| /frameworks/ml/nn/runtime/test/specs/V1_0/ |
| avg_pool_float_2.mod.py | 34 act0 = Int32Scalar("activation", 0) variable
42 "AVERAGE_POOL_2D", i0, padding, padding, padding, padding, stride, stride, filt, filt, act0).To(output)
|
| avg_pool_float_3.mod.py | 34 act0 = Int32Scalar("activation", 0) variable
42 "AVERAGE_POOL_2D", i0, padding, padding, padding, padding, stride, stride, filt, filt, act0).To(output)
|
| avg_pool_quant8_2.mod.py | 34 act0 = Int32Scalar("activation", 0) variable
42 "AVERAGE_POOL_2D", i0, padding, padding, padding, padding, stride, stride, filt, filt, act0).To(output)
|
| avg_pool_quant8_3.mod.py | 34 act0 = Int32Scalar("activation", 0) variable
42 "AVERAGE_POOL_2D", i0, padding, padding, padding, padding, stride, stride, filt, filt, act0).To(output)
|
| max_pool_float_2.mod.py | 34 act0 = Int32Scalar("activation", 0) variable
42 "MAX_POOL_2D", i0, padding, padding, padding, padding, stride, stride, filt, filt, act0).To(output)
|
| max_pool_quant8_2.mod.py | 34 act0 = Int32Scalar("activation", 0) variable
42 "MAX_POOL_2D", i0, padding, padding, padding, padding, stride, stride, filt, filt, act0).To(output)
|
| /frameworks/ml/nn/runtime/test/specs/V1_1/ |
| avg_pool_float_2_relaxed.mod.py | 34 act0 = Int32Scalar("activation", 0) variable
42 "AVERAGE_POOL_2D", i0, padding, padding, padding, padding, stride, stride, filt, filt, act0).To(output)
|
| avg_pool_float_3_relaxed.mod.py | 34 act0 = Int32Scalar("activation", 0) variable
42 "AVERAGE_POOL_2D", i0, padding, padding, padding, padding, stride, stride, filt, filt, act0).To(output)
|
| max_pool_float_2_relaxed.mod.py | 34 act0 = Int32Scalar("activation", 0) variable
42 "MAX_POOL_2D", i0, padding, padding, padding, padding, stride, stride, filt, filt, act0).To(output)
|
| /external/tensorflow/tensorflow/contrib/kfac/examples/ |
| mlp.py | 84 pre0, act0, params0 = fc_layer(layer_id=0, inputs=examples, output_size=128)
85 pre1, act1, params1 = fc_layer(layer_id=1, inputs=act0, output_size=64)
99 layer_collection.register_fully_connected(params1, act0, pre1)
|
| convnet.py | 149 pre0, act0, params0 = conv_layer(
151 act1 = max_pool_layer(layer_id=1, inputs=act0, kernel_size=3, stride=2)
|
| /prebuilts/tools/common/m2/repository/net/sf/saxon/Saxon-HE/9.8.0-5/ |
| Saxon-HE-9.8.0-5.jar | |
