From b6bc481fec60047b0ae7c9c0624d837c4e60d7c3 Mon Sep 17 00:00:00 2001
From: udlbook <110402648+udlbook@users.noreply.github.com>
Date: Thu, 27 Jul 2023 11:49:49 -0400
Subject: [PATCH] Created using Colaboratory

---
 Notebooks/Chap06/6_3_Stochastic_Gradient_Descent.ipynb | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/Notebooks/Chap06/6_3_Stochastic_Gradient_Descent.ipynb b/Notebooks/Chap06/6_3_Stochastic_Gradient_Descent.ipynb
index 3ce5285..eccb5aa 100644
--- a/Notebooks/Chap06/6_3_Stochastic_Gradient_Descent.ipynb
+++ b/Notebooks/Chap06/6_3_Stochastic_Gradient_Descent.ipynb
@@ -4,7 +4,7 @@
   "metadata": {
     "colab": {
       "provenance": [],
-      "authorship_tag": "ABX9TyNx6kL7nMnRBAp48GoXD/0c",
+      "authorship_tag": "ABX9TyN/trn2b+dsX0Qcg4ZzNZEf",
       "include_colab_link": true
     },
     "kernelspec": {
@@ -457,7 +457,7 @@
     {
       "cell_type": "code",
       "source": [
-        "def gradient_descent_step_fixed_learning_rate(phi, data,  model, alpha):\n",
+        "def gradient_descent_step_fixed_learning_rate(phi, data, alpha):\n",
         "  # TODO -- fill in this routine so that we take a fixed size step of size alpha without using line search\n",
         "\n",
         "  return phi"
@@ -483,7 +483,7 @@
         "\n",
         "for c_step in range (n_steps):\n",
         "  # Do gradient descent step\n",
-        "  phi_all[:,c_step+1:c_step+2] = gradient_descent_step_fixed_learning_rate(phi_all[:,c_step:c_step+1],data, model,alpha =0.2)\n",
+        "  phi_all[:,c_step+1:c_step+2] = gradient_descent_step_fixed_learning_rate(phi_all[:,c_step:c_step+1],data, alpha =0.2)\n",
         "  # Measure loss and draw model every 4th step\n",
         "  if c_step % 4 == 0:\n",
         "    loss =  compute_loss(data[0,:], data[1,:], model, phi_all[:,c_step+1:c_step+2])\n",
@@ -513,7 +513,7 @@
     {
       "cell_type": "code",
       "source": [
-        "def stochastic_gradient_descent_step(phi, data,  model, alpha, batch_size):\n",
+        "def stochastic_gradient_descent_step(phi, data, alpha, batch_size):\n",
         "  # TODO -- fill in this routine so that we take a fixed size step of size alpha but only using a subset (batch) of the data\n",
         "  # at each step\n",
         "  # You can use the function np.random.permutation to generate a random permutation of the n_data = data.shape[1] indices\n",
@@ -546,7 +546,7 @@
         "\n",
         "for c_step in range (n_steps):\n",
         "  # Do gradient descent step\n",
-        "  phi_all[:,c_step+1:c_step+2] = stochastic_gradient_descent_step(phi_all[:,c_step:c_step+1],data, model,alpha =0.8, batch_size=5)\n",
+        "  phi_all[:,c_step+1:c_step+2] = stochastic_gradient_descent_step(phi_all[:,c_step:c_step+1],data, alpha =0.8, batch_size=5)\n",
         "  # Measure loss and draw model every 4th step\n",
         "  if c_step % 8 == 0:\n",
         "    loss =  compute_loss(data[0,:], data[1,:], model, phi_all[:,c_step+1:c_step+2])\n",