 = np.sum(delta3,axis=0,keepdims=True)\n", - " delta2 = delta3.dot(W2.T) * (1-np.power(np.tanh(z1),2))\n", - " dW1 = np.dot(x.T,delta2)\n", - " db1 = np.sum(delta2,axis=0,keepdims=True)\n", + " delta3[range(x.shape[0]), y] -= 1 # (200,2)\n", + " dW2 = (a1.T).dot(delta3) # (3,2)\n", + " db2 = np.sum(delta3, axis=0, keepdims=True) # (1,2)\n", + " delta2 = delta3.dot(W2.T) * (1 - np.power(np.tanh(z1), 2))\n", + " dW1 = np.dot(x.T, delta2)\n", + " db1 = np.sum(delta2, axis=0)\n", + " \n", " # Add regularization terms\n", - " dW2 += lambda_reg * np.sum(W2)\n", - " dW1 += lambda_reg * np.sum(W1)\n", - " # Update weights\n", + " dW2 += lambda_reg * np.sum(W2) \n", + " dW1 += lambda_reg * np.sum(W1) \n", + " \n", + " # Update Weights: W = W + (-lr*gradient) = W - lr*gradient\n", " W1 += -learning_rate * dW1\n", " b1 += -learning_rate * db1\n", " W2 += -learning_rate * dW2\n", " b2 += -learning_rate * db2\n", + " \n", " # Update the model dictionary\n", - " model_dict = {'W1': W1, 'b1': b1,'W2': W2, 'b2': b2}\n", - " # Print loss every 50 epochs\n", + " model_dict = {'W1': W1, 'b1': b1, 'W2': W2, 'b2': b2}\n", + " \n", + " # Print the loss every 50 epochs\n", " if i%50 == 0:\n", - " print(\"Loss at epoch {} is: {}\".format(i,loss(probs,y,model_dict)))\n", + " print(\"Loss at epoch {} is: {:.3f}\".format(i,loss(probs, y, model_dict)))\n", " \n", - " return model_dict\n", + " return model_dict\n", " " ] }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Define Initial Weights\n", + "def init_network(input_dim, hidden_dim, output_dim):\n", + " model = {}\n", + " # Xavier Initialization \n", + " W1 = np.random.randn(input_dim, hidden_dim) / np.sqrt(input_dim)\n", + " b1 = np.zeros((1, hidden_dim))\n", + " W2 = np.random.randn(hidden_dim, output_dim) / np.sqrt(hidden_dim)\n", + " b2 = np.zeros((1, output_dim))\n", + " model['W1'] = W1\n", + " model['b1'] = b1\n", + " model['W2'] = W2\n", + " model['b2'] = b2\n", + " return model\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loss at epoch 0 is: 0.773\n", + "Loss at epoch 50 is: 0.330\n", + "Loss at epoch 100 is: 0.273\n", + "Loss at epoch 150 is: 0.262\n", + "Loss at epoch 200 is: 0.259\n", + "Loss at epoch 250 is: 0.258\n", + "Loss at epoch 300 is: 0.258\n", + "Loss at epoch 350 is: 0.258\n", + "Loss at epoch 400 is: 0.257\n", + "Loss at epoch 450 is: 0.257\n", + "Loss at epoch 500 is: 0.257\n", + "Loss at epoch 550 is: 0.256\n", + "Loss at epoch 600 is: 0.256\n", + "Loss at epoch 650 is: 0.256\n", + "Loss at epoch 700 is: 0.255\n", + "Loss at epoch 750 is: 0.255\n", + "Loss at epoch 800 is: 0.255\n", + "Loss at epoch 850 is: 0.255\n", + "Loss at epoch 900 is: 0.254\n", + "Loss at epoch 950 is: 0.254\n", + "Loss at epoch 1000 is: 0.254\n", + "Loss at epoch 1050 is: 0.254\n", + "Loss at epoch 1100 is: 0.253\n", + "Loss at epoch 1150 is: 0.253\n", + "Loss at epoch 1200 is: 0.253\n", + "Loss at epoch 1250 is: 0.253\n", + "Loss at epoch 1300 is: 0.252\n", + "Loss at epoch 1350 is: 0.252\n", + "Loss at epoch 1400 is: 0.252\n", + "Loss at epoch 1450 is: 0.252\n" + ] + } + ], + "source": [ + "model_dict = init_network(input_dim=input_neurons,hidden_dim=3,output_dim=output_neurons)\n", + "\n", + "model = backpropagation(X,y,model_dict,epochs=1500)" + ] + }, { "cell_type": "code", "execution_count": null,