- Sort Score
- Result 10 results
- Languages All
Results 1 - 10 of 10 for RELU (0.03 sec)
-
tensorflow/compiler/jit/mark_for_compilation_pass_test.cc
Node* b = ops::UnaryOp("Relu", a, builder.opts().WithName("B")); Node* c = ops::UnaryOp("Relu", b, builder.opts().WithName("C")); Node* d = ops::UnaryOp("UncompilableUnary", c, builder.opts().WithName("D")); Node* e = ops::UnaryOp("Relu", d, builder.opts().WithName("E")); ops::UnaryOp("Relu", e, builder.opts().WithName("F"));
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Feb 14 10:11:10 UTC 2024 - 79.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/python/integration_test/quantize_model_test.py
# If present the last op before return should be stablehlo.clamp for relu6 # and stablehlo.maximum for relu. if activation_fn is nn_ops.relu6: self.assertRegex(module_str, r'stablehlo.clamp.*\n.*return') elif activation_fn is nn_ops.relu: self.assertRegex(module_str, r'stablehlo.maximum.*\n.*return') else: # Check activation functions are implicit.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 14 06:31:57 UTC 2024 - 51.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/optimize_patterns.td
(HasRankAtMost<4> $a), (HasRankAtMost<4> $b)]>; } // We can eliminate Relu from Relu(SquaredDifference(x, y)), // since the result of SquaredDifference is always non-negative. // TFLite interpreter doesn't support Relu+int32 for now. So the test cases // are failing without the following pattern to optimize Relu away fixes // the problem. def OptimizeReluSquaredDifference : Pat<
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 16 20:31:41 UTC 2024 - 66.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/replace_cast_hacks_with_tf_xla_ops.mlir
%10 = "tf.Cast"(%9) {Truncate = false, device = ""} : (tensor<1x3xi32>) -> tensor<1x3xf32> %11 = "tf.Mul"(%10, %cst) {device = ""} : (tensor<1x3xf32>, tensor<f32>) -> tensor<1x3xf32> %12 = "tf.Relu"(%11) {device = ""} : (tensor<1x3xf32>) -> tensor<1x3xf32> return %12 : tensor<1x3xf32> } // CHECK-LABEL: func @matmul_with_relu
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/quantization/stablehlo/tests/passes/lift_quantizable_spots_as_functions.mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 10 04:07:09 UTC 2024 - 49.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tests/raise-target-subgraphs.mlir
%3 = "tfl.reshape"(%1, %2) {tac.device = "GPU", tac.inference_type = "FLOAT"} : (tensor<1x128x128xf32>, tensor<2xi32>) -> tensor<128x128xf32> %4 = "tfl.relu"(%3) {tac.device = "GPU", tac.inference_type = "FLOAT"} : (tensor<128x128xf32>) -> tensor<128x128xf32> %5 = "tfl.pseudo_const"() {value = dense<[1, 128, 128]> : tensor<3xi32>} : () -> tensor<3xi32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 74.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/python/integration_test/quantize_model_test_base.py
dilations: Sequence[int] = (1, 1, 1, 1), padding: str = 'SAME', ): class DepthwiseConvModel(module.Module): """A simple model with a single depthwise conv2d, bias and relu.""" def __init__(self): self.out_channel_size = filter_shape[2] * filter_shape[3] # This ensures filters will have different value range per out channel self.filters = np.stack(
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Mar 21 08:51:46 UTC 2024 - 51.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/prepare-quantize-post-training.mlir
%5 = "quantfork.stats"(%4) {layerStats = dense<[-56.2916565, 122.922478]> : tensor<2xf32>} : (tensor<1x4xf32>) -> tensor<1x4xf32> %6 = "tfl.svdf"(%0, %1, %2, %3, %5) {fused_activation_function = "RELU", rank = 1 : i32} : (tensor<1x3xf32>, tensor<2x3xf32>, tensor<2x1xf32>, tensor<2xf32>, tensor<1x4xf32>) -> tensor<1x2xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 52.6K bytes - Viewed (0) -
tensorflow/compiler/jit/mark_for_compilation_pass.cc
"TanhGrad", "Pow", "SquaredDifference", "ApproximateEqual", // Others "AddN", "Bitcast", "Cast", "ClipByValue", "Const", "Empty", "Identity", "IdentityN", "Relu", "Relu6", "ReluGrad", "Relu6Grad", "LeakyReluGrad", "Elu", "EluGrad", "Selu", "SeluGrad", "Select", "SelectV2", "Transpose", "ConjugateTranspose",
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Feb 21 12:19:41 UTC 2024 - 85.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/quantize_composite_functions.mlir
// CHECK-PER-TENSOR: return %[[UNIFORM_QUANTIZE_0]] : tensor<?x3x4x2x!quant.uniform<i8:f32, {{.*}}>> // ----- // Tests that fused pattern for convolution + bias + relu with // dynamic batch dimension is properly quantized. // Note that this checks for identical condition as // quantize_conv_with_bias_dynamic_fn, omitting stablehlo.maximum.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 09 05:56:10 UTC 2024 - 91.6K bytes - Viewed (0)