update_output = True
subplots = False
if update_output:
#First, we run our configurations
import mnist_loader
training_data, validation_data, test_data = mnist_loader.load_data_wrapper(
)
training_data = training_data[:1000]
import network_dev2
f = open('{0}_output.txt'.format(comparison_file), 'w').close()
config_count = 0
net = network_dev2.Network([784, 30, 10], output_filename=comparison_file)
net.SGD(
training_data,
epochs, #epochs
10, #m
3.0, #eta
5, #test_accuracy_check_interval
2, #eta_decrease_factor
.9, #u
training_data_subsections=training_data_subsections,
test_data=test_data,
config_num=config_count,
run_count=run_count)
config_count += 1
net = network_dev2.Network([784, 30, 10], output_filename=comparison_file)
if update_output:
#First, we run our configurations
import mnist_loader
training_data, validation_data, test_data = mnist_loader.load_data_wrapper()
#If we want to speed up our training or magnify differences for comparisons:
#Note: will lower accuracy and learning speed.
training_data = training_data[:1000]
import network_dev2
f = open('{0}_output.txt'.format(comparison_file), 'w').close()
config_count = 0
net = network_dev2.Network([784, 30, 10], output_filename=comparison_file, softmax=False, cost=network_dev2.quadratic_cost, weight_init=network_dev2.large_weight_initializer)
net.SGD(training_data,
epochs, #epochs
10,#m
0.5,#eta
5,#test_accuracy_check_interval
2,#eta_decrease_factor
0,#u
0,#lmbda / regularization rate
training_data_subsections=training_data_subsections,
validation_data=validation_data,
test_data=test_data,
early_stopping=early_stopping,
output_training_cost=output_training_cost,
output_training_accuracy=output_training_accuracy,
output_validation_cost=output_validation_cost,
#First, we run our configurations
import mnist_loader
training_data, validation_data, test_data = mnist_loader.load_data_wrapper(
)
#If we want to speed up our training or magnify differences for comparisons
#Note: will lower accuracy and learning speed.
training_data = training_data[:1000]
import network_dev2
f = open('{0}_output.txt'.format(comparison_file), 'w').close()
config_count = 0
net = network_dev2.Network(
[784, 30, 10],
output_filename=comparison_file,
softmax=False,
cost=network_dev2.quadratic_cost,
weight_init=network_dev2.large_weight_initializer)
net.SGD(
training_data,
epochs, #epochs
10, #m
0.5, #eta
5, #test_accuracy_check_interval
2, #eta_decrease_factor
0, #u
0, #lmbda / regularization rate
training_data_subsections=training_data_subsections,
validation_data=validation_data,
test_data=test_data,
early_stopping=early_stopping,