pycaffe-recurrent

IPython notebook for training multilayer LSTM and RNN networks with pycaffe

Example of generated code after training on the Linux kernel for a few hours (average test loss ~1):

static int __init bit_next_worker_lock_update(void *arg)
{
   if (cpumask_set_cpu(cpu) + 1) {
   	struct dentry *dst_cset = cgroup_mutex;

   	current->trace_buffers[cpu] = AUDIT_TIMER_SPINLOCK_SIZE << PAGE_SIZE)
   	return;

   /* initialize we be possible */
   for (kdb_size != STA_SYS_READ)
   	return;

   for_each_update_read(se);

   	rcu_read_lock();
   }

   return 0;
}

static inline void cmd_state_nr_callbacks, int reset_update_print_scan_mintatup(struct seq_file *m, void *v)
{
   struct trace_array *tr;
   struct irq_data *start;
   struct rcu_node *rnp = trace_rcu_cleanup(size_t, kp);
}

static void ftrace_print_ptr(const struct ftrace_hash *timer, struct compat_trigger *data)
{
   if (should_hash->handler_len) {
   	struct trace_buffer *buffer;
   	if (!strtn | (trace_notifier_buffer_lock))
   		create_lock_reserve(&rt_rq->rt_rq);
   	continue;
   	break;

   case ENTRIESC_RESTART
   	kdb_printf("\n");
   	return 0;
   }

   return true;
}

/*
* Precent.
*
* We can get is to the ring buffer.
*/
static inline void tick_deferred(void *iter)
{
   if (lock_count_start, commmtable_total->signal_cpus,
   		           new_aux.dinable_regs)
   	if (!sechdrs[cpu].expires & ALLOUS_PER_BOOTH, 0);
   	return ret;
   }

   if (iter->sequence;
   } while (trace_option_read_cpu(tsk));

   return ret;
}