165 lines
5.5 KiB
Plaintext
165 lines
5.5 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"id": "initial_id",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-11-04T14:28:00.043927Z",
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"start_time": "2025-11-04T14:27:59.939813Z"
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}
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},
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"source": "import numpy as np",
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"outputs": [],
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"execution_count": 3
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-11-04T14:28:09.512985Z",
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"start_time": "2025-11-04T14:28:09.508856Z"
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}
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},
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"cell_type": "code",
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"source": [
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"nn_architecture = [\n",
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" {\"input_dim\": 2, \"output_dim\": 4, \"activation\": \"relu\"},\n",
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" {\"input_dim\": 4, \"output_dim\": 6, \"activation\": \"relu\"},\n",
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" {\"input_dim\": 6, \"output_dim\": 6, \"activation\": \"relu\"},\n",
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" {\"input_dim\": 6, \"output_dim\": 4, \"activation\": \"relu\"},\n",
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" {\"input_dim\": 4, \"output_dim\": 1, \"activation\": \"sigmoid\"},\n",
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"]"
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],
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"id": "48cafaf4b64967bb",
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"outputs": [],
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"execution_count": 4
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-11-04T14:28:39.907457Z",
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"start_time": "2025-11-04T14:28:39.903244Z"
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}
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},
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"cell_type": "code",
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"source": [
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"def init_layers(nn_architecture, seed = 99):\n",
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" np.random.seed(seed)\n",
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" number_of_layers = len(nn_architecture)\n",
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" params_values = {}\n",
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"\n",
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" for idx, layer in enumerate(nn_architecture):\n",
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" layer_idx = idx + 1\n",
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" layer_input_size = layer[\"input_dim\"]\n",
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" layer_output_size = layer[\"output_dim\"]\n",
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"\n",
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" params_values['W' + str(layer_idx)] = np.random.randn(\n",
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" layer_output_size, layer_input_size) * 0.1\n",
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" params_values['b' + str(layer_idx)] = np.random.randn(\n",
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" layer_output_size, 1) * 0.1\n",
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"\n",
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" return params_values\n"
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],
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"id": "d13137630b41b756",
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"outputs": [],
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"execution_count": 6
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-11-04T14:29:00.821197Z",
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"start_time": "2025-11-04T14:29:00.795742Z"
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}
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},
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"cell_type": "code",
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"source": "init_layers(nn_architecture)",
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"id": "31f205147667dea6",
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'W1': array([[-0.01423588, 0.20572217],\n",
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" [ 0.02832619, 0.1329812 ],\n",
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" [-0.01546219, -0.00690309],\n",
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" [ 0.07551805, 0.08256466]]),\n",
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" 'b1': array([[-0.01130692],\n",
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" [-0.23678376],\n",
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" [-0.01670494],\n",
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" [ 0.0685398 ]]),\n",
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" 'W2': array([[ 0.00235001, 0.04562013, 0.02704928, -0.14350081],\n",
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" [ 0.08828171, -0.05800817, -0.05015653, 0.05909533],\n",
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" [-0.07316163, 0.02617555, -0.08557956, -0.01875259],\n",
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" [-0.03734863, -0.0461971 , -0.08164661, -0.00451233],\n",
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" [ 0.01213278, 0.09259528, -0.05738197, 0.00527031],\n",
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" [ 0.22073106, 0.03918219, 0.04827134, 0.0433334 ]]),\n",
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" 'b2': array([[-0.17042917],\n",
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" [-0.02439081],\n",
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" [-0.21397038],\n",
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" [ 0.08613227],\n",
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" [ 0.17002844],\n",
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" [-0.05287848]]),\n",
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" 'W3': array([[ 0.17634779, -0.11216078, -0.11919342, 0.05527319, -0.08159809,\n",
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" -0.04966468],\n",
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" [ 0.10862256, -0.09746753, -0.02821358, -0.01172141, 0.03785473,\n",
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" 0.07321946],\n",
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" [-0.0103571 , -0.11987063, 0.10100356, 0.28753603, 0.08203126,\n",
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" 0.05606115],\n",
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" [-0.03756422, -0.02521043, -0.13896134, 0.06173323, -0.0135787 ,\n",
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" 0.1287905 ],\n",
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" [-0.10369944, 0.13643321, -0.03099566, -0.06111171, -0.04831058,\n",
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" -0.06089837],\n",
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" [-0.20883353, 0.0639322 , 0.0774304 , 0.12785694, 0.0705276 ,\n",
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" 0.06559774]]),\n",
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" 'b3': array([[-0.1678502 ],\n",
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" [ 0.01831099],\n",
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" [-0.11332241],\n",
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" [-0.02790857],\n",
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" [ 0.13966199],\n",
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" [ 0.00322194]]),\n",
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" 'W4': array([[-0.26136608, -0.10015776, -0.0567511 , -0.0225658 , 0.09380238,\n",
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" 0.08367841],\n",
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" [ 0.08121485, 0.0232307 , -0.02951077, -0.0361676 , 0.04321151,\n",
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" 0.09339585],\n",
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" [ 0.15526339, 0.00936234, 0.02948258, 0.14854308, -0.10868852,\n",
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" 0.08211628],\n",
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" [-0.07879492, 0.15938117, 0.14059044, 0.16447566, 0.15415987,\n",
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" 0.08406076]]),\n",
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" 'b4': array([[-0.10230944],\n",
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" [ 0.04947723],\n",
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" [ 0.08957326],\n",
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" [ 0.0477352 ]]),\n",
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" 'W5': array([[-0.01145305, 0.01568974, 0.03875967, -0.10262266]]),\n",
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" 'b5': array([[0.06791429]])}"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"execution_count": 7
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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": "3.13.7"
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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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