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/external/tensorflow/tensorflow/python/keras/layers/
Dkernelized_test.py79 rff_layer = kernel_layers.RandomFourierFeatures(output_dim=10, scale=3.0)
95 _ = kernel_layers.RandomFourierFeatures(output_dim=-3, scale=2.0)
107 _ = kernel_layers.RandomFourierFeatures(output_dim=10, scale=0.0)
111 rff_layer = kernel_layers.RandomFourierFeatures(output_dim=10, scale=3.0)
122 output_dim=10,
126 self.assertEqual(rff_layer.output_dim, 10)
136 output_dim=10,
152 kernel_layers.RandomFourierFeatures(output_dim=4, name='rff')(inputs)
153 kernel_layers.RandomFourierFeatures(output_dim=10, scale=2.0)(inputs)
158 output_dim=7, name='random_fourier_features', trainable=True)
[all …]
Dembeddings.py93 output_dim, argument
106 if input_dim <= 0 or output_dim <= 0:
109 input_dim, output_dim))
123 self.output_dim = output_dim
143 shape=(self.input_dim, self.output_dim),
151 shape=(self.input_dim, self.output_dim),
168 return input_shape + (self.output_dim,)
187 return (input_shape[0],) + tuple(in_lens) + (self.output_dim,)
206 'output_dim': self.output_dim,
Dkernelized.py152 output_dim, argument
158 if output_dim <= 0:
161 output_dim))
172 self.output_dim = output_dim
193 self.kernel_initializer, shape=(input_dim, self.output_dim))
197 shape=(input_dim, self.output_dim),
204 shape=(self.output_dim,),
236 return input_shape[:-1].concatenate(self.output_dim)
243 'output_dim': self.output_dim,
Dembeddings_test.py84 layer = keras.layers.Embedding(output_dim=2, input_dim=2)
94 keras.layers.Embedding(input_dim=0, output_dim=1)
97 keras.layers.Embedding(input_dim=1, output_dim=0)
101 l = keras.layers.Embedding(output_dim=2, input_dim=2)
115 output_dim=2,
138 layer = keras.layers.Embedding(input_dim=5, output_dim=2)
154 layer = keras.layers.Embedding(input_dim=5, output_dim=2)
Dcudnn_recurrent_test.py407 output_dim = 2
411 target_dim = 2 * output_dim if mode == 'concat' else output_dim
418 rnn(output_dim), merge_mode=mode, input_shape=(None, dim)))
431 rnn(output_dim, return_sequences=True),
434 model.add(keras.layers.Bidirectional(rnn(output_dim), merge_mode=mode))
441 rnn(output_dim), merge_mode=mode)(
450 rnn(output_dim, stateful=True), merge_mode=mode)(
Dwrappers_test.py541 output_dim = 2
544 target_dim = 2 * output_dim if mode == 'concat' else output_dim
551 rnn(output_dim), merge_mode=mode, input_shape=(timesteps, dim)))
586 output_dim = 2
592 rnn(output_dim), input_shape=(timesteps, dim)))
605 output_dim = 2
610 target_dim = 2 * output_dim if mode == 'concat' else output_dim
616 rnn(output_dim, return_sequences=True),
619 model.add(keras.layers.Bidirectional(rnn(output_dim), merge_mode=mode))
626 rnn(output_dim), merge_mode=mode)(inputs)
[all …]
/external/tensorflow/tensorflow/python/keras/saving/
Dsave_weights_test.py90 output_dim = 3
104 (keras.layers.Conv1D(output_dim, size, use_bias=False)),
105 [np.random.random((output_dim, input_dim, size, 1))],
109 (keras.layers.Conv2D(output_dim, size,
111 [np.random.random((output_dim, input_dim, size, size))],
115 (keras.layers.Conv2DTranspose(output_dim, size,
118 [np.random.random((output_dim, input_dim, size, size))],
122 (keras.layers.Conv2DTranspose(output_dim, size,
125 [np.random.random((size, size, input_dim, output_dim))],
129 (keras.layers.Conv3D(output_dim, size,
[all …]
/external/tensorflow/tensorflow/compiler/xla/client/lib/
Dconv_grad_size_util.cc70 TF_ASSIGN_OR_RETURN(SpatialDimensionOutputSizeAndPadding output_dim, in ConvGradExtractAndVerifyDimension()
73 if (output_size != output_dim.output_size) { in ConvGradExtractAndVerifyDimension()
76 output_size, ", computed = ", output_dim.output_size, in ConvGradExtractAndVerifyDimension()
83 dim.output_size = (output_dim.output_size - 1) * stride + 1; in ConvGradExtractAndVerifyDimension()
85 dim.pad_before = effective_filter_size - 1 - output_dim.pad_before; in ConvGradExtractAndVerifyDimension()
/external/tensorflow/tensorflow/core/util/
Dbcast.h202 int output_dim = -1; in BCastList() local
206 output_dim = -1; in BCastList()
217 if (!output_dim_set || copy[i][j] == output_dim) { in BCastList()
218 output_dim = copy[i][j]; in BCastList()
226 output_.push_back(output_dim_set ? output_dim : 1); in BCastList()
262 result_.back() = mul_dims(result_.back(), output_dim); in BCastList()
266 mul_dims(bcast_[i].back(), current_is_one[i] ? output_dim : 1); in BCastList()
272 result_.push_back(output_dim); in BCastList()
275 bcast_[i].push_back(current_is_one[i] ? output_dim : 1); in BCastList()
/external/tensorflow/tensorflow/lite/kernels/internal/
Dbatch_to_space_nd_test.cc25 int input_dim, int output_dim) { in GetIndexRange() argument
29 output_dim, &index_start, &index_end); in GetIndexRange()
/external/tensorflow/tensorflow/lite/delegates/xnnpack/
Dpad_tester.cc41 int32_t output_dim = InputShape()[i]; in OutputShape() local
43 output_dim += InputPrePaddings()[i]; in OutputShape()
46 output_dim += InputPostPaddings()[i]; in OutputShape()
48 output_shape.push_back(output_dim); in OutputShape()
Dreshape_tester.h46 for (int32_t output_dim : output_shape) { in OutputShape() local
47 EXPECT_GT(output_dim, 0); in OutputShape()
/external/tensorflow/tensorflow/core/ops/
Dimage_ops.cc227 DimensionHandle output_dim; in CombinedNMSShapeFn() local
230 TF_RETURN_IF_ERROR(c->MakeDimForScalarInput(3, &output_dim)); in CombinedNMSShapeFn()
231 if (c->ValueKnown(output_dim) && c->Value(output_dim) <= 0) { in CombinedNMSShapeFn()
241 output_size = c->Value(output_dim); in CombinedNMSShapeFn()
248 output_size = std::min(c->Value(output_dim), in CombinedNMSShapeFn()
1026 DimensionHandle output_dim; in __anon30b5031d1b02() local
1027 TF_RETURN_IF_ERROR(c->MakeDimForScalarInput(2, &output_dim)); in __anon30b5031d1b02()
1028 c->set_output(0, c->MakeShape({output_dim})); in __anon30b5031d1b02()
1053 DimensionHandle output_dim; in __anon30b5031d1c02() local
1054 TF_RETURN_IF_ERROR(c->MakeDimForScalarInput(2, &output_dim)); in __anon30b5031d1c02()
[all …]
/external/tensorflow/tensorflow/python/keras/benchmarks/keras_examples_benchmarks/
Dantirectifier_benchmark_test.py138 output_dim = input_shape[-1]
140 shape=(output_dim * 2, output_dim),
Dtext_classification_transformer_benchmark_test.py224 input_dim=vocab_size, output_dim=embed_dim)
226 input_dim=maxlen, output_dim=embed_dim)
/external/tensorflow/tensorflow/compiler/xla/service/
Ddynamic_padder.cc355 for (int64 output_dim : output_dims) { in RewriteDynamicReshapeSplitInput() local
356 reshaped_dims.push_back(reshape->shape().dimensions(output_dim)); in RewriteDynamicReshapeSplitInput()
379 const int64 output_dim = output_dims[i]; in RewriteDynamicReshapeSplitInput() local
380 HloInstruction* dynamic_size = output_dynamic_dims[output_dim]; in RewriteDynamicReshapeSplitInput()
472 for (int64 output_dim : output_dims) { in RewriteDynamicReshapeSplitInput() local
474 dynamic_dimension_inference->GetDynamicSize(reshape, {}, output_dim); in RewriteDynamicReshapeSplitInput()
479 output_dim)); in RewriteDynamicReshapeSplitInput()
557 int64 output_dim, absl::Span<HloInstruction*> input_dynamic_dims, in RewriteDynamicReshapeCombineInput() argument
569 ShapeUtil::MakeShape(xla::S32, {output_shape.dimensions(output_dim)}); in RewriteDynamicReshapeCombineInput()
655 if (i != output_dim) { in RewriteDynamicReshapeCombineInput()
[all …]
Dindexed_array_analysis.cc215 for (int64 output_dim : output_dims) { in FoldGatherOfGather() local
216 simulated_index.insert(simulated_index.begin() + output_dim, in FoldGatherOfGather()
537 for (int64 output_dim : operand->output_dims()) { in ReshapeToAddDegenerateDims() local
538 output_dims_bitvector[output_dim] = true; in ReshapeToAddDegenerateDims()
564 for (int64 output_dim : new_output_dims) { in ReshapeToAddDegenerateDims() local
565 EraseAt(&new_source_shape_dims, output_dim); in ReshapeToAddDegenerateDims()
711 int64 output_dim = scalar_indexed->output_dims()[i]; in FoldReshapeOfGatherNoDegenerateDims() local
713 reshape_passthrough_dims, output_dim); in FoldReshapeOfGatherNoDegenerateDims()
877 auto is_broadcasted_dim = [&](int64 output_dim) { in ComputeArrayForElementwiseBinaryOp() argument
878 return absl::c_find(broadcast_dims, output_dim) == broadcast_dims.end(); in ComputeArrayForElementwiseBinaryOp()
/external/tensorflow/tensorflow/python/debug/examples/v2/
Ddebug_mnist_v2.py164 def get_dense_weights(input_dim, output_dim): argument
168 kernel = tf.Variable(initial_kernel([input_dim, output_dim]))
169 bias = tf.Variable(tf.constant(0.1, shape=[output_dim]))
/external/tensorflow/tensorflow/python/debug/examples/v1/
Ddebug_mnist_v1.py162 def nn_layer(input_tensor, input_dim, output_dim, layer_name, act=tf.nn.relu): argument
168 weights = weight_variable([input_dim, output_dim])
170 biases = bias_variable([output_dim])
/external/XNNPACK/test/
Dconstant-pad-operator-tester.h84 inline size_t output_dim(size_t i) const { in output_dim() function
91 elements *= output_dim(i); in num_output_elements()
126 output_dims[XNN_MAX_TENSOR_DIMS - num_dims() + i] = output_dim(i); in TestX32()
/external/tensorflow/tensorflow/python/keras/distribute/
Dkeras_embedding_model_correctness_test.py42 word_embed = keras.layers.Embedding(input_dim=20, output_dim=10)(word_ids)
100 output_dim=10,
Dkeras_stateful_lstm_model_correctness_test.py64 word_embed = keras.layers.Embedding(input_dim=20, output_dim=10)(word_ids)
/external/tensorflow/tensorflow/python/keras/utils/
Dmulti_gpu_utils_test.py59 output_dim = 1
71 model.add(keras.layers.Dense(output_dim))
74 y = np.random.random((num_samples, output_dim))
/external/tensorflow/tensorflow/python/keras/
Dbackend_test.py1184 output_dim = 3
1190 (num_samples, output_dim)).astype(np.float32)
1191 w_i_val = np.random.random((input_dim, output_dim)).astype(np.float32)
1192 w_o_val = np.random.random((output_dim, output_dim)).astype(np.float32)
1250 self.assertEqual(last_output.shape.as_list(), [num_samples, output_dim])
1252 [num_samples, timesteps, output_dim])
1254 self.assertEqual(state.shape.as_list(), [num_samples, output_dim])
1294 output_dim = 3
1300 (num_samples, output_dim)).astype(np.float32)
1301 w_i_val = np.random.random((input_dim, output_dim)).astype(np.float32)
[all …]
/external/tensorflow/tensorflow/python/kernel_tests/
Dbroadcast_to_ops_test.py58 for output_dim in range(input_dim, 6):
61 output_shape = [2] * output_dim

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