Searched refs:dropout (Results 1 - 25 of 64) sorted by relevance

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/external/tensorflow/tensorflow/contrib/estimator/python/estimator/
H A Ddnn.py93 dropout=None,
114 dropout: When not `None`, the probability we will drop out a given
130 dropout=dropout,
H A Ddnn_linear_combined_test.py50 dropout=None,
61 dnn_dropout=dropout,
/external/tensorflow/tensorflow/python/estimator/canned/
H A Ddnn.py49 dropout, input_layer_partitioner):
59 dropout: When not `None`, the probability we will drop out a given
102 if dropout is not None and mode == model_fn.ModeKeys.TRAIN:
103 net = core_layers.dropout(net, rate=dropout, training=True)
128 dropout=None,
146 dropout: When not `None`, the probability we will drop out a given
182 dropout=dropout,
279 dropout
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/external/tensorflow/tensorflow/contrib/cudnn_rnn/python/ops/
H A Dcudnn_rnn_ops.py812 dropout=0.,
837 dropout: whether to enable dropout. With it is 0, dropout is disabled.
838 seed: the op seed used for initializing dropout. See @{tf.set_random_seed}
857 dropout=dropout,
871 dropout=0.,
895 dropout: whether to enable dropout
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/external/tensorflow/tensorflow/contrib/slim/python/slim/nets/
H A Dvgg.py89 dropout_keep_prob: the probability that activations are kept in the dropout
117 net = layers_lib.dropout(
120 net = layers_lib.dropout(
154 dropout_keep_prob: the probability that activations are kept in the dropout
182 net = layers_lib.dropout(
185 net = layers_lib.dropout(
219 dropout_keep_prob: the probability that activations are kept in the dropout
247 net = layers_lib.dropout(
250 net = layers_lib.dropout(
H A Dalexnet.py86 dropout_keep_prob: the probability that activations are kept in the dropout
117 net = layers_lib.dropout(
120 net = layers_lib.dropout(
H A Doverfeat.py82 dropout_keep_prob: the probability that activations are kept in the dropout
113 net = layers_lib.dropout(
116 net = layers_lib.dropout(
/external/tensorflow/tensorflow/contrib/learn/python/learn/estimators/
H A Ddnn.py103 * dropout: When not `None`, the probability we will drop out a given
124 dropout = params.get("dropout")
173 if dropout is not None and mode == model_fn.ModeKeys.TRAIN:
174 net = layers.dropout(net, keep_prob=(1.0 - dropout))
300 dropout=None,
332 dropout: When not `None`, the probability we will drop out a given
374 "dropout": dropout,
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H A Dnonlinear_test.py101 dropout=0.0,
113 dropout=0.1,
125 dropout=0.9,
H A Dcomposable_model.py264 dropout=None,
279 dropout: When not None, the probability we will drop out
301 self._dropout = dropout
374 net = layers.dropout(net, keep_prob=(1.0 - self._dropout))
/external/tensorflow/tensorflow/python/ops/
H A Dnn_test.py277 # Runs dropout with 0-1 tensor 10 times, sum the number of ones and validate
287 dropout = nn_ops.dropout(t, keep_prob)
289 self.assertEqual([x_dim, y_dim], dropout.get_shape())
291 value = dropout.eval()
304 # Runs dropout with 0-1 tensor 10 times, sum the number of ones and validate
315 dropout = nn_ops.dropout(t, keep_prob, noise_shape=[x_dim, 1])
316 self.assertEqual([x_dim, y_dim], dropout.get_shape())
319 value = dropout
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/external/tensorflow/tensorflow/examples/tutorials/layers/
H A Dcnn_mnist.py80 # Add dropout operation; 0.6 probability that element will be kept
81 dropout = tf.layers.dropout(
87 logits = tf.layers.dense(inputs=dropout, units=10)
/external/tensorflow/tensorflow/examples/speech_commands/
H A Dmodels.py79 placeholder node that can be used to control the dropout amount.
92 TensorFlow node outputting logits results, and optionally a dropout
148 TensorFlow node outputting logits results, and optionally a dropout
201 During training, dropout nodes are introduced after each relu, controlled by a
210 TensorFlow node outputting logits results, and optionally a dropout
231 first_dropout = tf.nn.dropout(first_relu, dropout_prob)
250 second_dropout = tf.nn.dropout(second_relu, dropout_prob)
307 During training, dropout nodes are introduced after the relu, controlled by a
316 TensorFlow node outputting logits results, and optionally a dropout
340 first_dropout = tf.nn.dropout(first_rel
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/external/tensorflow/tensorflow/python/keras/_impl/keras/layers/
H A Drecurrent.py833 dropout: Float between 0 and 1.
854 dropout=0.,
874 self.dropout = min(1., max(0., dropout))
907 if 0 < self.dropout < 1 and self._dropout_mask is None:
911 self.dropout,
937 if 0 < self.dropout + self.recurrent_dropout:
968 'dropout':
969 self.dropout,
1006 dropout
1137 def dropout(self): member in class:SimpleRNN
1622 def dropout(self): member in class:GRU
2157 def dropout(self): member in class:LSTM
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H A Dconvolutional_recurrent.py261 dropout: Float between 0 and 1.
323 dropout=0.,
355 self.dropout = min(1., max(0., dropout))
485 if self.implementation == 0 and 0 < self.dropout < 1:
491 return K.dropout(ones, self.dropout)
510 return K.dropout(ones, self.recurrent_dropout)
595 'dropout':
596 self.dropout,
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H A Dcore_test.py52 dropout = keras.layers.Dropout(0.5)
53 self.assertEqual(True, dropout.supports_masking)
/external/tensorflow/tensorflow/contrib/cudnn_rnn/python/kernel_tests/
H A Dcudnn_rnn_ops_test.py66 dropout=0.):
84 dropout=dropout)
420 batch_size, seq_length, dir_count, dropout,
431 dropout=dropout)
522 # Cudnn scales result for dropout during training, therefore dropout has no
525 # demonstrative of the dropout-invariant nature of CudnnRnn.)
527 for (config, dropout) i
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/external/tensorflow/tensorflow/contrib/cudnn_rnn/python/layers/
H A Dcudnn_rnn.py158 dropout=0.,
179 dropout: dropout rate, a number between [0, 1]. Dropout is applied between
180 each layer (no dropout is applied for a model with a single layer).
181 When set to 0, dropout is disabled.
182 seed: the op seed used for initializing dropout. See @{tf.set_random_seed}
208 self._dropout = dropout
464 dropout=self._dropout,
477 dropout=self._dropout,
/external/tensorflow/tensorflow/contrib/eager/python/examples/rnn_ptb/
H A Drnn_ptb.py66 input_seq = tf.nn.dropout(input_seq, self.keep_ratio)
116 num_layers, hidden_dim, dropout=dropout_ratio)
138 y = tf.nn.dropout(y, self.keep_ratio)
312 FLAGS.hidden_dim, FLAGS.num_layers, FLAGS.dropout,
353 "--dropout", type=float, default=0.2, help="Drop out ratio.")
/external/tensorflow/tensorflow/examples/learn/
H A Diris_custom_model.py30 """DNN with three hidden layers, and dropout of 0.1 probability."""
32 # each layer having a dropout probability of 0.1.
36 net = tf.layers.dropout(net, rate=0.1)
H A Dmultiple_gpu.py34 """DNN with three hidden layers, and dropout of 0.1 probability.
48 # each layer having a dropout probability of 0.1.
53 net = tf.layers.dropout(net, rate=0.1)
/external/tensorflow/tensorflow/examples/tutorials/mnist/
H A Dmnist_with_summaries.py103 with tf.name_scope('dropout'):
106 dropped = tf.nn.dropout(hidden1, keep_prob)
153 k = FLAGS.dropout
198 parser.add_argument('--dropout', type=float, default=0.9,
199 help='Keep probability for training dropout.')
/external/tensorflow/tensorflow/python/layers/
H A Dcore.py265 rate: The dropout rate, between 0 and 1. E.g. `rate=0.1` would drop out
268 binary dropout mask that will be multiplied with the input.
270 `(batch_size, timesteps, features)`, and you want the dropout mask
300 return nn.dropout(inputs, 1 - self.rate,
311 @tf_export('layers.dropout')
312 def dropout(inputs, function
327 rate: The dropout rate, between 0 and 1. E.g. "rate=0.1" would drop out
330 binary dropout mask that will be multiplied with the input.
332 `(batch_size, timesteps, features)`, and you want the dropout mask
340 (apply dropout) o
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/external/tensorflow/tensorflow/contrib/boosted_trees/python/kernel_tests/
H A Dprediction_ops_test.py188 # Empty dropout.
216 # Empty dropout.
246 # Empty dropout.
295 # Empty dropout.
383 # Empty dropout.
430 # Empty dropout.
479 # Empty dropout.
528 # Empty dropout.
576 # Empty dropout.
627 # Empty dropout
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/external/tensorflow/tensorflow/contrib/receptive_field/python/util/
H A Dreceptive_field_test.py165 """Aligned network with dropout for test.
168 has dropout normalization.
184 dropout = slim.dropout(l3)
186 nn.relu(l1 + dropout, name='output')
383 # uniform variable of the dropout.
388 ['Dropout/dropout/random_uniform']))

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