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Results 1 - 4 of 4 for 3x3x3x8x16xf32 (0.14 sec)

  2. github.com/tensorflow/tensorflow

    tensorflow/compiler/mlir/lite/quantization/tensorflow/tests/tf_to_quant_4bit.mlir 

      func.return %rst : tensor<256x8x7x16xf32>

// CHECK: %[[CONSTANT0:.*]] = "tf.Const"() <{value = dense<0.000000e+00> : tensor<3x3x3x16xf32>}>
// CHECK: %[[QUANTIZE:.*]] = "quantfork.qcast"(%[[CONSTANT0]]) : (tensor<3x3x3x16xf32>) -> tensor<3x3x3x16x!quant.uniform<i4:f32, 1.000000e+00:-8>>
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Mon Oct 30 06:52:55 UTC 2023
    - 9.4K bytes
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  3. github.com/tensorflow/tensorflow

    tensorflow/compiler/mlir/lite/quantization/tensorflow/tests/tf_to_quant.mlir 

      func.return %rst : tensor<256x8x7x16xf32>

// CHECK: %[[CONSTANT0:.*]] = "tf.Const"() <{value = dense<0.000000e+00> : tensor<3x3x3x16xf32>}>
// CHECK: %[[QUANTIZE:.*]] = "quantfork.qcast"(%[[CONSTANT0]]) : (tensor<3x3x3x16xf32>) -> tensor<3x3x3x16x!quant.uniform<i8:f32, 1.000000e+00:-128>>
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Mon Oct 30 06:52:55 UTC 2023
    - 9.5K bytes
    - Viewed (0)
  4. github.com/tensorflow/tensorflow

    tensorflow/compiler/mlir/tensorflow/tests/optimize.mlir 

      %filter = arith.constant dense<2.0> : tensor<3x3x3x16xf32>
  %bias = arith.constant dense<3.0> : tensor<16xf32>
  %value = arith.constant dense<4.0> : tensor<16xf32>
  %0 = "tf.Conv2D"(%arg, %filter) {T = "tfdtype$DT_FLOAT", data_format = "NHWC", dilations = [1, 2, 3, 1], padding = "SAME", strides = [1, 4, 5, 1]} : (tensor<256x32x32x3xf32>, tensor<3x3x3x16xf32>) -> tensor<256x8x7x16xf32>
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Fri Jan 05 18:35:42 UTC 2024
    - 3.3K bytes
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  5. github.com/tensorflow/tensorflow

    tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/nchw_convolution_to_nhwc.mlir 

    // CHECK-LABEL: convolution_3d
func.func @convolution_3d(%arg0: tensor<1x4x28x28x1xf32>, %arg1: tensor<2x3x3x1x16xf32>) -> tensor<1x3x26x26x16xf32> {
  %0 = stablehlo.convolution(%arg0, %arg1) dim_numbers = [b, 0, 1, 2, f]x[0, 1, 2, i, o]->[b, 0, 1, 2, f], window = {} {batch_group_count = 1 : i64, feature_group_count = 1 : i64} : (tensor<1x4x28x28x1xf32>, tensor<2x3x3x1x16xf32>) -> tensor<1x3x26x26x16xf32>
  return %0 : tensor<1x3x26x26x16xf32>
}
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Mon Mar 25 23:00:47 UTC 2024
    - 5.5K bytes
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