updates 2023

Author: avaamini

lab2/solutions/Part1_MNIST_Solution.ipynb

@@ -26,9 +26,9 @@
         "  <td align=\"center\"><a target=\"_blank\" href=\"http://introtodeeplearning.com\">\n",
         "        <img src=\"https://i.ibb.co/Jr88sn2/mit.png\" style=\"padding-bottom:5px;\" />\n",
         "      Visit MIT Deep Learning</a></td>\n",
-        "  <td align=\"center\"><a target=\"_blank\" href=\"https://colab.research.google.com/github/aamini/introtodeeplearning/blob/master/lab2/solutions/Part1_MNIST_Solution.ipynb\">\n",
+        "  <td align=\"center\"><a target=\"_blank\" href=\"https://colab.research.google.com/github/aamini/introtodeeplearning/blob/2023/lab2/solutions/Part1_MNIST_Solution.ipynb\">\n",
         "        <img src=\"https://i.ibb.co/2P3SLwK/colab.png\"  style=\"padding-bottom:5px;\" />Run in Google Colab</a></td>\n",
-        "  <td align=\"center\"><a target=\"_blank\" href=\"https://github.com/aamini/introtodeeplearning/blob/master/lab2/solutions/Part1_MNIST_Solution.ipynb\">\n",
+        "  <td align=\"center\"><a target=\"_blank\" href=\"https://github.com/aamini/introtodeeplearning/blob/2023/lab2/solutions/Part1_MNIST_Solution.ipynb\">\n",
         "        <img src=\"https://i.ibb.co/xfJbPmL/github.png\"  height=\"70px\" style=\"padding-bottom:5px;\"  />View Source on GitHub</a></td>\n",
         "</table>\n",
         "\n",
@@ -41,13 +41,13 @@
         "id": "gKA_J7bdP33T"
       },
       "source": [
-        "# Copyright 2022 MIT 6.S191 Introduction to Deep Learning. All Rights Reserved.\n",
+        "# Copyright 2023 MIT Introduction to Deep Learning. All Rights Reserved.\n",
         "# \n",
         "# Licensed under the MIT License. You may not use this file except in compliance\n",
-        "# with the License. Use and/or modification of this code outside of 6.S191 must\n",
-        "# reference:\n",
+        "# with the License. Use and/or modification of this code outside of MIT Introduction\n",
+        "# to Deep Learning must reference:\n",
         "#\n",
-        "# © MIT 6.S191: Introduction to Deep Learning\n",
+        "# © MIT Introduction to Deep Learning\n",
         "# http://introtodeeplearning.com\n",
         "#"
       ],
@@ -690,7 +690,7 @@
         "\n",
         "    #'''TODO: compute the categorical cross entropy loss\n",
         "    loss_value = tf.keras.backend.sparse_categorical_crossentropy(labels, logits)\n",
-        "    # loss_value = tf.keras.backend.sparse_categorical_crossentropy() # TODO\n",
+        "    # loss_value = tf.keras.backend.sparse_categorical_crossentropy('''TODO''', '''TODO''') # TODO\n",
         "\n",
         "  loss_history.append(loss_value.numpy().mean()) # append the loss to the loss_history record\n",
         "  plotter.plot(loss_history.get())\n",
@@ -716,4 +716,4 @@
       ]
     }
   ]
-}
+}