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TF mostly fixed an issue so remove workaround for ReconstructingRegressor

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Aurélien Geron 2021-10-07 19:08:15 +13:00
parent fdb5d1695e
commit 495de15361
1 changed files with 8 additions and 8 deletions
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12_custom_models_and_training_with_tensorflow.ipynb
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@ -2209,7 +2209,9 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "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."
] ]
}, },
{ {
@ -2218,21 +2220,19 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"class ReconstructingRegressor(keras.models.Model):\n", "class ReconstructingRegressor(keras.Model):\n",
" def __init__(self, output_dim, **kwargs):\n", " def __init__(self, output_dim, **kwargs):\n",
" super().__init__(**kwargs)\n", " super().__init__(**kwargs)\n",
" self.hidden = [keras.layers.Dense(30, activation=\"selu\",\n", " self.hidden = [keras.layers.Dense(30, activation=\"selu\",\n",
" kernel_initializer=\"lecun_normal\")\n", " kernel_initializer=\"lecun_normal\")\n",
" for _ in range(5)]\n", " for _ in range(5)]\n",
" self.out = keras.layers.Dense(output_dim)\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", " self.reconstruction_mean = keras.metrics.Mean(name=\"reconstruction_error\")\n",
"\n", "\n",
" #Commented out due to TF issue #46858, see the note above\n", " def build(self, batch_input_shape):\n",
" #def build(self, batch_input_shape):\n", " n_inputs = batch_input_shape[-1]\n",
" # n_inputs = batch_input_shape[-1]\n", " self.reconstruct = keras.layers.Dense(n_inputs)\n",
" # self.reconstruct = keras.layers.Dense(n_inputs)\n", " #super().build(batch_input_shape)\n",
" # super().build(batch_input_shape)\n",
"\n", "\n",
" def call(self, inputs, training=None):\n", " def call(self, inputs, training=None):\n",
" Z = inputs\n", " Z = inputs\n",