From 6b166b372974f0afad5a9240a78e34e54e64b2ad Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Aur=C3=A9lien=20Geron?= <ageron@users.noreply.github.com>
Date: Wed, 29 Nov 2017 21:43:08 +0100
Subject: [PATCH] Fix a few errors in extra_capsnets.ipynb

---
 extra_capsnets.ipynb | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/extra_capsnets.ipynb b/extra_capsnets.ipynb
index cbe6086..67e67bd 100644
--- a/extra_capsnets.ipynb
+++ b/extra_capsnets.ipynb
@@ -512,10 +512,10 @@
     "We can apply this function to compute $\\hat{\\mathbf{u}}_{j|i}$ for every pair of capsules ($i$, $j$) like this (recall that there are 6×6×32=1152 capsules in the first layer, and 10 in the second layer):\n",
     "$\n",
     "\\pmatrix{\n",
-    "  \\mathbf{W}_{1,1} & \\mathbf{W}_{2,1} & \\cdots & \\mathbf{W}_{10,1} \\\\\n",
-    "  \\mathbf{W}_{1,2} & \\mathbf{W}_{2,2} & \\cdots & \\mathbf{W}_{10,2} \\\\\n",
+    "  \\mathbf{W}_{1,1} & \\mathbf{W}_{1,2} & \\cdots & \\mathbf{W}_{1,10} \\\\\n",
+    "  \\mathbf{W}_{2,1} & \\mathbf{W}_{2,2} & \\cdots & \\mathbf{W}_{2,10} \\\\\n",
     "  \\vdots & \\vdots & \\ddots & \\vdots \\\\\n",
-    "  \\mathbf{W}_{1,1152} & \\mathbf{W}_{2,1152} & \\cdots & \\mathbf{W}_{10,1152}\n",
+    "  \\mathbf{W}_{1152,1} & \\mathbf{W}_{1152,2} & \\cdots & \\mathbf{W}_{1152,10}\n",
     "} \\times\n",
     "\\pmatrix{\n",
     "  \\mathbf{u}_1 & \\mathbf{u}_1 & \\cdots & \\mathbf{u}_1 \\\\\n",
@@ -1434,7 +1434,7 @@
    "source": [
     "Note that the `tf.cond()` function expects the if-True and if-False tensors to be passed _via_ functions: these functions will be called just once during the graph construction phase (not during the execution phase), similar to `tf.while_loop()`. This allows TensorFlow to add the necessary operations to handle the conditional evaluation of the if-True or if-False tensors. However, in our case, the tensors `y` and `y_pred` are already created by the time we call `tf.cond()`, so unfortunately TensorFlow will consider both `y` and `y_pred` to be dependencies of the `reconstruction_targets` tensor. The `reconstruction_targets` tensor will end up with the correct value, but:\n",
     "1. whenever we evaluate a tensor that depends on `reconstruction_targets`, the `y_pred` tensor will be evaluated (even if `mask_with_layers` is `True`). This is not a big deal because computing `y_pred` adds no computing overhead during training, since we need it anyway to compute the margin loss. And during testing, if we are doing classification, we won't need reconstructions, so `reconstruction_targets` won't be evaluated at all.\n",
-    "2. we will always need to feed a value for the `y` placeholder (even if `mask_with_layers` is `False`). This is a bit annoying, but we can pass any array of the appropriate shape, because TensorFlow won't use it anyway (it just does not know it yet when it checks for dependencies)."
+    "2. we will always need to feed a value for the `y` placeholder (even if `mask_with_layers` is `False`). This is a bit annoying, but we can pass an empty array, because TensorFlow won't use it anyway (it just does not know it yet when it checks for dependencies)."
    ]
   },
   {
@@ -1935,14 +1935,14 @@
     "    caps2_output_value, decoder_output_value, y_pred_value = sess.run(\n",
     "            [caps2_output, decoder_output, y_pred],\n",
     "            feed_dict={X: sample_images,\n",
-    "                       y: np.full(n_samples, np.nan)})"
+    "                       y: np.array([], dtype=np.int64)})"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Note: we feed `y` with an array of the appropriate size (full of `nan` values), but TensorFlow will not use it, as explained earlier."
+    "Note: we feed `y` with an empty array, but TensorFlow will not use it, as explained earlier."
    ]
   },
   {