part III working

tutorial/tutorial.ipynb

@@ -1190,12 +1190,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We load the training/validation dataset from the same HDF5 database that contains the test dataset used previously."
+    "We load the training/validation dataset from the HDF5 table that we created previously."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1207,27 +1207,27 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We'll split this dataset into training and validation using an stratified sampling algorithm ([scikit-learn's StratifiedShuffleSplit](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedShuffleSplit.html)). This yields training and validation datasets with the same proportions of positive (upcall) and negative (no upcall) examples. \n"
+    "We will split the dataset into a training set of 30 (randomly selected) samples and a validation set consisting of the 10 remaining samples.  "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[26 12  9 23 38 24 18 35 10 14 11  0 19  8 39 32 30 17  1 13 16 28  3  2\n",
-      " 21 36  4 33  6 34]\n",
-      "[5, 7, 15, 20, 22, 25, 27, 29, 31, 37]\n"
+      "[24  2 17 15 39 25  9 22 28 16 30  5 14  1 32  6  3 35 13  8 27 33 19 10\n",
+      " 34 31  7 26 36 21]\n",
+      "[0, 4, 11, 12, 18, 20, 23, 29, 37, 38]\n"
      ]
     }
    ],
    "source": [
-    "train_indices = np.random.choice(np.arange(40), 30, replace=False)\n",
-    "val_indices = [i for i in range(40) if i not in train_indices]\n",
+    "train_indices = np.random.choice(np.arange(40), 30, replace=False) # select 30 random indices out of 0...39\n",
+    "val_indices = [i for i in range(40) if i not in train_indices]     # 10 indices that were not selected\n",
     "\n",
     "print(train_indices)\n",
     "print(val_indices)"
@@ -1262,7 +1262,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now we have configured two batch generators, which will load 32 spectrograms and associated labels at a time during the training process. After attaching these generators to the new_resnet_model, we can run the training loop for a couple of epochs."
+    "Now we have configured two batch generators, which will load 10 spectrograms and associated labels at a time during the training process. After attaching these generators to the new_resnet_model, we can run the training loop for a couple of epochs."
    ]
   },
   {