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Results 1 - 10 of 11 for _backprop (0.56 sec)
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tensorflow/cc/framework/while_gradients.cc
return result; } // The backprop loop counter and main backprop loop run in their own execution // frame (conceptually, the main forward loop and forward loop counter run // together in a frame, then the backprop loop counter and backprop loop run // together in a different frame). This returns the frame name to use for the // backprop while loops.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Apr 13 05:57:22 UTC 2024 - 8.1K bytes - Viewed (0) -
tensorflow/cc/framework/gradients.cc
DCHECK(while_ctx != nullptr); // Record 'summed_grads' as the backprop input associated with 'exit_node' std::map<Node*, Output>& backprops = while_backprops_[while_ctx]; DCHECK(backprops.find(exit_node) == backprops.end()); backprops[exit_node] = summed_grads; // Wait until we have all exit nodes' backprops collected before processing // the while loop.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Apr 13 05:57:22 UTC 2024 - 22K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/tpu_space_to_depth_pass.cc
// Build new BackPropFilterOp. auto loc = backprop.getLoc(); auto new_backprop = builder.create<TF::Conv2DBackpropFilterOp>( loc, new_result_type, input, new_filter_sizes, backprop.getOutBackprop(), strides, backprop.getUseCudnnOnGpu(), backprop.getPadding(), backprop.getExplicitPaddings(), backprop.getDataFormat(), backprop.getDilations());
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 29.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/lower_tf.td
// computes loss and backprop of the loss with respect to 'features'. // // Softmax cross entropy loss is defined as follows: // // loss = Sum(-labels * Log(Exp(features) / Sum(Exp(features))) // loss = Sum(-labels * LogSoftmax(features)) // // Computing gradient of the loss with respect to features gives us, // // backprop = (Exp(features) / Sum(Exp(features))) - labels
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 04 13:30:42 UTC 2024 - 24.7K 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/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/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) -
RELEASE.md
* `tf.compat.v1.nn.fused_batch_norm` backprop to `offset` when `is_training=False` * `tf.image.adjust_contrast` forward * `tf.nn.depthwise_conv2d` backprop to `filter` when not using cuDNN convolution * `tf.image.resize` with `method=ResizeMethod.NEAREST` backprop * `tf.math.bincount` - TODO: confirm exception added
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 11 23:24:08 UTC 2024 - 730.3K bytes - Viewed (0)