TrainingExample([-91.31429,-114.3718,-170.2887,-209.5479,51,24,32,10,-178.6222,-81.19243,-101.9763,-167.3187,-200.8598,47,23,31,10,-179.9829],0.0),
TrainingExample([-42.08201,-57.71526,-56.0271,-73.74921,30,16,18,4,-144.6631,-41.72422,-53.60718,-74.16974,-80.0265,30,15,18,5,-141.0959],1.0),
TrainingExample([-41.72422,-53.60718,-74.16974,-80.0265,30,15,18,5,-141.0959,-42.08201,-57.71526,-56.0271,-73.74921,30,16,18,4,-144.6631],0.0),
TrainingExample([-27.97045,-42.6892,-62.42385,-72.11889,22,13,13,2,-138.0798,-37.23659,-52.0332,-47.41673,-59.97505,29,13,15,4,-147.1644],1.0),
TrainingExample([-37.23659,-52.0332,-47.41673,-59.97505,29,13,15,4,-147.1644,-27.97045,-42.6892,-62.42385,-72.11889,22,13,13,2,-138.0798],0.0),
TrainingExample([-42.23076,-57.38282,-80.06391,-91.61021,24,15,19,6,-146.6433,-45.00954,-46.97235,-108.6906,-114.4459,18,19,20,3,-133.4822],1.0),
TrainingExample([-45.00954,-46.97235,-108.6906,-114.4459,18,19,20,3,-133.4822,-42.23076,-57.38282,-80.06391,-91.61021,24,15,19,6,-146.6433],0.0)]
# Create a video and image writer
fig, writer = generate_writer()
print "Generating an image of the neural network loaded"
network.load("dnn.10.__AllNodes__.txt")
network.draw()
take_still("neural_network_before.png")
# Generate a video of the neural network learning
print "Generating a video of the neural network learning."
with writer.saving(fig, parameters.video_file_name, 100):
for e, example in enumerate(examples):
print e, example
network.think(example.inputs)
network.draw()
annotate_frame(example)
writer.grab_frame()
print "Success! Open the file " + parameters.video_file_name + " to view the video."
examples = [TrainingExample([0, 0, 1], 0),
TrainingExample([0, 1, 1], 1),
TrainingExample([1, 0, 1], 1),
TrainingExample([0, 1, 0], 1),
TrainingExample([1, 0, 0], 1),
TrainingExample([1, 1, 1], 0),
TrainingExample([0, 0, 0], 0)]
# Create a video and image writer
fig, writer = generate_writer()
# Generate an image of the neural network before training
print "Generating an image of a new neural network"
network.do_not_think()
network.draw()
take_still("results/new_neural_network.png")
# Generate a video of the neural network learning
print "Generating a video of the neural network learning."
print "There will be " + str(len(examples) * len(parameters.show_iterations)) + " frames."
print "This may take a long time. Please wait..."
with writer.saving(fig, parameters.video_file_name, 100):
for i in range(1, parameters.training_iterations + 1):
cumulative_error = 0
for e, example in enumerate(examples):
cumulative_error += network.train(example)
if i in parameters.show_iterations:
network.draw()
annotate_frame(i, e, average_error, example)
writer.grab_frame()
average_error = calculate_average_error(cumulative_error, len(examples))
TrainingExample([0, 1, 1], 1),
TrainingExample([1, 0, 1], 1),
TrainingExample([0, 1, 0], 1),
TrainingExample([1, 0, 0], 1),
TrainingExample([1, 1, 1], 0),
TrainingExample([0, 0, 0], 0)
]
# Create a video and image writer
fig, writer = generate_writer()
# Generate an image of the neural network before training
print "Generating an image of the neural network before"
network.do_not_think()
network.draw()
take_still("neural_network_before.png")
# Generate a video of the neural network learning
print "Generating a video of the neural network learning."
print "There will be " + str(
len(examples) * len(parameters.show_iterations)) + " frames."
print "This may take a long time. Please wait..."
with writer.saving(fig, parameters.video_file_name, 100):
for i in range(1, parameters.training_iterations + 1):
cumulative_error = 0
for e, example in enumerate(examples):
cumulative_error += network.train(example)
if i in parameters.show_iterations:
network.draw()
annotate_frame(i, e, average_error, example)
writer.grab_frame()
examples = [TrainingExample([0, 0, 1], 0),
TrainingExample([0, 1, 1], 1),
TrainingExample([1, 0, 1], 1),
TrainingExample([0, 1, 0], 1),
TrainingExample([1, 0, 0], 1),
TrainingExample([1, 1, 1], 0),
TrainingExample([0, 0, 0], 0)]
# Create a video and image writer
fig, writer = generate_writer()
# Generate an image of the neural network before training
print "Generating an image of the neural network before"
network.do_not_think()
network.draw()
take_still("neural_network_before.png")
# Generate a video of the neural network learning
print "Generating a video of the neural network learning."
print "There will be " + str(len(examples) * len(parameters.show_iterations)) + " frames."
print "This may take a long time. Please wait..."
with writer.saving(fig, parameters.video_file_name, 100):
for i in range(1, parameters.training_iterations + 1):
cumulative_error = 0
for e, example in enumerate(examples):
cumulative_error += network.train(example)
if i in parameters.show_iterations:
network.draw()
annotate_frame(i, e, average_error, example)
writer.grab_frame()
average_error = calculate_average_error(cumulative_error, len(examples))