我與纖薄庫的預訓練inceptionV2同樣的問題。
首先，我用這個arg_scope，我得到了這個問題：
def _batch_norm_arg_scope(list_ops,
use_batch_norm=True,
batch_norm_decay=0.9997,
batch_norm_epsilon=0.001,
batch_norm_scale=False,
train_batch_norm=False):
"""Slim arg scope for InceptionV2 batch norm."""
if use_batch_norm:
batch_norm_params = {
'is_training': train_batch_norm,
'scale': batch_norm_scale,
'decay': batch_norm_decay,
'epsilon': batch_norm_epsilon
}
normalizer_fn = slim.batch_norm
else:
normalizer_fn = None
batch_norm_params = None
return slim.arg_scope(list_ops,
normalizer_fn=normalizer_fn,
normalizer_params=batch_norm_params)
我使用arg_scope在纖薄庫解決。
with slim.arg_scope(inception_v2.inception_v2_arg_scope()):
，僅僅是：
def inception_arg_scope(weight_decay=0.00004,
use_batch_norm=True,
batch_norm_decay=0.9997,
batch_norm_epsilon=0.001,
activation_fn=tf.nn.relu):
"""Defines the default arg scope for inception models.
Args:
weight_decay: The weight decay to use for regularizing the model.
use_batch_norm: "If `True`, batch_norm is applied after each convolution.
batch_norm_decay: Decay for batch norm moving average.
batch_norm_epsilon: Small float added to variance to avoid dividing by zero
in batch norm.
activation_fn: Activation function for conv2d.
Returns:
An `arg_scope` to use for the inception models.
"""
batch_norm_params = {
# Decay for the moving averages.
'decay': batch_norm_decay,
# epsilon to prevent 0s in variance.
'epsilon': batch_norm_epsilon,
# collection containing update_ops.
'updates_collections': tf.GraphKeys.UPDATE_OPS,
# use fused batch norm if possible.
'fused': None,
}
if use_batch_norm:
normalizer_fn = slim.batch_norm
normalizer_params = batch_norm_params
else:
normalizer_fn = None
normalizer_params = {}
# Set weight_decay for weights in Conv and FC layers.
with slim.arg_scope([slim.conv2d, slim.fully_connected],
weights_regularizer=slim.l2_regularizer(weight_decay)):
with slim.arg_scope(
[slim.conv2d],
weights_initializer=slim.variance_scaling_initializer(),
activation_fn=activation_fn,
normalizer_fn=normalizer_fn,
normalizer_params=normalizer_params) as sc:
return sc