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Results 1 - 10 of 18 for conv2d_backprop_input (0.51 sec)
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tensorflow/compiler/mlir/tfr/examples/mnist/ops_defs.py
strides = [1, op.get_attr('stride_w'), op.get_attr('stride_h'), 1] padding = op.get_attr('padding') shape_0, shape_1 = tf.shape_n([op.inputs[0], op.inputs[1]]) return [ tf.compat.v1.nn.conv2d_backprop_input( shape_0, op.inputs[1], grad, strides=strides, padding=padding, dilations=dilations, data_format='NHWC'),
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Aug 31 20:23:51 UTC 2023 - 6.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/legalize-tf.mlir
} func.func @conv2d_backprop_input(%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: Wed Jun 05 01:54:33 UTC 2024 - 153.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/tests/legalize-tf.mlir
return %result : tensor<?x512x512x1xf32> } // ----- // CHECK-LABEL: @conv2d_backprop_input func.func @conv2d_backprop_input( %filter: tensor<3x3x1x32xf32>, %out_backprop: tensor<100x26x26x32xf32> ) -> tensor<100x28x28x1xf32> { // CHECK: %[[REV_FILTER:.*]] = "mhlo.reverse"(%arg0) <{dimensions = dense<[0, 1]> : tensor<2xi64>}>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon May 06 18:46:23 UTC 2024 - 335.5K bytes - Viewed (0) -
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)