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fix version bugs #27

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fix version bugs #27

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960ab04
fix version bugs
MrStronger Jun 25, 2018
79c4f28
Merge branch 'master' into master
MrStronger Aug 31, 2018
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12 changes: 12 additions & 0 deletions README.md
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# 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

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2 changes: 1 addition & 1 deletion model.py
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Expand Up @@ -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
8 changes: 4 additions & 4 deletions train.py
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Expand Up @@ -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
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