/external/tensorflow/tensorflow/python/layers/ |
core.py | 16 """Contains the core layers: Dense, Dropout. 191 @tf_export(v1=['layers.Dropout']) 192 class Dropout(keras_layers.Dropout, base.Layer): 193 """Applies Dropout to the input. 195 Dropout consists in randomly setting a fraction `rate` of input units to 0 201 rate: The dropout rate, between 0 and 1. E.g. `rate=0.1` would drop out 204 binary dropout mask that will be multiplied with the input. 206 `(batch_size, timesteps, features)`, and you want the dropout mask 220 super(Dropout, self).__init__(rate=rate 234 def dropout(inputs, function [all...] |
layers.py | 31 from tensorflow.python.layers.core import Dropout 35 from tensorflow.python.layers.core import dropout
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core_test.py | 387 dp = core_layers.Dropout(0.5, name='dropout') 391 self.assertEqual(dp.name, 'dropout') 395 dp = core_layers.Dropout(0.5) 409 dp = core_layers.Dropout(0.5, seed=1) 423 dp = core_layers.Dropout(0.5, noise_shape=noise_shape, seed=1) 433 dp = core_layers.Dropout(0.5, noise_shape=noise_shape, seed=1) 444 dropped = core_layers.dropout(inputs, 0.5, training=True, seed=1) 448 dropped = core_layers.dropout(inputs, 0.5, training=False, seed=1) 456 dp = core_layers.Dropout(rate, name='dropout' [all...] |
/external/tensorflow/tensorflow/python/keras/layers/ |
time_distributed_learning_phase_test.py | 35 y = keras.layers.TimeDistributed(keras.layers.Dropout(.999))(
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core.py | 109 @keras_export('keras.layers.Dropout') 110 class Dropout(Layer): 111 """Applies Dropout to the input. 113 Dropout consists in randomly setting 120 binary dropout mask that will be multiplied with the input. 123 you want the dropout mask to be the same for all timesteps, 130 training mode (adding dropout) or in inference mode (doing nothing). 134 super(Dropout, self).__init__(**kwargs) 141 # Subclasses of `Dropout` may implement `_get_noise_shape(self, inputs)`, 153 return nn.dropout( [all...] |
core_test.py | 39 keras.layers.Dropout, kwargs={'rate': 0.5}, input_shape=(3, 2)) 42 keras.layers.Dropout, 48 dropout = keras.layers.Dropout(0.5) 49 self.assertEqual(True, dropout.supports_masking)
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__init__.py | 68 from tensorflow.python.keras.layers.core import Dropout
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/external/tensorflow/tensorflow/contrib/distribute/python/examples/ |
keras_mnist.py | 97 model.add(tf.keras.layers.Dropout(0.25)) 100 model.add(tf.keras.layers.Dropout(0.5))
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mnist_eager_multigpu.py | 61 tf.keras.layers.Dropout(0.4),
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mnist_tf1_tpu.py | 59 tf.keras.layers.Dropout(0.4),
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/external/libopus/scripts/ |
rnn_train.py | 12 from keras.layers import Dropout 25 #model.add(GRU(12, dropout=0.0, recurrent_dropout=0.0, activation='tanh', recurrent_activation='sigmoid', return_sequences=True)) 30 x = GRU(12, dropout=0.1, recurrent_dropout=0.1, activation='tanh', recurrent_activation='sigmoid', return_sequences=True)(x)
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/external/tensorflow/tensorflow/examples/saved_model/integration_tests/ |
use_mnist_cnn.py | 49 'If set, dropout rate passed to the SavedModel.') 67 net = tf.keras.layers.Dropout(dropout_rate)(net)
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export_mnist_cnn.py | 60 net = tf.keras.layers.Dropout(dropout_rate, name='dropout1')(net) 76 net = tf.keras.layers.Dropout(dropout_rate)(net)
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/external/tensorflow/tensorflow/python/keras/saving/ |
saving_utils_test.py | 104 dropout = keras.layers.Dropout(0.5, name='dropout') 106 branch_b = [input_b, dense, dense2, dropout] 220 e = keras.layers.Dropout(0.5, name='dropout')(c)
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/external/tensorflow/tensorflow/python/keras/engine/ |
training_test.py | 189 dropout = keras.layers.Dropout(0.5, name='dropout') 191 branch_b = [input_b, dense, dropout] 275 'dropout': output_e_np 286 'dropout': output_e_np 297 'dropout': output_e_np 304 'dropout': output_e_np 314 'dropout': output_e_np 332 loss = {'dense': 'mse', 'dropout': 'mae' [all...] |
sequential_test.py | 43 model.add(keras.layers.Dropout(0.3, name='dp')) 55 model.add(keras.layers.Dropout(0.3, name='dp'))
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training_eager_test.py | 76 dropout = keras.layers.Dropout(0.5, name='dropout') 79 [input_a, dense], [input_b, dense, dropout])
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/external/tensorflow/tensorflow/python/keras/ |
models_test.py | 69 keras.layers.Dropout(0.5), 168 x_a = keras.layers.Dropout(0.5)(x_a)
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integration_test.py | 49 keras.layers.Dropout(0.1),
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model_subclassing_test.py | 58 self.dp = keras.layers.Dropout(0.5) 99 self.dp = keras.layers.Dropout(0.5) 710 # test that dropout is applied in training and not inference 719 self.dp = keras.layers.Dropout(0.5) [all...] |
/external/tensorflow/tensorflow/contrib/distribute/python/ |
keras_backward_compat_test.py | 51 model.add(keras.layers.Dropout(0.1)) 59 b = keras.layers.Dropout(0.1)(b) 206 e = keras.layers.Dropout(0.5, name='dropout')(c) 701 z = keras.layers.Dropout(0.9999)(y) [all...] |
keras_test.py | 59 model.add(keras.layers.Dropout(0.1)) 67 b = keras.layers.Dropout(0.1)(b) 256 e = keras.layers.Dropout(0.5, name='dropout')(c) [all...] |
/external/tensorflow/tensorflow/contrib/eager/python/examples/densenet/ |
densenet.py | 38 dropout_rate: dropout rate. 57 self.dropout = tf.keras.layers.Dropout(dropout_rate) 77 output = self.dropout(output, training=training) 89 dropout_rate: dropout rate. 124 dropout_rate: dropout rate. 166 rate: dropout rate.
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/external/tensorflow/tensorflow/examples/tf2_showcase/ |
mnist.py | 100 l.Dropout(0.4),
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/external/tensorflow/tensorflow/contrib/keras/api/keras/layers/ |
__init__.py | 66 from tensorflow.python.keras.layers.core import Dropout
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