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Results 31 - 40 of 97 for 5x1xf32 (1.46 sec)
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tensorflow/compiler/mlir/tensorflow/tests/mlir2graphdef/function-resource-args-handle-info.mlir
func.func @main(%arg0: tensor<*x!tf_type.resource<tensor<8x1xf32>>>) -> tensor<8x1xf32> { %0 = tf_executor.graph { %outputs, %control = tf_executor.island wraps "tf.ReadVariableOp"(%arg0) : (tensor<*x!tf_type.resource<tensor<8x1xf32>>>) -> tensor<8x1xf32> tf_executor.fetch %outputs : tensor<8x1xf32> } func.return %0 : tensor<8x1xf32> } // Check that we generate _handle_dtypes and _handle_shapes for the resource
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Mar 25 12:28:56 UTC 2022 - 1.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfrt/tests/ifrt/tf_restore_pruning.mlir
%0 = "tf.RestoreV2"(%cst, %cst_1, %cst_0): (tensor<!tf_type.string>, tensor<1x!tf_type.string>, tensor<1x!tf_type.string>) -> tensor<3x1xf32> %1 = "tf.VarHandleOp"() <{container = "", shared_name = "y"}> : () -> tensor<!tf_type.resource<tensor<3x1xf32>>> "tf.AssignVariableOp"(%1, %0) : (tensor<!tf_type.resource<tensor<3x1xf32>>>, tensor<3x1xf32>) -> () return
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Mar 25 22:02:06 UTC 2024 - 1.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/prepare-quantize-post-training-16bits.mlir
%5 = "tfl.pseudo_const"() {value = dense<[[0.5]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32> %6 = "tfl.pseudo_const"() {value = dense<[[0.6]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32> %7 = "tfl.pseudo_const"() {value = dense<[[0.7]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32> %8 = "tfl.pseudo_const"() {value = dense<[[0.8]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32> %9 = "tfl.no_value"() {value} : () -> none
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 26.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tests/e2e/simple-graph.mlir
module { func.func @main(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>, %arg2: tensor<1xf32>, %arg3: tensor<1xf32>) -> tensor<2x1xf32> attributes {tf.entry_function = {inputs = "input0,input1,input2,input3", outputs = "output"}} { %0 = "tfl.add"(%arg0, %arg1) {fused_activation_function = "RELU6"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32> %1 = "tfl.mul"(%0, %arg2) {fused_activation_function = "RELU6"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 1.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/README.md
%2 = "tfl.add"(%arg0, %arg3) {tac.device = "GPU", fused_activation_function = "RELU6", tac.inference_type = "FLOAT"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32> %3 = "tfl.pack"(%1, %2) {tac.device = "CPU", tac.inference_type = "FLOAT", axis = 0 : i32, values_count = 2 : i32} : (tensor<1xf32>, tensor<1xf32>) -> tensor<2x1xf32> return %3 : tensor<2x1xf32> } ```
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Mar 29 18:32:13 UTC 2022 - 11.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/tests/tfl_legalize_hlo.mlir
func.func @main(%arg0: tensor<5x7xf32>) -> tensor<5x7xf32> { func.return %arg0: tensor<5x7xf32> // CHECK-LABEL: main // CHECK: return %arg0 : tensor<5x7xf32> } // - transpose // func.func @transpose_2d(%arg0: tensor<2x3xf32>) -> tensor<3x2xf32> { %0 = "mhlo.transpose"(%arg0) <{permutation = dense<[1, 0]> : tensor<2xi64>}> : (tensor<2x3xf32>) -> tensor<3x2xf32> func.return %0 : tensor<3x2xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 40.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/tests/legalize-tf-with-tf2xla-hlo-importer.mlir
// CHECK-LABEL: @xla_svd func.func @xla_svd(%arg0: tensor<1x1xf32>) -> (tensor<1xf32>, tensor<1x1xf32>, tensor<1x1xf32>) { // CHECK-NOT: XlaSvd %s, %u, %v = "tf.XlaSvd"(%arg0) {max_iter = 1, epsilon = 1.0E-09 : f32, precision_config = ""} : (tensor<1x1xf32>) -> (tensor<1xf32>, tensor<1x1xf32>, tensor<1x1xf32>) func.return %s, %u, %v : tensor<1xf32>, tensor<1x1xf32>, tensor<1x1xf32> } func.func @identity(%arg0: f32) -> f32 {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Apr 06 15:32:52 UTC 2024 - 38.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/default_quant_params.mlir
// CHECK-LABEL: hardcode_all func.func @hardcode_all(%arg0: tensor<2x2xf32>, %arg1: tensor<2x1xf32>) -> tensor<2x2xf32> { %0 = "tfl.add"(%arg0, %arg1) {fused_activation_function="NONE"}: (tensor<2x2xf32>, tensor<2x1xf32>) -> tensor<2x2xf32> func.return %0 : tensor<2x2xf32> // CHECK: %[[q0:.*]] = "tfl.quantize"(%arg1) <{qtype = tensor<2x1x!quant.uniform<u8:f32, 0.0078431372549019607:128>>}>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 8.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/const-fold.mlir
func.func @add_dense_dense_float_mixfng_1_n() -> tensor<2x2xf32> { %cst_0 = arith.constant dense<[[1.5, -2.5]]> : tensor<1x2xf32> %cst_1 = arith.constant dense<[[-3.], [4.]]> : tensor<2x1xf32> %0 = "tfl.add"(%cst_0, %cst_1) {fused_activation_function = "NONE"} : (tensor<1x2xf32>, tensor<2x1xf32>) -> tensor<2x2xf32> func.return %0 : tensor<2x2xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 45.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/tests/legalize-tf.mlir
// CHECK-SAME: (%[[A:.*]]: tensor<5x7xf32>, %[[B:.*]]: tensor<7x11xf32>) func.func @matmul_notranspose(%a: tensor<5x7xf32>, %b: tensor<7x11xf32>) -> tensor<5x11xf32> { // CHECK: "mhlo.dot"(%[[A]], %[[B]]) %0 = "tf.MatMul"(%a, %b) {transpose_a = false, transpose_b = false} : (tensor<5x7xf32>, tensor<7x11xf32>) -> tensor<5x11xf32> func.return %0 : tensor<5x11xf32> } // -----
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon May 06 18:46:23 UTC 2024 - 335.5K bytes - Viewed (0)