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Results 1 - 10 of 12 for 2x4x3x6xi32 (0.13 sec)
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tensorflow/compiler/mlir/quantization/tensorflow/tests/replace_cast_hacks_with_tf_xla_ops.mlir
%19 = "tf.Cast"(%18) {Truncate = false, device = ""} : (tensor<2x4x3x6xi32>) -> tensor<2x4x3x6xf32> %20 = "tf.Mul"(%19, %cst_0) {device = ""} : (tensor<2x4x3x6xf32>, tensor<f32>) -> tensor<2x4x3x6xf32> %21 = "tf.Relu"(%20) {device = ""} : (tensor<2x4x3x6xf32>) -> tensor<2x4x3x6xf32> %22 = "tf.Minimum"(%21, %cst) {device = ""} : (tensor<2x4x3x6xf32>, tensor<f32>) -> tensor<2x4x3x6xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 81K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/mlir2graphdef/aliasing_arg_attr.mlir
attributes {tf.entry_function = {inputs = "args_0,args_1", outputs = "rets_0,rets_1"}} { %0:2 = tf_executor.graph { %1:3 = tf_executor.island wraps "tf.IdentityN"(%arg0, %arg1) {T = ["tfdtype$DT_FLOAT", "tfdtype$DT_INT32"], device = "", name = "identity"} : (tensor<*xf32>, tensor<2x4x6x8xi32>) -> (tensor<*xf32>, tensor<2x4x6x8xi32>)
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Mar 25 12:28:56 UTC 2022 - 958 bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/mlir2graphdef/device-arg-retval-attr.mlir
attributes {tf.entry_function = {inputs = "args_0,args_1", outputs = "rets_0,rets_1"}} { %0:2 = tf_executor.graph { %1:3 = tf_executor.island wraps "tf.IdentityN"(%arg0, %arg1) {T = ["tfdtype$DT_FLOAT", "tfdtype$DT_INT32"], device = "", name = "identity"} : (tensor<*xf32>, tensor<2x4x6x8xi32>) -> (tensor<*xf32>, tensor<2x4x6x8xi32>)
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Mar 25 12:28:56 UTC 2022 - 1.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/push-tpose-through-ewise.mlir
// ----- // CHECK-LABEL: doubleTposeInputPermNotEqualNoChange func.func @doubleTposeInputPermNotEqualNoChange(%arg0: tensor<2x4x3x5xf32>, %arg1: tensor<2x3x4x5xf32>) -> tensor<5x2x3x4xf32> { %perm = arith.constant dense<[3, 0, 2, 1]> : tensor<4xi32> %0 = "tfl.transpose"(%arg0, %perm) : (tensor<2x4x3x5xf32>, tensor<4xi32>) -> tensor<5x2x3x4xf32> %perm1 = arith.constant dense<[3, 0, 1, 2]> : tensor<4xi32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 8.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/graphdef2mlir/device-arg-retval-attr.pbtxt
# Verify arg and ret devices are added as arg and ret attributes. # CHECK-LABEL: func @main # CHECK-SAME: (%[[ARG_0:[a-z0-9]+]]: tensor<*xf32> {tf.device = "/CPU:0"}, %[[ARG_1:[a-z0-9]+]]: tensor<2x4x6x8xi32>) -> (tensor<*xf32>, tensor<*xi32> {tf.device = "/CPU:1"}) node { name: "args_0" op: "_Arg" device: "/CPU:0" attr { key: "T" value { type: DT_FLOAT } } attr {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Dec 07 17:45:22 UTC 2020 - 1.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/mlir2graphdef/graph-as-function.mlir
// RUN: tf-mlir-translate -mlir-to-graphdef %s -tf-graph-as-function -o - | FileCheck %s func.func @main(%arg0: tensor<*x!tf_type.resource>, %arg1: tensor<*x!tf_type.resource<tensor<3x3x1x32xf32>>>, %arg2: tensor<*xf32>, %arg3: tensor<2x4x6x8xi32>) -> (tensor<f32>, tensor<f32>) attributes {tf.entry_function = {inputs = "args_0,args_1,args_2,args_3", outputs = "rets_0_RetVal,rets_1_RetVal"}} { %graph:2 = tf_executor.graph {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Mar 25 12:28:56 UTC 2022 - 3.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/graphdef2mlir/graph-as-function.pbtxt
# functions are converted. # CHECK: func @main(%arg0: tensor<*x!tf_type.resource>, %arg1: tensor<*x!tf_type.resource<tensor<3x3x1x32xf32>>>, %arg2: tensor<*xf32>, %arg3: tensor<2x4x6x8xi32>) -> (tensor<*xf32>, tensor<*xf32>) # CHECK-SAME: _xla_compile_device_type = "GPU" # CHECK-SAME: allow_soft_placement # CHECK-SAME: control_outputs = "" # CHECK-SAME: inputs = "args_0,args_1,args_2,args_3"
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 24 00:18:34 UTC 2023 - 5K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/ops.mlir
// ----- func.func @transpose_output_type_bad(%arg0 : tensor<3x4x5x6xi32>) -> tensor<3x4x5x6xi32> { %cst = arith.constant dense<[0, 3, 1, 2]> : tensor<4xi32> // expected-error @+1 {{expect output type tensor<3x6x4x5xi32>, got tensor<3x4x5x6xi32>}} %0 = "tfl.transpose"(%arg0, %cst) : (tensor<3x4x5x6xi32>, tensor<4xi32>) -> tensor<3x4x5x6xi32> func.return %0 : tensor<3x4x5x6xi32> } // -----
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jun 06 19:09:08 UTC 2024 - 189.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/prepare-composite-functions-tf.mlir
func.func private @dense_image_warp_invalid_input_type(%arg0: tensor<2x4x4x1xi32>, %arg1: tensor<2x4x4x2xf32>) -> tensor<2x4x4x1xf32> attributes {tf._implements = "addons:DenseImageWarp"} // expected-warning @+1 {{Flow should be a 4D float tensor}} func.func private @dense_image_warp_invalid_flow_type(%arg0: tensor<2x4x4x1xf32>, %arg1: tensor<2x4x4x2xi32>) -> tensor<2x4x4x1xf32> attributes {tf._implements = "addons:DenseImageWarp"}
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 122.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/legalize-tf.mlir
} func.func @gatherWithBatchDims(%arg0 : tensor<2x3x6xf32>, %arg1 : tensor<2x5xi32>) -> tensor<2x5x3x6xf32> { %0 = "tf.Const"() { value = dense<[1]> : tensor<1xi32> } : () -> tensor<1xi32> %1 = "tf.GatherV2"(%arg0, %arg1, %0) {batch_dims = 1 : i64} : (tensor<2x3x6xf32>, tensor<2x5xi32>, tensor<1xi32>) -> tensor<2x5x3x6xf32> func.return %1 : tensor<2x5x3x6xf32> // CHECK-LABEL:gatherWithBatchDims
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jun 05 01:54:33 UTC 2024 - 153.4K bytes - Viewed (0)