1098 lines
29 KiB
Text
1098 lines
29 KiB
Text
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "ea3e9a09-3257-4b1a-9245-d42bbf88d06b",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd \n",
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"from matplotlib import pyplot as plt \n",
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"\n",
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"data = pd.read_csv('gtsrb_data_test.csv')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "fe7852fc-5353-478d-9c7e-ad5fd251d963",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"data = np.array(data)\n",
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"\n",
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"m,n = data.shape\n",
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"data_train = data[1000:m].T\n",
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"\n",
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"Y_train = data_train[0].astype(int)\n",
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"\n",
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"X_train = data_train[1:n]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "f3d23a27-d544-44ea-9291-5697fac2b8c2",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0.5568628\n"
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]
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}
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],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "42435b4b-6390-4c20-b04a-cec2730879fd",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"def init_params():\n",
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" W1 = np.random.rand(10,1024) - 0.5\n",
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" b1 = np.random.rand(10,1) - 0.5\n",
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" W2 = np.random.rand(43,10) - 0.5\n",
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" b2 = np.random.rand(43,1) - 0.5\n",
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" return W1, b1 , W2, b2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "f9da376f-6b2a-4c7c-b392-c34f7dc7dee4",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"def ReLU(Z):\n",
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" return np.maximum(Z,0)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "6cd98c98-9f06-4362-a3cd-a0a9b168ea97",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"def softmax(Z):\n",
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" A = np.exp(Z) / sum(np.exp(Z))\n",
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" return A"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "039f4e5d-9d7c-4f59-81d6-f64310cc5b1f",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"def forward_prop(W1, b1, W2, b2, X):\n",
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" Z1 = W1.dot(X) + b1\n",
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" A1 = ReLU(Z1)\n",
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" Z2 = W2.dot(A1) + b2\n",
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" A2 = softmax(Z2)\n",
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" return Z1, A1, Z2, A2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "24588282-2ec1-4c64-9b9d-4e7d395449fb",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"def ReLU_deriv(Z):\n",
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" return Z > 0"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "d4977d9c-38c9-4758-9262-dd03b8bbd015",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"def one_hot(Y):\n",
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" one_hot_Y = np.zeros((Y.size, Y.max() + 1))\n",
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" one_hot_Y[np.arange(Y.size), Y] = 1\n",
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" one_hot_Y = one_hot_Y.T\n",
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" return one_hot_Y"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "941b2ec2-980f-4577-abe3-d13ac252e86b",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"def backward_prop(Z1, A1, Z2, A2, W1, W2, X, Y):\n",
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" one_hot_Y = one_hot(Y)\n",
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" dZ2 = A2 - one_hot_Y\n",
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" dW2 = 1 / m * dZ2.dot(A1.T)\n",
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" db2 = 1 / m * np.sum(dZ2)\n",
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" dZ1 = W2.T.dot(dZ2) * ReLU_deriv(Z1)\n",
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" dW1 = 1 / m * dZ1.dot(X.T)\n",
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" db1 = 1 / m * np.sum(dZ1)\n",
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" return dW1, db1, dW2, db2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "20ab4235-86ac-4708-8b16-8d0169ecd97f",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"def update_params(W1, b1, W2, b2, dW1, db1, dW2, db2, alpha):\n",
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" W1 = W1 - alpha * dW1\n",
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" b1 = b1 - alpha * db1 \n",
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" W2 = W2 - alpha * dW2 \n",
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" b2 = b2 - alpha * db2 \n",
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" return W1, b1, W2, b2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "b82641d2-7ebb-4adb-9c40-3a6a28ccb49b",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"def get_predictions(A2):\n",
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" return np.argmax(A2, 0)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "2f38d2cc-316a-48f3-a275-57253249f132",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"def get_accuracy(predictions, Y):\n",
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" #print(predictions, Y)\n",
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" return np.sum(predictions == Y) / Y.size"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "c46ae58a-9362-4f7d-8ce4-d668230b1647",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"acc_store = [] "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"id": "54095b38-ca0a-41e6-9e33-c7b484e73c86",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"def gradient_descent(X, Y, alpha, iterations):\n",
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" W1, b1, W2, b2 = init_params()\n",
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" for i in range(iterations):\n",
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" Z1, A1, Z2, A2 = forward_prop(W1, b1, W2, b2, X)\n",
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" dW1, db1, dW2, db2 = backward_prop(Z1, A1, Z2, A2, W1, W2, X, Y)\n",
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" W1, b1, W2, b2 = update_params(W1, b1, W2, b2, dW1, db1, dW2, db2, alpha)\n",
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" if i % 10 == 0:\n",
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" print(\"Iteration: \", i)\n",
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" predictions = get_predictions(A2)\n",
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" pred = get_accuracy(predictions, Y)\n",
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" print(pred)\n",
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" acc_store.append(pred)\n",
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" return W1, b1, W2, b2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"id": "bfa77e8c-04e4-4d1e-85f0-ea6cdd71e924",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Iteration: 0\n",
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"0.025193465176268273\n",
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}
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],
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"source": [
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"W1, b1, W2, b2 = gradient_descent(X_train, Y_train, 0.10, 4000)\n",
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"df = pd.DataFrame(acc_store)\n",
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"df.to_csv('gt_acc.csv', index=False)\n",
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"np.savez(\"gt_weights\", W1, b1, W2, b2)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c8c9bb8d-0379-4b46-941b-410bf0da29ef",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.17"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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