| /external/tensorflow/tensorflow/contrib/slim/python/slim/nets/ |
| D | inception_v3.py | 107 [layers.conv2d, layers_lib.max_pool2d, layers_lib.avg_pool2d],
112 net = layers.conv2d(inputs, depth(32), [3, 3], stride=2, scope=end_point)
118 net = layers.conv2d(net, depth(32), [3, 3], scope=end_point)
124 net = layers.conv2d(
137 net = layers.conv2d(net, depth(80), [1, 1], scope=end_point)
143 net = layers.conv2d(net, depth(192), [3, 3], scope=end_point)
157 [layers.conv2d, layers_lib.max_pool2d, layers_lib.avg_pool2d],
164 branch_0 = layers.conv2d(
167 branch_1 = layers.conv2d(
169 branch_1 = layers.conv2d(
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| D | inception_v1.py | 62 [layers.conv2d, layers_lib.fully_connected],
65 [layers.conv2d, layers_lib.max_pool2d], stride=1, padding='SAME'):
67 net = layers.conv2d(inputs, 64, [7, 7], stride=2, scope=end_point)
77 net = layers.conv2d(net, 64, [1, 1], scope=end_point)
82 net = layers.conv2d(net, 192, [3, 3], scope=end_point)
95 branch_0 = layers.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
97 branch_1 = layers.conv2d(net, 96, [1, 1], scope='Conv2d_0a_1x1')
98 branch_1 = layers.conv2d(
101 branch_2 = layers.conv2d(net, 16, [1, 1], scope='Conv2d_0a_1x1')
102 branch_2 = layers.conv2d(
[all …]
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| D | inception_v2.py | 84 layers.conv2d, layers_lib.max_pool2d, layers_lib.avg_pool2d,
121 net = layers.conv2d(
131 net = layers.conv2d(net, depth(192), [3, 3], scope=end_point)
146 branch_0 = layers.conv2d(
149 branch_1 = layers.conv2d(
154 branch_1 = layers.conv2d(
157 branch_2 = layers.conv2d(
162 branch_2 = layers.conv2d(
164 branch_2 = layers.conv2d(
168 branch_3 = layers.conv2d(
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| D | vgg.py | 66 [layers.conv2d, layers_lib.fully_connected],
70 with arg_scope([layers.conv2d], padding='SAME') as arg_sc:
102 [layers.conv2d, layers_lib.max_pool2d],
105 inputs, 1, layers.conv2d, 64, [3, 3], scope='conv1')
107 net = layers_lib.repeat(net, 1, layers.conv2d, 128, [3, 3], scope='conv2')
109 net = layers_lib.repeat(net, 2, layers.conv2d, 256, [3, 3], scope='conv3')
111 net = layers_lib.repeat(net, 2, layers.conv2d, 512, [3, 3], scope='conv4')
113 net = layers_lib.repeat(net, 2, layers.conv2d, 512, [3, 3], scope='conv5')
116 net = layers.conv2d(net, 4096, [7, 7], padding='VALID', scope='fc6')
119 net = layers.conv2d(net, 4096, [1, 1], scope='fc7')
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| D | overfeat.py | 50 [layers.conv2d, layers_lib.fully_connected],
54 with arg_scope([layers.conv2d], padding='SAME'):
96 [layers.conv2d, layers_lib.fully_connected, layers_lib.max_pool2d],
98 net = layers.conv2d(
101 net = layers.conv2d(net, 256, [5, 5], padding='VALID', scope='conv2')
103 net = layers.conv2d(net, 512, [3, 3], scope='conv3')
104 net = layers.conv2d(net, 1024, [3, 3], scope='conv4')
105 net = layers.conv2d(net, 1024, [3, 3], scope='conv5')
108 [layers.conv2d],
112 net = layers.conv2d(net, 3072, [6, 6], padding='VALID', scope='fc6')
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| D | alexnet.py | 54 [layers.conv2d, layers_lib.fully_connected],
58 with arg_scope([layers.conv2d], padding='SAME'):
99 [layers.conv2d, layers_lib.fully_connected, layers_lib.max_pool2d],
101 net = layers.conv2d(
104 net = layers.conv2d(net, 192, [5, 5], scope='conv2')
106 net = layers.conv2d(net, 384, [3, 3], scope='conv3')
107 net = layers.conv2d(net, 384, [3, 3], scope='conv4')
108 net = layers.conv2d(net, 256, [3, 3], scope='conv5')
113 [layers.conv2d],
116 net = layers.conv2d(net, 4096, [5, 5], padding='VALID', scope='fc6')
[all …]
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| D | resnet_v2.py | 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_v1.py | 109 shortcut = layers.conv2d(
116 residual = layers.conv2d(
120 residual = layers.conv2d(
196 [layers.conv2d, bottleneck, resnet_utils.stack_blocks_dense],
212 net = layers.conv2d(
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| D | resnet_utils.py | 124 return layers_lib.conv2d(
139 return layers_lib.conv2d(
256 [layers_lib.conv2d],
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| /external/tensorflow/tensorflow/core/grappler/optimizers/ |
| D | remapper.cc | 74 Conv2DWithBiasAdd(const NodeDef* conv2d, const NodeDef* bias_add) in Conv2DWithBiasAdd()
75 : conv2d(conv2d), bias_add(bias_add) {} in Conv2DWithBiasAdd()
77 const NodeDef* conv2d = nullptr; member
84 Conv2DWithBiasAddAndRelu(const NodeDef* conv2d, const NodeDef* bias_add, in Conv2DWithBiasAddAndRelu()
86 : conv2d(conv2d), bias_add(bias_add), relu(relu) {} in Conv2DWithBiasAddAndRelu()
88 const NodeDef* conv2d = nullptr; member
96 Conv2DWithSqueezeAndBiasAdd(const NodeDef* conv2d, const NodeDef* squeeze, in Conv2DWithSqueezeAndBiasAdd()
98 : conv2d(conv2d), squeeze(squeeze), bias_add(bias_add) {} in Conv2DWithSqueezeAndBiasAdd()
100 const NodeDef* conv2d = nullptr; member
108 Conv2DWithBatchNorm(const NodeDef* conv2d, const NodeDef* fused_batch_norm, in Conv2DWithBatchNorm()
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| /external/tensorflow/tensorflow/contrib/receptive_field/python/util/ |
| D | receptive_field_test.py | 51 l1 = slim.conv2d(x, 1, [1, 1], stride=4, scope='L1', padding='VALID')
54 l2 = slim.conv2d(l2_pad, 1, [3, 3], stride=2, scope='L2', padding='VALID')
55 l3 = slim.conv2d(l2, 1, [1, 1], stride=2, scope='L3', padding='VALID')
79 l1 = slim.conv2d(x, 1, [1, 1], stride=4, scope='L1', padding='VALID')
105 l1 = slim.conv2d(l1_pad, 1, [5, 5], stride=2, scope='L1', padding='VALID')
107 l2 = slim.conv2d(x, 1, [3, 3], stride=1, scope='L2', padding='VALID')
108 l3 = slim.conv2d(l2, 1, [3, 3], stride=1, scope='L3', padding='VALID')
132 l1 = slim.conv2d(x, 1, [1, 1], stride=4, scope='L1', padding='VALID')
134 l2 = slim.conv2d(x, 1, [3, 3], stride=2, scope='L2', padding='SAME')
135 l3 = slim.conv2d(l2, 1, [1, 1], stride=2, scope='L3', padding='VALID')
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| D | graph_compute_order_test.py | 43 l1 = slim.conv2d(x, 1, [1, 1], stride=4, scope='L1', padding='VALID')
46 l2 = slim.conv2d(l2_pad, 1, [3, 3], stride=2, scope='L2', padding='VALID')
51 l5 = slim.conv2d(l4, 1, [1, 1], stride=2, scope='L5', padding='SAME')
53 l6 = slim.conv2d(l4, 1, [3, 3], stride=2, scope='L6', padding='SAME')
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| D | parse_layer_parameters_test.py | 45 l1 = slim.conv2d(x, 1, [1, 1], stride=4, scope='L1', padding='VALID')
48 l2 = slim.conv2d(l2_pad, 1, [3, 3], stride=2, scope='L2', padding='VALID')
53 l5 = slim.conv2d(l4, 1, [1, 1], stride=2, scope='L5', padding='SAME')
55 l6 = slim.conv2d(l4, 1, [3, 3], stride=2, scope='L6', padding='SAME')
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| /external/tensorflow/tensorflow/contrib/quantize/python/ |
| D | quantize_test.py | 36 conv2d = layers.conv2d variable
55 conv = conv2d(inputs, 32, [5, 5], stride=2, padding='SAME',
77 conv = conv2d(input1, 32, [5, 5], stride=2, padding='SAME',
207 _ = conv2d(
234 _ = conv2d(
257 conv = conv2d(
267 _ = conv2d(
295 conv1 = conv2d(
306 conv2 = conv2d(
354 _ = conv2d(
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| D | common_test.py | 34 conv2d = layers.conv2d variable
103 node = conv2d(
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| /external/tensorflow/tensorflow/contrib/specs/python/ |
| D | specs_ops.py | 80 Cx = Fun(layers.conv2d)
81 Cs = Fun(layers.conv2d, activation_fn=math_ops.sigmoid)
82 Ct = Fun(layers.conv2d, activation_fn=math_ops.tanh)
83 Cr = Fun(layers.conv2d, activation_fn=nn_ops.relu)
84 Cm = Fun(layers.conv2d, activation_fn=nn_ops.softmax)
85 Cl = Fun(layers.conv2d, activation_fn=None)
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| /external/tensorflow/tensorflow/python/profiler/internal/ |
| D | model_analyzer_testlib.py | 49 x = nn_ops.conv2d(image, kernel, [1, 2, 2, 1], padding='SAME')
54 x = nn_ops.conv2d(x, kernel, [1, 2, 2, 1], padding='SAME')
83 r1 = nn_ops.conv2d(image, kernel1, [1, 2, 2, 1], padding='SAME')
89 r2 = nn_ops.conv2d(image, kernel2, [1, 2, 2, 1], padding='SAME')
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| /external/tensorflow/tensorflow/contrib/slim/ |
| D | README.md | 120 created by a `slim.fully_connected` or `slim.conv2d` layer. Non-model variables
185 conv = tf.nn.conv2d(input, kernel, [1, 1, 1, 1], padding='SAME')
199 net = slim.conv2d(input, 128, [3, 3], scope='conv1_1')
209 Conv2d | [slim.conv2d](https://www.tensorflow.org/code/tensorflow/contrib/layers/python/layers/laye…
229 net = slim.conv2d(net, 256, [3, 3], scope='conv3_1')
230 net = slim.conv2d(net, 256, [3, 3], scope='conv3_2')
231 net = slim.conv2d(net, 256, [3, 3], scope='conv3_3')
240 net = slim.conv2d(net, 256, [3, 3], scope='conv3_%d' % (i+1))
247 net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3], scope='conv3')
253 subsequent call of `slim.conv2d` are appended with an underscore and iteration
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| /external/tensorflow/tensorflow/python/compiler/tensorrt/test/ |
| D | const_broadcast_test.py | 45 y1 = nn.conv2d(x, filt1, strides=[1, 1, 1, 1], padding='SAME', name='y1')
49 y2 = nn.conv2d(z1, filt2, strides=[1, 1, 1, 1], padding='SAME', name='y2')
56 y3 = nn.conv2d(z2, filt3, strides=[1, 1, 1, 1], padding='SAME', name='y3')
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| D | memory_alignment_test.py | 49 conv = nn.conv2d(
55 out = nn.conv2d(
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| /external/tensorflow/tensorflow/python/kernel_tests/ |
| D | conv_ops_test.py | 213 conv = nn_ops.conv2d(
248 conv = nn_ops.conv2d(
287 computed = nn_ops.conv2d(
393 conv2d_result = nn_ops.conv2d(
1091 conv_forward = nn_ops.conv2d(
1137 conv_forward = nn_ops.conv2d(
1685 conv = nn_ops.conv2d(
2243 c1 = nn_ops.conv2d(
2252 nn_ops.conv2d(
2261 nn_ops.conv2d(
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| D | atrous_conv2d_test.py | 82 y2 = nn_ops.conv2d(
134 y2 = nn_ops.conv2d(y2, f, strides=[1, 1, 1, 1], padding=padding)
135 y2 = nn_ops.conv2d(y2, f, strides=[1, 1, 1, 1], padding=padding)
136 y2 = nn_ops.conv2d(y2, f, strides=[1, 1, 1, 1], padding=padding)
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| /external/tensorflow/tensorflow/core/kernels/ |
| D | conv_ops_test.cc | 1068 Node* conv2d; member
1073 Node* conv2d; member
1079 Node* conv2d; member
1086 Node* conv2d; member
1092 Node* conv2d; member
1114 Node* conv2d; in Conv2D() local
1121 .Finalize(graph, &conv2d)); in Conv2D()
1123 return {graph, conv2d}; in Conv2D()
1134 Node* conv2d = conv_graph.conv2d; in Conv2DWithBias() local
1141 .Input(conv2d) in Conv2DWithBias()
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| /external/tensorflow/tensorflow/python/ops/ |
| D | conv2d_benchmark.py | 72 conv2d_op = nn_ops.conv2d(
77 conv2d_op = nn_ops.conv2d(
82 warmup_conv2d_op = nn_ops.conv2d(
87 warmup_conv2d_op = nn_ops.conv2d(
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| /external/tensorflow/tensorflow/python/tools/ |
| D | optimize_for_inference_test.py | 140 conv_op = nn_ops.conv2d(
187 conv_op = nn_ops.conv2d(
240 nn_ops.conv2d(
270 nn_ops.conv2d(
300 nn_ops.conv2d(
|