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Results 1 - 10 of 19 for Conv2DBackpropInput (0.95 sec)
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tensorflow/compiler/mlir/lite/quantization/tensorflow/tests/fallback_to_flex_ops_legacy.mlir
} // CHECK-LABEL: conv2d_backprop_input_with_add func.func @conv2d_backprop_input_with_add(%arg0: tensor<4xi32>, %arg1: tensor<3x3x1x32xf32>, %arg2: tensor<15x14x14x32xf32>) -> tensor<15x28x28x1xf32> { %0 = "tf.Conv2DBackpropInput"(%arg0, %arg1, %arg2) {strides = [1, 2, 2, 1], padding="SAME", dilations=[1, 1, 1, 1]}: (tensor<4xi32>, tensor<3x3x1x32xf32>, tensor<15x14x14x32xf32>) -> tensor<15x28x28x1xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 5.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/quantization/tensorflow/tests/fallback_to_flex_ops_default.mlir
} // CHECK-LABEL: conv2d_backprop_input_with_add func.func @conv2d_backprop_input_with_add(%arg0: tensor<4xi32>, %arg1: tensor<3x3x1x32xf32>, %arg2: tensor<15x14x14x32xf32>) -> tensor<15x28x28x1xf32> { %0 = "tf.Conv2DBackpropInput"(%arg0, %arg1, %arg2) {strides = [1, 2, 2, 1], padding="SAME", dilations=[1, 1, 1, 1]}: (tensor<4xi32>, tensor<3x3x1x32xf32>, tensor<15x14x14x32xf32>) -> tensor<15x28x28x1xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 13.4K bytes - Viewed (0) -
tensorflow/cc/gradients/nn_grad.cc
TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "use_cudnn_on_gpu", &use_cudnn_on_gpu)); auto dx_1 = Conv2DBackpropInput(scope, Shape(scope, op.input(0)), op.input(1), grad_inputs[0], strides, padding, Conv2DBackpropInput::DataFormat(data_format) .UseCudnnOnGpu(use_cudnn_on_gpu)); grad_outputs->push_back(dx_1);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 27 23:34:33 UTC 2022 - 24.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/layout_optimization_layout_assignment_gpu_cc_70.mlir
%input_size: tensor<4xi32>, %filter: tensor<1x28x28x64xf32>, %out_backprop: tensor<1x28x28x64xf32> ) -> tensor<1x28x28x64xf32> { // CHECK: "tf.Conv2DBackpropInput" // CHECK-SAME: data_format = "NCHW" %0 = "tf.Conv2DBackpropInput"(%input_size, %filter, %out_backprop) { data_format = "NHWC", padding = "VALID", strides = [1, 1, 1, 1]
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 21 08:41:18 UTC 2022 - 8.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/layout_optimization_layout_assignment_gpu_cc_60.mlir
%input_size: tensor<4xi32>, %filter: tensor<1x28x28x64xf16>, %out_backprop: tensor<1x28x28x64xf16> ) -> tensor<1x28x28x64xf16> { // CHECK: "tf.Conv2DBackpropInput" // CHECK-SAME: data_format = "NCHW" %0 = "tf.Conv2DBackpropInput"(%input_size, %filter, %out_backprop) { data_format = "NHWC", padding = "VALID", strides = [1, 1, 1, 1]
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 21 08:41:18 UTC 2022 - 5.8K bytes - Viewed (0) -
tensorflow/compiler/jit/tests/keras_imagenet_main_graph_mode.golden_summary
_Retval 2 cluster 0 size 2178 Add 17 AddN 72 ArgMax 1 AssignAddVariableOp 1 AssignSubVariableOp 106 BiasAdd 1 BiasAddGrad 1 Cast 3 Const 357 Conv2D 53 Conv2DBackpropFilter 53 Conv2DBackpropInput 52 DivNoNan 1 Equal 1 FusedBatchNorm 53 FusedBatchNormGrad 53 Identity 2 MatMul 3 MaxPool 1 MaxPoolGrad 1 Mean 1 Mul 164 Pad 1 ReadVariableOp 646 Relu 49 ReluGrad 49
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Jan 06 10:38:14 UTC 2023 - 740 bytes - Viewed (0) -
tensorflow/compiler/jit/tests/keras_imagenet_main.golden_summary
_Arg 435 _Retval 2 cluster 0 size 1910 Add 16 AddN 71 ArgMax 1 AssignAddVariableOp 1 BiasAdd 1 BiasAddGrad 1 Cast 115 Const 407 Conv2D 53 Conv2DBackpropFilter 53 Conv2DBackpropInput 52 Equal 1 FusedBatchNormGradV2 53 FusedBatchNormV2 53 MatMul 3 MaxPool 1 MaxPoolGrad 1 Mean 1 Mul 218 Pad 2 ReadVariableOp 538 Relu 49 ReluGrad 49 Reshape 2
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Jan 06 10:38:14 UTC 2023 - 874 bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/layout_optimization_layout_assignment_to_nchw.mlir
// CHECK: %[[ARG_PERM:.*]] = "tf.Const"() <{value = dense<[0, 3, 1, 2]> : tensor<4xi64>}> // CHECK: %[[OUT_BP_TRANSPOSE:[0-9]*]] = "tf.Transpose"(%arg2, %[[ARG_PERM]]) // CHECK: %[[CONV2D_BACKPROP:[0-9]*]] = "tf.Conv2DBackpropInput" // CHECK-SAME: (%[[INPUT_PERM]], %arg1, %[[OUT_BP_TRANSPOSE]]) // CHECK-SAME: data_format = "NCHW" // CHECK-SAME: dilations = [1, 4, 2, 3] // CHECK-SAME: explicit_paddings = [1, 2, 7, 8, 3, 4, 5, 6]
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 9K bytes - Viewed (0) -
tensorflow/compiler/jit/tests/keras_imagenet_main.pbtxt
} attr { key: "index" value { i: 1 } } } node { name: "training/LossScaleOptimizer/gradients/res5c_branch2c_1/Conv2D_grad/Conv2DBackpropInput" op: "Conv2DBackpropInput" input: "ConstantFolding/training/LossScaleOptimizer/gradients/res5c_branch2c_1/Conv2D_grad/ShapeN-matshapes-0" input: "res5c_branch2c_1/Conv2D/Cast"
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 30 02:52:54 UTC 2019 - 1.3M bytes - Viewed (0) -
tensorflow/compiler/jit/tests/keras_imagenet_main_graph_mode.pbtxt
} } attr { key: "use_nesterov" value { b: false } } } node { name: "training/SGD/gradients/res5c_branch2c_1/Conv2D_grad/Conv2DBackpropInput" op: "Conv2DBackpropInput" input: "training/SGD/gradients/res5c_branch2c_1/Conv2D_grad/ShapeN" input: "res5c_branch2c_1/Conv2D/ReadVariableOp"
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 30 02:52:54 UTC 2019 - 1.1M bytes - Viewed (0)