diff --git a/04_modern_net.ipynb b/04_modern_net.ipynb
index bcabb07..c1cff59 100644
--- a/04_modern_net.ipynb
+++ b/04_modern_net.ipynb
@@ -16,24 +16,25 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def init_weights(shape):\n",
     "    return tf.Variable(tf.random_normal(shape, stddev=0.01))\n",
     "\n",
-    "def model(X, w_h, w_h2, w_o, p_keep_input, p_keep_hidden): # this network is the same as the previous one except with an extra hidden layer + dropout\n",
-    "    X = tf.nn.dropout(X, p_keep_input)\n",
-    "    h = tf.nn.relu(tf.matmul(X, w_h))\n",
+    "def init_biases(shape):\n",
+    "    return tf.Variable(tf.zeros(shape))\n",
     "\n",
+    "def model(X, w_h, b_h, w_h2, b_h2, w_o, b_o, p_keep_input, p_keep_hidden): # this network is the same as the previous one except with an extra hidden layer + dropout\n",
+    "    X = tf.nn.dropout(X, p_keep_input)\n",
+    "    h = tf.nn.relu(tf.matmul(X, w_h)) + b_h\n",
+    "    \n",
     "    h = tf.nn.dropout(h, p_keep_hidden)\n",
-    "    h2 = tf.nn.relu(tf.matmul(h, w_h2))\n",
+    "    h2 = tf.nn.relu(tf.matmul(h, w_h2)) + b_h2\n",
     "\n",
     "    h2 = tf.nn.dropout(h2, p_keep_hidden)\n",
     "\n",
-    "    return tf.matmul(h2, w_o)\n",
+    "    return tf.matmul(h2, w_o) + b_o\n",
     "\n",
     "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)\n",
     "trX, trY, teX, teY = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labels"
@@ -42,21 +43,22 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "X = tf.placeholder(\"float\", [None, 784])\n",
     "Y = tf.placeholder(\"float\", [None, 10])\n",
     "\n",
     "w_h = init_weights([784, 625])\n",
+    "b_h = init_biases([625])\n",
     "w_h2 = init_weights([625, 625])\n",
+    "b_h2 = init_biases([625])\n",
     "w_o = init_weights([625, 10])\n",
+    "b_o = init_biases([10])\n",
     "\n",
     "p_keep_input = tf.placeholder(\"float\")\n",
     "p_keep_hidden = tf.placeholder(\"float\")\n",
-    "py_x = model(X, w_h, w_h2, w_o, p_keep_input, p_keep_hidden)\n",
+    "py_x = model(X, w_h, b_h, w_h2, b_h2, w_o, b_o, p_keep_input, p_keep_hidden)\n",
     "\n",
     "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=py_x, labels=Y))\n",
     "train_op = tf.train.RMSPropOptimizer(0.001, 0.9).minimize(cost)\n",
@@ -66,9 +68,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Launch the graph in a session\n",
@@ -105,16 +105,16 @@
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.13"
+   "pygments_lexer": "ipython3",
+   "version": "3.6.1"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }