diff --git a/13_loading_and_preprocessing_data.ipynb b/13_loading_and_preprocessing_data.ipynb
index 0c82bb2..0986941 100644
--- a/13_loading_and_preprocessing_data.ipynb
+++ b/13_loading_and_preprocessing_data.ipynb
@@ -2040,8 +2040,8 @@
    "outputs": [],
    "source": [
     "train_set = mnist_dataset(train_filepaths, shuffle_buffer_size=60000)\n",
-    "valid_set = mnist_dataset(train_filepaths)\n",
-    "test_set = mnist_dataset(train_filepaths)"
+    "valid_set = mnist_dataset(valid_filepaths)\n",
+    "test_set = mnist_dataset(test_filepaths)"
    ]
   },
   {
@@ -2274,7 +2274,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "But let's pretend the dataset does not fit in memory, just to make things more interesting. Luckily, each review fits on just one line (they use `<br />` to indicate line breaks), so we can read the reviews using a `TextLineDataset`. If they didn't we would have to preprocess the input files (e.g., converting them to TFRecords). For very large datasets, it would make sense a tool like Apache Beam for that."
+    "But let's pretend the dataset does not fit in memory, just to make things more interesting. Luckily, each review fits on just one line (they use `<br />` to indicate line breaks), so we can read the reviews using a `TextLineDataset`. If they didn't we would have to preprocess the input files (e.g., converting them to TFRecords). For very large datasets, it would make sense to use a tool like Apache Beam for that."
    ]
   },
   {
@@ -2473,7 +2473,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's run it on the same `X_example`, just to make sure the word IDs are larger now, since the vocabulary bigger:"
+    "Let's run it on the same `X_example`, just to make sure the word IDs are larger now, since the vocabulary is bigger:"
    ]
   },
   {
@@ -2540,7 +2540,7 @@
    "source": [
     "class BagOfWords(keras.layers.Layer):\n",
     "    def __init__(self, n_tokens, dtype=tf.int32, **kwargs):\n",
-    "        super().__init__(dtype=tf.int32, **kwargs)\n",
+    "        super().__init__(dtype=dtype, **kwargs)\n",
     "        self.n_tokens = n_tokens\n",
     "    def call(self, inputs):\n",
     "        one_hot = tf.one_hot(inputs, self.n_tokens)\n",