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Results 61 - 70 of 111 for RELU (0.18 sec)
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tensorflow/compiler/mlir/tfr/integration/graph_decompose_test.py
t1 = constant_op.constant([[1.0, 2.0], [3.0, 4.0]]) t2 = constant_op.constant([[1.0, 2.0], [3.0, 4.0]]) t3 = constant_op.constant([[-10.0, -10.0], [-10.0, -10.0]]) sq = biased_dense(t1, t2, t3, act='relu') self.assertAllEqual(sq.numpy().reshape(-1), [0, 0, 5, 12]) def testWithKnownKernel(self): @def_function.function def biasd_dense_elu(x, y, z): dot = gen_composite_ops.my_biased_dense(x, y, z)
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Sep 28 21:37:05 UTC 2021 - 3.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/python/integration_test/quantize_model_test.py
('none', None, False, False, quant_opts_pb2.TF, False, 'SAME'), ('relu', nn_ops.relu, False, False, quant_opts_pb2.TF, False, 'SAME'), ('relu6', nn_ops.relu6, False, False, quant_opts_pb2.TF, False, 'SAME'), ('with_bias', None, True, False, quant_opts_pb2.TF, False, 'SAME'), ( 'with_bias_and_relu', nn_ops.relu, True, False, quant_opts_pb2.TF,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 17 03:36:50 UTC 2024 - 235.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfr/integration/node_expansion_test.py
t1 = constant_op.constant([[1.0, 2.0], [3.0, 4.0]]) t2 = constant_op.constant([[1.0, 2.0], [3.0, 4.0]]) t3 = constant_op.constant([[-10.0, -10.0], [-10.0, -10.0]]) sq = gen_composite_ops.my_biased_dense(t1, t2, t3, act='relu') self.assertAllEqual(sq.numpy().reshape(-1), [0, 0, 5, 12]) def testWithKnownKernel(self): def biasd_dense_elu(x, y, z): dot = gen_composite_ops.my_biased_dense(x, y, z)
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Sep 28 21:37:05 UTC 2021 - 3.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/mlir2flatbuffer/custom_op_with_tflite_op.mlir
// tf.MyCustomOp is the result of conversion to a Custom op %2 = "tf.MyCustomOp"(%1, %0) {fused_activation_function = "RELU", int_attr = 2 : i32} : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32> loc("MyCustomOp") %3 = "tfl.exp"(%2) : (tensor<4xf32>) -> tensor<4xf32> loc("exp") func.return %3 : tensor<4xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jul 14 16:41:28 UTC 2022 - 4.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/optimize_no_verify.mlir
%cst = arith.constant dense<0.0> : tensor<2x3xbf16> %0 = "tfl.maximum"(%arg0, %cst) : (tensor<2x3xbf16>, tensor<2x3xbf16>) -> tensor<2x3xbf16> func.return %0 : tensor<2x3xbf16> // CHECK: %[[RESULT:.*]] = "tfl.relu"(%arg0) // CHECK: return %[[RESULT]] } // CHECK-LABEL: fuseScalarAddIntoConv2dBf16 func.func @fuseScalarAddIntoConv2dBf16(%arg0: tensor<256x32x32x3xbf16>, %arg1: tensor<16x3x3x3xbf16>) -> tensor<256x8x7x16xbf16> {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 5.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/ir/tfl_op_enums.td
} // Allowed activation function cases // These should match the ActivationFunctionType enum in TFLite schema. def TFL_AFEnum_None : I32EnumAttrCase<"NONE", 0>; def TFL_AFEnum_Relu : I32EnumAttrCase<"RELU", 1>; def TFL_AFEnum_Relu1 : I32EnumAttrCase<"RELU_N1_TO_1", 2>; def TFL_AFEnum_Relu6 : I32EnumAttrCase<"RELU6", 3>; def TFL_AFEnum_Tanh : I32EnumAttrCase<"TANH", 4>; def TFL_AFEnum_Sign : I32EnumAttrCase<"SIGN_BIT", 5>;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Oct 20 00:05:24 UTC 2022 - 6.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/optimize.mlir
// Fusing: %[[add1:[0-9].*]] = tfl.add %arg0, %[[add]] {fused_activation_function = "RELU"} : tensor<1xf32> // Fusing: %[[relu:[0-9].*]] = "tfl.relu"(%arg0) : (tensor<1xf32>) -> tensor<1xf32> // Fusing: %[[add2:[0-9].*]] = tfl.add %[[relu]], %[[add1]] {fused_activation_function = "RELU6"} : tensor<1xf32> // Fusing: %[[add3:[0-9].*]] = tfl.add %[[add2]], %[[relu]] {fused_activation_function = "RELU6"} : tensor<1xf32> // Fusing: return
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 16 20:31:41 UTC 2024 - 284.1K 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/lite/tests/tfl_while_outline.mlir
%14 = "tfl.relu"(%10#1) : (tensor<4x2xf32>) -> tensor<4x2xf32> %15 = "tfl.logistic"(%10#0) : (tensor<4x2xf32>) -> tensor<4x2xf32> %16 = tfl.mul %15, %14 {fused_activation_function = "NONE"} : tensor<4x2xf32> %17 = tfl.add %13, %16 {fused_activation_function = "NONE"} : tensor<4x2xf32> %18 = "tfl.relu"(%17) : (tensor<4x2xf32>) -> tensor<4x2xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 13.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/hardwares/gpu_hardware.cc
// tfl.Abs / tfl.Average_pool_2d / tfl.Cos / tfl.div / tfl.exp / tfl.hardswish / // tfl.log / tfl.logistic / tfl.max_pool_2d / tfl.mirror_pad / tfl.maximum / // tfl.custom / tfl.mean / tfl.minimum / tfl.pad / tfl.pow / tfl.prelu / // tfl.relu / tfl.relu6 / tfl.rsqrt / tfl.sin / tfl.slice / tfl.softmax / // tfl.space_to_depth / tfl.sqrt / tfl.square / tfl.squared_difference / // tfl.strided_slice / tfl.tanh / tfl.transpose / tfl.transpose_conv
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 06 03:08:33 UTC 2023 - 7.8K bytes - Viewed (0)