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models.BUILD
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tensorflow/c/eager/unified_api_testutil.h
// outputs = tf.function(model)(inputs) // else: // outputs = model(inputs) Status RunModel(Model model, AbstractContext* ctx, absl::Span<AbstractTensorHandle* const> inputs, absl::Span<AbstractTensorHandle*> outputs, bool use_function); Status BuildImmediateExecutionContext(bool use_tfrt, AbstractContext** ctx); // Return a tensor handle with given type, values and dimensions.
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.github/ISSUE_TEMPLATE/tflite-other.md
false - type: input id: Cuda attributes: label: CUDA/cuDNN version description: placeholder: validations: required: false - type: input id: Gpu attributes: label: GPU model and memory description: if compiling from source placeholder: validations: required: false - type: textarea id: what-happened attributes: label: Current Behaviour?
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tensorflow/c/experimental/gradients/nn_grad_test.cc
TF_RETURN_IF_ERROR(ops::SparseSoftmaxCrossEntropyWithLogits( ctx, inputs[0], inputs[1], &loss, &backprop, "SparseSoftmaxCrossEntropyWithLogits")); // `gradient_checker` only works with model that returns only 1 tensor. // Although, `ops::SparseSoftmaxCrossEntropyWithLogits` returns 2 tensors, the // second tensor isn't needed for computing gradient so we could safely drop // it. outputs[0] = loss;
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tensorflow/c/experimental/gradients/grad_test_helper.h
void CompareNumericalAndAutodiffGradients( Model model, Model grad_model, AbstractContext* ctx, absl::Span<AbstractTensorHandle* const> inputs, bool use_function, double abs_error = 1e-2); void CheckTensorValue(AbstractTensorHandle* t, absl::Span<const float> manuals, absl::Span<const int64_t> dims, double abs_error = 1e-2); Model BuildGradModel(Model forward, GradientRegistry registry);
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SECURITY.md
TensorFlow [**models**](https://developers.google.com/machine-learning/glossary/#model) (to use a term commonly used by machine learning practitioners) are expressed as programs that TensorFlow executes. TensorFlow programs are encoded as computation [**graphs**](https://developers.google.com/machine-learning/glossary/#graph). Since models are practically programs that TensorFlow executes, using untrusted
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.github/ISSUE_TEMPLATE/tflite-converter-issue.md
1) Reference [TensorFlow Model Colab](https://colab.research.google.com/gist/ymodak/e96a4270b953201d5362c61c1e8b78aa/tensorflow-datasets.ipynb?authuser=1): Demonstrate how to build your TF model.
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tensorflow/c/eager/gradient_checker.h
namespace tensorflow { namespace gradients { /* Returns numerical grad inside `dtheta_approx` given `forward` model and * parameter specified by `input_index`. * * I.e. if y = <output of the forward model> and w = inputs[input_index], * this will calculate dy/dw numerically. * * `use_function` indicates whether to use graph mode(true) or eager(false). * * `numerical_grad` is the pointer to the AbstractTensorHandle* which will
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.github/bot_config.yml
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tensorflow/c/eager/gradient_checker.cc
RunModel(forward, ctx, inputs, model_outputs, use_function)); AbstractTensorHandlePtr model_out(model_outputs[0]); TF_Tensor* model_out_tensor; TF_RETURN_IF_ERROR(GetValue(model_out.get(), &model_out_tensor)); int num_dims_out = TF_NumDims(model_out_tensor); TF_DeleteTensor(model_out_tensor); // If the output is a scalar, then return the scalar output if (num_dims_out == 0) { outputs[0] = model_out.release();
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