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)) print "Success! Open the file " + parameters.video_file_name + " to view the video." # Generate an image of the neural network after training print "Generating an image of the neural network after" network.do_not_think() network.draw() take_still("neural_network_after.png") # Consider a new situation new_situation = [1, 1, 0] print "Considering a new situation " + str(new_situation) + "?" print network.think(new_situation)
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."
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)) print "Success! Open the file " + parameters.video_file_name + " to view the video." # Generate an image of the neural network after training print "Generating an image of a trained neural network" network.do_not_think() network.draw() take_still("results/trained_neural_network.png") # Consider a new situation while True: print "Enter new input sequence" new_input = raw_input() new_input_list = [int(i) for i in new_input]