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tensorflow/compiler/mlir/tfr/integration/graph_decompose_test.py
t3 = constant_op.constant([[1.0, 2.0], [3.0, 4.0]]) sq1 = add([t1]) sq2 = add([t1, t2]) sq3 = add([t1, t2, t3]) self.assertAllEqual(sq1.numpy().reshape(-1), [1, 2, 3, 4]) self.assertAllEqual(sq2.numpy().reshape(-1), [2, 4, 6, 8]) self.assertAllEqual(sq3.numpy().reshape(-1), [3, 6, 9, 12]) def testBiasedDense(self): biased_dense = def_function.function(gen_composite_ops.my_biased_dense)
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Sep 28 21:37:05 UTC 2021 - 3.2K bytes - Viewed (0) -
ci/official/containers/linux_arm64/jax.requirements.txt
setuptools wheel cloudpickle colorama>=0.4.4 matplotlib pillow>=9.1.0 rich absl-py portpicker six opt-einsum auditwheel typing_extensions importlib_metadata>=4.6 numpy==1.26.0;python_version=="3.12" numpy==1.23.4;python_version=="3.11" numpy==1.22.4;python_version<"3.11" scipy==1.11.2;python_version=="3.12" scipy==1.9.2;python_version=="3.11" scipy==1.7.3;python_version<"3.11" ml_dtypes>=0.2.0
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jan 30 20:02:17 UTC 2024 - 570 bytes - Viewed (0) -
tensorflow/compiler/mlir/tfr/integration/node_expansion_test.py
sq2 = gen_composite_ops.my_add_n([t1, t2]) sq3 = gen_composite_ops.my_add_n([t1, t2, t3]) self.assertAllEqual(sq1.numpy().reshape(-1), [1, 2, 3, 4]) self.assertAllEqual(sq2.numpy().reshape(-1), [2, 4, 6, 8]) self.assertAllEqual(sq3.numpy().reshape(-1), [3, 6, 9, 12]) def testBiasedDense(self): t1 = constant_op.constant([[1.0, 2.0], [3.0, 4.0]])
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Sep 28 21:37:05 UTC 2021 - 3.9K bytes - Viewed (0) -
ci/official/requirements_updater/requirements.in
# TODO(b/304751256): Adjust the numpy pin to a single version, when ready numpy ~= 1.23.5 ; python_version <= "3.11" numpy ~= 1.26.0 ; python_version >= "3.12" wheel ~= 0.41.2 h5py >= 3.10.0 lit ~= 17.0.2 opt_einsum == 3.3.0 astunparse == 1.6.3 dill == 0.3.7 astor == 0.7.1 typing_extensions == 4.8.0 gast == 0.4.0 termcolor == 2.3.0 wrapt == 1.16.0 tblib == 2.0.0 ml_dtypes >= 0.4.0, < 0.5.0 # Install tensorboard, and keras
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jun 12 19:00:33 UTC 2024 - 834 bytes - Viewed (0) -
WORKSPACE
NUMPY_ANNOTATIONS = { "numpy": package_annotation( additive_build_content = """\ filegroup( name = "includes", srcs = glob(["site-packages/numpy/core/include/**/*.h"]), ) cc_library( name = "numpy_headers", hdrs = [":includes"], strip_include_prefix="site-packages/numpy/core/include/", ) """, ), } pip_parse( name = "pypi",
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Apr 05 22:27:48 UTC 2024 - 3K bytes - Viewed (0) -
tensorflow/c/experimental/ops/math_ops.cc
namespace tensorflow { namespace ops { // Op: Mul() // Summary: Returns x * y element-wise. // // Description: // *NOTE*: `Multiply` supports broadcasting. More about broadcasting // [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) Status Mul(AbstractContext* ctx, AbstractTensorHandle* const x, AbstractTensorHandle* const y, AbstractTensorHandle** z, const char* name, const char* raw_device_name) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 10 19:11:36 UTC 2022 - 12.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/python/integration_test/stablehlo_quantizer_odml_oss.ipynb
"metadata": { "id": "c9JX9RJTPaoW" }, "outputs": [], "source": [ "import os\n", "os.environ['KERAS_BACKEND'] = 'jax'\n", "import jax.numpy as jnp\n", "import numpy as np\n", "import tensorflow as tf\n", "from keras_core.applications import ResNet50\n", "from jax.experimental import jax2tf" ] }, {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Apr 12 03:40:43 UTC 2024 - 5.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/python/representative_dataset.py
"""Defines types required for representative datasets for quantization.""" from collections.abc import Collection, Sized import os from typing import Iterable, Mapping, Optional, Union import numpy as np from tensorflow.compiler.mlir.quantization.tensorflow import quantization_options_pb2 from tensorflow.core.protobuf import meta_graph_pb2 from tensorflow.python.client import session
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Mar 22 22:55:22 UTC 2024 - 14.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/python/BUILD
"//tensorflow/python/saved_model:tag_constants", "//tensorflow/python/training:checkpoint_utils", "//tensorflow/python/types:core", "//third_party/py/numpy", "@absl_py//absl/testing:parameterized", ], ) pytype_strict_library( name = "quantize_model_test_base", testonly = 1, srcs = ["integration_test/quantize_model_test_base.py"],
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 17 03:36:50 UTC 2024 - 18.8K bytes - Viewed (0) -
src/math/rand/v2/pcg.go
// Use of this source code is governed by a BSD-style // license that can be found in the LICENSE file. package rand import ( "errors" "internal/byteorder" "math/bits" ) // https://numpy.org/devdocs/reference/random/upgrading-pcg64.html // https://github.com/imneme/pcg-cpp/commit/871d0494ee9c9a7b7c43f753e3d8ca47c26f8005 // A PCG is a PCG generator with 128 bits of internal state.
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Mon May 13 21:31:58 UTC 2024 - 2.9K bytes - Viewed (0)