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tensorflow/c/experimental/gradients/custom_gradient_test.cc
{ AbstractTensorHandle* x_raw = nullptr; Status s = TestScalarTensorHandle<float, TF_FLOAT>(ctx.get(), 1.0f, &x_raw); ASSERT_EQ(errors::OK, s.code()) << s.message(); x.reset(x_raw); } // Pseudo-code: // // tape.watch(x) // y = exp(x) // outputs = tape.gradient(y, x) std::vector<AbstractTensorHandle*> outputs(1); Status s = RunModel(ExpWithPassThroughGrad, ctx.get(), {x.get()},
C++ - Registered: Tue Mar 26 12:39:09 GMT 2024 - Last Modified: Wed Feb 28 13:53:47 GMT 2024 - 4.8K bytes - Viewed (0) -
RELEASE.md
* Clean up `BatchNormalization` layer's `trainable` property to act like standard python state when it's used inside `tf.functions` (frozen at tracing time), instead of acting like a pseudo-variable whose updates *kind of sometimes* get reflected in already-traced `tf.function` traces. * Add the `Conv1DTranspose` layer. * Refine the semantics of `SensitivitySpecificityBase` derived metrics. See
Plain Text - Registered: Tue May 07 12:40:20 GMT 2024 - Last Modified: Mon Apr 29 19:17:57 GMT 2024 - 727.7K bytes - Viewed (8)