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Results 1 - 4 of 4 for 1x34x1x3xf32 (0.31 sec)

  1. tensorflow/compiler/mlir/quantization/tensorflow/tests/replace_cast_hacks_with_tf_xla_ops.mlir

        %0 = "tf.Div"(%arg0, %cst_2) {device = ""} : (tensor<1x3x4x3x3xf32>, tensor<f32>) -> tensor<1x3x4x3x3xf32>
        %1 = "tf.AddV2"(%0, %cst_0) {device = ""} : (tensor<1x3x4x3x3xf32>, tensor<f32>) -> tensor<1x3x4x3x3xf32>
        %2 = "tf.Floor"(%1) {device = ""} : (tensor<1x3x4x3x3xf32>) -> tensor<1x3x4x3x3xf32>
        %3 = "tf.ClipByValue"(%2, %cst_4, %cst_5) {device = ""} : (tensor<1x3x4x3x3xf32>, tensor<f32>, tensor<f32>) -> tensor<1x3x4x3x3xf32>
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Mon Oct 30 06:52:55 UTC 2023
    - 81K bytes
    - Viewed (0)
  2. tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/quantize_composite_functions.mlir

      func.func private @quantize_conv_fn(%arg0: tensor<1x3x4x3xf32>) -> tensor<1x3x4x2xf32> attributes {tf._original_func_name = "main_0"} {
        %cst = "tf.Const"() {value = dense<3.00000000e-1> : tensor<2x3x3x2xf32>} : () -> tensor<2x3x3x2xf32>
        %0 = "quantfork.stats"(%arg0) {layerStats = dense<[6.00000000e-6, 9.00000000e-1]> : tensor<2xf32>} : (tensor<1x3x4x3xf32>) -> tensor<1x3x4x3xf32>
        %1 = "tf.XlaCallModule"(%0, %cst) {
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Thu May 09 05:56:10 UTC 2024
    - 91.6K bytes
    - Viewed (0)
  3. tensorflow/compiler/mlir/tensorflow/tests/lower_tf.mlir

        // CHECK-NEXT:  %[[SELU_VAL:.*]] = "tf.Mul"(%[[ELU_VAL]], %[[SCALED_ALPHA]]) : (tensor<1x4x4x3xf32>, tensor<f32>) -> tensor<1x4x4x3xf32>
        // CHECK-NEXT:  %[[RES:.*]] = "tf.SelectV2"(%[[PRED]], %[[SCALED_FEATURES]], %[[SELU_VAL]]) : (tensor<1x4x4x3xi1>, tensor<1x4x4x3xf32>, tensor<1x4x4x3xf32>) -> tensor<1x4x4x3xf32>
        // CHECK-NEXT:  return %[[RES]] : tensor<1x4x4x3xf32>
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Fri Jan 05 18:35:42 UTC 2024
    - 92K bytes
    - Viewed (0)
  4. tensorflow/compiler/mlir/lite/tests/prepare-tf.mlir

      } : (tensor<6x7x8xf32>, tensor<4xi32>, tensor<4xi32>, tensor<4xi32>) -> tensor<1x4x1x8xf32>
      func.return %0 : tensor<1x4x1x8xf32>
    
      // CHECK-DAG: %[[NEW_DIMS:.*]] = arith.constant dense<[6, 1, 7, 1, 8]> : tensor<5xi32>
      // CHECK-DAG: %[[BEGIN:.*]] = arith.constant dense<0> : tensor<5xi32>
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Wed May 29 07:26:59 UTC 2024
    - 59.8K bytes
    - Viewed (0)
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