fix version bugs

README.md

@@ -1,6 +1,16 @@
 # 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
+
 # How to Use
 Download the [dataset](https://drive.google.com/file/d/0B-KJCaaF7elleG1RbzVPZWV4Tlk/view?usp=sharing) and extract into the repository folder
 

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

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