| /dports/misc/py-mxnet/incubator-mxnet-1.9.0/scala-package/examples/src/main/scala/org/apache/mxnetexamples/neuralstyle/ |
| H A D | ModelVgg19.scala | 49 val relu1_1 = ConvRelu(data, s"${prefix}conv1_1", s"${prefix}relu1_1", 64) constant
50 val relu1_2 = ConvRelu(relu1_1, s"${prefix}conv1_2", s"${prefix}relu1_2", 64)
80 val style = if (contentOnly) null else Symbol.Group(relu1_1, relu2_1, relu3_1, relu4_1, relu5_1)
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| /dports/misc/mxnet/incubator-mxnet-1.9.0/scala-package/examples/src/main/scala/org/apache/mxnetexamples/neuralstyle/ |
| H A D | ModelVgg19.scala | 49 val relu1_1 = ConvRelu(data, s"${prefix}conv1_1", s"${prefix}relu1_1", 64) constant
50 val relu1_2 = ConvRelu(relu1_1, s"${prefix}conv1_2", s"${prefix}relu1_2", 64)
80 val style = if (contentOnly) null else Symbol.Group(relu1_1, relu2_1, relu3_1, relu4_1, relu5_1)
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| /dports/misc/py-mxnet/incubator-mxnet-1.9.0/scala-package/examples/src/main/scala/org/apache/mxnetexamples/visualization/ |
| H A D | VGG.scala | 33 val relu1_1 = Symbol.Activation("relu1_1")()(Map("data" -> conv1_1, "act_type" -> "relu")) constant
35 Map("data" -> relu1_1, "pool_type" -> "max", "kernel" -> "(2, 2)", "stride" -> "(2,2)"))
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| /dports/misc/mxnet/incubator-mxnet-1.9.0/scala-package/examples/src/main/scala/org/apache/mxnetexamples/visualization/ |
| H A D | VGG.scala | 33 val relu1_1 = Symbol.Activation("relu1_1")()(Map("data" -> conv1_1, "act_type" -> "relu")) constant
35 Map("data" -> relu1_1, "pool_type" -> "max", "kernel" -> "(2, 2)", "stride" -> "(2,2)"))
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| /dports/misc/mxnet/incubator-mxnet-1.9.0/example/neural-style/ |
| H A D | model_vgg19.py | 29 relu1_1 = mx.symbol.Activation(name='relu1_1', data=conv1_1 , act_type='relu')
30 …conv1_2 = mx.symbol.Convolution(name='conv1_2', data=relu1_1 , num_filter=64, pad=(1,1), kernel=(3…
60 style = mx.sym.Group([relu1_1, relu2_1, relu3_1, relu4_1, relu5_1])
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| /dports/misc/py-mxnet/incubator-mxnet-1.9.0/example/neural-style/ |
| H A D | model_vgg19.py | 29 relu1_1 = mx.symbol.Activation(name='relu1_1', data=conv1_1 , act_type='relu')
30 …conv1_2 = mx.symbol.Convolution(name='conv1_2', data=relu1_1 , num_filter=64, pad=(1,1), kernel=(3…
60 style = mx.sym.Group([relu1_1, relu2_1, relu3_1, relu4_1, relu5_1])
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| /dports/misc/mxnet/incubator-mxnet-1.9.0/example/image-classification/ |
| H A D | symbol_vgg.R | 26 relu1_1 = mx.symbol.Activation(data = conv1_1, act_type = "relu", name = "relu1_1") functionVar
27 pool1 = mx.symbol.Pooling(data = relu1_1, pool_type = "max", kernel = c(2, 2),
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| /dports/misc/py-mxnet/incubator-mxnet-1.9.0/example/image-classification/ |
| H A D | symbol_vgg.R | 26 relu1_1 = mx.symbol.Activation(data = conv1_1, act_type = "relu", name = "relu1_1") functionVar
27 pool1 = mx.symbol.Pooling(data = relu1_1, pool_type = "max", kernel = c(2, 2),
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| /dports/misc/mxnet/incubator-mxnet-1.9.0/example/neural-style/end_to_end/ |
| H A D | model_vgg19.py | 28 relu1_1 = mx.symbol.Activation(data=conv1_1 , act_type='relu')
29 …conv1_2 = mx.symbol.Convolution(name='%s_conv1_2' % prefix, data=relu1_1 , num_filter=64, pad=(1,1…
62 style = mx.sym.Group([relu1_1, relu2_1, relu3_1, relu4_1, relu5_1])
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| /dports/misc/py-mxnet/incubator-mxnet-1.9.0/example/neural-style/end_to_end/ |
| H A D | model_vgg19.py | 28 relu1_1 = mx.symbol.Activation(data=conv1_1 , act_type='relu')
29 …conv1_2 = mx.symbol.Convolution(name='%s_conv1_2' % prefix, data=relu1_1 , num_filter=64, pad=(1,1…
62 style = mx.sym.Group([relu1_1, relu2_1, relu3_1, relu4_1, relu5_1])
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| /dports/misc/mxnet/incubator-mxnet-1.9.0/example/ssd/symbol/ |
| H A D | vgg16_reduced.py | 32 relu1_1 = mx.symbol.Activation(data=conv1_1, act_type="relu", name="relu1_1")
34 data=relu1_1, kernel=(3, 3), pad=(1, 1), num_filter=64, name="conv1_2")
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| H A D | legacy_vgg16_ssd_300.py | 51 relu1_1 = mx.symbol.Activation(data=conv1_1, act_type="relu", name="relu1_1")
53 data=relu1_1, kernel=(3, 3), pad=(1, 1), num_filter=64, name="conv1_2")
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| H A D | legacy_vgg16_ssd_512.py | 50 relu1_1 = mx.symbol.Activation(data=conv1_1, act_type="relu", name="relu1_1")
52 data=relu1_1, kernel=(3, 3), pad=(1, 1), num_filter=64, name="conv1_2")
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| /dports/misc/py-mxnet/incubator-mxnet-1.9.0/example/ssd/symbol/ |
| H A D | vgg16_reduced.py | 32 relu1_1 = mx.symbol.Activation(data=conv1_1, act_type="relu", name="relu1_1")
34 data=relu1_1, kernel=(3, 3), pad=(1, 1), num_filter=64, name="conv1_2")
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| H A D | legacy_vgg16_ssd_300.py | 51 relu1_1 = mx.symbol.Activation(data=conv1_1, act_type="relu", name="relu1_1")
53 data=relu1_1, kernel=(3, 3), pad=(1, 1), num_filter=64, name="conv1_2")
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| H A D | legacy_vgg16_ssd_512.py | 50 relu1_1 = mx.symbol.Activation(data=conv1_1, act_type="relu", name="relu1_1")
52 data=relu1_1, kernel=(3, 3), pad=(1, 1), num_filter=64, name="conv1_2")
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| /dports/graphics/realsr-ncnn-vulkan/realsr-ncnn-vulkan-20210210/src/ncnn/benchmark/ |
| H A D | vgg16_int8.param | 5 ReLU relu1_1 1 1 conv1_1 conv1_1_relu1_1
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| /dports/misc/mxnet/incubator-mxnet-1.9.0/example/rcnn/symnet/ |
| H A D | symbol_vgg.py | 26 relu1_1 = mx.symbol.Activation(data=conv1_1, act_type="relu", name="relu1_1")
28 data=relu1_1, kernel=(3, 3), pad=(1, 1), num_filter=64, workspace=2048, name="conv1_2")
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| /dports/misc/py-mxnet/incubator-mxnet-1.9.0/example/rcnn/symnet/ |
| H A D | symbol_vgg.py | 26 relu1_1 = mx.symbol.Activation(data=conv1_1, act_type="relu", name="relu1_1")
28 data=relu1_1, kernel=(3, 3), pad=(1, 1), num_filter=64, workspace=2048, name="conv1_2")
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| /dports/misc/mxnet/incubator-mxnet-1.9.0/example/fcn-xs/ |
| H A D | symbol_fcnxs.py | 87 relu1_1 = mx.symbol.Activation(data=conv1_1, act_type="relu", name="relu1_1")
88 conv1_2 = mx.symbol.Convolution(data=relu1_1, kernel=(3, 3), pad=(1, 1), num_filter=64,
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| /dports/misc/py-mxnet/incubator-mxnet-1.9.0/example/fcn-xs/ |
| H A D | symbol_fcnxs.py | 87 relu1_1 = mx.symbol.Activation(data=conv1_1, act_type="relu", name="relu1_1")
88 conv1_2 = mx.symbol.Convolution(data=relu1_1, kernel=(3, 3), pad=(1, 1), num_filter=64,
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| /dports/graphics/opencv/opencv-4.5.3/contrib/modules/text/samples/ |
| H A D | textbox.prototxt | 36 name: "relu1_1"
|