results.append(epoch_and_MSE[0]) #print "\n\nNet After Training\n", network # save the network network.save_to_file( "trained_configuration.pkl" ) # load a stored network # network = NeuralNet.load_from_file( "trained_configuration.pkl" ) dfs_concatenated = intermediate_post_process_weights(seed_value, data_collector, dfs_concatenated) # print out the result for example_number, example in enumerate(training_set): inputs_for_training_example = example.features network.inputs_for_training_example = inputs_for_training_example output_from_network = network.calc_networks_output() print "\tnetworks input:", example.features, "\tnetworks output:", output_from_network, "\ttarget:", example.targets print results print print np.median(results) print print dfs_concatenated print end_angle_values = dfs_concatenated["end"]["hyperplane_angle"] treatment_values = dfs_concatenated["treatment"]["hyperplane_angle"] list_of_dfs = [treatment_values] + [ (dfs_concatenated[epochs]["hyperplane_angle"]) for epochs in [0] ] + [end_angle_values] selected_df = pd.concat( list_of_dfs, axis=1 ) print selected_df
experimental_weight_setting_function(network) data_collection_interval = 1000 data_collector = NetworkDataCollector(network, data_collection_interval) # start training on test set one epoch_and_MSE = network.backpropagation(training_set, 0.01, 3000, data_collector) results.append(epoch_and_MSE[0]) # save the network network.save_to_file("trained_configuration.pkl") # load a stored network # network = NeuralNet.load_from_file( "trained_configuration.pkl" ) print "\n\nNetwork State after backpropagation\n", network, "\n" # print out the result # print out the result for example_number, example in enumerate(training_set): inputs_for_training_example = example.features network.inputs_for_training_example = inputs_for_training_example output_from_network = network.calc_networks_output() print "\tnetworks input:", example.features, "\tnetworks output:", output_from_network, "\ttarget:", example.targets print results print print np.median(results) print