From 3cdb675cefcf7633eacf1a4fcf92640c423d38de Mon Sep 17 00:00:00 2001 From: udlbook <110402648+udlbook@users.noreply.github.com> Date: Sun, 24 Dec 2023 10:16:00 -0500 Subject: [PATCH] Created using Colaboratory --- .../5_3_Multiclass_Cross_entropy_Loss.ipynb | 27 ++++++++++++++----- 1 file changed, 20 insertions(+), 7 deletions(-) diff --git a/Notebooks/Chap05/5_3_Multiclass_Cross_entropy_Loss.ipynb b/Notebooks/Chap05/5_3_Multiclass_Cross_entropy_Loss.ipynb index a629520..0fc5eb1 100644 --- a/Notebooks/Chap05/5_3_Multiclass_Cross_entropy_Loss.ipynb +++ b/Notebooks/Chap05/5_3_Multiclass_Cross_entropy_Loss.ipynb @@ -4,7 +4,7 @@ "metadata": { "colab": { "provenance": [], - "authorship_tag": "ABX9TyPNAZtbS+8jYc+tZqhDHNev", + "authorship_tag": "ABX9TyOPv/l+ToaApJV7Nz+8AtpV", "include_colab_link": true }, "kernelspec": { @@ -401,12 +401,25 @@ { "cell_type": "code", "source": [ - "# Now let's plot the likelihood, negative log likelihood as a function the value of the offset beta1\n", - "fig, ax = plt.subplots(1,2)\n", - "fig.set_size_inches(10.5, 3.5)\n", - "fig.tight_layout(pad=3.0)\n", - "ax[0].plot(beta_1_vals, likelihoods); ax[0].set_xlabel('beta_1[0,0]'); ax[0].set_ylabel('likelihood')\n", - "ax[1].plot(beta_1_vals, nlls); ax[1].set_xlabel('beta_1[0,0]'); ax[1].set_ylabel('negative log likelihood')\n", + "# Now let's plot the likelihood, negative log likelihood, and least squares as a function the value of the offset beta1\n", + "fig, ax = plt.subplots()\n", + "fig.tight_layout(pad=5.0)\n", + "likelihood_color = 'tab:red'\n", + "nll_color = 'tab:blue'\n", + "\n", + "\n", + "ax.set_xlabel('beta_1[0, 0]')\n", + "ax.set_ylabel('likelihood', color = likelihood_color)\n", + "ax.plot(beta_1_vals, likelihoods, color = likelihood_color)\n", + "ax.tick_params(axis='y', labelcolor=likelihood_color)\n", + "\n", + "ax1 = ax.twinx()\n", + "ax1.plot(beta_1_vals, nlls, color = nll_color)\n", + "ax1.set_ylabel('negative log likelihood', color = nll_color)\n", + "ax1.tick_params(axis='y', labelcolor = nll_color)\n", + "\n", + "plt.axvline(x = beta_1_vals[np.argmax(likelihoods)], linestyle='dotted')\n", + "\n", "plt.show()" ], "metadata": {