| /external/tensorflow/tensorflow/contrib/nn/python/ops/ |
| D | alpha_dropout_test.py | 33 x_dim, y_dim = 40, 30
36 t = random_ops.random_normal([x_dim, y_dim])
38 self.assertEqual([x_dim, y_dim], output.get_shape())
46 x_dim = 40
49 t = constant_op.constant(1.0, shape=[x_dim, y_dim], dtype=dtypes.float32)
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])
62 _ = alpha_dropout(t, keep_prob, noise_shape=[x_dim, 1])
[all …]
|
| /external/tensorflow/tensorflow/contrib/eager/python/examples/l2hmc/ |
| D | l2hmc_test.py | 31 x_dim=2,
67 shape=[n_samples, dynamics.x_dim], dtype=tf.float32)
106 x_dim=hparams.x_dim,
110 samples = tf.random_normal(shape=[hparams.n_samples, hparams.x_dim])
122 x_dim=hparams.x_dim,
126 x = tf.placeholder(tf.float32, shape=[None, hparams.x_dim])
128 samples = npr.normal(size=[hparams.n_samples, hparams.x_dim])
154 x = tf.random_normal([hparams.n_samples, hparams.x_dim],
157 x_dim=hparams.x_dim,
205 x_dim=hparams.x_dim,
[all …]
|
| D | neural_nets.py | 39 def __init__(self, x_dim, factor, n_hidden=10): argument
48 self.scale_layer = _custom_dense(x_dim, .001)
50 initial_value=tf.zeros([1, x_dim]), name='coeff_scale', trainable=True)
52 self.translation_layer = _custom_dense(x_dim, factor=.001)
54 self.transformation_layer = _custom_dense(x_dim, .001)
56 initial_value=tf.zeros([1, x_dim]),
|
| D | l2hmc.py | 38 x_dim, argument
55 self.x_dim = x_dim
62 self.position_fn = neural_nets.GenericNet(x_dim, factor=2.)
63 self.momentum_fn = neural_nets.GenericNet(x_dim, factor=1.)
259 idx = npr.permutation(np.arange(self.x_dim))[:self.x_dim // 2]
260 mask = np.zeros((self.x_dim,))
|
| D | main.py | 46 x_dim = 2
55 x_dim=x_dim, minus_loglikelihood_fn=energy_fn, n_steps=n_steps, eps=eps)
78 samples = tf.random_normal(shape=[n_samples, x_dim])
111 samples = tf.random_normal(shape=[n_samples, x_dim])
|
| /external/tensorflow/tensorflow/python/ops/ |
| D | nn_test.py | 311 x_dim = 40
315 t = constant_op.constant(1.0, shape=[x_dim, y_dim], dtype=dtypes.float32)
318 self.assertEqual([x_dim, y_dim], dropout.get_shape())
328 expected_count = x_dim * y_dim * keep_prob * num_iter
338 x_dim = 40 * 30
342 t = constant_op.constant(1.0, shape=[x_dim, y_dim], dtype=dtypes.float32)
343 dropout = nn_ops.dropout(t, keep_prob, noise_shape=[x_dim, 1])
344 self.assertEqual([x_dim, y_dim], dropout.get_shape())
355 expected_count = x_dim * y_dim * keep_prob * num_iter
362 x_dim = 40
[all …]
|
| /external/tensorflow/tensorflow/contrib/image/ops/ |
| D | single_image_random_dot_stereograms_ops.cc | 55 DimensionHandle x_dim = c->Dim(output_image_shape, 0); in __anonbda156eb0102() local
63 {y_dim, x_dim, colors > 256 ? c->MakeDim(3) : c->MakeDim(1)})); in __anonbda156eb0102()
|
| /external/tensorflow/tensorflow/contrib/learn/python/learn/estimators/ |
| D | tensor_signature.py | 81 for dim, (x_dim, y_dim) in enumerate(zip(this.dims, other.dims)):
84 if not x_dim.is_compatible_with(y_dim):
|
| /external/tensorflow/tensorflow/python/framework/ |
| D | tensor_shape.py | 1054 for x_dim, y_dim in zip(self._dims, other.dims):
1055 if not x_dim.is_compatible_with(y_dim):
|
| /external/libcups/cups/ |
| D | ppd-cache.c | 2953 *x_dim, *y_dim; /* Media dimensions */ in _ppdCreateFromIPP() local
3235 x_dim = ippFindAttribute(media_size, "x-dimension", IPP_TAG_INTEGER); in _ppdCreateFromIPP()
3238 …if (x_dim && y_dim && (pwg = pwgMediaForSize(ippGetInteger(x_dim, 0), ippGetInteger(y_dim, 0))) !=… in _ppdCreateFromIPP()
3266 x_dim = ippFindAttribute(media_size, "x-dimension", IPP_TAG_INTEGER); in _ppdCreateFromIPP()
3269 pwg = pwgMediaForSize(ippGetInteger(x_dim, 0), ippGetInteger(y_dim, 0)); in _ppdCreateFromIPP()
3306 x_dim = ippFindAttribute(media_size, "x-dimension", IPP_TAG_INTEGER); in _ppdCreateFromIPP()
3309 pwg = pwgMediaForSize(ippGetInteger(x_dim, 0), ippGetInteger(y_dim, 0)); in _ppdCreateFromIPP()
3342 x_dim = ippFindAttribute(media_size, "x-dimension", IPP_TAG_INTEGER); in _ppdCreateFromIPP()
3345 pwg = pwgMediaForSize(ippGetInteger(x_dim, 0), ippGetInteger(y_dim, 0)); in _ppdCreateFromIPP()
|
| /external/tensorflow/tensorflow/core/ops/ |
| D | array_ops.cc | 1750 int64 x_dim = c->Value(c->Dim(shape_x, 0)); in __anon7c94107b2902() local
1754 c->set_output(0, c->Vector(std::max(x_dim, y_dim))); in __anon7c94107b2902()
|