Python keras.applications.imagenet_utils._obtain_input_shape() Examples
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code examples of keras.applications.imagenet_utils._obtain_input_shape().
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Example #1
| Source File: models.py From Keras-FCN with MIT License | 5 votes |
|
def DenseNet_FCN(input_shape=None, weight_decay=1E-4,
batch_momentum=0.9, batch_shape=None, classes=21,
include_top=False, activation='sigmoid'):
if include_top is True:
# TODO(ahundt) Softmax is pre-applied, so need different train, inference, evaluate.
# TODO(ahundt) for multi-label try per class sigmoid top as follows:
# x = Reshape((row * col * classes))(x)
# x = Activation('sigmoid')(x)
# x = Reshape((row, col, classes))(x)
return densenet.DenseNetFCN(input_shape=input_shape,
weights=None, classes=classes,
nb_layers_per_block=[4, 5, 7, 10, 12, 15],
growth_rate=16,
dropout_rate=0.2)
# if batch_shape:
# img_input = Input(batch_shape=batch_shape)
# image_size = batch_shape[1:3]
# else:
# img_input = Input(shape=input_shape)
# image_size = input_shape[0:2]
input_shape = _obtain_input_shape(input_shape,
default_size=32,
min_size=16,
data_format=K.image_data_format(),
include_top=False)
img_input = Input(shape=input_shape)
x = densenet.__create_fcn_dense_net(classes, img_input,
input_shape=input_shape,
nb_layers_per_block=[4, 5, 7, 10, 12, 15],
growth_rate=16,
dropout_rate=0.2,
include_top=include_top)
x = top(x, input_shape, classes, activation, weight_decay)
# TODO(ahundt) add weight loading
model = Model(img_input, x, name='DenseNet_FCN')
return model
Example #2
| Source File: densenet_models.py From EvadeML-Zoo with MIT License | 4 votes |
|
def densenet_cifar10_model(logits=False, input_range_type=1, pre_filter=lambda x:x):
assert input_range_type == 1
batch_size = 64
nb_classes = 10
img_rows, img_cols = 32, 32
img_channels = 3
img_dim = (img_channels, img_rows, img_cols) if K.image_dim_ordering() == "th" else (img_rows, img_cols, img_channels)
depth = 40
nb_dense_block = 3
growth_rate = 12
nb_filter = 16
dropout_rate = 0.0 # 0.0 for data augmentation
input_tensor = None
include_top=True
if logits is True:
activation = None
else:
activation = "softmax"
# Determine proper input shape
input_shape = _obtain_input_shape(img_dim,
default_size=32,
min_size=8,
data_format=K.image_data_format(),
include_top=include_top)
if input_tensor is None:
img_input = Input(shape=input_shape)
else:
if not K.is_keras_tensor(input_tensor):
img_input = Input(tensor=input_tensor, shape=input_shape)
else:
img_input = input_tensor
x = __create_dense_net(nb_classes, img_input, True, depth, nb_dense_block,
growth_rate, nb_filter, -1, False, 0.0,
dropout_rate, 1E-4, activation)
# Ensure that the model takes into account
# any potential predecessors of `input_tensor`.
if input_tensor is not None:
inputs = get_source_inputs(input_tensor)
else:
inputs = img_input
# Create model.
model = Model(inputs, x, name='densenet')
return model
# Source: https://github.com/titu1994/DenseNet
Example #3
| Source File: models.py From Keras-FCN with MIT License | 4 votes |
|
def Atrous_DenseNet(input_shape=None, weight_decay=1E-4,
batch_momentum=0.9, batch_shape=None, classes=21,
include_top=False, activation='sigmoid'):
# TODO(ahundt) pass the parameters but use defaults for now
if include_top is True:
# TODO(ahundt) Softmax is pre-applied, so need different train, inference, evaluate.
# TODO(ahundt) for multi-label try per class sigmoid top as follows:
# x = Reshape((row * col * classes))(x)
# x = Activation('sigmoid')(x)
# x = Reshape((row, col, classes))(x)
return densenet.DenseNet(depth=None, nb_dense_block=3, growth_rate=32,
nb_filter=-1, nb_layers_per_block=[6, 12, 24, 16],
bottleneck=True, reduction=0.5, dropout_rate=0.2,
weight_decay=1E-4,
include_top=True, top='segmentation',
weights=None, input_tensor=None,
input_shape=input_shape,
classes=classes, transition_dilation_rate=2,
transition_kernel_size=(1, 1),
transition_pooling=None)
# if batch_shape:
# img_input = Input(batch_shape=batch_shape)
# image_size = batch_shape[1:3]
# else:
# img_input = Input(shape=input_shape)
# image_size = input_shape[0:2]
input_shape = _obtain_input_shape(input_shape,
default_size=32,
min_size=16,
data_format=K.image_data_format(),
include_top=False)
img_input = Input(shape=input_shape)
x = densenet.__create_dense_net(classes, img_input,
depth=None, nb_dense_block=3, growth_rate=32,
nb_filter=-1, nb_layers_per_block=[6, 12, 24, 16],
bottleneck=True, reduction=0.5, dropout_rate=0.2,
weight_decay=1E-4, top='segmentation',
input_shape=input_shape,
transition_dilation_rate=2,
transition_kernel_size=(1, 1),
transition_pooling=None,
include_top=include_top)
x = top(x, input_shape, classes, activation, weight_decay)
model = Model(img_input, x, name='Atrous_DenseNet')
# TODO(ahundt) add weight loading
return model
