style fixes

Author: 0x00b1

keras_resnet/blocks/_1d.py

@@ -1,12 +1,10 @@
 # -*- coding: utf-8 -*-
 
 """
-
 keras_resnet.blocks._1d
 ~~~~~~~~~~~~~~~~~~~~~~~
 
 This module implements a number of popular one-dimensional residual blocks.
-
 """
 
 import keras.layers
@@ -19,7 +17,6 @@
 
 def basic_1d(filters, stage=0, block=0, kernel_size=3, numerical_name=False, stride=None):
     """
-
     A one-dimensional basic block.
 
     :param filters: the output’s feature space
@@ -39,7 +36,6 @@ def basic_1d(filters, stage=0, block=0, kernel_size=3, numerical_name=False, str
         >>> import keras_resnet.blocks
 
         >>> keras_resnet.blocks.basic_1d(64)
-
     """
     if stride is None:
         if block != 0 or stage == 0:
@@ -83,7 +79,6 @@ def f(x):
 
 def bottleneck_1d(filters, stage=0, block=0, kernel_size=3, numerical_name=False, stride=None):
     """
-
     A one-dimensional bottleneck block.
 
     :param filters: the output’s feature space
@@ -103,7 +98,6 @@ def bottleneck_1d(filters, stage=0, block=0, kernel_size=3, numerical_name=False
         >>> import keras_resnet.blocks
 
         >>> keras_resnet.blocks.bottleneck_1d(64)
-
     """
     if stride is None:
         stride = 1 if block != 0 or stage == 0 else 2

keras_resnet/blocks/_2d.py

@@ -1,12 +1,10 @@
 # -*- coding: utf-8 -*-
 
 """
-
 keras_resnet.blocks._2d
 ~~~~~~~~~~~~~~~~~~~~~~~
 
 This module implements a number of popular two-dimensional residual blocks.
-
 """
 
 import keras.layers
@@ -19,7 +17,6 @@
 
 def basic_2d(filters, stage=0, block=0, kernel_size=3, numerical_name=False, stride=None):
     """
-
     A two-dimensional basic block.
 
     :param filters: the output’s feature space
@@ -39,7 +36,6 @@ def basic_2d(filters, stage=0, block=0, kernel_size=3, numerical_name=False, str
         >>> import keras_resnet.blocks
 
         >>> keras_resnet.blocks.basic_2d(64)
-
     """
     if stride is None:
         if block != 0 or stage == 0:
@@ -83,7 +79,6 @@ def f(x):
 
 def bottleneck_2d(filters, stage=0, block=0, kernel_size=3, numerical_name=False, stride=None):
     """
-
     A two-dimensional bottleneck block.
 
     :param filters: the output’s feature space
@@ -103,7 +98,6 @@ def bottleneck_2d(filters, stage=0, block=0, kernel_size=3, numerical_name=False
         >>> import keras_resnet.blocks
 
         >>> keras_resnet.blocks.bottleneck_2d(64)
-
     """
     if stride is None:
         if block != 0 or stage == 0:

keras_resnet/blocks/_3d.py

@@ -1,12 +1,10 @@
 # -*- coding: utf-8 -*-
 
 """
-
 keras_resnet.blocks._3d
 ~~~~~~~~~~~~~~~~~~~~~~~
 
 This module implements a number of popular three-dimensional residual blocks.
-
 """
 
 import keras.layers
@@ -19,7 +17,6 @@
 
 def basic_3d(filters, stage=0, block=0, kernel_size=3, numerical_name=False, stride=None):
     """
-
     A three-dimensional basic block.
 
     :param filters: the output’s feature space
@@ -39,7 +36,6 @@ def basic_3d(filters, stage=0, block=0, kernel_size=3, numerical_name=False, str
         >>> import keras_resnet.blocks
 
         >>> keras_resnet.blocks.basic_3d(64)
-
     """
     if stride is None:
         if block != 0 or stage == 0:
@@ -83,7 +79,6 @@ def f(x):
 
 def bottleneck_3d(filters, stage=0, block=0, kernel_size=3, numerical_name=False, stride=None):
     """
-
     A three-dimensional bottleneck block.
 
     :param filters: the output’s feature space
@@ -103,7 +98,6 @@ def bottleneck_3d(filters, stage=0, block=0, kernel_size=3, numerical_name=False
         >>> import keras_resnet.blocks
 
         >>> keras_resnet.blocks.bottleneck_3d(64)
-
     """
     if stride is None:
         if block != 0 or stage == 0:

keras_resnet/blocks/__init__.py

@@ -1,12 +1,10 @@
 # -*- coding: utf-8 -*-
 
 """
-
 keras_resnet.blocks
 ~~~~~~~~~~~~~~~~~~~
 
 This module implements a number of popular residual blocks.
-
 """
 
 from ._1d import (

keras_resnet/blocks/_time_distributed_2d.py

@@ -1,12 +1,10 @@
 # -*- coding: utf-8 -*-
 
 """
-
 keras_resnet.blocks._time_distributed_2d
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 This module implements a number of popular time distributed two-dimensional residual blocks.
-
 """
 
 import keras.layers

keras_resnet/classifiers/_2d.py

@@ -1,12 +1,10 @@
 # -*- coding: utf-8 -*-
 
 """
-
 keras_resnet.classifiers
 ~~~~~~~~~~~~~~~~~~~~~~~~
 
 This module implements popular residual two-dimensional classifiers.
-
 """
 
 import keras.backend
@@ -19,7 +17,6 @@
 
 class ResNet18(keras.models.Model):
     """
-
     A :class:`ResNet18 <ResNet18>` object.
 
     :param inputs: input tensor (e.g. an instance of `keras.layers.Input`)
@@ -35,9 +32,7 @@ class ResNet18(keras.models.Model):
         >>> model = keras_resnet.classifiers.ResNet18(x)
 
         >>> model.compile("adam", "categorical_crossentropy", ["accuracy"])
-
     """
-
     def __init__(self, inputs, classes):
         outputs = keras_resnet.models.ResNet18(inputs)
 
@@ -50,7 +45,6 @@ def __init__(self, inputs, classes):
 
 class ResNet34(keras.models.Model):
     """
-
     A :class:`ResNet34 <ResNet34>` object.
 
     :param inputs: input tensor (e.g. an instance of `keras.layers.Input`)
@@ -66,9 +60,7 @@ class ResNet34(keras.models.Model):
         >>> model = keras_resnet.classifiers.ResNet34(x)
 
         >>> model.compile("adam", "categorical_crossentropy", ["accuracy"])
-
     """
-
     def __init__(self, inputs, classes):
         outputs = keras_resnet.models.ResNet34(inputs)
 
@@ -81,7 +73,6 @@ def __init__(self, inputs, classes):
 
 class ResNet50(keras.models.Model):
     """
-
     A :class:`ResNet50 <ResNet50>` object.
 
     :param inputs: input tensor (e.g. an instance of `keras.layers.Input`)
@@ -97,9 +88,7 @@ class ResNet50(keras.models.Model):
         >>> model = keras_resnet.classifiers.ResNet50(x)
 
         >>> model.compile("adam", "categorical_crossentropy", ["accuracy"])
-
     """
-
     def __init__(self, inputs, classes):
         outputs = keras_resnet.models.ResNet50(inputs)
 
@@ -112,7 +101,6 @@ def __init__(self, inputs, classes):
 
 class ResNet101(keras.models.Model):
     """
-
     A :class:`ResNet101 <ResNet101>` object.
 
     :param inputs: input tensor (e.g. an instance of `keras.layers.Input`)
@@ -128,9 +116,7 @@ class ResNet101(keras.models.Model):
         >>> model = keras_resnet.classifiers.ResNet101(x)
 
         >>> model.compile("adam", "categorical_crossentropy", ["accuracy"])
-
     """
-
     def __init__(self, inputs, classes):
         outputs = keras_resnet.models.ResNet101(inputs)
 
@@ -143,7 +129,6 @@ def __init__(self, inputs, classes):
 
 class ResNet152(keras.models.Model):
     """
-
     A :class:`ResNet152 <ResNet152>` object.
 
     :param inputs: input tensor (e.g. an instance of `keras.layers.Input`)
@@ -161,7 +146,6 @@ class ResNet152(keras.models.Model):
         >>> model.compile("adam", "categorical_crossentropy", ["accuracy"])
 
     """
-
     def __init__(self, inputs, classes):
         outputs = keras_resnet.models.ResNet152(inputs)
 
@@ -174,7 +158,6 @@ def __init__(self, inputs, classes):
 
 class ResNet200(keras.models.Model):
     """
-
     A :class:`ResNet200 <ResNet200>` object.
 
     :param inputs: input tensor (e.g. an instance of `keras.layers.Input`)
@@ -190,9 +173,7 @@ class ResNet200(keras.models.Model):
         >>> model = keras_resnet.classifiers.ResNet200(x)
 
         >>> model.compile("adam", "categorical_crossentropy", ["accuracy"])
-
     """
-
     def __init__(self, inputs, classes):
         outputs = keras_resnet.models.ResNet200(inputs)
 

keras_resnet/classifiers/__init__.py

@@ -1,12 +1,10 @@
 # -*- coding: utf-8 -*-
 
 """
-
 keras_resnet.classifiers
 ~~~~~~~~~~~~~~~~~~~~~~~~
 
 This module implements popular residual classifiers.
-
 """
 
 from ._2d import (