Merge branch 'master' of github.com:ageron/handson-ml2

10_neural_nets_with_keras.ipynb

@@ -1089,15 +1089,6 @@
     "y_pred_main, y_pred_aux = model.predict((X_new_A, X_new_B))"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = WideAndDeepModel(30, activation=\"relu\")"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},

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",

18_reinforcement_learning.ipynb

@@ -565,7 +565,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's create a neural network that will take observations as inputs, and output the action to take for each observation. To choose an action, the network will estimate a probability for each action, then we will select an action randomly according to the estimated probabilities. In the case of the Cart-Pole environment, there are just two possible actions (left or right), so we only need one output neuron: it will output the probability `p` of the action 0 (left), and of course the probability of action 1 (right) will be `1 - p`."
+    "Let's create a neural network that will take observations as inputs, and output the probabilities of actions to take for each observation. To choose an action, the network will estimate a probability for each action, then we will select an action randomly according to the estimated probabilities. In the case of the Cart-Pole environment, there are just two possible actions (left or right), so we only need one output neuron: it will output the probability `p` of the action 0 (left), and of course the probability of action 1 (right) will be `1 - p`."
    ]
   },
   {