本文整理汇总了Python中cntk.Trainer.restore_from_checkpoint方法的典型用法代码示例。如果您正苦于以下问题：Python Trainer.restore_from_checkpoint方法的具体用法？Python Trainer.restore_from_checkpoint怎么用？Python Trainer.restore_from_checkpoint使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类cntk.Trainer的用法示例。
在下文中一共展示了Trainer.restore_from_checkpoint方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: entrenar
# 需要导入模块: from cntk import Trainer [as 别名]
# 或者: from cntk.Trainer import restore_from_checkpoint [as 别名]
def entrenar(checkpoint, entrRuedas, entrOperaciones, input_dim, num_output_classes, testRuedas, testOperaciones):
minibatch_size = 100;
epocs=900;
minibatchIteraciones = int(len(entrOperaciones) / minibatch_size);
# Input variables denoting the features and label data
feature = input((input_dim), np.float32)
label = input((num_output_classes), np.float32)
netout = crearRed(input_dim, num_output_classes, feature);
ce = cross_entropy_with_softmax(netout, label)
pe = classification_error(netout, label)
lr_per_minibatch=learning_rate_schedule(0.25, UnitType.minibatch)
# Instantiate the trainer object to drive the model training
learner = sgd(netout.parameters, lr=lr_per_minibatch)
progress_printer = ProgressPrinter(log_to_file=checkpoint+".log", num_epochs=epocs);
trainer = Trainer(netout, (ce, pe), learner, progress_printer)
if os.path.isfile(checkpoint):
trainer.restore_from_checkpoint(checkpoint);
npentrRuedas = np.array(entrRuedas).astype(np.float32);
npentrOperaciones = np.array(entrOperaciones).astype(np.float32);
#iteramos una vez por cada "epoc"
for i in range(0, epocs):
p = np.random.permutation(len(entrRuedas));
npentrOperaciones = npentrOperaciones[p];
npentrRuedas = npentrRuedas[p];
#ahora partimos los datos en "minibatches" y entrenamos
for j in range(0, minibatchIteraciones):
features = npentrRuedas[j*minibatch_size:(j+1)*minibatch_size];
labels = npentrOperaciones[j*minibatch_size:(j+1)*minibatch_size];
trainer.train_minibatch({feature: features, label: labels});
trainer.summarize_training_progress()
trainer.save_checkpoint(checkpoint);
minibatchIteraciones = int(len(testOperaciones) / minibatch_size);
avg_error = 0;
for j in range(0, minibatchIteraciones):
test_features = np.array(testRuedas[j*minibatch_size:(j+1)*minibatch_size]).astype(np.float32);
test_labels = np.array(testOperaciones[j*minibatch_size:(j+1)*minibatch_size]).astype(np.float32);
#test_features = np.array( entrRuedas[0:minibatch_size]).astype(np.float32);
#test_labels = np.array(entrOperaciones[0:minibatch_size]).astype(np.float32);
avg_error = avg_error + ( trainer.test_minibatch(
{feature: test_features, label: test_labels}) / minibatchIteraciones)
return avg_error示例2: cargarRedDesdeArchivo
# 需要导入模块: from cntk import Trainer [as 别名]
# 或者: from cntk.Trainer import restore_from_checkpoint [as 别名]
def cargarRedDesdeArchivo(archivo):
input_dim = 800;
num_output_classes = 3;
feature = input((input_dim), np.float32);
label = input((num_output_classes), np.float32)
netout = crearRed(input_dim, 3, feature);
ce = cross_entropy_with_softmax(netout, label)
pe = classification_error(netout, label)
lr_per_minibatch=learning_rate_schedule(0.5, UnitType.minibatch)
# Instantiate the trainer object to drive the model training
learner = sgd(netout.parameters, lr=lr_per_minibatch)
progress_printer = ProgressPrinter(1)
trainer = Trainer(netout, (ce, pe), learner, progress_printer)
trainer.restore_from_checkpoint(archivo);
return netout;