| /external/tensorflow/tensorflow/contrib/slim/python/slim/nets/ |
| D | vgg.py | 47 from tensorflow.contrib.layers.python.layers import layers as layers_lib unknown
66 [layers.conv2d, layers_lib.fully_connected],
102 [layers.conv2d, layers_lib.max_pool2d],
104 net = layers_lib.repeat(
106 net = layers_lib.max_pool2d(net, [2, 2], scope='pool1')
107 net = layers_lib.repeat(net, 1, layers.conv2d, 128, [3, 3], scope='conv2')
108 net = layers_lib.max_pool2d(net, [2, 2], scope='pool2')
109 net = layers_lib.repeat(net, 2, layers.conv2d, 256, [3, 3], scope='conv3')
110 net = layers_lib.max_pool2d(net, [2, 2], scope='pool3')
111 net = layers_lib.repeat(net, 2, layers.conv2d, 512, [3, 3], scope='conv4')
[all …]
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| D | inception_v1.py | 24 from tensorflow.contrib.layers.python.layers import layers as layers_lib unknown
62 [layers.conv2d, layers_lib.fully_connected],
65 [layers.conv2d, layers_lib.max_pool2d], stride=1, padding='SAME'):
72 net = layers_lib.max_pool2d(net, [3, 3], stride=2, scope=end_point)
87 net = layers_lib.max_pool2d(net, [3, 3], stride=2, scope=end_point)
105 branch_3 = layers_lib.max_pool2d(
127 branch_3 = layers_lib.max_pool2d(
137 net = layers_lib.max_pool2d(net, [3, 3], stride=2, scope=end_point)
155 branch_3 = layers_lib.max_pool2d(
177 branch_3 = layers_lib.max_pool2d(
[all …]
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| D | overfeat.py | 37 from tensorflow.contrib.layers.python.layers import layers as layers_lib unknown
50 [layers.conv2d, layers_lib.fully_connected],
55 with arg_scope([layers_lib.max_pool2d], padding='VALID') as arg_sc:
96 [layers.conv2d, layers_lib.fully_connected, layers_lib.max_pool2d],
100 net = layers_lib.max_pool2d(net, [2, 2], scope='pool1')
102 net = layers_lib.max_pool2d(net, [2, 2], scope='pool2')
106 net = layers_lib.max_pool2d(net, [2, 2], scope='pool5')
113 net = layers_lib.dropout(
116 net = layers_lib.dropout(
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| D | alexnet.py | 41 from tensorflow.contrib.layers.python.layers import layers as layers_lib unknown
54 [layers.conv2d, layers_lib.fully_connected],
59 with arg_scope([layers_lib.max_pool2d], padding='VALID') as arg_sc:
99 [layers.conv2d, layers_lib.fully_connected, layers_lib.max_pool2d],
103 net = layers_lib.max_pool2d(net, [3, 3], 2, scope='pool1')
105 net = layers_lib.max_pool2d(net, [3, 3], 2, scope='pool2')
109 net = layers_lib.max_pool2d(net, [3, 3], 2, scope='pool5')
117 net = layers_lib.dropout(
120 net = layers_lib.dropout(
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| D | inception_v3.py | 24 from tensorflow.contrib.layers.python.layers import layers as layers_lib unknown
107 [layers.conv2d, layers_lib.max_pool2d, layers_lib.avg_pool2d],
131 net = layers_lib.max_pool2d(net, [3, 3], stride=2, scope=end_point)
149 net = layers_lib.max_pool2d(net, [3, 3], stride=2, scope=end_point)
157 [layers.conv2d, layers_lib.max_pool2d, layers_lib.avg_pool2d],
179 branch_3 = layers_lib.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
206 branch_3 = layers_lib.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
233 branch_3 = layers_lib.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
263 branch_2 = layers_lib.max_pool2d(
295 branch_3 = layers_lib.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
[all …]
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| D | inception_v2.py | 24 from tensorflow.contrib.layers.python.layers import layers as layers_lib unknown
84 layers.conv2d, layers_lib.max_pool2d, layers_lib.avg_pool2d,
115 net = layers_lib.max_pool2d(net, [3, 3], scope=end_point, stride=2)
137 net = layers_lib.max_pool2d(net, [3, 3], scope=end_point, stride=2)
167 branch_3 = layers_lib.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
202 branch_3 = layers_lib.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
234 branch_2 = layers_lib.max_pool2d(
265 branch_3 = layers_lib.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
300 branch_3 = layers_lib.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
335 branch_3 = layers_lib.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
[all …]
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| D | resnet_v2.py | 55 from tensorflow.contrib import layers as layers_lib unknown
105 shortcut = layers_lib.conv2d(
113 residual = layers_lib.conv2d(
117 residual = layers_lib.conv2d(
200 [layers_lib.conv2d, bottleneck, resnet_utils.stack_blocks_dense],
213 [layers_lib.conv2d], activation_fn=None, normalizer_fn=None):
226 net = layers_lib.conv2d(
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| D | resnet_utils.py | 43 from tensorflow.contrib import layers as layers_lib unknown
124 return layers_lib.conv2d(
139 return layers_lib.conv2d(
256 [layers_lib.conv2d],
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| D | resnet_v1.py | 64 from tensorflow.contrib.layers.python.layers import layers as layers_lib unknown
206 net = layers_lib.max_pool2d(net, [3, 3], stride=2, scope='pool1')
221 end_points['predictions'] = layers_lib.softmax(
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| /external/tensorflow/tensorflow/contrib/layers/python/layers/ |
| D | layers_test.py | 25 from tensorflow.contrib import layers as layers_lib unknown
318 layers_lib.convolution3d(images_2d, 32, 3)
322 layers_lib.convolution2d(images_3d, 32, 3)
329 layers_lib.convolution2d(images, 32, 3, data_format='CHWN')
335 output = layers_lib.convolution2d(images, 32, [3, 3])
347 output = layers_lib.convolution2d(images, 32, [3, 3], data_format='NCHW')
359 output = layers_lib.convolution2d(images, 32, 3)
367 output = layers_lib.convolution2d(images, 32, images.get_shape()[1:3])
375 output = layers_lib.convolution2d(
393 layers_lib.convolution2d(images, 64, images.get_shape()[1:3])
[all …]
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| /external/tensorflow/tensorflow/contrib/bayesflow/python/kernel_tests/ |
| D | monte_carlo_test.py | 23 from tensorflow.contrib import layers as layers_lib unknown
37 layers = layers_lib
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| /external/tensorflow/tensorflow/contrib/learn/python/learn/estimators/ |
| D | head.py | 31 from tensorflow.contrib import layers as layers_lib unknown
617 return layers_lib.linear(logits_input, logits_dimension, scope="logits")
|