本文整理汇总了Python中nets.nasnet.nasnet_utils.calc_reduction_layers方法的典型用法代码示例。如果您正苦于以下问题：Python nasnet_utils.calc_reduction_layers方法的具体用法？Python nasnet_utils.calc_reduction_layers怎么用？Python nasnet_utils.calc_reduction_layers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nets.nasnet.nasnet_utils的用法示例。
在下文中一共展示了nasnet_utils.calc_reduction_layers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: testCalcReductionLayers
# 需要导入模块: from nets.nasnet import nasnet_utils [as 别名]
# 或者: from nets.nasnet.nasnet_utils import calc_reduction_layers [as 别名]
def testCalcReductionLayers(self):
num_cells = 18
num_reduction_layers = 2
reduction_layers = nasnet_utils.calc_reduction_layers(
num_cells, num_reduction_layers)
self.assertEqual(len(reduction_layers), 2)
self.assertEqual(reduction_layers[0], 6)
self.assertEqual(reduction_layers[1], 12) 示例2: _build_pnasnet_base
# 需要导入模块: from nets.nasnet import nasnet_utils [as 别名]
# 或者: from nets.nasnet.nasnet_utils import calc_reduction_layers [as 别名]
def _build_pnasnet_base(
hidden_previous, hidden, normal_cell, hparams, true_cell_num,
start_cell_num):
"""Constructs a PNASNet image model for proposal classifier features."""
# Find where to place the reduction cells or stride normal cells
reduction_indices = nasnet_utils.calc_reduction_layers(
hparams.num_cells, hparams.num_reduction_layers)
filter_scaling = _filter_scaling(reduction_indices, start_cell_num)
# Note: The None is prepended to match the behavior of _imagenet_stem()
cell_outputs = [None, hidden_previous, hidden]
net = hidden
# Run the cells
for cell_num in range(start_cell_num, hparams.num_cells):
is_reduction = cell_num in reduction_indices
stride = 2 if is_reduction else 1
if is_reduction: filter_scaling *= hparams.filter_scaling_rate
prev_layer = cell_outputs[-2]
net = normal_cell(
net,
scope='cell_{}'.format(cell_num),
filter_scaling=filter_scaling,
stride=stride,
prev_layer=prev_layer,
cell_num=true_cell_num)
true_cell_num += 1
cell_outputs.append(net)
# Final nonlinearity.
# Note that we have dropped the final pooling, dropout and softmax layers
# from the default pnasnet version.
with tf.variable_scope('final_layer'):
net = tf.nn.relu(net)
return net
# TODO(shlens): Only fixed_shape_resizer is currently supported for PNASNet
# featurization. The reason for this is that pnasnet.py only supports
# inputs with fully known shapes. We need to update pnasnet.py to handle
# shapes not known at compile time.