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Results 1 - 10 of 62 for tanh (0.06 sec)
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tensorflow/compiler/mlir/tensorflow/tests/layout_optimization_move_transposes_begin.mlir
// CHECK: %[[ARG_TRANSPOSE:[0-9]*]] = "tf.Transpose"(%arg0, %[[ARG_PERM]]) // CHECK: %[[TANH:[0-9]*]] = "tf.Tanh"(%[[ARG_TRANSPOSE]]) {{.*}} tensor<1x8x4x4xf32> // CHECK: %[[ADD:[0-9]*]] = "tf.AddV2"(%[[TANH]], %[[TANH]]) {{.*}} tensor<1x8x4x4xf32> // CHECK: return %[[ADD]] %0 = "tf.Tanh"(%arg0) : (tensor<1x4x4x8xf32>) -> tensor<1x4x4x8xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 6.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfr/examples/mnist/ops_defs.py
elif act == 'TANH': return tf.raw_ops.Tanh(x=res) else: return res @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)
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/compiler/mlir/tensorflow/tests/layout_optimization_move_transposes_end.mlir
// CHECK: %[[RES_PERM:.*]] = "tf.Const"() <{value = dense<[0, 3, 1, 2]> : tensor<4xi32>}> // CHECK: %[[TANH:[0-9]*]] = "tf.Tanh"(%arg0) {{.*}} tensor<1x4x4x8xf32> // CHECK: %[[RES_TRANSPOSE:[0-9]*]] = "tf.Transpose"(%[[TANH]], %[[RES_PERM]]) {{.*}} tensor<1x8x4x4xf32> // CHECK: return %[[RES_TRANSPOSE]] %0 = "tf.Const"() {value = dense<[0, 3, 1, 2]> : tensor<4xi32>} : () -> tensor<4xi32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 9.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/quantize-dynamic-range-float16.mlir
%5, %6, %7, %8, %9, %9, %9, %10, %11, %10, %10, %9, %9, %recurrent_input, %cell_input, %9, %9, %9, %9) { cell_clip = 1.000000e+01 : f32, fused_activation_function = "TANH", proj_clip = 0.000000e+00 : f32, time_major = false} : ( tensor<1x2x3xf32>, tensor<1x1xf32>, tensor<1x1xf32>, tensor<1x1xf32>, tensor<1x1xf32>,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 4.6K bytes - Viewed (0) -
tensorflow/compiler/jit/mark_for_compilation_pass_test.cc
builder.opts().WithName("A")); Node* tanh0 = ops::UnaryOp("Tanh", call, builder.opts().WithName("tanh0")); Node* tanh1 = ops::UnaryOp("Tanh", tanh0, builder.opts().WithName("tanh1")); ops::UnaryOp("Tanh", tanh1, builder.opts().WithName("tanh2")); TF_EXPECT_OK(GraphDefBuilderToGraph(builder, graph.get())); }
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/tfrt/tests/tf_to_corert/basic.mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 08 00:18:59 UTC 2024 - 3.9K bytes - Viewed (0) -
tensorflow/cc/gradients/math_grad_test.cc
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Aug 25 18:20:20 UTC 2023 - 36K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/prepare-quantize-post-training-16bits.mlir
%9, %9, %9, %9) { asymmetric_quantize_inputs = false, cell_clip = 1.000000e+01 : f32, effective_hidden_scale_intermediate = tensor<0x!quant.calibrated<f32<0.0:1.0>>>, fused_activation_function = "TANH", input_to_cell_intermediate = tensor<0xf32>, input_to_forget_intermediate = tensor<0xf32>, input_to_input_intermediate = tensor<0xf32>, input_to_output_intermediate = tensor<0xf32>,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 26.1K bytes - Viewed (0) -
src/math/all_test.go
for i := 0; i < len(vf); i++ { if f := Tanh(vf[i]); !veryclose(tanh[i], f) { t.Errorf("Tanh(%g) = %g, want %g", vf[i], f, tanh[i]) } } for i := 0; i < len(vftanhSC); i++ { if f := Tanh(vftanhSC[i]); !alike(tanhSC[i], f) { t.Errorf("Tanh(%g) = %g, want %g", vftanhSC[i], f, tanhSC[i]) } } } func TestTrunc(t *testing.T) { for i := 0; i < len(vf); i++ {
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Fri Jul 07 17:39:26 UTC 2023 - 86.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/flatbuffer2mlir/lstm.mlir
"tfl.lstm"(%arg0, %arg1, %arg2, %arg3, %arg4, %arg5, %arg6, %arg7, %arg8, %cst, %cst, %cst, %arg9, %arg10, %arg11, %arg12, %arg13, %arg14, %arg19, %arg20, %arg15, %arg16, %arg17, %arg18) ({}) {cell_clip = 1.000000e+01 : f32, fused_activation_function = "TANH", input_to_input_intermediate = tensor<0x!quant.uniform<i16:f32, 0.0049890000373125076>>, input_to_forget_intermediate = tensor<0x!quant.uniform<i16:f32, 0.0078849997371435165>>, input_to_cell_intermediate = tensor<0x!quant.uniform<i16:f32, 0.0087630003690719604>>,...
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 20.4K bytes - Viewed (0)