1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
|
%global _empty_manifest_terminate_build 0
Name: python-params-flow
Version: 0.8.2
Release: 1
Summary: Tensorflow Keras utilities for reducing boilerplate code.
License: MIT
URL: https://github.com/kpe/params-flow/
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/a9/95/ff49f5ebd501f142a6f0aaf42bcfd1c192dc54909d1d9eb84ab031d46056/params-flow-0.8.2.tar.gz
BuildArch: noarch
%description
|Build Status| |Coverage Status| |Version Status| |Python Versions| |Downloads|
`params-flow`_ provides an alternative style for defining your `Keras`_ model
or layer configuration in order to reduce the boilerplate code related to
passing and (de)serializing your model/layer configuration arguments.
`params-flow`_ encourages this:
import params_flow as pf
class MyDenseLayer(pf.Layer): # using params_flow Layer/Model instead of Keras ones
class Params(pf.Layer.Params): # extend one or more base Params configurations
num_outputs = None # declare all configuration arguments
activation = "gelu" # provide or override super() defaults
# do not define an __init__()
def build(self, in_shape):
self.kernel = self.add_variable("kernel",
[int(in_shape[-1]),
self.params.num_outputs]) # access config arguments
which would be sufficient to pass the right configuration arguments to the
super layer/model, as well as take care of (de)serialization, so you can concentrate
on the ``build()`` or ``call()`` implementations, instead of writing boilerplate
code like this:
from tf.keras.layers import Layer
class MyDenseLayer(Layer):
def __init__(self,
num_outputs, # put all of the layer configuration in the constructor
activation = "gelu", # provide defaults
**kwargs): # allow base layer configuration to be passed to super
self.num_outputs = num_outputs
self.activation = activation
super().__init__(**kwargs)
def build(self, in_shape):
self.kernel = self.add_variable("kernel",
[int(in_shape[-1]),
self.num_outputs]) # access config arguments
def get_config(self): # serialize layer configuration, __init__() is the deserializer
config = {
'num_outputs': self.num_outputs,
'activation': self.activation
}
base_config = super().get_config()
return dict(list(base_config.items())) + list(config.items())
%package -n python3-params-flow
Summary: Tensorflow Keras utilities for reducing boilerplate code.
Provides: python-params-flow
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-params-flow
|Build Status| |Coverage Status| |Version Status| |Python Versions| |Downloads|
`params-flow`_ provides an alternative style for defining your `Keras`_ model
or layer configuration in order to reduce the boilerplate code related to
passing and (de)serializing your model/layer configuration arguments.
`params-flow`_ encourages this:
import params_flow as pf
class MyDenseLayer(pf.Layer): # using params_flow Layer/Model instead of Keras ones
class Params(pf.Layer.Params): # extend one or more base Params configurations
num_outputs = None # declare all configuration arguments
activation = "gelu" # provide or override super() defaults
# do not define an __init__()
def build(self, in_shape):
self.kernel = self.add_variable("kernel",
[int(in_shape[-1]),
self.params.num_outputs]) # access config arguments
which would be sufficient to pass the right configuration arguments to the
super layer/model, as well as take care of (de)serialization, so you can concentrate
on the ``build()`` or ``call()`` implementations, instead of writing boilerplate
code like this:
from tf.keras.layers import Layer
class MyDenseLayer(Layer):
def __init__(self,
num_outputs, # put all of the layer configuration in the constructor
activation = "gelu", # provide defaults
**kwargs): # allow base layer configuration to be passed to super
self.num_outputs = num_outputs
self.activation = activation
super().__init__(**kwargs)
def build(self, in_shape):
self.kernel = self.add_variable("kernel",
[int(in_shape[-1]),
self.num_outputs]) # access config arguments
def get_config(self): # serialize layer configuration, __init__() is the deserializer
config = {
'num_outputs': self.num_outputs,
'activation': self.activation
}
base_config = super().get_config()
return dict(list(base_config.items())) + list(config.items())
%package help
Summary: Development documents and examples for params-flow
Provides: python3-params-flow-doc
%description help
|Build Status| |Coverage Status| |Version Status| |Python Versions| |Downloads|
`params-flow`_ provides an alternative style for defining your `Keras`_ model
or layer configuration in order to reduce the boilerplate code related to
passing and (de)serializing your model/layer configuration arguments.
`params-flow`_ encourages this:
import params_flow as pf
class MyDenseLayer(pf.Layer): # using params_flow Layer/Model instead of Keras ones
class Params(pf.Layer.Params): # extend one or more base Params configurations
num_outputs = None # declare all configuration arguments
activation = "gelu" # provide or override super() defaults
# do not define an __init__()
def build(self, in_shape):
self.kernel = self.add_variable("kernel",
[int(in_shape[-1]),
self.params.num_outputs]) # access config arguments
which would be sufficient to pass the right configuration arguments to the
super layer/model, as well as take care of (de)serialization, so you can concentrate
on the ``build()`` or ``call()`` implementations, instead of writing boilerplate
code like this:
from tf.keras.layers import Layer
class MyDenseLayer(Layer):
def __init__(self,
num_outputs, # put all of the layer configuration in the constructor
activation = "gelu", # provide defaults
**kwargs): # allow base layer configuration to be passed to super
self.num_outputs = num_outputs
self.activation = activation
super().__init__(**kwargs)
def build(self, in_shape):
self.kernel = self.add_variable("kernel",
[int(in_shape[-1]),
self.num_outputs]) # access config arguments
def get_config(self): # serialize layer configuration, __init__() is the deserializer
config = {
'num_outputs': self.num_outputs,
'activation': self.activation
}
base_config = super().get_config()
return dict(list(base_config.items())) + list(config.items())
%prep
%autosetup -n params-flow-0.8.2
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-params-flow -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Sun Apr 23 2023 Python_Bot <Python_Bot@openeuler.org> - 0.8.2-1
- Package Spec generated
|
