Python keras.layers.SpatialDropout3D() Examples
The following are 10
code examples of keras.layers.SpatialDropout3D().
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keras.layers
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Example #1
| Source File: layers.py From nni with MIT License | 6 votes |
|
def keras_dropout(layer, rate):
"""
Keras dropout layer.
"""
from keras import layers
input_dim = len(layer.input.shape)
if input_dim == 2:
return layers.SpatialDropout1D(rate)
elif input_dim == 3:
return layers.SpatialDropout2D(rate)
elif input_dim == 4:
return layers.SpatialDropout3D(rate)
else:
return layers.Dropout(rate)
Example #2
| Source File: core_test.py From DeepLearning_Wavelet-LSTM with MIT License | 5 votes |
|
def test_dropout():
layer_test(layers.Dropout,
kwargs={'rate': 0.5},
input_shape=(3, 2))
layer_test(layers.Dropout,
kwargs={'rate': 0.5, 'noise_shape': [3, 1]},
input_shape=(3, 2))
layer_test(layers.Dropout,
kwargs={'rate': 0.5, 'noise_shape': [None, 1]},
input_shape=(3, 2))
layer_test(layers.SpatialDropout1D,
kwargs={'rate': 0.5},
input_shape=(2, 3, 4))
for data_format in ['channels_last', 'channels_first']:
for shape in [(4, 5), (4, 5, 6)]:
if data_format == 'channels_last':
input_shape = (2,) + shape + (3,)
else:
input_shape = (2, 3) + shape
layer_test(layers.SpatialDropout2D if len(shape) == 2 else layers.SpatialDropout3D,
kwargs={'rate': 0.5,
'data_format': data_format},
input_shape=input_shape)
# Test invalid use cases
with pytest.raises(ValueError):
layer_test(layers.SpatialDropout2D if len(shape) == 2 else layers.SpatialDropout3D,
kwargs={'rate': 0.5,
'data_format': 'channels_middle'},
input_shape=input_shape)
Example #3
| Source File: core_test.py From DeepLearning_Wavelet-LSTM with MIT License | 5 votes |
|
def test_dropout():
layer_test(layers.Dropout,
kwargs={'rate': 0.5},
input_shape=(3, 2))
layer_test(layers.Dropout,
kwargs={'rate': 0.5, 'noise_shape': [3, 1]},
input_shape=(3, 2))
layer_test(layers.Dropout,
kwargs={'rate': 0.5, 'noise_shape': [None, 1]},
input_shape=(3, 2))
layer_test(layers.SpatialDropout1D,
kwargs={'rate': 0.5},
input_shape=(2, 3, 4))
for data_format in ['channels_last', 'channels_first']:
for shape in [(4, 5), (4, 5, 6)]:
if data_format == 'channels_last':
input_shape = (2,) + shape + (3,)
else:
input_shape = (2, 3) + shape
layer_test(layers.SpatialDropout2D if len(shape) == 2 else layers.SpatialDropout3D,
kwargs={'rate': 0.5,
'data_format': data_format},
input_shape=input_shape)
# Test invalid use cases
with pytest.raises(ValueError):
layer_test(layers.SpatialDropout2D if len(shape) == 2 else layers.SpatialDropout3D,
kwargs={'rate': 0.5,
'data_format': 'channels_middle'},
input_shape=input_shape)
Example #4
| Source File: core_test.py From DeepLearning_Wavelet-LSTM with MIT License | 5 votes |
|
def test_dropout():
layer_test(layers.Dropout,
kwargs={'rate': 0.5},
input_shape=(3, 2))
layer_test(layers.Dropout,
kwargs={'rate': 0.5, 'noise_shape': [3, 1]},
input_shape=(3, 2))
layer_test(layers.Dropout,
kwargs={'rate': 0.5, 'noise_shape': [None, 1]},
input_shape=(3, 2))
layer_test(layers.SpatialDropout1D,
kwargs={'rate': 0.5},
input_shape=(2, 3, 4))
for data_format in ['channels_last', 'channels_first']:
for shape in [(4, 5), (4, 5, 6)]:
if data_format == 'channels_last':
input_shape = (2,) + shape + (3,)
else:
input_shape = (2, 3) + shape
layer_test(layers.SpatialDropout2D if len(shape) == 2 else layers.SpatialDropout3D,
kwargs={'rate': 0.5,
'data_format': data_format},
input_shape=input_shape)
# Test invalid use cases
with pytest.raises(ValueError):
layer_test(layers.SpatialDropout2D if len(shape) == 2 else layers.SpatialDropout3D,
kwargs={'rate': 0.5,
'data_format': 'channels_middle'},
input_shape=input_shape)
Example #5
| Source File: core_test.py From DeepLearning_Wavelet-LSTM with MIT License | 5 votes |
|
def test_dropout():
layer_test(layers.Dropout,
kwargs={'rate': 0.5},
input_shape=(3, 2))
layer_test(layers.Dropout,
kwargs={'rate': 0.5, 'noise_shape': [3, 1]},
input_shape=(3, 2))
layer_test(layers.Dropout,
kwargs={'rate': 0.5, 'noise_shape': [None, 1]},
input_shape=(3, 2))
layer_test(layers.SpatialDropout1D,
kwargs={'rate': 0.5},
input_shape=(2, 3, 4))
for data_format in ['channels_last', 'channels_first']:
for shape in [(4, 5), (4, 5, 6)]:
if data_format == 'channels_last':
input_shape = (2,) + shape + (3,)
else:
input_shape = (2, 3) + shape
layer_test(layers.SpatialDropout2D if len(shape) == 2 else layers.SpatialDropout3D,
kwargs={'rate': 0.5,
'data_format': data_format},
input_shape=input_shape)
# Test invalid use cases
with pytest.raises(ValueError):
layer_test(layers.SpatialDropout2D if len(shape) == 2 else layers.SpatialDropout3D,
kwargs={'rate': 0.5,
'data_format': 'channels_middle'},
input_shape=input_shape)
Example #6
| Source File: core_test.py From DeepLearning_Wavelet-LSTM with MIT License | 5 votes |
|
def test_dropout():
layer_test(layers.Dropout,
kwargs={'rate': 0.5},
input_shape=(3, 2))
layer_test(layers.Dropout,
kwargs={'rate': 0.5, 'noise_shape': [3, 1]},
input_shape=(3, 2))
layer_test(layers.Dropout,
kwargs={'rate': 0.5, 'noise_shape': [None, 1]},
input_shape=(3, 2))
layer_test(layers.SpatialDropout1D,
kwargs={'rate': 0.5},
input_shape=(2, 3, 4))
for data_format in ['channels_last', 'channels_first']:
for shape in [(4, 5), (4, 5, 6)]:
if data_format == 'channels_last':
input_shape = (2,) + shape + (3,)
else:
input_shape = (2, 3) + shape
layer_test(layers.SpatialDropout2D if len(shape) == 2 else layers.SpatialDropout3D,
kwargs={'rate': 0.5,
'data_format': data_format},
input_shape=input_shape)
# Test invalid use cases
with pytest.raises(ValueError):
layer_test(layers.SpatialDropout2D if len(shape) == 2 else layers.SpatialDropout3D,
kwargs={'rate': 0.5,
'data_format': 'channels_middle'},
input_shape=input_shape)
Example #7
| Source File: core_test.py From DeepLearning_Wavelet-LSTM with MIT License | 5 votes |
|
def test_dropout():
layer_test(layers.Dropout,
kwargs={'rate': 0.5},
input_shape=(3, 2))
layer_test(layers.Dropout,
kwargs={'rate': 0.5, 'noise_shape': [3, 1]},
input_shape=(3, 2))
layer_test(layers.Dropout,
kwargs={'rate': 0.5, 'noise_shape': [None, 1]},
input_shape=(3, 2))
layer_test(layers.SpatialDropout1D,
kwargs={'rate': 0.5},
input_shape=(2, 3, 4))
for data_format in ['channels_last', 'channels_first']:
for shape in [(4, 5), (4, 5, 6)]:
if data_format == 'channels_last':
input_shape = (2,) + shape + (3,)
else:
input_shape = (2, 3) + shape
layer_test(layers.SpatialDropout2D if len(shape) == 2 else layers.SpatialDropout3D,
kwargs={'rate': 0.5,
'data_format': data_format},
input_shape=input_shape)
# Test invalid use cases
with pytest.raises(ValueError):
layer_test(layers.SpatialDropout2D if len(shape) == 2 else layers.SpatialDropout3D,
kwargs={'rate': 0.5,
'data_format': 'channels_middle'},
input_shape=input_shape)
Example #8
| Source File: core_test.py From DeepLearning_Wavelet-LSTM with MIT License | 5 votes |
|
def test_dropout():
layer_test(layers.Dropout,
kwargs={'rate': 0.5},
input_shape=(3, 2))
layer_test(layers.Dropout,
kwargs={'rate': 0.5, 'noise_shape': [3, 1]},
input_shape=(3, 2))
layer_test(layers.Dropout,
kwargs={'rate': 0.5, 'noise_shape': [None, 1]},
input_shape=(3, 2))
layer_test(layers.SpatialDropout1D,
kwargs={'rate': 0.5},
input_shape=(2, 3, 4))
for data_format in ['channels_last', 'channels_first']:
for shape in [(4, 5), (4, 5, 6)]:
if data_format == 'channels_last':
input_shape = (2,) + shape + (3,)
else:
input_shape = (2, 3) + shape
layer_test(layers.SpatialDropout2D if len(shape) == 2 else layers.SpatialDropout3D,
kwargs={'rate': 0.5,
'data_format': data_format},
input_shape=input_shape)
# Test invalid use cases
with pytest.raises(ValueError):
layer_test(layers.SpatialDropout2D if len(shape) == 2 else layers.SpatialDropout3D,
kwargs={'rate': 0.5,
'data_format': 'channels_middle'},
input_shape=input_shape)
Example #9
| Source File: isensee2017.py From 3DUnetCNN with MIT License | 5 votes |
|
def create_context_module(input_layer, n_level_filters, dropout_rate=0.3, data_format="channels_first"):
convolution1 = create_convolution_block(input_layer=input_layer, n_filters=n_level_filters)
dropout = SpatialDropout3D(rate=dropout_rate, data_format=data_format)(convolution1)
convolution2 = create_convolution_block(input_layer=dropout, n_filters=n_level_filters)
return convolution2
Example #10
| Source File: isensee2017.py From Keras-Brats-Improved-Unet3d with MIT License | 5 votes |
|
def create_context_module(input_layer, n_level_filters, dropout_rate=0.3, data_format="channels_first"):
convolution1 = create_convolution_block(input_layer=input_layer, n_filters=n_level_filters)
dropout = SpatialDropout3D(rate=dropout_rate, data_format=data_format)(convolution1)
convolution2 = create_convolution_block(input_layer=dropout, n_filters=n_level_filters)
return convolution2
