diff --git a/notebooks/03_Multiple_layer_models.ipynb b/notebooks/03_Multiple_layer_models.ipynb
index aee4226..613e9ef 100644
--- a/notebooks/03_Multiple_layer_models.ipynb
+++ b/notebooks/03_Multiple_layer_models.ipynb
@@ -176,8 +176,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -218,7 +220,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "def train_model_and_plot_stats(\n",
@@ -234,7 +238,7 @@
     "\n",
     "    # Run the optimiser for 5 epochs (full passes through the training set)\n",
     "    # printing statistics every epoch.\n",
-    "    stats, keys = optimiser.train(num_epochs=num_epochs, stats_interval=stats_interval)\n",
+    "    stats, keys, _ = optimiser.train(num_epochs=num_epochs, stats_interval=stats_interval)\n",
     "\n",
     "    # Plot the change in the validation and training set error over training.\n",
     "    fig_1 = plt.figure(figsize=(8, 4))\n",
@@ -270,7 +274,22 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 5: 1.2s to complete\n",
+      "    error(train)=3.11e-01, acc(train)=9.13e-01, error(valid)=2.92e-01, acc(valid)=9.18e-01\n",
+      "Epoch 10: 1.2s to complete\n",
+      "    error(train)=2.89e-01, acc(train)=9.20e-01, error(valid)=2.77e-01, acc(valid)=9.23e-01\n",
+      "Epoch 15: 1.1s to complete\n",
+      "    error(train)=2.79e-01, acc(train)=9.22e-01, error(valid)=2.70e-01, acc(valid)=9.24e-01\n",
+      "Epoch 20: 0.7s to complete\n",
+      "    error(train)=2.72e-01, acc(train)=9.24e-01, error(valid)=2.66e-01, acc(valid)=9.26e-01\n"
+     ]
+    }
+   ],
    "source": [
     "# Set training run hyperparameters\n",
     "batch_size = 100  # number of data points in a batch\n",
@@ -356,7 +375,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# Set training run hyperparameters\n",
@@ -437,7 +458,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": []
   },
@@ -451,7 +474,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": []
   },
@@ -465,7 +490,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": []
   },
@@ -479,7 +506,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": []
   },
@@ -536,7 +565,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -589,7 +620,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "test_inputs = np.array([[0.1, -0.2, 0.3], [-0.4, 0.5, -0.6]])\n",
@@ -624,7 +657,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "test_inputs = np.array([[0.1, -0.2, 0.3], [-0.4, 0.5, -0.6]])\n",
@@ -659,18 +694,6 @@
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.2"
   }
  },
  "nbformat": 4,