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Corrected some comments

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Somshubra Majumdar
2017-02-21 09:50:21 -06:00
parent 8f9d387265
commit 3b2b5eca77
1 changed files with 9 additions and 7 deletions
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keras_contrib/applications/densenet.py
+9 -7
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@@ -320,6 +320,8 @@ def __transition_block(ip, nb_filter, compression=1.0, dropout_rate=None, weight
Args:
ip: keras tensor
nb_filter: number of filters
compression: calculated as 1 - reduction. Reduces the number of feature maps
in the transition block.
dropout_rate: dropout rate
weight_decay: weight decay factor
@@ -345,14 +347,14 @@ def __dense_block(x, nb_layers, nb_filter, growth_rate, bottleneck=False, dropou
Args:
x: keras tensor
nb_layers: the number of layers of __conv_block to append to the model.
nb_layers: the number of layers of conv_block to append to the model.
nb_filter: number of filters
growth_rate: growth rate
bottleneck: bottleneck block
dropout_rate: dropout rate
weight_decay: weight decay factor
Returns: keras tensor with nb_layers of __conv_block appended
Returns: keras tensor with nb_layers of conv_block appended
'''
concat_axis = 1 if K.image_dim_ordering() == "th" else -1
@@ -378,7 +380,7 @@ def __transition_up_block(ip, nb_filters, type='upsampling', output_shape=None,
output_shape: required if type = 'deconv'. Output shape of tensor
weight_decay: weight decay factor
Returns: keras tensor, after applying batch_norm, relu-conv, dropout, maxpool
Returns: keras tensor, after applying upsampling operation.
'''
if type == 'upsampling':
@@ -469,8 +471,8 @@ def __create_dense_net(nb_classes, img_input, include_top, depth=40, nb_dense_bl
def __create_fcn_dense_net(nb_classes, img_input, include_top, nb_dense_block=5, growth_rate=12,
nb_filter=-1, bottleneck=False, reduction=0.0, dropout_rate=None, weight_decay=1E-4,
nb_layers_per_block=4, upsampling_conv=128, upsampling_type='upsampling', batchsize=None,
input_shape=None):
nb_layers_per_block=4, nb_upsampling_conv=128, upsampling_type='upsampling',
batchsize=None, input_shape=None):
''' Build the DenseNet model
Args:
@@ -489,7 +491,7 @@ def __create_fcn_dense_net(nb_classes, img_input, include_top, nb_dense_block=5,
If positive integer, a set number of layers per dense block.
If list, nb_layer is used as provided. Note that list size must
be (nb_dense_block + 1)
upsampling_conv: number of convolutional layers in upsampling via subpixel convolution
nb_upsampling_conv: number of convolutional layers in upsampling via subpixel convolution
upsampling_type: Can be one of 'upsampling', 'deconv', 'atrous' and
'subpixel'. Defines type of upsampling algorithm used.
batchsize: Fixed batch size. This is a temporary requirement for
@@ -513,7 +515,7 @@ def __create_fcn_dense_net(nb_classes, img_input, include_top, nb_dense_block=5,
# check if upsampling_conv has minimum number of filters
# minimum is set to 12, as at least 3 color channels are needed for correct upsampling
assert upsampling_conv > 12 and upsampling_conv % 4 == 0, "Parameter `upsampling_conv` number of channels must " \
assert nb_upsampling_conv > 12 and nb_upsampling_conv % 4 == 0, "Parameter `upsampling_conv` number of channels must " \
"be a positive number divisible by 4 and greater " \
"than 12"