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Results 1 - 6 of 6 for _backprop (0.48 sec)
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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) -
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) -
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)