ada/nn420-private-pine64backup
Archived
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

Fix this indention

Browse source
This commit is contained in:
Alexander Huddleston 2017-12-07 22:08:36 -06:00
parent fef852dbc7
commit ae19d80d7d
1 changed files with 47 additions and 43 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

42
neural_network.py
View file

@ -81,7 +81,7 @@ def format_man_dist(elem):
target = []
for i in range(29):
filename = join('data', str(i) + '.bin')
filename = join('/pub/faculty_share/daugher/datafiles/data/' + str(i) + 'states.bin')
# Debugging to print the current file from which states are being parsed.
#print(i)
@ -91,7 +91,7 @@ for i in range(29):
data = f.read(8)
counter = 0
while(data and counter < 1000):
while(data and counter < 2000):
temp.append(format_man_dist(i))
data = f.read(8)
@ -123,7 +123,7 @@ model.compile(optimizer='sgd',
for i in range(29):
filename = join('data', str(i) + '.bin')
filename = join('/pub/faculty_share/daugher/datafiles/data/' + str(i) + 'states.bin')
# Debugging to print the current file from which states are being parsed.
print(i)
@ -133,7 +133,7 @@ for i in range(29):
counter = 0
training = []
while(data and counter < 1000):
while(data and counter < 2000):
bin_data = reduce(format_input, list(data), [])
bin_data.reverse()
bin_data = bin_data[16:]
@ -146,14 +146,19 @@ for i in range(29):
#print(training[0])
# Train the network.
model.fit(np.array(training), np.array(target[i]), epochs=5, batch_size=1000)
model.fit(np.array(training), np.array(target[i]), epochs=8, batch_size=2000)
#model.train_on_batch(np.array(temp), np.array(target))
# Used for testing data
with open('data/18.bin', 'rb') as f:
for i in range(11, 29):
filename = join('/pub/faculty_share/daugher/datafiles/data/', str(i) + '.bin')
for i in range(1000):
print(i)
with open('/pub/faculty_share/daugher/datafiles/data/18states.bin', 'rb') as f:
for i in range(2000):
data = f.read(8)
data = f.read(8)
@ -187,29 +192,28 @@ with open('data/18.bin', 'rb') as f:
counter += 1
data = f.read(8)
#print(testing_target)
# Evaluate accuracy
# Evaluate accuracy
loss_and_metrics = model.evaluate(np.array(testing),np.array(testing_target), batch_size=1000)
loss_and_metrics = model.evaluate(np.array(testing),np.array(testing_target), batch_size=1000)
# Generating predictions:
# Generating predictions:
predictions = model.predict(np.array(testing), batch_size=1000)
predictions = model.predict(np.array(testing), batch_size=1000)
output = []
output = []
for p in range(len(predictions)):
for p in range(len(predictions)):
if np.argmax(testing_target[p]) < 18:
output.append(100*((18 - (28 - np.argmax(predictions[p]))) / (18 - np.argmax(testing_target[p]))))
else:
output.append(0)
#for i in range(len(output)):
# print(output[i])
#for i in range(len(output)):
# print(output[i])
print(np.array(output).mean())
print("Percentage possible improvement: ", np.array(output).mean())
print(loss_and_metrics)
print(model.metrics_names[0], loss_and_metrics[0])
print(model.metrics_names)
print(model.metrics_names[1], loss_and_metrics[1])