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Fix some issues with TF 2.2 breaking changes

main
Aurélien Geron 2020-04-02 12:52:31 +13:00
parent 6adb7253b5
commit 1a19694d57
1 changed files with 18 additions and 17 deletions
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35
12_custom_models_and_training_with_tensorflow.ipynb
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@ -1588,13 +1588,20 @@
"model.fit(X_train_scaled.astype(np.float32), y_train.astype(np.float32), epochs=2)" "model.fit(X_train_scaled.astype(np.float32), y_train.astype(np.float32), epochs=2)"
] ]
}, },
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Warning**: In TF 2.2, tf.keras adds an extra first metric in `model.metrics` at position 0 (see [TF issue #38150](https://github.com/tensorflow/tensorflow/issues/38150)). This forces us to use `model.metrics[-1]` rather than `model.metrics[0]` to access the `HuberMetric`."
]
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 124, "execution_count": 124,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"model.metrics[0].threshold" "model.metrics[-1].threshold"
] ]
}, },
{ {
@ -1713,6 +1720,13 @@
"model.fit(X_train_scaled.astype(np.float32), y_train.astype(np.float32), epochs=2)" "model.fit(X_train_scaled.astype(np.float32), y_train.astype(np.float32), epochs=2)"
] ]
}, },
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Warning**: In TF 2.2, tf.keras adds an extra first metric in `model.metrics` at position 0 (see [TF issue #38150](https://github.com/tensorflow/tensorflow/issues/38150)). This forces us to use `model.metrics[-1]` rather than `model.metrics[0]` to access the `HuberMetric`."
]
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 134, "execution_count": 134,
@ -1721,7 +1735,7 @@
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
"model.metrics[0].threshold" "model.metrics[-1].threshold"
] ]
}, },
{ {
@ -1976,8 +1990,6 @@
"class ResidualBlock(keras.layers.Layer):\n", "class ResidualBlock(keras.layers.Layer):\n",
" def __init__(self, n_layers, n_neurons, **kwargs):\n", " def __init__(self, n_layers, n_neurons, **kwargs):\n",
" super().__init__(**kwargs)\n", " super().__init__(**kwargs)\n",
" self.n_layers = n_layers # not shown in the book\n",
" self.n_neurons = n_neurons # not shown\n",
" self.hidden = [keras.layers.Dense(n_neurons, activation=\"elu\",\n", " self.hidden = [keras.layers.Dense(n_neurons, activation=\"elu\",\n",
" kernel_initializer=\"he_normal\")\n", " kernel_initializer=\"he_normal\")\n",
" for _ in range(n_layers)]\n", " for _ in range(n_layers)]\n",
@ -1986,12 +1998,7 @@
" Z = inputs\n", " Z = inputs\n",
" for layer in self.hidden:\n", " for layer in self.hidden:\n",
" Z = layer(Z)\n", " Z = layer(Z)\n",
" return inputs + Z\n", " return inputs + Z"
" \n",
" def get_config(self): # not shown\n",
" base_config = super().get_config() # not shown\n",
" return {**base_config, # not shown\n",
" \"n_layers\": self.n_layers, \"n_neurons\": self.n_neurons} # not shown"
] ]
}, },
{ {
@ -2003,7 +2010,6 @@
"class ResidualRegressor(keras.models.Model):\n", "class ResidualRegressor(keras.models.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.output_dim = output_dim # not shown in the book\n",
" self.hidden1 = keras.layers.Dense(30, activation=\"elu\",\n", " self.hidden1 = keras.layers.Dense(30, activation=\"elu\",\n",
" kernel_initializer=\"he_normal\")\n", " kernel_initializer=\"he_normal\")\n",
" self.block1 = ResidualBlock(2, 30)\n", " self.block1 = ResidualBlock(2, 30)\n",
@ -2015,12 +2021,7 @@
" for _ in range(1 + 3):\n", " for _ in range(1 + 3):\n",
" Z = self.block1(Z)\n", " Z = self.block1(Z)\n",
" Z = self.block2(Z)\n", " Z = self.block2(Z)\n",
" return self.out(Z)\n", " return self.out(Z)"
"\n",
" def get_config(self): # not shown\n",
" base_config = super().get_config() # not shown\n",
" return {**base_config, # not shown\n",
" \"output_dim\": self.output_dim} # not shown"
] ]
}, },
{ {