Viewing file:  __init__.py (1.8 KB) -rw-r--r--
Select action/file-type:
 (+) |  (+) |  (+) | Code (+) | Session (+) |  (+) | SDB (+) |  (+) |  (+) |  (+) |  (+) |  (+) |
"""Enables dynamic setting of underlying Keras module.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
_KERAS_BACKEND = None
_KERAS_LAYERS = None
_KERAS_MODELS = None
_KERAS_UTILS = None
def get_submodules_from_kwargs(kwargs):
backend = kwargs.get('backend', _KERAS_BACKEND)
layers = kwargs.get('layers', _KERAS_LAYERS)
models = kwargs.get('models', _KERAS_MODELS)
utils = kwargs.get('utils', _KERAS_UTILS)
for key in kwargs.keys():
if key not in ['backend', 'layers', 'models', 'utils']:
raise TypeError('Invalid keyword argument: %s', key)
return backend, layers, models, utils
def correct_pad(backend, inputs, kernel_size):
"""Returns a tuple for zero-padding for 2D convolution with downsampling.
# Arguments
input_size: An integer or tuple/list of 2 integers.
kernel_size: An integer or tuple/list of 2 integers.
# Returns
A tuple.
"""
img_dim = 2 if backend.image_data_format() == 'channels_first' else 1
input_size = backend.int_shape(inputs)[img_dim:(img_dim + 2)]
if isinstance(kernel_size, int):
kernel_size = (kernel_size, kernel_size)
if input_size[0] is None:
adjust = (1, 1)
else:
adjust = (1 - input_size[0] % 2, 1 - input_size[1] % 2)
correct = (kernel_size[0] // 2, kernel_size[1] // 2)
return ((correct[0] - adjust[0], correct[0]),
(correct[1] - adjust[1], correct[1]))
__version__ = '1.0.8'
from . import vgg16
from . import vgg19
from . import resnet50
from . import inception_v3
from . import inception_resnet_v2
from . import xception
from . import mobilenet
from . import mobilenet_v2
from . import densenet
from . import nasnet
from . import resnet
from . import resnet_v2
from . import resnext
|