| /frameworks/ml/nn/runtime/test/specs/V1_1/ |
| embedding_lookup_relaxed.mod.py | 20 features = 4 variable
22 actual_values = [x for x in range(rows * columns * features)]
25 for k in range(features):
26 actual_values[(i * columns + j) * features + k] = i + j / 10. + k / 100.
30 value = Input("value", "TENSOR_FLOAT32", "{%d, %d, %d}" % (rows, columns, features))
31 output = Output("output", "TENSOR_FLOAT32", "{%d, %d, %d}" % (lookups, columns, features))
|
| hashtable_lookup_float_relaxed.mod.py | 20 features = 2 variable
22 table = [x for x in range(rows * features)]
24 for j in range(features):
25 table[i * features + j] = i + j / 10.
31 value = Input("value", "TENSOR_FLOAT32", "{%d, %d}" % (rows, features))
32 output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (lookups, features))
|
| /external/libpng/contrib/arm-neon/ |
| android-ndk.c | 23 * with an implementation of the Android ARM 'cpu-features' library. The code
27 #include <cpu-features.h>
|
| /external/mockito/src/main/java/org/mockito/internal/creation/bytebuddy/ |
| BytecodeGenerator.java | 9 <T> Class<? extends T> mockClass(MockFeatures<T> features);
|
| /external/webrtc/webrtc/common_audio/vad/ |
| vad_filterbank.h | 30 // The values are given in Q4 and written to |features|. Further, an approximate
38 // - features [o] : 10 * log10(energy in each frequency band), Q4.
42 size_t data_length, int16_t* features);
|
| /prebuilts/gcc/linux-x86/host/x86_64-linux-glibc2.15-4.8/sysroot/usr/include/i386-linux-gnu/sys/ |
| klog.h | 22 #include <features.h>
|
| perm.h | 22 #include <features.h>
|
| prctl.h | 22 #include <features.h>
|
| vm86.h | 22 #include <features.h>
|
| /prebuilts/gcc/linux-x86/host/x86_64-linux-glibc2.15-4.8/sysroot/usr/include/ |
| libgen.h | 22 #include <features.h>
|
| /prebuilts/gcc/linux-x86/host/x86_64-linux-glibc2.15-4.8/sysroot/usr/include/net/ |
| if_packet.h | 23 #include <features.h>
|
| /prebuilts/gcc/linux-x86/host/x86_64-linux-glibc2.15-4.8/sysroot/usr/include/x86_64-linux-gnu/sys/ |
| klog.h | 22 #include <features.h>
|
| perm.h | 22 #include <features.h>
|
| prctl.h | 22 #include <features.h>
|
| /device/linaro/bootloader/edk2/AppPkg/Applications/Python/Python-2.7.10/Lib/xml/dom/ |
| domreg.py | 32 def _good_enough(dom, features):
33 "_good_enough(dom, features) -> Return 1 if the dom offers the features"
34 for f,v in features:
39 def getDOMImplementation(name = None, features = ()):
40 """getDOMImplementation(name = None, features = ()) -> DOM implementation.
49 be found, raise an ImportError. The features list must be a sequence
64 # order, returning the one that has the required features
65 if isinstance(features, StringTypes):
66 features = _parse_feature_string(features)
[all...] |
| /device/linaro/bootloader/edk2/AppPkg/Applications/Python/Python-2.7.2/Lib/xml/dom/ |
| domreg.py | 32 def _good_enough(dom, features):
33 "_good_enough(dom, features) -> Return 1 if the dom offers the features"
34 for f,v in features:
39 def getDOMImplementation(name = None, features = ()):
40 """getDOMImplementation(name = None, features = ()) -> DOM implementation.
49 be found, raise an ImportError. The features list must be a sequence
64 # order, returning the one that has the required features
65 if isinstance(features, StringTypes):
66 features = _parse_feature_string(features)
[all...] |
| /external/python/cpython2/Lib/xml/dom/ |
| domreg.py | 32 def _good_enough(dom, features):
33 "_good_enough(dom, features) -> Return 1 if the dom offers the features"
34 for f,v in features:
39 def getDOMImplementation(name = None, features = ()):
40 """getDOMImplementation(name = None, features = ()) -> DOM implementation.
49 be found, raise an ImportError. The features list must be a sequence
64 # order, returning the one that has the required features
65 if isinstance(features, StringTypes):
66 features = _parse_feature_string(features
[all...] |
| /prebuilts/gdb/darwin-x86/lib/python2.7/xml/dom/ |
| domreg.py | 32 def _good_enough(dom, features):
33 "_good_enough(dom, features) -> Return 1 if the dom offers the features"
34 for f,v in features:
39 def getDOMImplementation(name = None, features = ()):
40 """getDOMImplementation(name = None, features = ()) -> DOM implementation.
49 be found, raise an ImportError. The features list must be a sequence
64 # order, returning the one that has the required features
65 if isinstance(features, StringTypes):
66 features = _parse_feature_string(features
[all...] |
| /prebuilts/gdb/linux-x86/lib/python2.7/xml/dom/ |
| domreg.py | 32 def _good_enough(dom, features):
33 "_good_enough(dom, features) -> Return 1 if the dom offers the features"
34 for f,v in features:
39 def getDOMImplementation(name = None, features = ()):
40 """getDOMImplementation(name = None, features = ()) -> DOM implementation.
49 be found, raise an ImportError. The features list must be a sequence
64 # order, returning the one that has the required features
65 if isinstance(features, StringTypes):
66 features = _parse_feature_string(features
[all...] |
| /prebuilts/python/darwin-x86/2.7.5/lib/python2.7/xml/dom/ |
| domreg.py | 32 def _good_enough(dom, features):
33 "_good_enough(dom, features) -> Return 1 if the dom offers the features"
34 for f,v in features:
39 def getDOMImplementation(name = None, features = ()):
40 """getDOMImplementation(name = None, features = ()) -> DOM implementation.
49 be found, raise an ImportError. The features list must be a sequence
64 # order, returning the one that has the required features
65 if isinstance(features, StringTypes):
66 features = _parse_feature_string(features
[all...] |
| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/xml/dom/ |
| domreg.py | 32 def _good_enough(dom, features):
33 "_good_enough(dom, features) -> Return 1 if the dom offers the features"
34 for f,v in features:
39 def getDOMImplementation(name = None, features = ()):
40 """getDOMImplementation(name = None, features = ()) -> DOM implementation.
49 be found, raise an ImportError. The features list must be a sequence
64 # order, returning the one that has the required features
65 if isinstance(features, StringTypes):
66 features = _parse_feature_string(features
[all...] |
| /external/harfbuzz_ng/src/ |
| hb-shape.h | 46 const hb_feature_t *features,
52 const hb_feature_t *features,
|
| /external/tensorflow/tensorflow/contrib/gan/python/features/ |
| __init__.py | 15 """TFGAN features module.
25 # Collapse features into a single namespace.
27 from tensorflow.contrib.gan.python.features.python import clip_weights
28 from tensorflow.contrib.gan.python.features.python import conditioning_utils
29 from tensorflow.contrib.gan.python.features.python import random_tensor_pool
30 from tensorflow.contrib.gan.python.features.python import virtual_batchnorm
32 from tensorflow.contrib.gan.python.features.python.clip_weights import *
33 from tensorflow.contrib.gan.python.features.python.conditioning_utils import *
34 from tensorflow.contrib.gan.python.features.python.random_tensor_pool import *
35 from tensorflow.contrib.gan.python.features.python.virtual_batchnorm import
[all...] |
| /frameworks/ml/nn/runtime/test/specs/V1_0/ |
| svdf.mod.py | 18 features = 4 variable
20 units = int(features / rank)
27 weights_feature = Input("weights_feature", "TENSOR_FLOAT32", "{%d, %d}" % (features, input_size))
28 weights_time = Input("weights_time", "TENSOR_FLOAT32", "{%d, %d}" % (features, memory_size))
30 state_in = Input("state_in", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*features))
33 state_out = IgnoredOutput("state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*features))
60 state_in: [0 for _ in range(batches * memory_size * features)],
127 output0 = {state_out: [0 for _ in range(batches * memory_size * features)],
|
| svdf2.mod.py | 18 features = 8 variable
20 units = int(features / rank)
27 weights_feature = Input("weights_feature", "TENSOR_FLOAT32", "{%d, %d}" % (features, input_size))
28 weights_time = Input("weights_time", "TENSOR_FLOAT32", "{%d, %d}" % (features, memory_size))
30 state_in = Input("state_in", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*features))
33 state_out = IgnoredOutput("state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*features))
75 state_in: [0 for _ in range(batches * memory_size * features)],
142 output0 = {state_out: [0 for _ in range(batches * memory_size * features)],
|
