本文整理汇总了Python中tensorflow.contrib.slim.softmax方法的典型用法代码示例。如果您正苦于以下问题:Python slim.softmax方法的具体用法?Python slim.softmax怎么用?Python slim.softmax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tensorflow.contrib.slim
的用法示例。
在下文中一共展示了slim.softmax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: _add_seglink_layers
# 需要导入模块: from tensorflow.contrib import slim [as 别名]
# 或者: from tensorflow.contrib.slim import softmax [as 别名]
def _add_seglink_layers(self):
all_seg_scores = []
all_seg_offsets = []
all_within_layer_link_scores = []
all_cross_layer_link_scores = []
for layer_name in self.feat_layers:
with tf.variable_scope(layer_name):
seg_scores, seg_offsets, within_layer_link_scores, cross_layer_link_scores = self._build_seg_link_layer(layer_name)
all_seg_scores.append(seg_scores)
all_seg_offsets.append(seg_offsets)
all_within_layer_link_scores.append(within_layer_link_scores)
all_cross_layer_link_scores.append(cross_layer_link_scores)
self.seg_score_logits = reshape_and_concat(all_seg_scores) # (batch_size, N, 2)
self.seg_scores = slim.softmax(self.seg_score_logits) # (batch_size, N, 2)
self.seg_offsets = reshape_and_concat(all_seg_offsets) # (batch_size, N, 5)
self.cross_layer_link_scores = reshape_and_concat(all_cross_layer_link_scores) # (batch_size, 8N, 2)
self.within_layer_link_scores = reshape_and_concat(all_within_layer_link_scores) # (batch_size, 4(N - N_conv4_3), 2)
self.link_score_logits = tf.concat([self.within_layer_link_scores, self.cross_layer_link_scores], axis = 1)
self.link_scores = slim.softmax(self.link_score_logits)
tf.summary.histogram('link_scores', self.link_scores)
tf.summary.histogram('seg_scores', self.seg_scores)
示例2: colorized_image_from_softmax
# 需要导入模块: from tensorflow.contrib import slim [as 别名]
# 或者: from tensorflow.contrib.slim import softmax [as 别名]
def colorized_image_from_softmax(self, targets, decoder_output):
''' Regenerate colorized image from softmax distribution for all colors
Notes:
This is a constant mapping from distribution to actual image
Args:
decoder_output: list of input images (scaled between -1 and 1) with the
dimensions specified in the cfg
'''
resize_shape = tf.stack([self.input_size[0],self.input_size[1]])
softmax_to_ab = tf.nn.convolution(decoder_output, self.trans_kernel, 'SAME' )
resized_output = tf.image.resize_images(softmax_to_ab,
resize_shape,
method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
softmax_to_ab = tf.nn.convolution(targets, self.trans_kernel, 'SAME' )
resized_target = tf.image.resize_images(softmax_to_ab,
resize_shape,
method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
return resized_target, resized_output
示例3: attention_weights
# 需要导入模块: from tensorflow.contrib import slim [as 别名]
# 或者: from tensorflow.contrib.slim import softmax [as 别名]
def attention_weights(features, tau=10.0, num_hidden=512):
"""computing attention weights
Args:
features: [B,N,F]
Returns:
[B,N] tensor with soft attention weights for each sample
"""
B, N, F = features.get_shape().as_list()
with tf.variable_scope('attention'):
x = tf.reshape(features, [-1, F])
x = slim.fully_connected(x, num_hidden, scope='fc0')
x = slim.fully_connected(x, 1, activation_fn=None, scope='fc1')
x = tf.reshape(x, features.get_shape()[:2])
alpha = tf.reshape(slim.softmax(x / tau), [B,N,])
return alpha
示例4: test_vgg
# 需要导入模块: from tensorflow.contrib import slim [as 别名]
# 或者: from tensorflow.contrib.slim import softmax [as 别名]
def test_vgg(self):
with slim.arg_scope(vgg.vgg_arg_scope()):
net, end_points = vgg.vgg_16(self.inputs, self.nbclasses, is_training=False)
net = slim.softmax(net)
saver = tf.train.Saver(tf.global_variables())
check_point = 'test/data/vgg_16.ckpt'
sess = tf.InteractiveSession()
saver.restore(sess, check_point)
self.sess = sess
self.graph_origin = tf.get_default_graph()
self.target_op_name = darkon.Gradcam.candidate_featuremap_op_names(sess, self.graph_origin)[-2]
self.model_name = 'vgg'
self.assertEqual('vgg_16/conv5/conv5_3/Relu', self.target_op_name)
示例5: instantiate_softmax
# 需要导入模块: from tensorflow.contrib import slim [as 别名]
# 或者: from tensorflow.contrib.slim import softmax [as 别名]
def instantiate_softmax(self, node, tensor, params):
return slim.softmax(tensor, **params)
示例6: encoder_multilayers_fc
# 需要导入模块: from tensorflow.contrib import slim [as 别名]
# 或者: from tensorflow.contrib.slim import softmax [as 别名]
def encoder_multilayers_fc(input_placeholder, is_training,
layer_num, hidden_size, output_size,
weight_decay=0.0001, scope="three_layer_fc_network", dropout=0.5, reuse=None):
''' An encoder with three FC layers with every but last FC layer
output to hidden_size, the final FC layer will have no
acitvation instead of relu for other layers'''
print('\t building multilayers FC encoder', scope)
with tf.variable_scope(scope, reuse=reuse) as sc:
end_points_collection = sc.original_name_scope + '_end_points'
with slim.arg_scope([slim.conv2d, slim.fully_connected],
activation_fn=tf.nn.relu,
weights_initializer=tf.truncated_normal_initializer(stddev=0.01),
#weights_regularizer=slim.l2_regularizer(weight_decay) ):
weights_regularizer=slim.l2_regularizer(weight_decay)):
print('\t\tinput with size:', input_placeholder.get_shape())
net = input_placeholder
# FC layer 1~(i-1)
for i in range(layer_num - 1):
net = add_fc_with_dropout_layer(net, is_training, hidden_size, activation_fn=tf.nn.relu, dropout=dropout, scope='fc'+str(i))
# Last FC layer
net = add_fc_layer(net, is_training, output_size, activation_fn=None, scope='fc'+str(layer_num))
# Softmax Activation
#net = slim.softmax(net, scope='predictions')
end_points = convert_collection_to_dict(end_points_collection)
return net, end_points
示例7: det_net
# 需要导入模块: from tensorflow.contrib import slim [as 别名]
# 或者: from tensorflow.contrib.slim import softmax [as 别名]
def det_net(features, num_resnet_blocks, num_resnet_features,
num_keep, in_size,
nms_kind='greedy',
scope=None):
with tf.variable_scope(scope, 'DetNet'):
out_size = features.get_shape()[1:3]
x = nnutil.stack(features,
num_resnet_blocks,
num_resnet_features,
downsample=False)
with tf.variable_scope('seg'):
seg_logits = slim.conv2d(x, 2, [1, 1],
activation_fn=None,
weights_initializer=tf.random_normal_initializer(stddev=1e-1),
scope='logits')
seg_preds = slim.softmax(seg_logits)
with tf.variable_scope('reg'):
# TODO: use reg masks instead
reg_preds = slim.conv2d(x, 4, [1, 1],
weights_initializer=tf.random_normal_initializer(stddev=1e-3),
activation_fn=tf.nn.relu,
scope='reg_preds')
with tf.variable_scope('boxes'):
boxes_proposals = reg_to_boxes(reg_preds, in_size, out_size)
boxes_preds = compute_detections_batch(seg_preds, boxes_proposals,
num_keep, nms_kind=nms_kind)
return seg_preds, reg_preds, boxes_proposals, boxes_preds
示例8: build_output
# 需要导入模块: from tensorflow.contrib import slim [as 别名]
# 或者: from tensorflow.contrib.slim import softmax [as 别名]
def build_output(
self,
inputs: tf.Tensor,
is_training: tf.placeholder,
output_name: str
) -> [tf.Tensor, tf.Tensor, tf.Tensor, Dict]:
logits, endpoints = self.build_inference(inputs, is_training=is_training)
output = slim.softmax(logits, scope=output_name + "/softmax")
output = tf.identity(output, name=output_name)
return inputs, logits, output, endpoints
示例9: resnet_v2
# 需要导入模块: from tensorflow.contrib import slim [as 别名]
# 或者: from tensorflow.contrib.slim import softmax [as 别名]
def resnet_v2(inputs,
blocks,
num_classes=None,
global_pool=True,
model_type='vanilla',
scope=None,
reuse=None,
end_points=None):
with tf.variable_scope(scope, 'resnet_v2', [inputs], reuse=reuse) as sc:
if end_points is None:
end_points = {}
end_points['inputs'] = inputs
end_points['flops'] = end_points.get('flops', 0)
net = inputs
# We do not include batch normalization or activation functions in conv1
# because the first ResNet unit will perform these. Cf. Appendix of [2].
with slim.arg_scope([slim.conv2d], activation_fn=None, normalizer_fn=None):
net, current_flops = flopsometer.conv2d_same(
net, 64, 7, stride=2, scope='conv1')
end_points['flops'] += current_flops
net = slim.max_pool2d(net, [3, 3], stride=2, scope='pool1')
# Early stopping is broken in distributed training.
net, end_points = resnet_act.stack_blocks(
net,
blocks,
model_type=model_type,
end_points=end_points)
if global_pool or num_classes is not None:
# This is needed because the pre-activation variant does not have batch
# normalization or activation functions in the residual unit output. See
# Appendix of [2].
net = slim.batch_norm(net, activation_fn=tf.nn.relu, scope='postnorm')
if global_pool:
# Global average pooling.
net = tf.reduce_mean(net, [1, 2], name='pool5', keep_dims=True)
if num_classes is not None:
net, current_flops = flopsometer.conv2d(
net,
num_classes, [1, 1],
activation_fn=None,
normalizer_fn=None,
scope='logits')
end_points['flops'] += current_flops
end_points['predictions'] = slim.softmax(net, scope='predictions')
return net, end_points
示例10: build_model
# 需要导入模块: from tensorflow.contrib import slim [as 别名]
# 或者: from tensorflow.contrib.slim import softmax [as 别名]
def build_model(self, input_imgs, is_training, targets, masks=None, privileged_input=None):
'''Builds the model. Assumes that the input is from range [-1, 1].
Notes:
Stocasticity is not supplied in this function. If desired, it must
be defined in the encoder/decoder model method.
Args:
input_imgs: list of input images (scaled between -1 and 1) with the
dimensions specified in the cfg
is_training: flag for whether the model is in training mode or not
mask: mask used for computing sum of squares loss. If None, we assume
it is np.ones.
'''
print('building model')
self.input_images = input_imgs
self.privileged_input = privileged_input
if self.privileged_input is None:
self.privileged_input = input_imgs
self.target_images = targets
self.targets = targets
self.masks = masks
# build generator
if masks is None:
masks = tf.constant( 1, dtype=tf.float32, shape=[], name='constant_mask' )
if self.decoder_only:
self.encoder_output = input_imgs # Assume that the input is the representation
else:
self.encoder_output = self.build_encoder(input_imgs, is_training)
self.decoder_output = self.build_decoder( self.encoder_output, is_training )
temp = slim.softmax(self.decoder_output * 2.606)
self.generated_target, self.generated_output = self.colorized_image_from_softmax(self.target_images, temp)
# build discriminator
self.augmented_images = []
self.discriminator_endpoints = []
self.discriminator_output_real = self.build_discriminator( # run once on real targets
self.privileged_input, self.generated_target, is_training )
self.discriminator_output_fake = self.build_discriminator( # run once on the output
self.privileged_input, self.generated_output, is_training, reuse=True )
# self.discriminator_output_real = self.build_discriminator( # run once on real targets
# self.privileged_input, self.target_images, is_training )
# self.discriminator_output_fake = self.build_discriminator( # run once on the output
# self.privileged_input, self.decoder_output, is_training, reuse=True )
resized_output = tf.reshape(self.decoder_output, [-1, 313])
resized_target = tf.reshape(targets, [-1, 313])
masks = tf.reshape(masks, [-1])
# set up losses
_ = self.get_losses( resized_output, resized_target, masks,
discriminator_predictions_real=self.discriminator_output_real,
discriminator_predictions_fake=self.discriminator_output_fake )
# record accuracies
self._build_metrics( scope='metrics')
# add summaries
self._build_summaries()
# discriminator accuracies
self.model_built = True
示例11: encoder_multilayers_fc_bn
# 需要导入模块: from tensorflow.contrib import slim [as 别名]
# 或者: from tensorflow.contrib.slim import softmax [as 别名]
def encoder_multilayers_fc_bn(input_placeholder, is_training,
layer_num, hidden_size, output_size,
weight_decay=0.0001, scope="three_layer_fc_network", dropout=0.5, reuse=None, batch_norm_decay=0.9,
batch_norm_epsilon=1e-5, batch_norm_scale=True,batch_norm_center=True, initial_dropout=False):
''' An encoder with three FC layers with every but last FC layer
output to hidden_size, the final FC layer will have no
acitvation instead of relu for other layers'''
print('\t building multilayers FC encoder', scope)
batch_norm_params = {
'is_training': is_training,
'decay': batch_norm_decay,
'epsilon': batch_norm_epsilon,
'scale': batch_norm_scale,
'center': batch_norm_center,
'updates_collections': tf.GraphKeys.UPDATE_OPS
}
with tf.variable_scope(scope, reuse=reuse) as sc:
end_points_collection = sc.original_name_scope + '_end_points'
with slim.arg_scope([slim.conv2d, slim.fully_connected],
activation_fn=tf.nn.relu,
weights_initializer=tf.truncated_normal_initializer(stddev=0.01),
#weights_regularizer=slim.l2_regularizer(weight_decay) ):
weights_regularizer=slim.l2_regularizer(weight_decay),
normalizer_fn=slim.batch_norm,
normalizer_params=batch_norm_params):
print('\t\tinput with size:', input_placeholder.get_shape())
net = input_placeholder
if initial_dropout:
net = tf.layers.dropout(
net,
rate=1.-dropout,
training=is_training)
# FC layer 1~(i-1)
for i in range(layer_num - 1):
net = add_fc_with_dropout_layer(net, is_training, hidden_size, activation_fn=tf.nn.relu, dropout=dropout, scope='fc'+str(i))
with slim.arg_scope([slim.conv2d, slim.fully_connected],
activation_fn=None,
normalizer_fn=None,
weights_initializer=tf.truncated_normal_initializer(stddev=0.01),
weights_regularizer=slim.l2_regularizer(weight_decay) ):
# Last FC layer
net = add_fc_layer(net, is_training, output_size, activation_fn=None, scope='fc'+str(layer_num))
# Softmax Activation
#net = slim.softmax(net, scope='predictions')
end_points = convert_collection_to_dict(end_points_collection)
return net, end_points
示例12: encoder_multilayers_fc_bn_res_no_dropout
# 需要导入模块: from tensorflow.contrib import slim [as 别名]
# 或者: from tensorflow.contrib.slim import softmax [as 别名]
def encoder_multilayers_fc_bn_res_no_dropout(input_placeholder, is_training,
layer_num, hidden_size, output_size,
weight_decay=0.0001, scope="three_layer_fc_network", dropout=0.8,
batch_norm_decay=0.9, batch_norm_epsilon=1e-5,
reuse=None):
''' An encoder with three FC layers with every but last FC layer
output to hidden_size, the final FC layer will have no
acitvation instead of relu for other layers'''
batch_norm_params = {'center': True,
'scale': True,
'decay': batch_norm_decay,
'epsilon': batch_norm_epsilon,
'is_training': is_training}
print('\t building multilayers FC encoder', scope)
with tf.variable_scope(scope, reuse=reuse) as sc:
end_points_collection = sc.original_name_scope + '_end_points'
with slim.arg_scope([slim.conv2d, slim.fully_connected],
activation_fn=tf.nn.relu,
normalizer_fn=slim.batch_norm,
normalizer_params=batch_norm_params,
weights_initializer=tf.truncated_normal_initializer(stddev=0.01),
weights_regularizer=slim.l2_regularizer(weight_decay) ):
print('\t\tinput with size:', input_placeholder.get_shape())
net = input_placeholder
# FC layer 1~(i-1)
for i in range(layer_num - 1):
net = add_fc_layer(net, is_training, hidden_size, activation_fn=tf.nn.relu, scope='fc'+str(i))
with slim.arg_scope([slim.conv2d, slim.fully_connected],
activation_fn=None,
normalizer_fn=None,
weights_initializer=tf.truncated_normal_initializer(stddev=0.01),
weights_regularizer=slim.l2_regularizer(weight_decay) ):
# Last FC layer
net = add_fc_layer(net, is_training, output_size, activation_fn=None, scope='fc'+str(layer_num-1))
# Make residual connection
net = net + input_placeholder
# Softmax Activation
#net = slim.softmax(net, scope='predictions')
end_points = convert_collection_to_dict(end_points_collection)
return net, end_points
示例13: encoder_multilayers_fc_bn_res_no_dropout_normalize_input
# 需要导入模块: from tensorflow.contrib import slim [as 别名]
# 或者: from tensorflow.contrib.slim import softmax [as 别名]
def encoder_multilayers_fc_bn_res_no_dropout_normalize_input(input_placeholder, is_training,
layer_num, hidden_size, output_size,
weight_decay=0.0001, scope="three_layer_fc_network", dropout=0.8,
batch_norm_decay=0.9, batch_norm_epsilon=1e-5,
reuse=None):
''' An encoder with three FC layers with every but last FC layer
output to hidden_size, the final FC layer will have no
acitvation instead of relu for other layers'''
batch_norm_params = {'center': True,
'scale': True,
'decay': batch_norm_decay,
'epsilon': batch_norm_epsilon,
'is_training': is_training}
print('\t building multilayers FC encoder', scope)
with tf.variable_scope(scope, reuse=reuse) as sc:
end_points_collection = sc.original_name_scope + '_end_points'
with slim.arg_scope([slim.conv2d, slim.fully_connected],
activation_fn=tf.nn.relu,
normalizer_fn=slim.batch_norm,
normalizer_params=batch_norm_params,
weights_initializer=tf.truncated_normal_initializer(stddev=0.01),
weights_regularizer=slim.l2_regularizer(weight_decay) ):
inputs = tf.layers.batch_normalization(
input_placeholder,
axis=-1,
momentum=batch_norm_decay,
epsilon=batch_norm_epsilon,
training=is_training)
print('\t\tinput with size:', input_placeholder.get_shape())
net = inputs
# FC layer 1~(i-1)
for i in range(layer_num - 1):
net = add_fc_layer(net, is_training, hidden_size, activation_fn=tf.nn.relu, scope='fc'+str(i))
with slim.arg_scope([slim.conv2d, slim.fully_connected],
activation_fn=None,
normalizer_fn=None,
weights_initializer=tf.truncated_normal_initializer(stddev=0.01),
weights_regularizer=slim.l2_regularizer(weight_decay) ):
# Last FC layer
net = add_fc_layer(net, is_training, output_size, activation_fn=None, scope='fc'+str(layer_num-1))
# Make residual connection
net = net + inputs
# Softmax Activation
#net = slim.softmax(net, scope='predictions')
end_points = convert_collection_to_dict(end_points_collection)
return net, end_points
示例14: encoder_multilayers_fc_bn_res
# 需要导入模块: from tensorflow.contrib import slim [as 别名]
# 或者: from tensorflow.contrib.slim import softmax [as 别名]
def encoder_multilayers_fc_bn_res(input_placeholder, is_training,
layer_num, hidden_size, output_size,
batch_norm_decay=0.95, batch_norm_epsilon=1e-5,
weight_decay=0.0001, scope="three_layer_fc_network", dropout=0.8, reuse=None):
''' An encoder with three FC layers with every but last FC layer
output to hidden_size, the final FC layer will have no
acitvation instead of relu for other layers'''
batch_norm_params = {'center': True,
'scale': True,
'decay': batch_norm_decay,
'epsilon': batch_norm_epsilon,
'is_training': is_training}
print('\t building multilayers FC encoder', scope)
with tf.variable_scope(scope, reuse=reuse) as sc:
end_points_collection = sc.original_name_scope + '_end_points'
with slim.arg_scope([slim.conv2d, slim.fully_connected],
activation_fn=tf.nn.relu,
normalizer_fn=slim.batch_norm,
normalizer_params=batch_norm_params,
weights_initializer=tf.truncated_normal_initializer(stddev=0.01),
weights_regularizer=slim.l2_regularizer(weight_decay) ):
print('\t\tinput with size:', input_placeholder.get_shape())
net = input_placeholder
# FC layer 1~(i-1)
for i in range(layer_num - 1):
if dropout < 1.0:
is_training_dropout = False
net = add_fc_with_dropout_layer(net, is_training_dropout, hidden_size,
activation_fn=tf.nn.relu, dropout=dropout, scope='fc'+str(i))
else:
net = add_fc_layer(net, is_training, hidden_size, activation_fn=tf.nn.relu, dropout=dropout, scope='fc'+str(i))
with slim.arg_scope([slim.conv2d, slim.fully_connected],
activation_fn=None,
normalizer_fn=None,
weights_initializer=tf.truncated_normal_initializer(stddev=0.01),
weights_regularizer=slim.l2_regularizer(weight_decay) ):
# Last FC layer
net = add_fc_layer(net, is_training, output_size, activation_fn=None, scope='fc'+str(layer_num))
# Make residual connection
net = net + input_placeholder
# Softmax Activation
#net = slim.softmax(net, scope='predictions')
end_points = convert_collection_to_dict(end_points_collection)
return net, end_points
示例15: _construct_sequence
# 需要导入模块: from tensorflow.contrib import slim [as 别名]
# 或者: from tensorflow.contrib.slim import softmax [as 别名]
def _construct_sequence(batch):
hidden, boxes = batch
# initializing the state with features
states = [hidden[0]]
# TODO: make this dependent on the data
# TODO: make it with scan ?
for t in range(1, T):
# find the matching boxes. TODO: try with the soft matching function
if c.match_kind == 'boxes':
dists = nnutil.cdist(boxes[t], boxes[t-1])
idxs = tf.argmin(dists, 1, 'idxs')
state_prev = tf.gather(states[t-1], idxs)
elif c.match_kind == 'hidden':
# TODO: actually it makes more sense to compare on states
dists = nnutil.cdist(hidden[t], hidden[t-1])
idxs = tf.argmin(dists, 1, 'idxs')
state_prev = tf.gather(states[t-1], idxs)
elif c.match_kind == 'hidden-soft':
dists = nnutil.cdist(hidden[t], hidden[t-1])
weights = slim.softmax(-dists)
state_prev = tf.matmul(weights, states[t-1])
else:
raise RuntimeError('Unknown match_kind: %s' % c.match_kind)
def _construct_update(reuse):
state = tf.concat(1, [state_prev, hidden[t]])
# TODO: initialize jointly
reset = slim.fully_connected(state, NFH, tf.nn.sigmoid,
reuse=reuse,
scope='reset')
step = slim.fully_connected(state, NFH, tf.nn.sigmoid,
reuse=reuse,
scope='step')
state_r = tf.concat(1, [reset * state_prev, hidden[t]])
state_up = slim.fully_connected(state_r, NFH, tf.nn.tanh,
reuse=reuse,
scope='state_up')
return state_up, step
try:
state_up, step = _construct_update(reuse=True)
except ValueError:
state_up, step = _construct_update(reuse=False)
state = step * state_up + (1.0 - step) * state_prev
states.append(state)
return tf.pack(states)