outputs_fname = sys.argv[8]
num_epochs = int(sys.argv[9])
lr_decay = float(sys.argv[10])
assert model_name in model_names, "model name {0} not in " \
"list of models".format(model_name)
rel_cod_idxs = range(-7, 6)
rel_nt_idxs = range(-21, 18)
print model_name
print rel_cod_idxs
print rel_nt_idxs
name = model_name
neural_net = inter.make_lasagne_feedforward_nn(name,
expt_dir,
gene_seq_fname,
gene_len_fname,
tr_codons_fname,
te_codons_fname,
outputs_fname,
rel_cod_idxs=rel_cod_idxs,
rel_nt_idxs=rel_nt_idxs,
lr_decay=lr_decay,
nonlinearity="tanh",
widths=[200],
update_method="nesterov")
neural_net.run_epochs(num_epochs)
#skip plotting
print rel_cod_idxs
print rel_nt_idxs
if model_name == "n17n15_cod_n5p4_nt_n15p14":
rel_struc_idxs = range(-17, -14)
name = "str_" + model_name + "_rep{0}".format(model_rep)
success = False
while not success:
my_nn = inter.make_lasagne_feedforward_nn(
name,
expt_dir,
gene_seq_fname,
gene_len_fname,
tr_codons_fname,
te_codons_fname,
outputs_fname,
rel_cod_idxs=rel_cod_idxs,
rel_nt_idxs=rel_nt_idxs,
rel_struc_idxs=rel_struc_idxs,
struc_fname=struc_fname,
lr_decay=lr_decay,
nonlinearity="tanh",
widths=[200],
update_method="nesterov")
failed = False
for i in range(num_epochs + 1):
my_nn.run_epochs(i)
if np.isnan(my_nn.test_err_by_epoch[-1]):
failed = True
shutil.rmtree(my_nn.out_dir + "/" + my_nn.name)
break
if not failed: