From 51a06aaf26763abe21ec0f2f6d1d08413a31e3e8 Mon Sep 17 00:00:00 2001
From: Mark Daoust <markdaoust@google.com>
Date: Thu, 10 Aug 2023 20:48:45 -0700
Subject: [PATCH] Fix accuracy metric for logit output.

Fixes: https://github.com/tensorflow/tensorflow/issues/41413
PiperOrigin-RevId: 555773892
---
 .../tutorials/images/transfer_learning.ipynb  | 61 +++++++++++++------
 1 file changed, 41 insertions(+), 20 deletions(-)

diff --git a/site/en/tutorials/images/transfer_learning.ipynb b/site/en/tutorials/images/transfer_learning.ipynb
index 7268909c71f..85a1551977b 100644
--- a/site/en/tutorials/images/transfer_learning.ipynb
+++ b/site/en/tutorials/images/transfer_learning.ipynb
@@ -516,9 +516,9 @@
       "source": [
         "### Important note about BatchNormalization layers\n",
         "\n",
-        "Many models contain `tf.keras.layers.BatchNormalization` layers. This layer is a special case and precautions should be taken in the context of fine-tuning, as shown later in this tutorial. \n",
+        "Many models contain `tf.keras.layers.BatchNormalization` layers. This layer is a special case and precautions should be taken in the context of fine-tuning, as shown later in this tutorial.\n",
         "\n",
-        "When you set `layer.trainable = False`, the `BatchNormalization` layer will run in inference mode, and will not update its mean and variance statistics. \n",
+        "When you set `layer.trainable = False`, the `BatchNormalization` layer will run in inference mode, and will not update its mean and variance statistics.\n",
         "\n",
         "When you unfreeze a model that contains BatchNormalization layers in order to do fine-tuning, you should keep the BatchNormalization layers in inference mode by passing `training = False` when calling the base model. Otherwise, the updates applied to the non-trainable weights will destroy what the model has learned.\n",
         "\n",
@@ -617,60 +617,71 @@
         "model = tf.keras.Model(inputs, outputs)"
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "I8ARiyMFsgbH"
+      },
+      "outputs": [],
+      "source": [
+        "model.summary()"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "g0ylJXE_kRLi"
+        "id": "lxOcmVr0ydFZ"
       },
       "source": [
-        "### Compile the model\n",
-        "\n",
-        "Compile the model before training it. Since there are two classes, use the `tf.keras.losses.BinaryCrossentropy` loss with `from_logits=True` since the model provides a linear output."
+        "The 8+ million parameters in MobileNet are frozen, but there are 1.2 thousand _trainable_ parameters in the Dense layer. These are divided between two `tf.Variable` objects, the weights and biases."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "RpR8HdyMhukJ"
+        "id": "krvBumovycVA"
       },
       "outputs": [],
       "source": [
-        "base_learning_rate = 0.0001\n",
-        "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=base_learning_rate),\n",
-        "              loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),\n",
-        "              metrics=['accuracy'])"
+        "len(model.trainable_variables)"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "I8ARiyMFsgbH"
+        "id": "jeGk93R2ahav"
       },
       "outputs": [],
       "source": [
-        "model.summary()"
+        "tf.keras.utils.plot_model(model, show_shapes=True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "lxOcmVr0ydFZ"
+        "id": "g0ylJXE_kRLi"
       },
       "source": [
-        "The 2.5 million parameters in MobileNet are frozen, but there are 1.2 thousand _trainable_ parameters in the Dense layer. These are divided between two `tf.Variable` objects, the weights and biases."
+        "### Compile the model\n",
+        "\n",
+        "Compile the model before training it. Since there are two classes, use the `tf.keras.losses.BinaryCrossentropy` loss with `from_logits=True` since the model provides a linear output."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "krvBumovycVA"
+        "id": "RpR8HdyMhukJ"
       },
       "outputs": [],
       "source": [
-        "len(model.trainable_variables)"
+        "base_learning_rate = 0.0001\n",
+        "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=base_learning_rate),\n",
+        "              loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),\n",
+        "              metrics=[tf.keras.metrics.BinaryAccuracy(threshold=0, name='accuracy')])"
       ]
     },
     {
@@ -681,7 +692,7 @@
       "source": [
         "### Train the model\n",
         "\n",
-        "After training for 10 epochs, you should see ~94% accuracy on the validation set.\n"
+        "After training for 10 epochs, you should see ~96% accuracy on the validation set.\n"
       ]
     },
     {
@@ -863,7 +874,7 @@
       "source": [
         "model.compile(loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),\n",
         "              optimizer = tf.keras.optimizers.RMSprop(learning_rate=base_learning_rate/10),\n",
-        "              metrics=['accuracy'])"
+        "              metrics=[tf.keras.metrics.BinaryAccuracy(threshold=0, name='accuracy')])"
       ]
     },
     {
@@ -1070,13 +1081,23 @@
         "\n",
         "To learn more, visit the [Transfer learning guide](https://www.tensorflow.org/guide/keras/transfer_learning).\n"
       ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "uKIByL01da8c"
+      },
+      "outputs": [],
+      "source": []
     }
   ],
   "metadata": {
     "accelerator": "GPU",
     "colab": {
-      "collapsed_sections": [],
       "name": "transfer_learning.ipynb",
+      "private_outputs": true,
+      "provenance": [],
       "toc_visible": true
     },
     "kernelspec": {