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Results 11 - 20 of 27 for _backprop (0.97 sec)
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tensorflow/c/eager/tape.h
// any gradients to be computed). // // Finally, we start a backprop stack with a set of tape entries for which we // have all gradients available. This set usually is a subset of the set of // targets (not all since targets which have outputs in the tape will not have // gradients available initially). // // Then we repeatedly pop an entry from the stack, run its backprop, and update
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Apr 02 12:40:29 UTC 2024 - 47.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/tests/legalize-tf-with-tf2xla-hlo-importer.mlir
// CHECK: %[[offset_backprop:.*]] = mhlo.convert %[[red2]] : tensor<8xf32> // CHECK: %[[x_backprop:.*]] = mhlo.convert %[[mul3]] : tensor<8x8x8x8xf32> // CHECK: return %[[x_backprop]] : tensor<8x8x8x8xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Apr 06 15:32:52 UTC 2024 - 38.6K bytes - Viewed (0) -
samples/bookinfo/src/productpage/static/tailwind/tailwind.css
s":" ","--tw-backdrop-contrast":" ","--tw-backdrop-grayscale":" ","--tw-backdrop-hue-rotate":" ","--tw-backdrop-invert":" ","--tw-backdrop-opacity":" ","--tw-backdrop-saturate":" ","--tw-backdrop-sepia":" "}),e({".backdrop-filter":{"@defaults backdrop-filter":{},"backdrop-filter":Fe},".backdrop-filter-none":{"backdrop-filter":"none"}})},transitionProperty:({matchUtilities:i,theme:e})=>{let t=e("transitionTimingFunction.DEFAULT"),r=e("transitionDuration.DEFAULT");i({transition:n=>({"transition-pr...
Registered: Fri Jun 14 15:00:06 UTC 2024 - Last Modified: Tue May 28 14:48:01 UTC 2024 - 357.1K bytes - Viewed (1) -
tensorflow/compiler/mlir/lite/stablehlo/transforms/fuse_convolution_pass.cc
}); } filter_value = filter.getValue(); mul_value = multiplier.getValue(); // In MHLO, Conv filter is in HWIO format, Depthwise conv filter is in HW1O // format and backprop input conv filter is in HWOI format. // Only fuses multiplier if all dimensions other than the out channel // dimension are equal to 1. if (!TFL::IsDimensionsDegenerateExceptLastOne(
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Feb 22 22:21:19 UTC 2024 - 8.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/tpu_space_to_depth_pass.mlir
// CHECK: %[[BACKPROP:.*]] = "tf.Conv2DBackpropFilter" // CHECK-SAME: strides = [1, 1, 1, 1] // CHECK-SAME: (tensor<2x115x115x12xf32>, tensor<4xi32>, tensor<2x112x112x64xf32>) -> tensor<4x4x12x64xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 37.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/tests/legalize-tf.mlir
// CHECK-NEXT: %[[offset_backprop:.*]] = mhlo.convert %[[red2]] : tensor<8xf32> // CHECK-NEXT: %[[x_backprop:.*]] = mhlo.convert %[[mul3]] : tensor<8x8x8x8xf32> // CHECK-NEXT: return %[[x_backprop]] : tensor<8x8x8x8xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon May 06 18:46:23 UTC 2024 - 335.5K bytes - Viewed (0) -
tensorflow/cc/framework/gradients_test.cc
{dx, dy, dz}, &grad_outputs)); } } CompareTestAndExpectedGraphs(); } TEST_F(GradientsTest, StackUnstack_StopBackprop) { // Tests that backprop stops before calculating gradients for Stack (because // only gradients w.r.t the output of Stack are requested). for (const bool expected : {false, true}) { const Scope& scope = expected ? scope_expected_ : scope_test_;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Apr 15 15:13:38 UTC 2023 - 25K bytes - Viewed (0) -
tensorflow/c/while_loop_test.cc
Add(params_->body_inputs[0], {one, 0}, params_->body_graph, s_); ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_); params_->body_outputs[0] = {add, 0}; ExpectOK(); // Create backprop graph TF_Output grad_output; TF_AddGradients(graph_, outputs_.data(), outputs_.size(), inputs_.data(), 1, nullptr, s_, &grad_output); ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 11 06:05:56 UTC 2024 - 15.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/transforms/legalize_tf.cc
Value scratch2 = ApplyReduction(loc, weighted_grad, reduce_dims, &rewriter); // x_backprop = y_backprop * (scale * scratch1) auto scaled_grad = rewriter.create<mhlo::MulOp>(loc, op.getScale(), scratch1); x_backprop = rewriter.create<mhlo::MulOp>( loc, grad, Broadcast1DToFeatureDim(loc, act, scaled_grad, feature_dim,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 11 20:00:43 UTC 2024 - 291.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/ir/tf_generated_ops.td
example, suppose y = f(x) and we wish to apply a custom function g for backprop such that dx = g(dy). In Python, ```python with tf.get_default_graph().gradient_override_map( {'IdentityN': 'OverrideGradientWithG'}): y, _ = identity_n([f(x), x]) @tf.RegisterGradient('OverrideGradientWithG') def ApplyG(op, dy, _): return [None, g(dy)] # Do not backprop to f(x). ``` }]; let arguments = (ins
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 11 23:24:08 UTC 2024 - 793K bytes - Viewed (0)