def run_experiments():
 shallow()
 basic_conv()
 omit_FC()
 dbl_conv(activation_fn=sigmoid)
 dbl_conv_relu()
 expanded_data(n=100)
 expanded_data(n=300)
 expanded_data(n=1000)
 expanded_data_double_fc(n=100)
 expanded_data_double_fc(n=300)
 expanded_data_double_fc(n=1000)
 nets = double_fc_dropout(0.5, 0.5, 0.5, 5)
 (error_locations, erroneous_predictions) = ensemble(nets)
 plt = plot_errors(error_locations, erroneous_predictions)
 plt.savefig('ensemble_errors.png')
 plt = plot_filters(nets[0], 0, 5, 4)
 plt.savefig('net_full_layer_0.png')
 plt = plot_filters(nets[0], 1, 8, 5)
 plt.savefig('net_full_layer_1.png')

def run_experiments ( ) : shallow ( ) basic_conv ( ) omit_FC ( ) dbl_conv ( activation_fn = sigmoid ) dbl_conv_relu ( ) expanded_data ( n = 100 ) expanded_data ( n = 300 ) expanded_data ( n = 1000 ) expanded_data_double_fc ( n = 100 ) expanded_data_double_fc ( n = 300 ) expanded_data_double_fc ( n = 1000 ) nets = double_fc_dropout ( 0.5 , 0.5 , 0.5 , 5 ) ( error_locations , erroneous_predictions ) = ensemble ( nets ) plt = plot_errors ( error_locations , erroneous_predictions ) plt . savefig ( 'ensemble_errors.png' ) plt = plot_filters ( nets [ 0 ] , 0 , 5 , 4 ) plt . savefig ( 'net_full_layer_0.png' ) plt = plot_filters ( nets [ 0 ] , 1 , 8 , 5 ) plt . savefig ( 'net_full_layer_1.png' )
run the experiments described in the book .