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Results 21 - 30 of 79 for RELU (0.39 sec)
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tensorflow/compiler/jit/tests/keras_imagenet_main.golden_summary
Conv2D 53 Conv2DBackpropFilter 53 Conv2DBackpropInput 52 Equal 1 FusedBatchNormGradV2 53 FusedBatchNormV2 53 MatMul 3 MaxPool 1 MaxPoolGrad 1 Mean 1 Mul 218 Pad 2 ReadVariableOp 538 Relu 49 ReluGrad 49 Reshape 2 ResourceApplyKerasMomentum 161 Slice 1 Softmax 1 SparseSoftmaxCrossEntropyWithLogits 1 Squeeze 1 Sum 1 Tile 1 Transpose 1 cluster 1 size 815 AddN 1
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Jan 06 10:38:14 UTC 2023 - 874 bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/mlir2flatbuffer/basic_lstm.mlir
// CHECK-NEXT: outputs: [ 5, 6, 7, 8 ], // CHECK-NEXT: builtin_options_type: LSTMOptions, // CHECK-NEXT: builtin_options: { // CHECK-NEXT: fused_activation_function: RELU, // CHECK-NEXT: cell_clip: 1.0, // CHECK-NEXT: proj_clip: 2.0, // CHECK-NEXT: kernel_type: BASIC // CHECK-NEXT: }, // CHECK-NEXT: intermediates: [ ] // CHECK-NEXT: } ],
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jul 14 16:41:28 UTC 2022 - 4.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/canonicalize.td
// Canonicalize tf.Maximum of zero to tf.Relu //===----------------------------------------------------------------------===// def IsInteger32Pred: CPred< "getElementTypeOrSelf($0.getType()).isInteger(32)">; // Whether the transformation is compatible with the device if given. // Currently, Relu with int32 is not supported on GPU. def IsDeviceCompatible: Constraint<
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Dec 06 18:42:28 UTC 2023 - 17K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/fused_kernel_matcher.mlir
// CHECK: %[[VAL_0:.*]] = "tf._FusedConv2D"(%arg2, %arg1, %arg0) <{data_format = "NHWC", dilations = [1, 1, 1, 1], epsilon = 0.000000e+00 : f32, explicit_paddings = [], fused_ops = ["BiasAdd", "Relu"], num_args = 1 : i64, operandSegmentSizes = array<i32: 1, 1, 1, 0>, padding = "SAME", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true}> {TArgs = [f32]} : (tensor<8x32x32x3xf32>, tensor<1x1x3x128xf32>, tensor<128xf32>) -> tensor<*xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 13.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/quantized_function_library.mlir
{"quantized_ops": ["${main_op}", "Relu"], "act_func": "internal_requantize_and_relu_fn", "output_type": "i8"}, {"quantized_ops": ["${main_op}", "Relu6"], "act_func": "internal_requantize_and_relu6_fn", "output_type": "i8"}, {"quantized_ops": ["${main_op}"], "act_func": "internal_dequantize_no_activation_fn", "output_type": "f32"},
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Jan 08 01:16:10 UTC 2024 - 30.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfr/passes/decompose_patterns.td
(TFR_ConstantTensorOp (Arith_ConstantOp ConstantAttr<I32Attr, "127">))]>; def QuantActRangeReluPattern : Pattern< (TFR_TFRQuantActRangeOp (TFR_ConstOp HasStringAttr<"RELU">:$act), (ConstantLikeMatcher F32Attr:$scale), (ConstantLikeMatcher I64Attr:$zp)), [(TFR_ConstantTensorOp (Arith_ConstantOp (Quantize<"0.0f"> $scale, $zp))),
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Sep 29 21:02:21 UTC 2022 - 2.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tests/target-annotation.mlir
// CHECK: tac.device = "GPU", tac.inference_type = "FLOAT" %1 = "tfl.add"(%arg0, %0) {fused_activation_function = "RELU"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32> // CHECK: tac.device = "GPU", tac.inference_type = "FLOAT" %2 = "tfl.relu"(%arg0) : (tensor<1xf32>) -> tensor<1xf32> // CHECK: tac.device = "CPU", tac.inference_type = "FLOAT"
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 19 19:32:06 UTC 2023 - 6.2K 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/quantization/common/quantization_lib/quantization_driver_test.cc
%0 = "tfl.conv_2d"(%arg0, %arg1, %arg2) {dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "RELU", padding = "VALID", stride_h = 1 : i32, stride_w = 1 : i32} : (tensor<1x4x4x3xf32>, tensor<3x1x1x3xf32>, tensor<3xf32>) -> tensor<1x4x4x3xf32> return %0 : tensor<1x4x4x3xf32> }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 7.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/insert_quantized_functions.mlir
// CHECK: func private @quantized_conv2d_with_relu6_fn // CHECK: func private @quantized_depthwise_conv2d_with_bias_and_relu_float_output_fn // CHECK-SAME: tf_quant.quantized_ops = ["DepthwiseConv2D", "BiasAdd", "Relu"] // CHECK: func private @quantized_matmul_with_bias_fn // CHECK: func private @quantized_matmul_with_bias_and_relu_fn // CHECK: func private @quantized_matmul_with_bias_and_relu6_fn // CHECK: func private @quantized_matmul_fn
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Aug 29 01:13:58 UTC 2023 - 3.3K bytes - Viewed (0)