| /external/tensorflow/tensorflow/contrib/nn/python/ops/ |
| alpha_dropout.py | 30 def alpha_dropout(x, keep_prob, noise_shape=None, seed=None, name=None): # pylint: disable=invalid-name
41 keep_prob: A scalar `Tensor` with the same type as x. The probability
53 ValueError: If `keep_prob` is not in `(0, 1]`.
58 if isinstance(keep_prob, numbers.Real) and not 0 < keep_prob <= 1.:
59 raise ValueError("keep_prob must be a scalar tensor or a float in the "
60 "range (0, 1], got %g" % keep_prob)
61 keep_prob = ops.convert_to_tensor(keep_prob,
63 name="keep_prob")
[all...] |
| alpha_dropout_test.py | 34 for keep_prob in [0.1, 0.5, 0.8]:
37 output = alpha_dropout(t, keep_prob)
48 keep_prob = 0.5
51 _ = alpha_dropout(t, keep_prob, noise_shape=[x_dim, y_dim + 10])
53 _ = alpha_dropout(t, keep_prob, noise_shape=[x_dim, y_dim, 5])
55 _ = alpha_dropout(t, keep_prob, noise_shape=[x_dim + 3])
57 _ = alpha_dropout(t, keep_prob, noise_shape=[x_dim])
60 _ = alpha_dropout(t, keep_prob, noise_shape=[y_dim])
61 _ = alpha_dropout(t, keep_prob, noise_shape=[1, y_dim])
62 _ = alpha_dropout(t, keep_prob, noise_shape=[x_dim, 1]
[all...] |
| /external/tensorflow/tensorflow/contrib/eager/python/examples/rnn_colorbot/ |
| rnn_colorbot_test.py | 53 keep_prob=1.0)
63 keep_prob=1.0)
|
| rnn_colorbot.py | 141 def __init__(self, rnn_cell_sizes, label_dimension, keep_prob):
148 keep_prob: (1 - dropout probability); dropout is applied to the outputs of
153 self.keep_prob = keep_prob
195 chars = tf.nn.dropout(chars, self.keep_prob)
260 keep_prob=FLAGS.keep_probability)
|
| /external/tensorflow/tensorflow/examples/tutorials/mnist/ |
| mnist_with_summaries.py | 104 keep_prob = tf.placeholder(tf.float32)
105 tf.summary.scalar('dropout_keep_probability', keep_prob)
106 dropped = tf.nn.dropout(hidden1, keep_prob)
157 return {x: xs, y_: ys, keep_prob: k}
|
| /external/tensorflow/tensorflow/python/ops/ |
| nn_test.py | 314 for keep_prob in [0.1, 0.5, 0.8]:
316 dropout = nn_ops.dropout(t, keep_prob)
322 # Verifies that there are only two values: 0 and 1/keep_prob.
325 self.assertAllClose(1 / keep_prob, sorted_value[1])
328 expected_count = x_dim * y_dim * keep_prob * num_iter
341 for keep_prob in [0.1, 0.5, 0.8]:
343 dropout = nn_ops.dropout(t, keep_prob, noise_shape=[x_dim, 1])
349 # Verifies that there are only two values: 0 and 1/keep_prob.
352 self.assertAllClose(1 / keep_prob, sorted_value[1])
355 expected_count = x_dim * y_dim * keep_prob * num_ite
[all...] |
| rnn_cell_impl.py | [all...] |
| nn_ops.py | [all...] |
| /external/tensorflow/tensorflow/contrib/text/python/ops/ |
| skip_gram_ops.py | 438 keep_prob = ((math_ops.sqrt(freq /
448 mask = math_ops.less_equal(random_prob, keep_prob)
|
| /external/tensorflow/tensorflow/tools/compatibility/ |
| tf_upgrade_v2.py | [all...] |
| /external/tensorflow/tensorflow/core/kernels/ |
| word2vec_kernels.cc | 154 float keep_prob = local
157 if (rng_.RandFloat() > keep_prob) {
|
| /external/tensorflow/tensorflow/contrib/learn/python/learn/estimators/ |
| composable_model.py | 389 net = layers.dropout(net, keep_prob=(1.0 - self._dropout))
|
| dnn.py | 179 net = layers.dropout(net, keep_prob=(1.0 - dropout))
[all...] |
| dnn_linear_combined.py | 267 keep_prob=(1.0 - dnn_dropout))
[all...] |
| /external/tensorflow/tensorflow/python/grappler/ |
| hierarchical_controller.py | 143 keep_prob=1.0,
[all...] |
| /external/tensorflow/tensorflow/contrib/boosted_trees/estimator_batch/ |
| dnn_tree_combined_estimator.py | 208 net = layers.dropout(net, keep_prob=(1.0 - dnn_dropout))
[all...] |
| /external/tensorflow/tensorflow/contrib/slim/python/slim/nets/ |
| inception_v2.py | 556 net, keep_prob=dropout_keep_prob, scope='Dropout_1b')
|
| inception_v3.py | 628 net, keep_prob=dropout_keep_prob, scope='Dropout_1b')
|
| /external/tensorflow/tensorflow/contrib/layers/python/layers/ |
| layers.py | [all...] |
| /external/tensorflow/tensorflow/contrib/rnn/python/kernel_tests/ |
| rnn_cell_test.py | [all...] |
