| /external/tensorflow/tensorflow/core/api_def/base_api/ |
| api_def_RFFT.pbtxt | 22 @compatibility(numpy)
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| api_def_RFFT2D.pbtxt | 23 @compatibility(numpy)
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| api_def_RFFT3D.pbtxt | 23 @compatibility(numpy)
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| /external/tensorflow/tensorflow/examples/tutorials/deepdream/ |
| README.md | 23 - NumPy
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| /external/tensorflow/tensorflow/python/data/ops/ |
| BUILD | 27 "//third_party/py/numpy",
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| /external/tensorflow/tensorflow/python/data/util/ |
| BUILD | 30 "//third_party/py/numpy",
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| /external/tensorflow/tensorflow/python/eager/ |
| pywrap_tensor.h | 20 #include "tensorflow/python/lib/core/numpy.h"
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| graph_callable_test.py | 44 2, my_function(constant_op.constant(2, dtype=dtypes.float32)).numpy())
48 3, my_function(constant_op.constant(2, dtype=dtypes.float32)).numpy())
83 2, my_function(constant_op.constant(2, dtype=dtypes.float32)).numpy())
87 3, my_function(constant_op.constant(2, dtype=dtypes.float32)).numpy())
103 dtype=dtypes.float32)).numpy())
129 dtype=dtypes.float32)).numpy())
234 constant_op.constant([1, 2, 3], dtype=dtypes.float32)).numpy()).all())
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| /external/tensorflow/tensorflow/python/kernel_tests/ |
| draw_bounding_box_op_test.py | 21 import numpy as np
38 image: Numpy array of shape [height, width, depth].
39 color: Numpy color of shape [depth] and either contents RGB/RGBA.
61 img: 3-D numpy image on which to draw.
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| /external/tensorflow/tensorflow/python/lib/core/ |
| numpy.cc | 20 #include "tensorflow/python/lib/core/numpy.h"
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| /external/tensorflow/tensorflow/python/tools/ |
| inspect_checkpoint.py | 23 import numpy as np
76 """Sets a single numpy printoption from a string of the form 'x=y'.
78 See documentation on numpy.set_printoptions() for details about what values
154 help="Argument for numpy.set_printoptions(), in the form 'k=v'.")
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| /external/walt/pywalt/pywalt/ |
| evparser.py | 2 from numpy import array
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| /frameworks/base/tests/JankBench/scripts/ |
| itr_collect.py | 7 import numpy
93 stddev = numpy.std(res.durations)
94 mean = numpy.mean(res.durations)
129 print "\t%s:\t%0.2f%% (%0.2f avg sample count)" % (test, 100 * scipy.stats.variation(per_test_score[test]), numpy.mean(per_test_sample_count[test]))
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| collect.py | 7 import numpy
134 stddev = round_to_2(numpy.std(res.durations))
135 mean = round_to_2(numpy.mean(res.durations))
155 geo_run = numpy.mean(scores)
188 print "\t%s:\t%0.2f%% (%0.2f avg sample count)" % (test, variation, numpy.mean(per_test_sample_count[test]))
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| /test/vts/testcases/host/camera_its/ |
| AndroidTest.xml | 19 <option name="dep-module" value="numpy" />
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| /cts/apps/CameraITS/tests/scene3/ |
| test_reprocess_edge_enhancement.py | 23 import numpy
77 ret["sharpness"] = numpy.mean(sharpness_list)
169 assert(numpy.isclose(sharpness_regular[3], sharpness_regular[0],
186 assert(numpy.isclose(sharpnesses_reprocess[reprocess_format][3],
202 assert(numpy.isclose(sharpnesses_reprocess[reprocess_format][2] /
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| /external/tensorflow/tensorflow/contrib/factorization/python/ops/ |
| factorization_ops_test_utils.py | 23 import numpy as np
44 np_matrix: A numpy array.
47 mat: A numpy matrix obtained by removing empty rows and columns from
49 nz_row_ids: A numpy array of the ids of non-empty rows, such that
51 nz_col_ids: A numpy array of the ids of non-empty columns, such that
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| /external/v8/tools/ignition/ |
| bytecode_dispatches_report.py | 13 import numpy
131 counters_matrix = numpy.empty([len(labels), len(labels)], dtype=int)
140 counters_matrix = numpy.flipud(counters_matrix)
158 ticks=numpy.arange(0.5, len(xlabels)),
166 ticks=numpy.arange(0.5, len(ylabels)),
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| /external/tensorflow/tensorflow/tools/docker/notebooks/ |
| 1_hello_tensorflow.ipynb | 102 "What we're doing is creating two vectors, [1.0, 1.0, 1.0, 1.0] and [2.0, 2.0, 2.0, 2.0], and then adding them. Here's equivalent code in raw Python and using numpy:"
190 "import numpy as np\n",
218 "The next two lines create tensors using a convenience function called `constant`, which is similar to numpy's `array` and numpy's `full`. If you look at the code for `constant`, you can see the details of what it is doing to create the tensor. In summary, it creates a tensor of the necessary shape and applies the constant operator to it to fill it with the provided values. The values to `constant` can be Python or numpy arrays. `constant` can take an optional shape parameter, which works similarly to numpy's `fill` if provided, and an optional name parameter, which can be used to put a more human-readable label on the operation in the TensorFlow operation graph.\n",
228 "`eval()` is also slightly more complicated than it looks. Yes, it does get the value of the vector (tensor) that results from the addition. It returns this as a numpy array, which can then be printed. But, it's important to realize it also runs the computation graph at this point, because we demanded the output from the operation node of the graph; to produce that, it had to run the computation graph. So, this is the point where the addition is actually performed, not when `add` was called, as `add` just put the addition operation into the TensorFlow computation graph."
304 "This version uses `constant` in a way similar to numpy's `fill`, specifying the optional shape and having the values copied out across it.\n",
433 "import numpy as np\n",
449 "Recall that you can pass numpy or Python arrays into `constant`.\n"
[all...] |
| /external/tensorflow/tensorflow/contrib/timeseries/python/timeseries/state_space_models/ |
| state_space_model.py | 24 import numpy
[all...] |
| /external/tensorflow/tensorflow/compiler/xla/python/ |
| numpy_bridge.cc | 24 namespace numpy { namespace in namespace:xla::swig
55 LOG(FATAL) << "No Numpy type for XLA primitive type " << primitive_type;
88 LOG(FATAL) << "No XLA primitive type for Numpy type " << np_type;
208 return error("Shape attribute np_dtype is not an integer numpy dtype");
211 return error("Shape attribute np_dtype is not a valid integer numpy dtype");
400 "Non-tuple or Numpy array encountered in conversion to XLA literal.");
436 "No XLA literal container for Numpy type number: %d", np_type);
472 LOG(FATAL) << "No XLA literal container for Numpy type" << np_type;
513 } // namespace numpy
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| /external/tensorflow/tensorflow/contrib/tpu/python/tpu/ |
| device_assignment.py | 22 import numpy as np
58 rank 3 numpy array. See the description of the `core_assignment`
63 ValueError: If `core_assignment` is not a rank 3 numpy array.
76 raise ValueError("core_assignment must be a rank {} numpy array".format(
143 A rank-1 int32 numpy array with size equal to the TPU topology rank.
167 A numpy array of rank `topology_rank + 2`, with shape
241 computation_shape: A rank 1 int32 numpy array of size 3, describing the
244 computation_stride: A rank 1 int32 numpy array of size 3, describing the
258 numpy arrays with shape [3] where all values are positive.
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| /external/tensorflow/tensorflow/python/keras/_impl/keras/ |
| models.py | 26 import numpy as np
102 # if obj is any numpy type
656 A flat list of Numpy arrays
668 of Numpy arrays with shapes and types matching
740 Numpy data for these targets at training time), you
798 x: Numpy array of training data.
800 dictionary mapping the input name to a Numpy array.
803 y: Numpy array of target (label) data.
805 dictionary mapping the output name to a Numpy array.
848 sample_weight: Optional Numpy array of weights fo
[all...] |
| /external/autotest/client/cros/audio/ |
| audio_quality_measurement_unittest.py | 8 import numpy
18 numpy.random.seed(0)
33 noise = standard_noise * numpy.random.standard_normal()
64 numpy.random.seed(0)
82 noise = noise_amplitude * numpy.random.standard_normal()
131 self.y[j] = self.amplitude * (3 + numpy.random.uniform(-1, 1))
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| /external/tensorflow/tensorflow/contrib/resampler/python/ops/ |
| resampler_ops_test.py | 22 import numpy as np
44 data: numpy array of shape `[data_height, data_width]` containing data
46 x: numpy array of shape `[warp_height, warp_width]` containing x coordinates
48 y: numpy array of shape `[warp_height, warp_width]` containing y coordinates
52 Numpy array of shape `[warp_height, warp_width]` containing interpolated
148 # Generate reference output via bilinear interpolation in numpy
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