| /external/tensorflow/tensorflow/contrib/tensor_forest/hybrid/python/layers/ |
| decisions_to_data_test.py | 38 num_features=31,
55 [[random.uniform(-1, 1) for i in range(self.params.num_features)]
|
| decisions_to_data.py | 42 shape=[params.num_nodes, params.num_features],
128 shape=[params.num_nodes, params.num_features],
166 shape=[params.num_nodes, params.num_features],
218 shape=[params.num_nodes, params.num_features],
|
| /external/tensorflow/tensorflow/core/tpu/ |
| tpu_embedding_output_layout_utils.cc | 37 two_d->set_dim0_size_per_sample(table.num_features());
46 for (int feature_index = 0; feature_index < table.num_features();
|
| /external/tensorflow/tensorflow/contrib/timeseries/python/timeseries/ |
| ar_model.py | 55 num_features,
62 num_features: number of input features per time step.
78 self._mean_transform = core.Dense(num_features * output_window_size,
80 self._covariance_transform = core.Dense(num_features * output_window_size,
82 self._prediction_shape = [-1, output_window_size, num_features]
101 [batch size, output window size, num_features], where num_features is the
137 num_features,
144 num_features: number of input features per time step.
156 self._mean_transform = core.Dense(num_features,
[all...] |
| math_utils_test.py | 278 self, stat_object, num_features, dtype, give_full_data,
285 + numpy.arange(num_features, dtype=numpy_dtype)[None, ...])[None])
306 range(num_features) + numpy.mean(numpy.arange(chunk_size))[None],
311 [num_features]),
335 for num_features in [1, 2, 3]:
338 num_features=num_features, dtype=dtype),
339 num_features=num_features,
|
| ar_model_test.py | 116 num_features=2,
223 periodicities=10, num_features=1,
245 num_features=1,
266 num_features=1,
287 num_features=1,
322 [1, 3, 1], # batch, window, num_features. The window size has 2
337 num_features=1,
356 [1, 2, 1], # batch, window, num_features. The window has two cut
|
| /external/tensorflow/tensorflow/contrib/tensor_forest/hybrid/core/ops/ |
| hard_routing_function_op.cc | 111 const int32 num_features = variable
147 tree_biases(j), num_features);
153 for (int k = 0; k < num_features; k++) {
|
| k_feature_routing_function_op.cc | 117 const int32 num_features = variable
140 tensorforest::GetFeatureSet(layer_num_, i, random_seed_, num_features,
149 tree_biases(j), num_features, num_features_per_node_);
|
| routing_function_op.cc | 102 const int32 num_features = variable
129 tree_biases(j), num_features);
|
| routing_gradient_op.cc | 101 const int32 num_features = variable
129 tree_biases(j), num_features);
|
| stochastic_hard_routing_function_op.cc | 122 const int32 num_features = variable
161 tree_biases(j), num_features);
|
| /external/tensorflow/tensorflow/contrib/timeseries/examples/ |
| lstm.py | 51 def __init__(self, num_units, num_features, exogenous_feature_columns=None,
60 num_features: The dimensionality of the time series (features per
72 num_features=num_features,
103 func_=functools.partial(tf.layers.dense, units=self.num_features),
112 tf.zeros([self.num_features], dtype=self.dtype),
133 current_values: A [batch size, self.num_features] floating point Tensor
203 model=_LSTMModel(num_features=5, num_units=128,
|
| known_anomaly.py | 62 num_features=1,
77 num_features=1,
|
| predict.py | 54 periodicities=100, num_features=1, cycle_num_latent_values=5)
64 num_features=1,
|
| /external/tensorflow/tensorflow/contrib/gan/python/eval/python/ |
| eval_utils_impl.py | 59 num_features = image_shape[0] * image_shape[1] * num_channels
60 if int(input_tensor.shape[1]) != num_features:
|
| /external/harfbuzz_ng/src/ |
| hb-directwrite.cc | 514 unsigned int num_features,
608 typographic_features.featureCount = num_features;
609 if (num_features)
611 typographic_features.features = new DWRITE_FONT_FEATURE[num_features];
612 for (unsigned int i = 0; i < num_features; ++i)
828 if (num_features)
840 unsigned int num_features)
843 features, num_features, 0);
854 unsigned int num_features,
860 features, num_features, &shapers)
[all...] |
| hb-fallback-shape.cc | 75 unsigned int num_features HB_UNUSED)
|
| hb-shape-plan.h | 100 unsigned int num_features);
|
| /external/tensorflow/tensorflow/contrib/tensor_forest/hybrid/python/ |
| hybrid_layer_test.py | 34 num_features=7,
|
| /external/tensorflow/tensorflow/contrib/timeseries/python/timeseries/state_space_models/ |
| state_space_model_test.py | 69 size=(configuration.num_features, state_dimension)).astype(
128 num_features=1))
177 dtype=dtypes.float64, num_features=1))
257 dtype=dtypes.float64, num_features=1))
368 dtype=dtype, num_features=1))
389 dtype=dtype, num_features=1))
422 dtype=dtype, num_features=1))
445 dtype=dtype, num_features=1))
690 num_features=1)
692 for feature in range(configuration.num_features)
[all...] |
| state_space_model.py | 51 "num_features", "use_observation_noise", "dtype",
64 num_features=1,
82 num_features: Output dimension for model
165 cls, num_features, use_observation_noise, dtype,
235 num_features=configuration.num_features,
472 .concatenate([self.num_features, self.num_features]))
474 (self.num_features,)))
476 (self.num_features, self.num_features))
[all...] |
| /external/tensorflow/tensorflow/contrib/distributions/python/kernel_tests/ |
| mixture_test.py | [all...] |
| /external/tensorflow/tensorflow/contrib/eager/python/examples/linear_regression/ |
| linear_regression.py | 105 def synthetic_dataset_helper(w, b, num_features, noise_level, batch_size,
113 x = tf.random_normal([batch_size, num_features])
|
| linear_regression_graph_test.py | 35 num_features=3,
|
| /external/tensorflow/tensorflow/lite/experimental/microfrontend/ops/ |
| audio_microfrontend_op.cc | 100 DimensionHandle num_features = ctx->MakeDim(num_channels);
102 ctx->Multiply(num_features, stack_size, &num_features));
104 ShapeHandle output = ctx->MakeShape({num_frames, num_features});
|
