Merge pull request #2240 from MedAymenF:mlp-core-fix

PiperOrigin-RevId: 554991816

Author: copybara-github

site/en/guide/core/mlp_core.ipynb

@@ -94,7 +94,7 @@
         "\n",
         "When these perceptrons are stacked, they form structures called dense layers which can then be connected to build a neural network. A dense layer's equation is similar to that of a perceptron's but uses a weight matrix and a bias vector instead: \n",
         "\n",
-        "$$Y = \\mathrm{W}⋅\\mathrm{X} + \\vec{b}$$\n",
+        "$$Z = \\mathrm{W}⋅\\mathrm{X} + \\vec{b}$$\n",
         "\n",
         "where\n",
         "\n",
@@ -234,7 +234,7 @@
       },
       "outputs": [],
       "source": [
-        "sns.countplot(y_viz.numpy());\n",
+        "sns.countplot(x=y_viz.numpy());\n",
         "plt.xlabel('Digits')\n",
         "plt.title(\"MNIST Digit Distribution\");"
       ]
@@ -386,8 +386,8 @@
         "    if not self.built:\n",
         "      # Infer the input dimension based on first call\n",
         "      self.in_dim = x.shape[1]\n",
-        "      # Initialize the weights and biases using Xavier scheme\n",
-        "      self.w = tf.Variable(xavier_init(shape=(self.in_dim, self.out_dim)))\n",
+        "      # Initialize the weights and biases\n",
+        "      self.w = tf.Variable(self.weight_init(shape=(self.in_dim, self.out_dim)))\n",
         "      self.b = tf.Variable(tf.zeros(shape=(self.out_dim,)))\n",
         "      self.built = True\n",
         "    # Compute the forward pass\n",
@@ -875,9 +875,9 @@
         "  label_ind = (y_test == label)\n",
         "  # extract predictions for specific true label\n",
         "  pred_label = test_classes[label_ind]\n",
-        "  label_filled = tf.cast(tf.fill(pred_label.shape[0], label), tf.int64)\n",
+        "  labels = y_test[label_ind]\n",
         "  # compute class-wise accuracy\n",
-        "  label_accs[accuracy_score(pred_label, label_filled).numpy()] = label\n",
+        "  label_accs[accuracy_score(pred_label, labels).numpy()] = label\n",
         "for key in sorted(label_accs):\n",
         "  print(f\"Digit {label_accs[key]}: {key:.3f}\")"
       ]
@@ -906,7 +906,7 @@
         "  plt.figure(figsize=(10,10))\n",
         "  confusion = sk_metrics.confusion_matrix(test_labels.numpy(), \n",
         "                                          test_classes.numpy())\n",
-        "  confusion_normalized = confusion / confusion.sum(axis=1)\n",
+        "  confusion_normalized = confusion / confusion.sum(axis=1, keepdims=True)\n",
         "  axis_labels = range(10)\n",
         "  ax = sns.heatmap(\n",
         "      confusion_normalized, xticklabels=axis_labels, yticklabels=axis_labels,\n",