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adds versioning to the model weight files #12

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adds versioning to the model weight files #12

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3d0390a
adds versioning to the model weight files
Dec 3, 2017
3b798e7
fix for eval app
Dec 3, 2017
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26 changes: 22 additions & 4 deletions riverraid.py
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Original file line number Diff line number Diff line change
Expand Up @@ -96,6 +96,23 @@ def executeKActions(action, prevObservation):
break
return recentKObservations, rewardTotal, done

def getNextModelName():
max = 0
for filename in os.listdir('.'):
if filename.startswith('model') and filename.endswith('.h5'):
i = filename.split('.')[0].split('model')[1]
if max < i:
max = i
return "model{}.h5".format(max+1)

def getMostRecentModel():
max = 0
for filename in os.listdir('.'):
if filename.startswith('model') and filename.endswith('.h5'):
i = filename.split('.')[0].split('model')[1]
if max < i:
max = i
return "model{}.h5".format(max)


if __name__ == '__main__':
Expand All @@ -104,9 +121,10 @@ def executeKActions(action, prevObservation):
Q = initNet()
Q.summary()
#plot_model(Q, to_file='model.png')
if os.path.exists("model.h5"):
recentModel = getMostRecentModel()
if os.path.exists(recentModel):
print "load weights from previous run"
Q.load_weights("model.h5")
Q.load_weights(recentModel)
else :
exit
# TODO: figure out if cnn creation is deterministic
Expand Down Expand Up @@ -219,7 +237,7 @@ def executeKActions(action, prevObservation):
if c == UPDATE_FREQUENCY:
weights = Q.get_weights()
QHat.set_weights(weights)
QHat.save_weights("model.h5")
QHat.save_weights(getNextModelName())
c = 0
print "target NN update={}".format(num_target_updates)
else:
Expand All @@ -230,4 +248,4 @@ def executeKActions(action, prevObservation):


print "average reward={}".format(average/NUM_EPISODES)
#QHat.save_weights("model.h5")
#QHat.save_weights("model0.h5")
17 changes: 13 additions & 4 deletions riverraid_eval.py
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Original file line number Diff line number Diff line change
Expand Up @@ -93,17 +93,26 @@ def executeKActions(action, prevObservation):
break
return recentKObservations, rewardTotal, done


def getMostRecentModel():
max = 0
for filename in os.listdir('.'):
if filename.startswith('model') and filename.endswith('.h5'):
i = filename.split('.')[0].split('model')[1]
if max < i:
max = i
return "model{}.h5".format(max)

if __name__ == '__main__':
env = gym.make('Riverraid-v0')
memory = deque([], REPLAY_MEMORY_SIZE)
Q = initNet()
Q.summary()
#plot_model(Q, to_file='model.png')
if os.path.exists("model.h5"):

recentModel = getMostRecentModel()
if os.path.exists(recentModel):
print "load weights from previous run"
Q.load_weights("model.h5")
Q.load_weights(recentModel)
else :
exit
# TODO: figure out if cnn creation is deterministic
Expand Down Expand Up @@ -153,4 +162,4 @@ def executeKActions(action, prevObservation):
print "average reward={}".format(numpy.mean(episode_rewards))
print "std dev reward={}".format(numpy.std(episode_rewards))
print "median reward={}".format(numpy.median(episode_rewards))
#QHat.save_weights("model.h5")
#QHat.save_weights("model0.h5")