TF mostly fixed an issue so remove workaround for ReconstructingRegre…

…ssor

12_custom_models_and_training_with_tensorflow.ipynb

@@ -3467,7 +3467,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Note**: due to an issue introduced in TF 2.2 ([#46858](https://github.com/tensorflow/tensorflow/issues/46858)), it is currently not possible to use `add_loss()` along with the `build()` method. So the following code differs from the book: I create the `reconstruct` layer in the constructor instead of the `build()` method. Unfortunately, this means that the number of units in this layer must be hard-coded (alternatively, it could be passed as an argument to the constructor)."
+    "**Note**: the following code has two differences with the code in the book:\n",
+    "1. It creates a `keras.metrics.Mean()` metric in the constructor and uses it in the `call()` method to track the mean reconstruction loss. Since we only want to do this during training, we add a `training` argument to the `call()` method, and if `training` is `True`, then we update `reconstruction_mean` and we call `self.add_metric()` to ensure it's displayed properly.\n",
+    "2. Due to an issue introduced in TF 2.2 ([#46858](https://github.com/tensorflow/tensorflow/issues/46858)), we must not call `super().build()` inside the `build()` method."
    ]
   },
   {
@@ -3476,21 +3478,19 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "class ReconstructingRegressor(keras.models.Model):\n",
+    "class ReconstructingRegressor(keras.Model):\n",
     "    def __init__(self, output_dim, **kwargs):\n",
     "        super().__init__(**kwargs)\n",
     "        self.hidden = [keras.layers.Dense(30, activation=\"selu\",\n",
     "                                          kernel_initializer=\"lecun_normal\")\n",
     "                       for _ in range(5)]\n",
     "        self.out = keras.layers.Dense(output_dim)\n",
-    "        self.reconstruct = keras.layers.Dense(8) # workaround for TF issue #46858\n",
     "        self.reconstruction_mean = keras.metrics.Mean(name=\"reconstruction_error\")\n",
     "\n",
-    "    #Commented out due to TF issue #46858, see the note above\n",
-    "    #def build(self, batch_input_shape):\n",
-    "    #    n_inputs = batch_input_shape[-1]\n",
-    "    #    self.reconstruct = keras.layers.Dense(n_inputs)\n",
-    "    #    super().build(batch_input_shape)\n",
+    "    def build(self, batch_input_shape):\n",
+    "        n_inputs = batch_input_shape[-1]\n",
+    "        self.reconstruct = keras.layers.Dense(n_inputs)\n",
+    "        #super().build(batch_input_shape)\n",
     "\n",
     "    def call(self, inputs, training=None):\n",
     "        Z = inputs\n",
@@ -3526,9 +3526,9 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/2\n",
-      "363/363 [==============================] - 1s 761us/step - loss: 1.6313 - reconstruction_error: 1.0474\n",
+      "363/363 [==============================] - 1s 810us/step - loss: 1.6313 - reconstruction_error: 1.0474\n",
       "Epoch 2/2\n",
-      "363/363 [==============================] - 0s 667us/step - loss: 0.4536 - reconstruction_error: 0.4022\n"
+      "363/363 [==============================] - 0s 683us/step - loss: 0.4536 - reconstruction_error: 0.4022\n"
      ]
     }
    ],