本文整理汇总了Python中net.Net方法的典型用法代码示例。如果您正苦于以下问题：Python net.Net方法的具体用法？Python net.Net怎么用？Python net.Net使用的例子？那么, 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net的用法示例。
在下文中一共展示了net.Net方法的15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: evaluate
# 需要导入模块: import net [as 别名]
# 或者: from net import Net [as 别名]
def evaluate(args):
if args.cuda:
ctx = mx.gpu(0)
else:
ctx = mx.cpu(0)
# images
content_image = utils.tensor_load_rgbimage(args.content_image,ctx, size=args.content_size, keep_asp=True)
style_image = utils.tensor_load_rgbimage(args.style_image, ctx, size=args.style_size)
style_image = utils.preprocess_batch(style_image)
# model
style_model = net.Net(ngf=args.ngf)
style_model.load_parameters(args.model, ctx=ctx)
# forward
style_model.set_target(style_image)
output = style_model(content_image)
utils.tensor_save_bgrimage(output[0], args.output_image, args.cuda) 示例2: __init__
# 需要导入模块: import net [as 别名]
# 或者: from net import Net [as 别名]
def __init__(self):
super(Predictor, self).__init__()
num_units = 512
num_layer = 2
batch_size = 1
data_dir = 'data/'
input_file = 'poetry.txt'
vocab_file = 'vocab.pkl'
tensor_file = 'tensor.npy'
self.data = Data(data_dir, input_file, vocab_file, tensor_file,
is_train=False, batch_size=batch_size)
self.model = Net(self.data, num_units, num_layer, batch_size)
self.sess = tf.Session()
saver = tf.train.Saver(tf.global_variables())
saver.restore(self.sess, 'model/model')
print('Load model done.' + '\n') 示例3: start
# 需要导入模块: import net [as 别名]
# 或者: from net import Net [as 别名]
def start():
global training_data, n, t
training_data = load_data()
print 'Data loaded...'
layers = []
layers.append({'type': 'input', 'out_sx': 1, 'out_sy': 1, 'out_depth': 255})
layers.append({'type': 'fc', 'num_neurons': 100, 'activation': 'sigmoid'})
layers.append({'type': 'softmax', 'num_classes': 255})
print 'Layers made...'
n = Net(layers)
print 'Net made...'
print n
t = Trainer(n, {'method': 'adadelta', 'batch_size': 10, 'l2_decay': 0.0001});
print 'Trainer made...' 示例4: start
# 需要导入模块: import net [as 别名]
# 或者: from net import Net [as 别名]
def start():
global network, sgd
layers = []
layers.append({'type': 'input', 'out_sx': 1, 'out_sy': 1, 'out_depth': 7})
#layers.append({'type': 'fc', 'num_neurons': 30, 'activation': 'relu'})
#layers.append({'type': 'fc', 'num_neurons': 30, 'activation': 'relu'})
layers.append({'type': 'softmax', 'num_classes': 2}) #svm works too
print 'Layers made...'
network = Net(layers)
print 'Net made...'
print network
sgd = Trainer(network, {'momentum': 0.2, 'l2_decay': 0.001})
print 'Trainer made...'
print sgd 示例5: start
# 需要导入模块: import net [as 别名]
# 或者: from net import Net [as 别名]
def start():
global network, t
layers = []
layers.append({'type': 'input', 'out_sx': 30, 'out_sy': 30, 'out_depth': 1})
layers.append({'type': 'fc', 'num_neurons': 100, 'activation': 'sigmoid'})
layers.append({'type': 'softmax', 'num_classes': 7})
print 'Layers made...'
network = Net(layers)
print 'Net made...'
print network
t = Trainer(network, {'method': 'adadelta', 'batch_size': 20, 'l2_decay': 0.001})
print 'Trainer made...'
print t 示例6: start
# 需要导入模块: import net [as 别名]
# 或者: from net import Net [as 别名]
def start():
global training_data, testing_data, network, t, N, labels
data = load_data()
shuffle(data)
size = int(len(data) * 0.01)
training_data, testing_data = data[size:], data[:size]
print 'Data loaded...'
layers = []
layers.append({'type': 'input', 'out_sx': 1, 'out_sy': 1, 'out_depth': N})
layers.append({'type': 'fc', 'num_neurons': 10, 'activation': 'sigmoid'})
layers.append({'type': 'softmax', 'num_classes': len(labels)})
print 'Layers made...'
network = Net(layers)
print 'Net made...'
print network
t = Trainer(network, {'method': 'adadelta', 'batch_size': 10, 'l2_decay': 0.0001}); 示例7: start
# 需要导入模块: import net [as 别名]
# 或者: from net import Net [as 别名]
def start():
global training_data, testing_data, n, t
training_data = load_data()
testing_data = load_data(False)
print 'Data loaded...'
layers = []
layers.append({'type': 'input', 'out_sx': 28, 'out_sy': 28, 'out_depth': 1})
layers.append({
'type': 'capsule', 'num_neurons': 30,
'num_recog': 3, 'num_gen': 4, 'num_pose': 2,
'dx': 1, 'dy': 0
})
layers.append({'type': 'regression', 'num_neurons': 28 * 28})
print 'Layers made...'
n = Net(layers)
print 'Net made...'
print n
t = Trainer(n, {'method': 'sgd', 'batch_size': 20, 'l2_decay': 0.001})
print 'Trainer made...' 示例8: start
# 需要导入模块: import net [as 别名]
# 或者: from net import Net [as 别名]
def start():
global training_data, network, t, N
training_data = load_data()
print 'Data loaded...'
layers = []
layers.append({'type': 'input', 'out_sx': 1, 'out_sy': 1, 'out_depth': N})
layers.append({'type': 'fc', 'num_neurons': 50, 'activation': 'sigmoid'})
layers.append({'type': 'fc', 'num_neurons': 10, 'activation': 'sigmoid'})
layers.append({'type': 'fc', 'num_neurons': 50, 'activation': 'sigmoid'})
layers.append({'type': 'regression', 'num_neurons': N})
print 'Layers made...'
network = Net(layers)
print 'Net made...'
print network
t = Trainer(network, {'method': 'adadelta', 'batch_size': 4, 'l2_decay': 0.0001}); 示例9: start
# 需要导入模块: import net [as 别名]
# 或者: from net import Net [as 别名]
def start():
global training_data, testing_data, n, t
training_data = load_data()
testing_data = load_data(False)
print 'Data loaded...'
layers = []
layers.append({'type': 'input', 'out_sx': 28, 'out_sy': 28, 'out_depth': 1})
layers.append({'type': 'fc', 'num_neurons': 50, 'activation': 'tanh'})
layers.append({'type': 'fc', 'num_neurons': 50, 'activation': 'tanh'})
layers.append({'type': 'fc', 'num_neurons': 2, 'activation': 'tanh'})
layers.append({'type': 'fc', 'num_neurons': 50, 'activation': 'tanh'})
layers.append({'type': 'fc', 'num_neurons': 50, 'activation': 'tanh'})
layers.append({'type': 'regression', 'num_neurons': 28 * 28})
print 'Layers made...'
n = Net(layers)
print 'Net made...'
print n
t = Trainer(n, {'method': 'adadelta', 'learning_rate': 1.0, 'batch_size': 50, 'l2_decay': 0.001, 'l1_decay': 0.001});
print 'Trainer made...' 示例10: start
# 需要导入模块: import net [as 别名]
# 或者: from net import Net [as 别名]
def start():
global training_data, testing_data, network, t, N
all_data = load_data()
shuffle(all_data)
size = int(len(all_data) * 0.1)
training_data, testing_data = all_data[size:], all_data[:size]
print 'Data loaded, size: {}...'.format(len(all_data))
layers = []
layers.append({'type': 'input', 'out_sx': 1, 'out_sy': 1, 'out_depth': N})
layers.append({'type': 'fc', 'num_neurons': 50, 'activation': 'sigmoid'})
layers.append({'type': 'fc', 'num_neurons': 10, 'activation': 'sigmoid'})
layers.append({'type': 'fc', 'num_neurons': 50, 'activation': 'sigmoid'})
layers.append({'type': 'softmax', 'num_classes': N})
print 'Layers made...'
network = Net(layers)
print 'Net made...'
print network
t = Trainer(network, {'method': 'adadelta', 'batch_size': 10, 'l2_decay': 0.0001}); 示例11: train2
# 需要导入模块: import net [as 别名]
# 或者: from net import Net [as 别名]
def train2():
global training_data2, n2, t2
layers = []
layers.append({'type': 'input', 'out_sx': 28, 'out_sy': 28, 'out_depth': 1})
layers.append({'type': 'fc', 'num_neurons': 100, 'activation': 'sigmoid'})
layers.append({'type': 'softmax', 'num_classes': 10})
print 'Layers made...'
n2 = Net(layers)
print 'Net made...'
print n2
t2 = Trainer(n2, {'method': 'adadelta', 'batch_size': 20, 'l2_decay': 0.001});
print 'Trainer made...'
print 'In training of smaller net...'
print 'k', 'time\t\t ', 'loss\t ', 'training accuracy'
print '----------------------------------------------------'
try:
for x, y in training_data2:
stats = t2.train(x, y)
print stats['k'], stats['time'], stats['loss'], stats['accuracy']
except: #hit control-c or other
return 示例12: start
# 需要导入模块: import net [as 别名]
# 或者: from net import Net [as 别名]
def start():
global training_data, testing_data, n, t
training_data = load_data()
testing_data = load_data(False)
print 'Data loaded...'
layers = []
layers.append({'type': 'input', 'out_sx': 1, 'out_sy': 1, 'out_depth': 10})
layers.append({'type': 'fc', 'num_neurons': 100, 'activation': 'sigmoid'})
layers.append({'type': 'regression', 'num_neurons': 28 * 28})
print 'Layers made...'
n = Net(layers)
print 'Net made...'
print n
t = Trainer(n, {'method': 'sgd', 'batch_size': 20, 'l2_decay': 0.001});
print 'Trainer made...'
print t 示例13: __init__
# 需要导入模块: import net [as 别名]
# 或者: from net import Net [as 别名]
def __init__(self, worker_id, env, global_ep, args):
self.name = 'worker_' + str(worker_id)
self.env = env
self.global_ep = global_ep
self.args = args
self.learning_rate = 1e-4
self.gamma = 0.99
self.trainer = tf.train.AdamOptimizer(self.learning_rate)
# create local copy of AC network
self.local_net = Net(self.env.state_dim,
self.env.action_dim,
scope=self.name,
trainer=self.trainer)
self.update_local_op = self._update_local_params() 示例14: evaluate
# 需要导入模块: import net [as 别名]
# 或者: from net import Net [as 别名]
def evaluate(args):
if args.cuda:
ctx = mx.gpu(0)
else:
ctx = mx.cpu(0)
# images
content_image = utils.tensor_load_rgbimage(args.content_image,ctx, size=args.content_size, keep_asp=True)
style_image = utils.tensor_load_rgbimage(args.style_image, ctx, size=args.style_size)
style_image = utils.preprocess_batch(style_image)
# model
style_model = net.Net(ngf=args.ngf)
style_model.load_params(args.model, ctx=ctx)
# forward
style_model.set_target(style_image)
output = style_model(content_image)
utils.tensor_save_bgrimage(output[0], args.output_image, args.cuda) 示例15: evaluate
# 需要导入模块: import net [as 别名]
# 或者: from net import Net [as 别名]
def evaluate(args):
if args.cuda:
ctx = mx.gpu(0)
else:
ctx = mx.cpu(0)
# images
content_image = utils.tensor_load_rgbimage(args.content_image,ctx, size=args.content_size, keep_asp=True)
style_image = utils.tensor_load_rgbimage(args.style_image, ctx, size=args.style_size)
style_image = utils.preprocess_batch(style_image)
# model
style_model = net.Net(ngf=args.ngf)
style_model.load_params(args.model, ctx=ctx)
# forward
style_model.setTarget(style_image)
output = style_model(content_image)
utils.tensor_save_bgrimage(output[0], args.output_image, args.cuda)