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Results 11 - 15 of 15 for reduce_max (0.34 sec)
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RELEASE.md
`tf.reduce_join`: `reduction_indices` becomes `axis` * `tf.reduce_logsumexp`: `reduction_indices` becomes `axis` * `tf.reduce_max`: `reduction_indices` becomes `axis` * `tf.reduce_mean`: `reduction_indices` becomes `axis` * `tf.reduce_min`: `reduction_indices` becomes `axis` * `tf.reduce_prod`: `reduction_indices` becomes `axis` * `tf.reduce_sum`: `reduction_indices`
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 11 23:24:08 UTC 2024 - 730.3K bytes - Viewed (0) -
tensorflow/cc/gradients/math_grad.cc
auto x_shape = Shape(scope, x); auto output_shape = Shape(scope, op.output(0)); // Reduce away broadcasted leading dims. auto reduce_x = internal::BroadcastGradientArgs(scope, x_shape, output_shape); auto gx_sum = ReduceSum(scope, gx, /*axis=*/reduce_x.r0, ReduceSum::KeepDims(true)); auto gx_sum_reshape = Reshape(scope, gx_sum, x_shape); auto gy = SelectV2(scope, c, zeros, grad_inputs[0]);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Aug 25 18:20:20 UTC 2023 - 50.7K bytes - Viewed (0) -
tensorflow/cc/gradients/math_grad_test.cc
// gradients when perturbing each entry in the Tensor (which then // changes how many minima exist.) // Instead, we use a single input that broadcast-multiplies a larger // tensor with equal values, and apply reduce_min to the multiplied // result. TensorShape x_shape({1}); auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape)); auto all_same = Mul(scope_, Const(scope_, {1.f, 1.f, 1.f}), x);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Aug 25 18:20:20 UTC 2023 - 36K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/schema/schema_generated.h
"SUM", "SQRT", "RSQRT", "SHAPE", "POW", "ARG_MIN", "FAKE_QUANT", "REDUCE_PROD", "REDUCE_MAX", "PACK", "LOGICAL_OR", "ONE_HOT", "LOGICAL_AND", "LOGICAL_NOT", "UNPACK", "REDUCE_MIN", "FLOOR_DIV", "REDUCE_ANY", "SQUARE", "ZEROS_LIKE", "FILL", "FLOOR_MOD", "RANGE",
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 21 18:21:50 UTC 2024 - 1M bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/python/integration_test/quantize_model_test.py
average_min_max_output = converted_model_average_min_max.signatures[ 'serving_default' ](input_tensor=sample_input)['output'] def get_mean_square_error(x, y): ret = tensorflow.reduce_mean(tensorflow.square(tensorflow.subtract(x, y))) try: ret = ret.numpy() except AttributeError: ret = ret.eval() return ret
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 17 03:36:50 UTC 2024 - 235.6K bytes - Viewed (0)