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;
}