diff --git a/01_intro.ipynb b/01_intro.ipynb
index a8911af..af7e15e 100644
--- a/01_intro.ipynb
+++ b/01_intro.ipynb
@@ -1679,7 +1679,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"images/layer1.png\" alt=\"Activations of early layers of a CNN\" width=\"300\" caption=\"Activations of early layers of a CNN\" id=\"img_layer1\">"
+    "<img src=\"images/layer1.png\" alt=\"Activations of early layers of a CNN\" width=\"300\" caption=\"Activations of early layers of a CNN (curtesy of Matthew D. Zeiler and Rob Fergus)\" id=\"img_layer1\">"
    ]
   },
   {
@@ -1693,7 +1693,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"images/layer2.png\" alt=\"Activations of early layers of a CNN\" width=\"800\" caption=\"Activations of early layers of a CNN\" id=\"img_layer2\">"
+    "<img src=\"images/layer2.png\" alt=\"Activations of early layers of a CNN\" width=\"800\" caption=\"Activations of early layers of a CNN (curtesy of Matthew D. Zeiler and Rob Fergus)\" id=\"img_layer2\">"
    ]
   },
   {
@@ -1709,7 +1709,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"images/chapter2_layer3.PNG\" alt=\"Activations of medium layers of a CNN\" width=\"800\" caption=\"Activations of medium layers of a CNN\" id=\"img_layer3\">"
+    "<img src=\"images/chapter2_layer3.PNG\" alt=\"Activations of medium layers of a CNN\" width=\"800\" caption=\"Activations of medium layers of a CNN (curtesy of Matthew D. Zeiler and Rob Fergus)\" id=\"img_layer3\">"
    ]
   },
   {
@@ -1723,7 +1723,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"images/chapter2_layer4and5.PNG\" alt=\"Activations of end layers of a CNN\" width=\"800\" caption=\"Activations of end layers of a CNN\" id=\"img_layer4\">"
+    "<img src=\"images/chapter2_layer4and5.PNG\" alt=\"Activations of end layers of a CNN\" width=\"800\" caption=\"Activations of end layers of a CNN (curtesy of Matthew D. Zeiler and Rob Fergus)\" id=\"img_layer4\">"
    ]
   },
   {
diff --git a/06_multicat.ipynb b/06_multicat.ipynb
index 4eec808..abb3b59 100644
--- a/06_multicat.ipynb
+++ b/06_multicat.ipynb
@@ -476,7 +476,8 @@
     {
      "data": {
       "text/plain": [
-       "(Path('train/002844.jpg'), ['train'])"
+       "(Path('/home/sgugger/.fastai/data/pascal_2007/train/008663.jpg'),\n",
+       " ['car', 'person'])"
       ]
      },
      "execution_count": null,
@@ -737,8 +738,8 @@
     {
      "data": {
       "text/plain": [
-       "tensor([-1.0028,  0.3400, -0.5906,  0.7806,  3.1160, -0.1994,  1.3180,  1.6361, -1.7553,  0.2217,  2.8052,  1.3229,  0.9369, -1.4760, -0.3204, -2.3116, -3.8615, -1.5931,  0.0745, -3.6006],\n",
-       "       device='cuda:5', grad_fn=<SelectBackward>)"
+       "tensor([ 2.0258, -1.3543,  1.4640,  1.7754, -1.2820, -5.8053,  3.6130,  0.7193, -4.3683, -2.5001, -2.8373, -1.8037,  2.0122,  0.6189,  1.9729,  0.8999, -2.6769, -0.3829,  1.2212,  1.6073],\n",
+       "       device='cuda:0', grad_fn=<SelectBackward>)"
       ]
      },
      "execution_count": null,
@@ -772,7 +773,25 @@
    "source": [
     "def binary_cross_entropy(inputs, targets):\n",
     "    inputs = inputs.sigmoid()\n",
-    "    return torch.where(targets==1, 1-inputs, inputs).log().mean()"
+    "    return torch.where(targets==1, inputs, 1-inputs).log().mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "binary_cross_entropy(activs, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "F.binary_cross_entropy_with_logits(activs, y)"
    ]
   },
   {
diff --git a/clean/06_multicat.ipynb b/clean/06_multicat.ipynb
index eaeecd3..cc3f979 100644
--- a/clean/06_multicat.ipynb
+++ b/clean/06_multicat.ipynb
@@ -313,7 +313,8 @@
     {
      "data": {
       "text/plain": [
-       "(Path('train/002844.jpg'), ['train'])"
+       "(Path('/home/sgugger/.fastai/data/pascal_2007/train/008663.jpg'),\n",
+       " ['car', 'person'])"
       ]
      },
      "execution_count": null,
@@ -488,8 +489,8 @@
     {
      "data": {
       "text/plain": [
-       "tensor([-1.0028,  0.3400, -0.5906,  0.7806,  3.1160, -0.1994,  1.3180,  1.6361, -1.7553,  0.2217,  2.8052,  1.3229,  0.9369, -1.4760, -0.3204, -2.3116, -3.8615, -1.5931,  0.0745, -3.6006],\n",
-       "       device='cuda:5', grad_fn=<SelectBackward>)"
+       "tensor([ 2.0258, -1.3543,  1.4640,  1.7754, -1.2820, -5.8053,  3.6130,  0.7193, -4.3683, -2.5001, -2.8373, -1.8037,  2.0122,  0.6189,  1.9729,  0.8999, -2.6769, -0.3829,  1.2212,  1.6073],\n",
+       "       device='cuda:0', grad_fn=<SelectBackward>)"
       ]
      },
      "execution_count": null,
@@ -509,7 +510,25 @@
    "source": [
     "def binary_cross_entropy(inputs, targets):\n",
     "    inputs = inputs.sigmoid()\n",
-    "    return torch.where(targets==1, 1-inputs, inputs).log().mean()"
+    "    return torch.where(targets==1, inputs, 1-inputs).log().mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "binary_cross_entropy(activs, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "F.binary_cross_entropy_with_logits(activs, y)"
    ]
   },
   {