diff --git a/10_neural_nets_with_keras.ipynb b/10_neural_nets_with_keras.ipynb
index 7ddf933..215853c 100644
--- a/10_neural_nets_with_keras.ipynb
+++ b/10_neural_nets_with_keras.ipynb
@@ -507,6 +507,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "keras.backend.clear_session()\n",
     "np.random.seed(42)\n",
     "tf.random.set_seed(42)"
    ]
@@ -543,21 +544,13 @@
     "model.summary()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Warning**: the following 2 cells do not work yet due to [TensorFlow issue 24622](https://github.com/tensorflow/tensorflow/issues/24622) (you are using a preview version of TensorFlow, hence there are still a few issues).\n",
-    "You can work around this issue by applying [PR 24626](https://github.com/tensorflow/tensorflow/pull/24625/files) to your copy of `tensorflow/python/keras/utils/vis_utils.py`."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#keras.utils.plot_model(model, \"my_mnist_model.png\", show_shapes=True)"
+    "keras.utils.plot_model(model, \"my_mnist_model.png\", show_shapes=True)"
    ]
   },
   {
@@ -565,34 +558,6 @@
    "execution_count": 29,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "%%html\n",
-    "<img src=\"my_mnist_model.png\" />"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Warning**: the following cell does not work yet due to [TensorFlow issue 24622](https://github.com/tensorflow/tensorflow/issues/24622) and [TensorFlow issue 24639](https://github.com/tensorflow/tensorflow/issues/24639).\n",
-    "You can work around issue 24639 by writing `from tensorflow.keras.utils.vis_utils import model_to_dot`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from IPython.display import SVG\n",
-    "#SVG(keras.utils.model_to_dot(model, show_shapes=True).create(prog='dot', format='svg'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "hidden1 = model.layers[1]\n",
     "hidden1.name"
@@ -600,16 +565,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
-    "model.get_layer(hidden1.name).name"
+    "model.get_layer(hidden1.name) is hidden1"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -618,7 +583,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -627,7 +592,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -636,7 +601,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -645,7 +610,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -654,7 +619,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -671,29 +636,29 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 39,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
+    "```python\n",
     "model.compile(loss=keras.losses.sparse_categorical_crossentropy,\n",
     "              optimizer=keras.optimizers.SGD(),\n",
-    "              metrics=[keras.metrics.sparse_categorical_accuracy])"
+    "              metrics=[keras.metrics.sparse_categorical_accuracy])\n",
+    "```"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
-    "history = model.fit(X_train, y_train, epochs=50,\n",
+    "history = model.fit(X_train, y_train, epochs=30,\n",
     "                    validation_data=(X_valid, y_valid))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -702,7 +667,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -711,7 +676,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -720,7 +685,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -735,7 +700,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -744,7 +709,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -755,7 +720,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -765,7 +730,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -774,7 +739,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -798,7 +763,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -819,7 +784,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -829,7 +794,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -837,7 +802,7 @@
     "    keras.layers.Dense(30, activation=\"relu\", input_shape=X_train.shape[1:]),\n",
     "    keras.layers.Dense(1)\n",
     "])\n",
-    "model.compile(loss=\"mean_squared_error\", optimizer=\"sgd\")\n",
+    "model.compile(loss=\"mean_squared_error\", optimizer=keras.optimizers.SGD(lr=1e-3))\n",
     "history = model.fit(X_train, y_train, epochs=20, validation_data=(X_valid, y_valid))\n",
     "mse_test = model.evaluate(X_test, y_test)\n",
     "X_new = X_test[:3]\n",
@@ -846,7 +811,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -858,7 +823,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -881,7 +846,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -891,21 +856,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [],
    "source": [
-    "input = keras.layers.Input(shape=X_train.shape[1:])\n",
-    "hidden1 = keras.layers.Dense(30, activation=\"relu\")(input)\n",
+    "input_ = keras.layers.Input(shape=X_train.shape[1:])\n",
+    "hidden1 = keras.layers.Dense(30, activation=\"relu\")(input_)\n",
     "hidden2 = keras.layers.Dense(30, activation=\"relu\")(hidden1)\n",
-    "concat = keras.layers.concatenate([input, hidden2])\n",
+    "concat = keras.layers.concatenate([input_, hidden2])\n",
     "output = keras.layers.Dense(1)(concat)\n",
-    "model = keras.models.Model(inputs=[input], outputs=[output])"
+    "model = keras.models.Model(inputs=[input_], outputs=[output])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -914,11 +879,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [],
    "source": [
-    "model.compile(loss=\"mean_squared_error\", optimizer=\"sgd\")\n",
+    "model.compile(loss=\"mean_squared_error\", optimizer=keras.optimizers.SGD(lr=1e-3))\n",
     "history = model.fit(X_train, y_train, epochs=20,\n",
     "                    validation_data=(X_valid, y_valid))\n",
     "mse_test = model.evaluate(X_test, y_test)\n",
@@ -934,7 +899,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -944,26 +909,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [],
    "source": [
-    "input_A = keras.layers.Input(shape=[5])\n",
-    "input_B = keras.layers.Input(shape=[6])\n",
+    "input_A = keras.layers.Input(shape=[5], name=\"wide_input\")\n",
+    "input_B = keras.layers.Input(shape=[6], name=\"deep_input\")\n",
     "hidden1 = keras.layers.Dense(30, activation=\"relu\")(input_B)\n",
     "hidden2 = keras.layers.Dense(30, activation=\"relu\")(hidden1)\n",
     "concat = keras.layers.concatenate([input_A, hidden2])\n",
-    "output = keras.layers.Dense(1)(concat)\n",
+    "output = keras.layers.Dense(1, name=\"output\")(concat)\n",
     "model = keras.models.Model(inputs=[input_A, input_B], outputs=[output])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [],
    "source": [
-    "model.compile(loss=\"mse\", optimizer=\"sgd\")\n",
+    "model.compile(loss=\"mse\", optimizer=keras.optimizers.SGD(lr=1e-3))\n",
     "\n",
     "X_train_A, X_train_B = X_train[:, :5], X_train[:, 2:]\n",
     "X_valid_A, X_valid_B = X_valid[:, :5], X_valid[:, 2:]\n",
@@ -985,7 +950,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -995,33 +960,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [],
    "source": [
-    "input_A = keras.layers.Input(shape=[5])\n",
-    "input_B = keras.layers.Input(shape=[6])\n",
+    "input_A = keras.layers.Input(shape=[5], name=\"wide_input\")\n",
+    "input_B = keras.layers.Input(shape=[6], name=\"deep_input\")\n",
     "hidden1 = keras.layers.Dense(30, activation=\"relu\")(input_B)\n",
     "hidden2 = keras.layers.Dense(30, activation=\"relu\")(hidden1)\n",
     "concat = keras.layers.concatenate([input_A, hidden2])\n",
-    "output = keras.layers.Dense(1)(concat)\n",
-    "aux_output = keras.layers.Dense(1)(hidden2)\n",
+    "output = keras.layers.Dense(1, name=\"main_output\")(concat)\n",
+    "aux_output = keras.layers.Dense(1, name=\"aux_output\")(hidden2)\n",
     "model = keras.models.Model(inputs=[input_A, input_B],\n",
     "                           outputs=[output, aux_output])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [],
    "source": [
-    "model.compile(loss=[\"mse\", \"mse\"], loss_weights=[0.9, 0.1], optimizer=\"sgd\")"
+    "model.compile(loss=[\"mse\", \"mse\"], loss_weights=[0.9, 0.1], optimizer=keras.optimizers.SGD(lr=1e-3))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1031,7 +996,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1049,7 +1014,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1075,11 +1040,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [],
    "source": [
-    "model.compile(loss=\"mse\", loss_weights=[0.9, 0.1], optimizer=\"sgd\")\n",
+    "model.compile(loss=\"mse\", loss_weights=[0.9, 0.1], optimizer=keras.optimizers.SGD(lr=1e-3))\n",
     "history = model.fit((X_train_A, X_train_B), (y_train, y_train), epochs=10,\n",
     "                    validation_data=((X_valid_A, X_valid_B), (y_valid, y_valid)))\n",
     "total_loss, main_loss, aux_loss = model.evaluate((X_test_A, X_test_B), (y_test, y_test))\n",
@@ -1088,7 +1053,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1104,7 +1069,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1114,7 +1079,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1127,18 +1092,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [],
    "source": [
-    "model.compile(loss=\"mse\", optimizer=\"sgd\")\n",
+    "model.compile(loss=\"mse\", optimizer=keras.optimizers.SGD(lr=1e-3))\n",
     "history = model.fit(X_train, y_train, epochs=10, validation_data=(X_valid, y_valid))\n",
     "mse_test = model.evaluate(X_test, y_test)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1147,7 +1112,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1156,7 +1121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1165,7 +1130,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1174,7 +1139,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1190,17 +1155,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [],
    "source": [
+    "keras.backend.clear_session()\n",
     "np.random.seed(42)\n",
     "tf.random.set_seed(42)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1213,11 +1179,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [],
    "source": [
-    "model.compile(loss=\"mse\", optimizer=\"sgd\")\n",
+    "model.compile(loss=\"mse\", optimizer=keras.optimizers.SGD(lr=1e-3))\n",
     "checkpoint_cb = keras.callbacks.ModelCheckpoint(\"my_keras_model.h5\", save_best_only=True)\n",
     "history = model.fit(X_train, y_train, epochs=10,\n",
     "                    validation_data=(X_valid, y_valid),\n",
@@ -1228,11 +1194,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [],
    "source": [
-    "model.compile(loss=\"mse\", optimizer=\"sgd\")\n",
+    "model.compile(loss=\"mse\", optimizer=keras.optimizers.SGD(lr=1e-3))\n",
     "early_stopping_cb = keras.callbacks.EarlyStopping(patience=10,\n",
     "                                                  restore_best_weights=True)\n",
     "history = model.fit(X_train, y_train, epochs=100,\n",
@@ -1243,7 +1209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1254,7 +1220,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1273,7 +1239,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1282,7 +1248,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1297,17 +1263,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 83,
    "metadata": {},
    "outputs": [],
    "source": [
+    "keras.backend.clear_session()\n",
     "np.random.seed(42)\n",
     "tf.random.set_seed(42)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1316,14 +1283,12 @@
     "    keras.layers.Dense(30, activation=\"relu\"),\n",
     "    keras.layers.Dense(1)\n",
     "])    \n",
-    "#model.compile(loss=\"mse\", optimizer=\"sgd\")\n",
-    "# or try another learning rate:\n",
-    "model.compile(loss=\"mse\", optimizer=keras.optimizers.SGD(lr=0.05))"
+    "model.compile(loss=\"mse\", optimizer=keras.optimizers.SGD(lr=1e-3))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 85,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1337,7 +1302,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To start the TensorBoard server, one option is to open a terminal, if needed activate the virtualenv where you installed TensorBoard, then type:\n",
+    "To start the TensorBoard server, one option is to open a terminal, if needed activate the virtualenv where you installed TensorBoard, go to this notebook's directory, then type:\n",
     "\n",
     "```bash\n",
     "$ tensorboard --logdir=./my_logs --port=6006\n",
@@ -1345,51 +1310,22 @@
     "\n",
     "You can then open your web browser to [localhost:6006](http://localhost:6006) and use TensorBoard. Once you are done, press Ctrl-C in the terminal window, this will shutdown the TensorBoard server.\n",
     "\n",
-    "Alternatively, you can create a Jupyter cell with this code:\n",
-    "\n",
-    "```bash\n",
-    "%%bash\n",
-    "tensorboard --logdir={run_logdir} --port=6006\n",
-    "```\n",
-    "\n",
-    "When you run this cell, the TensorBoard server will start and you can use it at [localhost:6006](http://localhost:6006), but Jupyter will be blocked until you interrupt this cell, which will shutdown the server.\n",
-    "\n",
-    "Lastly, you can use the following `tb()` function that starts the TensorBoard server in a way that does not block Jupyter, and directly opens a new browser tab for you. It returns a handle on the server's process, so you can call `server.kill()` when you want to shutdown the server. Note that interrupting this notebook will shutdown all TensorBoard servers that you started this way.\n",
-    "\n",
-    "You may also want to install the jupyter-tensorboard extension which integrates nicely into Jupyter to start/stop TensorBoard servers."
+    "Alternatively, you can load TensorBoard's Jupyter extension and run it like this:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 86,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def tb(logdir=root_logdir, port=6006, open_tab=True, sleep=3):\n",
-    "    import subprocess\n",
-    "    proc = subprocess.Popen(\n",
-    "        \"tensorboard --logdir={0} --port={1}\".format(logdir, port), shell=True)\n",
-    "    if open_tab:\n",
-    "        import time\n",
-    "        print(\"Waiting a few seconds for the TensorBoard Server to start...\")\n",
-    "        time.sleep(sleep)\n",
-    "        import webbrowser\n",
-    "        webbrowser.open(\"http://127.0.0.1:{}/\".format(port))\n",
-    "    return proc"
+    "%load_ext tensorboard\n",
+    "%tensorboard --logdir=./my_logs --port=6006"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "server = tb()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 87,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1399,17 +1335,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 88,
    "metadata": {},
    "outputs": [],
    "source": [
+    "keras.backend.clear_session()\n",
     "np.random.seed(42)\n",
     "tf.random.set_seed(42)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 89,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1418,17 +1355,17 @@
     "    keras.layers.Dense(30, activation=\"relu\"),\n",
     "    keras.layers.Dense(1)\n",
     "])    \n",
-    "model.compile(loss=\"mse\", optimizer=keras.optimizers.SGD(lr=0.015))"
+    "model.compile(loss=\"mse\", optimizer=keras.optimizers.SGD(lr=0.05))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 90,
    "metadata": {},
    "outputs": [],
    "source": [
     "tensorboard_cb = keras.callbacks.TensorBoard(run_logdir2)\n",
-    "history = model.fit(X_train, y_train, epochs=10,\n",
+    "history = model.fit(X_train, y_train, epochs=30,\n",
     "                    validation_data=(X_valid, y_valid),\n",
     "                    callbacks=[checkpoint_cb, tensorboard_cb])"
    ]
@@ -1441,21 +1378,19 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 95,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "help(keras.callbacks.TensorBoard.__init__)"
+    "Check out the other available logging options:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 91,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#server.kill() # uncomment and run this to stop the TensorBoard server"
+    "help(keras.callbacks.TensorBoard.__init__)"
    ]
   },
   {
@@ -1467,35 +1402,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 92,
    "metadata": {},
    "outputs": [],
    "source": [
+    "keras.backend.clear_session()\n",
     "np.random.seed(42)\n",
     "tf.random.set_seed(42)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 93,
    "metadata": {},
    "outputs": [],
    "source": [
     "def build_model(n_hidden=1, n_neurons=30, learning_rate=3e-3, input_shape=[8]):\n",
     "    model = keras.models.Sequential()\n",
-    "    options = {\"input_shape\": input_shape}\n",
+    "    model.add(keras.layers.InputLayer(input_shape=input_shape))\n",
     "    for layer in range(n_hidden):\n",
-    "        model.add(keras.layers.Dense(n_neurons, activation=\"relu\", **options))\n",
-    "        options = {}\n",
-    "    model.add(keras.layers.Dense(1, **options))\n",
-    "    optimizer = keras.optimizers.SGD(learning_rate)\n",
+    "        model.add(keras.layers.Dense(n_neurons, activation=\"relu\"))\n",
+    "    model.add(keras.layers.Dense(1))\n",
+    "    optimizer = keras.optimizers.SGD(lr=learning_rate)\n",
     "    model.compile(loss=\"mse\", optimizer=optimizer)\n",
     "    return model"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 94,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1504,7 +1439,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 95,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1515,7 +1450,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 96,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1524,7 +1459,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 97,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1533,7 +1468,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 98,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1543,7 +1478,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 99,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1564,7 +1499,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 100,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1573,7 +1508,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 101,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1582,7 +1517,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 102,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1591,7 +1526,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 103,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1600,7 +1535,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 104,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1610,7 +1545,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 105,
    "metadata": {},
    "outputs": [],
    "source": [