def run( file ) : ssd = ram.Ram( ) cmd = cpu.Cpu( 0, ssd, verbose ) ssd.loader( file ) #testRam( ssd ) #testCpu( ssd ) cmd.run( )
def deitel_version( ) :
print ' *** Welcome to Simpletron! ***\n\
*** Please enter your program one instruction ***\n\
*** (or data word) at a time into the input ***\n\
*** text field. I will display the location ***\n\
*** number and a question mark (?). You then ***\n\
*** type the word for that location. Enter the ***\n\
*** hash twice to stop entering your program. ***'
ssd = ram.Ram( )
addr = 0
while True :
unknown = raw_input( str( addr ) + "_?_" )
if exit( unknown ) :
print "Stopping input. Running Simpletron ..."
break
elif invalid( unknown ) :
print "\tinvalid, use {-9999 - 9999}"
continue
val = int( unknown )
ssd.setAt( addr, val )
addr += 1
cmd = cpu.Cpu( 0, ssd, not verbose )
cmd.run( )
def inicializar_rams(self):
numero_rams = int(
round(self.tamanho_entrada / self.numero_entradas_rams))
for i in range(0, numero_rams):
self.rams.append(ram.Ram(self.numero_entradas_rams))
def __init__(
self, tam
): #Construtor da classe SO, parametro de entrada é um objeto da classe Ram
self.ram = ram.Ram()
self.cpu = cpu.Cpu(self.ram, tam, self.ram.palavras)
import processor
import ram
import storage
import display
import keyboard
import mouse
import computer
processor = processor.Processor('3.4 GHz', 2)
ram = ram.Ram(500)
storage = storage.Storage(5000)
display = display.Display('LG', '1920x1080')
keyboard = keyboard.Keyboard('Samsung')
mouse = mouse.Mouse('Apple')
class ComputerBuilder:
def build(self):
return computer.Computer(processor, ram, storage, display, keyboard,
mouse)
# # Name each loss as '(raw)' and name the moving average version of the loss
# # as the original loss name.
# tf.scalar_summary(loss_name +' (raw)', l)
# tf.scalar_summary(loss_name, loss_averages.average(l))
#with tf.control_dependencies([loss_averages_op]):
# total_loss = tf.identity(total_loss)
#return total_loss, grads
debug = labels
return loss, grads, debug
def evaluate(self):
feed_dict = self.reader.next_train()
x = feed_dict['images']
labels = feed_dict['labels']
# Build inference Graph.
loc_mean_t, loc_t, h_t, prob, pred, debug = self.model.inference(x)
correct = tf.nn.in_top_k(prob, labels, 1)
return tf.reduce_sum(tf.cast(correct, tf.int32))
reader = reader.Reader('./utils/reader/read_img/translated_mnist_output/',
[40, 40, 3],
batch_size=16)
model = ram.Ram()
trainer = Trainer(model, reader, gpus=[0], max_steps=500000, truncation=False)
trainer.train()