def main(args):
#args_string = str(args)
argp = _argparse().parse_args(args[1:])
algo = argp.algo
# help_f = open('help.md', 'w')
# _argparse().print_help(file=help_f)
# help_f.close()
output_dir = None
if algo==RMS_PROP_ALGO:
classifier = NeuralNetwork(28 * 28,argp.hu,10,argp.activation,argp.dropout_rate);
trainer=rms_prop_trainer(argp.learning_rate,argp.l1,argp.l2,argp.t,argp.batch_size,argp.decay)
trainer.train_NN(classifier)
output_dir=trainer.output_directory
else:
classifier = NeuralNetwork(28 * 28,argp.hu,10,argp.activation,argp.dropout_rate);
trainer=gradient_descent_trainer(argp.learning_rate,argp.l1,argp.l2,argp.t,argp.batch_size)
trainer.train_NN(classifier)
output_dir=trainer.output_directory
#print "output"
#print output_dir
cmd_file_path=os.path.join(output_dir,"command.txt")
f = open(cmd_file_path,'w')
f.write("python ")
for a in args:
f.write(str(a))
f.write(" ")
f.close()
print "THE END"
def main(args):
#args_string = str(args)
argp = _argparse().parse_args(args[1:])
algo = argp.algo
help_f = open('help.md', 'w')
_argparse().print_help(file=help_f)
help_f.close()
output_dir = None
if algo==RMS_PROP_ALGO:
classifier = LogisticRegression(28 * 28,10);
trainer=climin_rmsprop_trainer(argp.batch_size,argp.learning_rate,argp.l1,argp.l2,argp.t)
trainer.train_LR(classifier)
output_dir=trainer.output_directory
elif algo == CLIMIN_GD_ALGO:
classifier = LogisticRegression(28 * 28,10);
trainer=climin_trainer(argp.batch_size,argp.learning_rate,argp.l1,argp.l2,argp.t)
trainer.train_LR(classifier)
output_dir=trainer.output_directory
else:
classifier = LogisticRegression(28 * 28,10);
trainer=gradient_descent_trainer(argp.batch_size,argp.learning_rate,argp.l1,argp.l2,argp.t)
trainer.train_LR(classifier)
output_dir=trainer.output_directory
#print "output"
#print output_dir
cmd_file_path=os.path.join(output_dir,"command.txt")
f = open(cmd_file_path,'w')
f.write("python ")
for a in args:
f.write(str(a))
f.write(" ")
f.close()
print "THE END"
