| /dports/devel/py-bullet3/bullet3-3.21/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/ |
| H A D | count_weights_test.py | 28 tf.Variable(tf.zeros((5, 3)), trainable=True)
29 tf.Variable(tf.zeros((1, 1)), trainable=True)
30 tf.Variable(tf.zeros((5,)), trainable=True)
34 tf.Variable(tf.zeros((5, 3)), trainable=False)
35 tf.Variable(tf.zeros((1, 1)), trainable=False)
36 tf.Variable(tf.zeros((5,)), trainable=False)
40 tf.Variable(tf.zeros((5, 3)), trainable=True)
42 tf.Variable(tf.zeros((1, 1)), trainable=True)
43 tf.Variable(tf.zeros((5,)), trainable=True)
48 tf.Variable(tf.zeros((3, 2)), trainable=True)
[all …]
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| H A D | in_graph_env.py | 46 trainable=False)
49 trainable=False)
50 self._reward = tf.Variable(0.0, dtype=tf.float32, name='reward', trainable=False)
51 self._done = tf.Variable(True, dtype=tf.bool, name='done', trainable=False)
52 self._step = tf.Variable(0, dtype=tf.int32, name='step', trainable=False)
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| H A D | in_graph_batch_env.py | 46 trainable=False)
49 trainable=False)
52 trainable=False)
55 trainable=False)
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| H A D | streaming_mean.py | 38 self._count = tf.Variable(lambda: 0, trainable=False)
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| /dports/math/py-keras/Keras-2.4.3/tests/ |
| H A D | test_dynamic_trainability.py | 20 layer.trainable = False
25 y = Dense(2, trainable=False)(x)
35 layer.trainable = False
44 model.trainable = False
50 model.trainable = False
63 inner_model.trainable = False
65 inner_model.trainable = True
75 inner_model.trainable = False
77 inner_model.trainable = True
91 inner_model.trainable = True
[all …]
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| /dports/games/leela-zero/leela-zero-0.17/training/tf/ |
| H A D | mixprec.py | 6 trainable=True, argument
10 storage_dtype = tf.float32 if trainable else dtype
14 trainable=trainable,
16 if trainable and dtype != tf.float32:
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| H A D | tfprocess.py | 153 self.global_step = tf.Variable(0, name='global_step', trainable=False)
236 self.swa_count = tf.Variable(0., name='swa_count', trainable=False)
239 trainable=False)
247 tf.zeros(shape=w.shape), name='swa/'+name, trainable=False)
267 trainable=False,
653 v = tf.Variable(var, name='save/'+name, trainable=False)
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| /dports/devel/py-bullet3/bullet3-3.21/examples/pybullet/gym/pybullet_envs/agents/tools/ |
| H A D | in_graph_env.py | 49 trainable=False)
52 trainable=False)
53 self._reward = tf.Variable(0.0, dtype=tf.float32, name='reward', trainable=False)
54 self._done = tf.Variable(True, dtype=tf.bool, name='done', trainable=False)
55 self._step = tf.Variable(0, dtype=tf.int32, name='step', trainable=False)
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| H A D | in_graph_batch_env.py | 49 trainable=False)
52 trainable=False)
55 trainable=False)
58 trainable=False)
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| /dports/misc/glow/glow-f24d960e3cc80db95ac0bc17b1900dbf60ca044a/tests/models/onnxModels/ |
| H A D | glow_custom_op_node_opts.onnxtxt | 83 doc_string: "$offline:0$trainable:0$layout:*$elemKind:float"
100 doc_string: "$offline:0$trainable:0$layout:*$elemKind:float"
117 doc_string: "$offline:0$trainable:0$layout:*$elemKind:float$saveName:save_save"
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| H A D | glow_custom_op_topk_quantized.onnxtxt | 81 doc_string: "$offline:0$trainable:0$layout:*$elemKind:i8$qScale:1.20000004768372$qOffset:5"
101 …doc_string: "$offline:0$trainable:0$layout:*$elemKind:i8$qScale:1.20000004768372$qOffset:5$saveNam…
121 doc_string: "$offline:0$trainable:0$layout:*$elemKind:index64$saveName:save_indices_save"
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| H A D | glow_custom_dag_multi_op.onnxtxt | 253 doc_string: "$offline:0$trainable:0$layout:*$elemKind:float"
270 doc_string: "$offline:0$trainable:0$layout:*$elemKind:float"
287 doc_string: "$offline:0$trainable:0$layout:*$elemKind:float"
304 doc_string: "$offline:0$trainable:0$layout:*$elemKind:float$saveName:res_save"
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| H A D | glow_custom_op_channelwise_quantized_group_conv.onnxtxt | 190 doc_string: "$offline:0$trainable:0$layout:*$elemKind:float"
213 doc_string: "$offline:0$trainable:0$layout:*$elemKind:float$saveName:save_save"
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| /dports/math/py-keras/Keras-2.4.3/docs/templates/layers/ |
| H A D | writing-your-own-keras-layers.md | 3 …sing `layers.core.Lambda` layers. But for any custom operation that has trainable weights, you sho…
22 # Create a trainable weight variable for this layer.
26 trainable=True)
50 # Create a trainable weight variable for this layer.
54 trainable=True)
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| /dports/misc/mmdnn/MMdnn-0.3.1/mmdnn/conversion/caffe/ |
| H A D | network.py | 31 def __init__(self, trainable=False): argument
34 self.trainable = trainable
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| /dports/math/py-keras/Keras-2.4.3/tests/keras/engine/ |
| H A D | layer_subclassing_tests.py | 87 'w3', shape=(), trainable=False, initializer='zeros')
139 trainable=True)
143 trainable=True)
163 trainable=True)
166 trainable=True)
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| H A D | test_topology.py | 39 model.trainable = False
43 model.trainable = True
47 model.layers[1].trainable = False
59 model.trainable = False
63 model.trainable = True
67 model.layers[0].trainable = False
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| /dports/devel/py-bullet3/bullet3-3.21/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/ |
| H A D | tf_normalizer.py | 52 trainable=False)
56 trainable=False)
60 trainable=False)
|
| /dports/misc/mnn/MNN-1.2.0/tools/train/source/optimizer/ |
| H A D | SGD.cpp | 16 auto train = ParameterOptimizer::trainable(); in SGD()
80 auto grad = OpGrad::grad(loss, trainable(), mGradBlockExprName); in onGetNextParameter()
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| H A D | ParameterOptimizer.hpp | 35 const std::set<Express::VARP>& trainable() const { in trainable() function in MNN::Train::ParameterOptimizer
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| /dports/graphics/tesseract/tesseract-5.0.0/src/training/unicharset/ |
| H A D | lstmtrainer.h | 272 Trainability trainable = TrainOnLine(image, batch); in TrainOnLine() local
273 if (trainable == UNENCODABLE || trainable == NOT_BOXED) { in TrainOnLine()
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| /dports/math/py-keras/Keras-2.4.3/tests/keras/layers/ |
| H A D | normalization_test.py | 193 model.trainable = False
204 model.trainable = True
212 layer.trainable = False
|
| /dports/misc/py-gluonnlp/gluon-nlp-0.10.0/scripts/parsing/parser/ |
| H A D | biaffine_parser.py | 77 def embedding_from_numpy(_we, trainable=True): argument
81 if not trainable:
88 trainable=False) if vocab.has_pret_embs() \
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| /dports/math/py-keras/Keras-2.4.3/tests/keras/ |
| H A D | metrics_correctness_test.py | 15 dense = layers.Dense(3, kernel_initializer='ones', trainable=False)
19 1, kernel_initializer='ones', name='output_1', trainable=False)(x_1)
21 1, kernel_initializer='ones', name='output_2', trainable=False)(x_2)
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| H A D | test_sequential_model.py | 295 inner_model.trainable = False
297 inner_model.trainable = True
423 model.trainable = False
434 model.trainable = True
|