本文整理汇总了Python中network.Network.test方法的典型用法代码示例。如果您正苦于以下问题:Python Network.test方法的具体用法?Python Network.test怎么用?Python Network.test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network.Network
的用法示例。
在下文中一共展示了Network.test方法的4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: demo
# 需要导入模块: from network import Network [as 别名]
# 或者: from network.Network import test [as 别名]
def demo():
params = iho_len(add_set[0])
first = Network(params[0], params[1], params[2], 5000, 0.08, momentum=0.1)
first.train(add_set)
first.test(add_set)
"""
Used when dealing with entirely binary inputs.
"""
"""
x = [0, 1, 2, 3, 4, 5, 6]
y = [9, 8, 7, 6, 4, 3, 3]
print ''
for i, j in zip(x, y):
first.test([[dec_bin(i) + dec_bin(j), dec_bin(i + j)]])
"""
"""
示例2: test_xor
# 需要导入模块: from network import Network [as 别名]
# 或者: from network.Network import test [as 别名]
def test_xor():
n = Network(2, 4, 1)
train = (
([0, 0], [0]),
([1, 1], [0]),
([0, 1], [1]),
([1, 0], [1]),
)
for i in xrange(20000):
inp, out = train[randint(0, 3)]
n.train(inp, out)
for inp, out in train:
assert round(n.test(inp)[0]) == out[0]
示例3: test_bindec
# 需要导入模块: from network import Network [as 别名]
# 或者: from network.Network import test [as 别名]
def test_bindec():
n = Network(4, 6, 16)
train = {}
for i in xrange(16):
bstr = bin(i)[2:]
bstr = '0' * (4 - len(bstr)) + bstr
inp = map(int, [c for c in bstr])
out = [0] * 16
out[i] = 1
train[i] = (inp, out)
for i in xrange(100000):
n.train(*train[randint(0, 15)])
for k, (inp, out) in train.items():
ret = n.test(inp)
assert ret.index(max(ret)) == k
示例4: main
# 需要导入模块: from network import Network [as 别名]
# 或者: from network.Network import test [as 别名]
def main(_):
model_dir = get_model_dir(conf,
['data_dir', 'sample_dir', 'max_epoch', 'test_step', 'save_step',
'is_train', 'random_seed', 'log_level', 'display'])
preprocess_conf(conf)
DATA_DIR = os.path.join(conf.data_dir, conf.data)
SAMPLE_DIR = os.path.join(conf.sample_dir, conf.data, model_dir)
check_and_create_dir(DATA_DIR)
check_and_create_dir(SAMPLE_DIR)
# 0. prepare datasets
if conf.data == "mnist":
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets(DATA_DIR, one_hot=True)
next_train_batch = lambda x: mnist.train.next_batch(x)[0]
next_test_batch = lambda x: mnist.test.next_batch(x)[0]
height, width, channel = 28, 28, 1
train_step_per_epoch = mnist.train.num_examples / conf.batch_size
test_step_per_epoch = mnist.test.num_examples / conf.batch_size
elif conf.data == "cifar":
from cifar10 import IMAGE_SIZE, inputs
maybe_download_and_extract(DATA_DIR)
images, labels = inputs(eval_data=False,
data_dir=os.path.join(DATA_DIR, 'cifar-10-batches-bin'), batch_size=conf.batch_size)
height, width, channel = IMAGE_SIZE, IMAGE_SIZE, 3
with tf.Session() as sess:
network = Network(sess, conf, height, width, channel)
stat = Statistic(sess, conf.data, model_dir, tf.trainable_variables(), conf.test_step)
stat.load_model()
if conf.is_train:
logger.info("Training starts!")
initial_step = stat.get_t() if stat else 0
iterator = trange(conf.max_epoch, ncols=70, initial=initial_step)
for epoch in iterator:
# 1. train
total_train_costs = []
for idx in xrange(train_step_per_epoch):
images = binarize(next_train_batch(conf.batch_size)) \
.reshape([conf.batch_size, height, width, channel])
cost = network.test(images, with_update=True)
total_train_costs.append(cost)
# 2. test
total_test_costs = []
for idx in xrange(test_step_per_epoch):
images = binarize(next_test_batch(conf.batch_size)) \
.reshape([conf.batch_size, height, width, channel])
cost = network.test(images, with_update=False)
total_test_costs.append(cost)
avg_train_cost, avg_test_cost = np.mean(total_train_costs), np.mean(total_test_costs)
stat.on_step(avg_train_cost, avg_test_cost)
# 3. generate samples
samples = network.generate()
save_images(samples, height, width, 10, 10,
directory=SAMPLE_DIR, prefix="epoch_%s" % epoch)
iterator.set_description("train l: %.3f, test l: %.3f" % (avg_train_cost, avg_test_cost))
print
else:
logger.info("Image generation starts!")
samples = network.generate()
save_images(samples, height, width, 10, 10, directory=SAMPLE_DIR)