| /external/tensorflow/tensorflow/core/api_def/base_api/ |
| api_def_FractionalAvgPool.pbtxt | 12 output tensor after fractional avg pooling.
18 row pooling sequence, needed to calculate gradient.
24 column pooling sequence, needed to calculate gradient.
30 Pooling ratio for each dimension of `value`, currently only
32 pooling ratio looks like [1.0, 1.44, 1.73, 1.0]. The first and last elements
33 must be 1.0 because we don't allow pooling on batch and channels
34 dimensions. 1.44 and 1.73 are pooling ratio on height and width dimensions
41 When set to True, generates the pooling sequence in a
43 Graham, Fractional Max-Pooling](http://arxiv.org/abs/1412.6071) for
50 When set to True, it means when pooling, the values at the boundar
[all...] |
| api_def_FractionalAvgPoolGrad.pbtxt | 20 row pooling sequence, form pooling region with
27 column pooling sequence, form pooling region with
40 When set to True, it means when pooling, the values at the boundary
41 of adjacent pooling cells are used by both cells. For example:
47 If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice.
48 The result would be [41/3, 26/3] for fractional avg pooling.
55 out_backprop to those indices that form the same pooling cell. Therefore, we
|
| api_def_FractionalMaxPool.pbtxt | 12 output tensor after fractional max pooling.
18 row pooling sequence, needed to calculate gradient.
24 column pooling sequence, needed to calculate gradient.
30 Pooling ratio for each dimension of `value`, currently only
32 pooling ratio looks like [1.0, 1.44, 1.73, 1.0]. The first and last elements
33 must be 1.0 because we don't allow pooling on batch and channels
34 dimensions. 1.44 and 1.73 are pooling ratio on height and width dimensions
41 When set to True, generates the pooling sequence in a
43 Graham, Fractional Max-Pooling](http://arxiv.org/abs/1412.6071) for
50 When set to True, it means when pooling, the values at the boundar
[all...] |
| api_def_FractionalMaxPoolGrad.pbtxt | 26 row pooling sequence, form pooling region with
33 column pooling sequence, form pooling region with
46 When set to True, it means when pooling, the values at the boundary
47 of adjacent pooling cells are used by both cells. For example:
53 If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice.
54 The result would be [20, 16] for fractional max pooling.
|
| /external/tensorflow/tensorflow/tools/api/golden/ |
| tensorflow.keras.applications.pbtxt | 41 argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], "
45 argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], "
49 argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], "
53 argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], "
57 argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], "
61 argspec: "args=[\'input_shape\', \'alpha\', \'depth_multiplier\', \'dropout\', \'include_top\', \'weights\', \'input_tensor\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'None\', \'1.0\', \'1\', \'0.001\', \'True\', \'imagenet\', \'None\', \'None\', \'1000\'], "
65 argspec: "args=[\'input_shape\', \'include_top\', \'weights\', \'input_tensor\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'None\', \'True\', \'imagenet\', \'None\', \'None\', \'1000\'], "
69 argspec: "args=[\'input_shape\', \'include_top\', \'weights\', \'input_tensor\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'None\', \'True\', \'imagenet\', \'None\', \'None\', \'1000\'], "
73 argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], "
77 argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\' (…)
[all...] |
| tensorflow.keras.applications.densenet.pbtxt | 5 argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], "
9 argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], "
13 argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], "
|
| tensorflow.keras.applications.nasnet.pbtxt | 5 argspec: "args=[\'input_shape\', \'include_top\', \'weights\', \'input_tensor\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'None\', \'True\', \'imagenet\', \'None\', \'None\', \'1000\'], "
9 argspec: "args=[\'input_shape\', \'include_top\', \'weights\', \'input_tensor\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'None\', \'True\', \'imagenet\', \'None\', \'None\', \'1000\'], "
|
| tensorflow.keras.applications.inception_resnet_v2.pbtxt | 5 argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], "
|
| tensorflow.keras.applications.inception_v3.pbtxt | 5 argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], "
|
| tensorflow.keras.applications.mobilenet.pbtxt | 5 argspec: "args=[\'input_shape\', \'alpha\', \'depth_multiplier\', \'dropout\', \'include_top\', \'weights\', \'input_tensor\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'None\', \'1.0\', \'1\', \'0.001\', \'True\', \'imagenet\', \'None\', \'None\', \'1000\'], "
|
| tensorflow.keras.applications.resnet50.pbtxt | 5 argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], "
|
| tensorflow.keras.applications.vgg16.pbtxt | 5 argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], "
|
| tensorflow.keras.applications.vgg19.pbtxt | 5 argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], "
|
| tensorflow.keras.applications.xception.pbtxt | 5 argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], "
|
| /external/tensorflow/tensorflow/contrib/lite/kernels/ |
| pooling.cc | 35 namespace pooling { namespace in namespace:tflite::ops::builtin
37 // This file has two implementation of each pooling op.
297 } // namespace pooling
300 static TfLiteRegistration r = {pooling::Init, pooling::Free,
301 pooling::GenericPrepare<pooling::kAverage>,
302 pooling::AverageEval<pooling::kReference>};
307 static TfLiteRegistration r = {pooling::Init, pooling::Free
[all...] |
| /external/autotest/site_utils/lxc/container_pool/ |
| __init__.py | 5 """This module provides code and classes related to container pooling."""
|
| /external/tensorflow/tensorflow/contrib/keras/api/keras/layers/ |
| __init__.py | 108 # Pooling layers.
109 from tensorflow.python.keras._impl.keras.layers.pooling import MaxPooling1D
110 from tensorflow.python.keras._impl.keras.layers.pooling import MaxPooling2D
111 from tensorflow.python.keras._impl.keras.layers.pooling import MaxPooling3D
112 from tensorflow.python.keras._impl.keras.layers.pooling import AveragePooling1D
113 from tensorflow.python.keras._impl.keras.layers.pooling import AveragePooling2D
114 from tensorflow.python.keras._impl.keras.layers.pooling import AveragePooling3D
115 from tensorflow.python.keras._impl.keras.layers.pooling import GlobalAveragePooling1D
116 from tensorflow.python.keras._impl.keras.layers.pooling import GlobalAveragePooling2D
117 from tensorflow.python.keras._impl.keras.layers.pooling import GlobalAveragePooling3
[all...] |
| /external/tensorflow/tensorflow/python/layers/ |
| layers.py | 105 # Pooling layers.
106 from tensorflow.python.layers.pooling import AveragePooling1D
107 from tensorflow.python.layers.pooling import MaxPooling1D
108 from tensorflow.python.layers.pooling import AveragePooling2D
109 from tensorflow.python.layers.pooling import MaxPooling2D
110 from tensorflow.python.layers.pooling import AveragePooling3D
111 from tensorflow.python.layers.pooling import MaxPooling3D
113 from tensorflow.python.layers.pooling import average_pooling1d
114 from tensorflow.python.layers.pooling import max_pooling1d
115 from tensorflow.python.layers.pooling import average_pooling2
[all...] |
| pooling.py | 17 """Contains the pooling layer classes and their functional aliases.
33 """Pooling layer for arbitrary pooling functions, for 1D inputs.
38 pool_function: The pooling function to apply, e.g. `tf.nn.max_pool`.
40 representing the size of the pooling window.
42 strides of the pooling operation.
65 # There is no TF op for 1D pooling, hence we make the inputs 4D.
102 """Average Pooling layer for 1D inputs.
106 representing the size of the pooling window.
108 strides of the pooling operation
[all...] |
| /external/tensorflow/tensorflow/core/kernels/ |
| fractional_pool_common.h | 38 // Generate pooling sequence for fractional pooling along one dimension.
40 // Regular max/avg pooling can be viewed as a special case, in which given the
43 // it will generate pooling sequence as
48 // In the case of fractional pooling, input_length is not an integer multiple of
49 // output_length, randomness plays a role when generating pooling sequence.
61 // valid pooling sequence:
72 // pooling_sequence: This is the cumulative pooling sequence.
|
| mkl_pooling_ops_common.cc | 94 // We only support 2D pooling across width/height and depthwise
95 // pooling, not a combination.
99 "MaxPooling supports exactly one of pooling across depth "
100 "or pooling across width/height."));
102 if (depth_window == 1) { // we are pooling in the H and W
124 } else { // we are pooling in the depth dimension
125 // Our current version of depthwise max pooling does not support
129 errors::Unimplemented("Depthwise max pooling requires the"
133 errors::Unimplemented("Depthwise max pooling requires the"
142 errors::Unimplemented("Depthwise max pooling is currently
[all...] |
| avgpooling_op.h | 53 // top_diff: backprop to the output of the pooling layer
58 // pooled_height: the height of the output to the pooling layer
59 // pooled_width: the width of the output to the pooling layer
60 // kernel_h: the height of the pooling kernel
61 // kernel_w: the width of the pooling kernel
66 // bottom_diff: backprop to the input of the pooling layer.
|
| /external/swiftshader/third_party/subzero/tests_lit/llvm2ice_tests/ |
| randomize-pool-immediate-basic.ll | 1 ; This is a smoke test of constant blinding and constant pooling.
15 ; RUN: | FileCheck %s --check-prefix=POOLING
19 ; RUN: | FileCheck %s --check-prefix=POOLING
35 ; POOLING-LABEL: add_arg_plus_200000
36 ; POOLING: mov e{{[a-z]*}},{{(DWORD PTR )?}}ds:0x0 {{[0-9a-f]*}}: R_386_32 .L$i32$00030d40
52 ; POOLING-LABEL: load_arg_plus_200000
53 ; POOLING: mov e{{[a-z]*}},{{(DWORD PTR )?}}ds:0x0 {{[0-9a-f]*}}: R_386_32 .L$i32$00030d40
72 ; POOLING-LABEL: add_arg_plus_64bits
73 ; POOLING: mov e{{[a-z]*}},{{(DWORD PTR )?}}ds:0x0 {{[0-9a-f]*}}: R_386_32 .L$i32$f46b0400
92 ; POOLING-LABEL: load_arg_plus_64bit
[all...] |
| /external/tensorflow/tensorflow/python/keras/_impl/keras/layers/ |
| pooling_test.py | 15 """Tests for pooling layers."""
30 testing_utils.layer_test(keras.layers.pooling.GlobalMaxPooling1D,
33 keras.layers.pooling.GlobalAveragePooling1D, input_shape=(3, 4, 5))
38 keras.layers.pooling.GlobalMaxPooling2D,
42 keras.layers.pooling.GlobalMaxPooling2D,
46 keras.layers.pooling.GlobalAveragePooling2D,
50 keras.layers.pooling.GlobalAveragePooling2D,
57 keras.layers.pooling.GlobalMaxPooling3D,
61 keras.layers.pooling.GlobalMaxPooling3D,
65 keras.layers.pooling.GlobalAveragePooling3D
[all...] |
| /external/tensorflow/tensorflow/python/keras/layers/ |
| __init__.py | 111 # Pooling layers.
112 from tensorflow.python.keras._impl.keras.layers.pooling import MaxPooling1D
113 from tensorflow.python.keras._impl.keras.layers.pooling import MaxPooling2D
114 from tensorflow.python.keras._impl.keras.layers.pooling import MaxPooling3D
115 from tensorflow.python.keras._impl.keras.layers.pooling import AveragePooling1D
116 from tensorflow.python.keras._impl.keras.layers.pooling import AveragePooling2D
117 from tensorflow.python.keras._impl.keras.layers.pooling import AveragePooling3D
118 from tensorflow.python.keras._impl.keras.layers.pooling import GlobalAveragePooling1D
119 from tensorflow.python.keras._impl.keras.layers.pooling import GlobalAveragePooling2D
120 from tensorflow.python.keras._impl.keras.layers.pooling import GlobalAveragePooling3
[all...] |
