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: