diff --git a/README.md b/README.md
index 6974abb..bb4adaf 100644
--- a/README.md
+++ b/README.md
@@ -1,11 +1,23 @@
 # Autopilot-TensorFlow
 A TensorFlow implementation of this [Nvidia paper](https://arxiv.org/pdf/1604.07316.pdf) with some changes.
 
+
+# Installation
+Download the latest version of [python](https://www.python.org/downloads/) and [pip](https://pip.pypa.io/en/stable/installing/)
+
+Install TensorFlow [How](https://www.tensorflow.org/install/)
+
+Use pip to install several packages
+`pip install scipy Pillow opencv-python`
+
+Activate tensorflow and enter the project directory
+
 # IMPORTANT
 Absolutely, under NO circumstance, should one ever pilot a car using computer vision software trained with this code (or any home made software for that matter). It is extremely dangerous to use your own self-driving software in a car, even if you think you know what you're doing, not to mention it is quite illegal in most places and any accidents will land you in huge lawsuits.
 
 This code is purely for research and statistics, absolutley NOT for application or testing of any sort.
 
+
 # How to Use
 Download the [dataset](https://github.com/SullyChen/driving-datasets) and extract into the repository folder
 
diff --git a/model.py b/model.py
index 0bc4576..84984a8 100644
--- a/model.py
+++ b/model.py
@@ -85,4 +85,4 @@ def conv2d(x, W, stride):
 W_fc5 = weight_variable([10, 1])
 b_fc5 = bias_variable([1])
 
-y = tf.mul(tf.atan(tf.matmul(h_fc4_drop, W_fc5) + b_fc5), 2) #scale the atan output
+y = tf.multiply(tf.atan(tf.matmul(h_fc4_drop, W_fc5) + b_fc5), 2) #scale the atan output
diff --git a/train.py b/train.py
index b1fad04..f21fbd0 100644
--- a/train.py
+++ b/train.py
@@ -12,20 +12,20 @@
 
 train_vars = tf.trainable_variables()
 
-loss = tf.reduce_mean(tf.square(tf.sub(model.y_, model.y))) + tf.add_n([tf.nn.l2_loss(v) for v in train_vars]) * L2NormConst
+loss = tf.reduce_mean(tf.square(tf.subtract(model.y_, model.y))) + tf.add_n([tf.nn.l2_loss(v) for v in train_vars]) * L2NormConst
 train_step = tf.train.AdamOptimizer(1e-4).minimize(loss)
 sess.run(tf.initialize_all_variables())
 
 # create a summary to monitor cost tensor
-tf.scalar_summary("loss", loss)
+tf.summary.scalar("loss", loss)
 # merge all summaries into a single op
-merged_summary_op = tf.merge_all_summaries()
+merged_summary_op = tf.summary.merge_all()
 
 saver = tf.train.Saver(write_version = saver_pb2.SaverDef.V1)
 
 # op to write logs to Tensorboard
 logs_path = './logs'
-summary_writer = tf.train.SummaryWriter(logs_path, graph=tf.get_default_graph())
+summary_writer = tf.summary.FileWriter(logs_path, graph=tf.get_default_graph())
 
 epochs = 30
 batch_size = 100