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Results 1 - 7 of 7 for relu6_grad (0.16 sec)
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tensorflow/compiler/mlir/tfr/examples/mnist/ops_defs.py
@tf.RegisterGradient('NewConv2D') def _conv_add_relu_grad(op: ops.Operation, grad): act = op.get_attr('act') y = op.outputs[0] if act == 'RELU': grad = gen_nn_ops.relu_grad(grad, y) elif act == 'RELU6': grad = gen_nn_ops.relu6_grad(grad, y) elif act == 'TANH': y = math_ops.conj(y) grad = gen_math_ops.tanh_grad(y, grad) broadcast_shape = tf.shape(y) input_value_shape = tf.shape(op.inputs[2])
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Aug 31 20:23:51 UTC 2023 - 6.8K bytes - Viewed (0) -
tensorflow/c/experimental/gradients/nn_grad.cc
AbstractTensorHandle* upstream_grad = grad_outputs[0]; AbstractTensorHandle* activations = forward_outputs_[0]; // Calculate Grad std::string name = "relu_grad"; TF_RETURN_IF_ERROR(ReluGrad(ctx, upstream_grad, activations, &grad_inputs[0], name.c_str())); return absl::OkStatus(); } ~ReluGradientFunction() override {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Apr 09 06:38:45 UTC 2024 - 5.7K bytes - Viewed (0) -
tensorflow/c/experimental/ops/nn_ops.cc
Status status = op_ptr->Execute(temp_outputs, &num_retvals); *loss = temp_outputs[0]; *backprop = temp_outputs[1]; return status; } // Op: ReluGrad() // Summary: Computes rectified linear gradients for a Relu operation. // // Description: Status ReluGrad(AbstractContext* ctx, AbstractTensorHandle* const gradients, AbstractTensorHandle* const features,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 10 19:11:36 UTC 2022 - 5.9K bytes - Viewed (0) -
tensorflow/c/experimental/ops/update_cpp_ops.sh
AddV2 \ MatMul \ Neg \ Sum \ Sub \ Div \ DivNoNan \ Exp \ Sqrt \ SqrtGrad \ Log1p ${generate} \ --category=nn \ SparseSoftmaxCrossEntropyWithLogits \ ReluGrad \ Relu \ BiasAdd \ BiasAddGrad ${generate} \ --category=resource_variable \ VarHandleOp \ ReadVariableOp \ AssignVariableOp \ DestroyResourceOp ${generate} \
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 17 17:54:34 UTC 2022 - 1.6K bytes - Viewed (0) -
tensorflow/compiler/jit/tests/keras_imagenet_main_graph_mode.golden_summary
Conv2DBackpropInput 52 DivNoNan 1 Equal 1 FusedBatchNorm 53 FusedBatchNormGrad 53 Identity 2 MatMul 3 MaxPool 1 MaxPoolGrad 1 Mean 1 Mul 164 Pad 1 ReadVariableOp 646 Relu 49 ReluGrad 49 Reshape 2 ResourceApplyKerasMomentum 161 ShapeN 50 Softmax 1 SparseSoftmaxCrossEntropyWithLogits 1 Square 55 Squeeze 1 Sub 106 Sum 57 Tile 1
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Jan 06 10:38:14 UTC 2023 - 740 bytes - Viewed (0) -
tensorflow/compiler/jit/tests/keras_imagenet_main.golden_summary
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 AssignAddVariableOp 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/c/experimental/ops/nn_ops.h
AbstractTensorHandle** backprop, const char* name = nullptr, const char* raw_device_name = nullptr); // Computes rectified linear gradients for a Relu operation. Status ReluGrad(AbstractContext* ctx, AbstractTensorHandle* const gradients, AbstractTensorHandle* const features, AbstractTensorHandle** backprops, const char* name = nullptr,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 10 19:11:36 UTC 2022 - 2.6K bytes - Viewed (0)