| /external/tensorflow/tensorflow/python/keras/tests/ |
| D | model_subclassing_compiled_test.py | 44 input_dim = 50
55 x = np.ones((num_samples, input_dim))
64 input_dim = 50
75 x1 = np.ones((num_samples, input_dim))
76 x2 = np.ones((num_samples, input_dim))
86 input_dim = 50
97 x = np.ones((num_samples, input_dim), dtype=np.float32)
111 input_dim = 50
116 x1 = np.ones((num_samples, input_dim))
117 x2 = np.ones((num_samples, input_dim))
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| D | model_subclassing_test.py | 136 input_dim = 50
146 model.build(input_shape=tensor_shape.Dimension(input_dim))
212 input_dim = 50
221 model.build(input_shape=(batch_size, input_dim))
225 model(array_ops.ones((32, input_dim)))
229 input_dim = tensor_shape.Dimension(50)
238 model.build(input_shape=(batch_size, input_dim))
242 model(array_ops.ones((32, input_dim)))
315 input_dim = 50
320 batch_input_shape = tensor_shape.TensorShape((batch_size, input_dim))
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| D | model_subclassing_test_util.py | 94 def get_functional_graph_model(input_dim, num_classes): argument
96 inputs = keras.Input(shape=(input_dim,))
109 def get_nested_model_3(input_dim, num_classes): argument
113 inputs = keras.Input(shape=(input_dim,))
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| /external/tensorflow/tensorflow/compiler/tf2xla/kernels/ |
| D | extract_image_patches_op.cc | 77 int input_dim = GetTensorSpatialDimIndex(num_dims, data_format, i); in Compile() local
79 ctx, ksizes_[input_dim] >= 0, in Compile()
82 dilations_[input_dim])); in Compile()
83 OP_REQUIRES(ctx, strides_[input_dim] >= 1, in Compile()
86 dilations_[input_dim])); in Compile()
87 OP_REQUIRES(ctx, dilations_[input_dim] >= 1, in Compile()
90 dilations_[input_dim])); in Compile()
110 int input_dim = GetTensorSpatialDimIndex(num_dims, data_format, i); in Compile() local
111 kernel_shape[i] = ksizes_[input_dim]; in Compile()
112 kernel_size *= ksizes_[input_dim]; in Compile()
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| /external/tensorflow/tensorflow/compiler/xla/service/ |
| D | dynamic_padder.cc | 247 HloInstruction* reshape, int64 input_dim, in RewriteDynamicReshapeSplitInput() argument
296 HloInstruction::CreateIota(mask_input_shape, input_dim)); in RewriteDynamicReshapeSplitInput()
325 input_dim, {input_shape_binary_mask, iota_mask}, compare_binary_iota, in RewriteDynamicReshapeSplitInput()
360 LiteralUtil::CreateR0<int32>(operand_shape.dimensions(input_dim)))); in RewriteDynamicReshapeSplitInput()
365 input_dim)); in RewriteDynamicReshapeSplitInput()
370 input_dim, {sorted_iota_mask, operand_static}, compare_iota_value, in RewriteDynamicReshapeSplitInput()
405 HloInstruction* reshape, int64 input_dim, int64 output_dim, in RewriteDynamicReshapeCombineInput() argument
424 PadWithScalar(broadcasted_zero, input_dim, dynamic_size, one); in RewriteDynamicReshapeCombineInput()
494 HloInstruction* reshape, int64 input_dim, HloInstruction* dynamic_size, in RewriteDynamicReshapeSingleDim() argument
497 << " input dim: " << input_dim; in RewriteDynamicReshapeSingleDim()
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| /external/tensorflow/tensorflow/python/keras/saving/ |
| D | saving_utils_test.py | 67 input_dim = 5 if testing_utils.get_model_type() == 'functional' else None
68 model = testing_utils.get_small_mlp(10, 3, input_dim)
71 if input_dim is None:
86 input_dim = 5 if testing_utils.get_model_type() == 'functional' else None
87 model = testing_utils.get_small_mlp(10, 3, input_dim)
107 input_dim = 5
110 input_a = keras.layers.Input(shape=(input_dim,), name='input_a')
111 input_b = keras.layers.Input(shape=(input_dim,), name='input_b')
121 input_a_np = np.random.random((10, input_dim)).astype(np.float32)
122 input_b_np = np.random.random((10, input_dim)).astype(np.float32)
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| D | hdf5_format_test.py | 88 input_dim = 3
104 [np.random.random((output_dim, input_dim, size, 1))],
105 (None, 4, input_dim),
110 [np.random.random((output_dim, input_dim, size, size))],
111 (None, input_dim, 4, 4),
117 [np.random.random((output_dim, input_dim, size, size))],
118 (None, input_dim, 4, 4),
124 [np.random.random((size, size, input_dim, output_dim))],
125 (None, 4, 4, input_dim),
130 [np.random.random((output_dim, input_dim, size, size, size))],
[all …]
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| /external/tensorflow/tensorflow/python/keras/layers/ |
| D | embeddings.py | 92 input_dim, argument
114 self.input_dim = input_dim
136 shape=(self.input_dim, self.output_dim),
143 shape=(self.input_dim, self.output_dim),
189 'input_dim': self.input_dim,
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| D | kernelized.py | 170 input_dim = input_shape.dims[1].value
173 self.kernel_initializer, shape=(input_dim, self.output_dim))
177 shape=(input_dim, self.output_dim),
191 self.scale = _get_default_scale(self.kernel_initializer, input_dim)
254 def _get_default_scale(initializer, input_dim): argument
257 return np.sqrt(input_dim / 2.0)
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| D | local.py | 176 input_dim, input_length = input_shape[1], input_shape[2]
178 input_dim, input_length = input_shape[2], input_shape[1]
180 if input_dim is None:
187 self.kernel_shape = (self.output_length, self.kernel_size[0] * input_dim,
199 self.kernel_shape = (input_dim, input_length,
202 self.kernel_shape = (input_length, input_dim,
221 input_length * input_dim)
229 filters_in=input_dim,
256 self.input_spec = InputSpec(ndim=3, axes={1: input_dim})
258 self.input_spec = InputSpec(ndim=3, axes={-1: input_dim})
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| D | embeddings_test.py | 80 layer = keras.layers.Embedding(output_dim=2, input_dim=2)
91 l = keras.layers.Embedding(output_dim=2, input_dim=2)
104 input_dim=3,
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| D | local_test.py | 91 input_dim = 5
114 input_shape=(num_samples, num_steps, input_dim))
121 input_dim = 5
140 layer.build((num_samples, num_steps, input_dim))
144 np.ones((num_samples, num_steps, input_dim))))
157 layer.build((num_samples, num_steps, input_dim))
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| D | recurrent_test.py | 68 def __init__(self, units, input_dim): argument
72 np.random.random((input_dim, units)))
110 def __init__(self, units, input_dim): argument
114 np.random.random((input_dim, units)))
1435 input_dim = 8
1438 x = keras.Input(batch_shape=(batch, None, input_dim))
1449 np.zeros((batch, timesteps, input_dim)),
1451 model.predict(np.ones((batch, timesteps, input_dim)))
1454 model.predict(np.ones((batch, timesteps, input_dim)))
1459 model.predict(np.ones((batch, timesteps, input_dim)))
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| /external/tensorflow/tensorflow/python/keras/engine/ |
| D | sequential_test.py | 43 model.add(keras.layers.Dense(1, input_dim=2))
71 input_dim = 3
76 num_hidden, num_classes, input_dim)
82 x = np.random.random((batch_size, input_dim))
98 model.add(keras.layers.Dense(num_hidden, input_dim=input_dim))
111 input_dim = 3
128 x = np.random.random((batch_size, input_dim))
138 input_dim = 3
156 x = array_ops.ones((num_samples, input_dim))
177 model = testing_utils.get_small_sequential_mlp(10, 4, input_dim=3)
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| D | training_generator_test.py | 99 num_hidden=3, num_classes=4, input_dim=2)
137 num_hidden=3, num_classes=4, input_dim=2)
166 num_hidden=3, num_classes=4, input_dim=2)
202 num_hidden=3, num_classes=4, input_dim=2)
241 num_hidden=3, num_classes=4, input_dim=2)
286 num_hidden=10, num_classes=1, input_dim=10)
303 num_hidden=3, num_classes=4, input_dim=2)
342 num_hidden=10, num_classes=1, input_dim=10)
404 keras.layers.Embedding(input_dim=len(vocab) + 1, output_dim=4),
428 num_hidden=3, num_classes=4, input_dim=2)
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| D | training_test.py | 94 num_hidden=10, num_classes=2, input_dim=3)
107 num_hidden=10, num_classes=2, input_dim=3)
184 num_hidden=10, num_classes=2, input_dim=3)
195 num_hidden=10, num_classes=2, input_dim=3)
209 num_hidden=10, num_classes=2, input_dim=3)
222 num_hidden=10, num_classes=2, input_dim=3)
237 num_hidden=10, num_classes=2, input_dim=3)
252 num_hidden=10, num_classes=2, input_dim=3)
1091 input_dim = 5
1112 num_hidden=10, num_classes=num_classes, input_dim=input_dim)
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| D | training_dataset_test.py | 58 model = testing_utils.get_small_mlp(1, 4, input_dim=3)
84 model = testing_utils.get_small_mlp(1, 4, input_dim=3)
220 model = testing_utils.get_small_mlp(1, 4, input_dim=3)
270 model = testing_utils.get_small_mlp(1, 4, input_dim=3)
340 model = testing_utils.get_small_functional_mlp(1, 4, input_dim=3)
369 model = testing_utils.get_small_mlp(1, 4, input_dim=3)
393 model = testing_utils.get_small_mlp(1, 4, input_dim=3)
433 model = testing_utils.get_small_mlp(1, 4, input_dim=3)
478 model = testing_utils.get_small_mlp(1, 4, input_dim=3)
577 keras.layers.Dense(8, activation='relu', input_dim=4,
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| /external/tensorflow/tensorflow/python/keras/optimizer_v2/ |
| D | optimizer_v2_test.py | 574 input_dim = 1
579 input_shape=(input_dim,),
653 input_dim = 3
658 input_shape=(input_dim,),
664 num_hidden=num_hidden, num_classes=num_classes, input_dim=input_dim)
674 num_hidden=num_hidden, num_classes=num_classes, input_dim=input_dim)
741 input_dim = 3
746 input_shape=(input_dim,),
752 num_hidden=num_hidden, num_classes=num_classes, input_dim=input_dim)
754 num_hidden=num_hidden, num_classes=num_classes, input_dim=input_dim)
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| /external/tensorflow/tensorflow/python/keras/utils/ |
| D | multi_gpu_utils_test.py | 57 input_dim = 10
69 input_shape=(input_dim,)))
72 x = np.random.random((num_samples, input_dim))
156 input_dim = 10
157 shape = (input_dim,)
185 input_shape=(input_dim,)))
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| /external/tensorflow/tensorflow/lite/kernels/internal/ |
| D | batch_to_space_nd_test.cc | 25 int input_dim, int output_dim) { in GetIndexRange() argument
28 optimized_ops::GetIndexRange(spatial_index_dim, block_shape_dim, input_dim, in GetIndexRange()
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| /external/tensorflow/tensorflow/python/keras/premade/ |
| D | wide_deep_test.py | 45 dnn_model = sequential.Sequential([core.Dense(units=1, input_dim=3)])
89 dnn_model = sequential.Sequential([core.Dense(units=1, input_dim=3)])
128 dnn_model = sequential.Sequential([core.Dense(units=1, input_dim=3)])
169 dnn_model = sequential.Sequential([core.Dense(units=1, input_dim=3)])
172 sequential.Sequential([core.Dense(units=1, input_dim=3)]))
266 dnn_model = sequential.Sequential([core.Dense(units=1, input_dim=3)])
281 dnn_model = sequential.Sequential([core.Dense(units=1, input_dim=3)])
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| /external/tensorflow/tensorflow/python/keras/ |
| D | testing_utils.py | 399 def get_small_sequential_mlp(num_hidden, num_classes, input_dim=None): argument
401 if input_dim:
403 input_dim=input_dim))
411 def get_small_functional_mlp(num_hidden, num_classes, input_dim): argument
412 inputs = keras.Input(shape=(input_dim,))
472 def get_small_mlp(num_hidden, num_classes, input_dim): argument
480 return get_small_sequential_mlp(num_hidden, num_classes, input_dim)
482 return get_small_functional_mlp(num_hidden, num_classes, input_dim)
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| D | callbacks_v1_test.py | 83 NUM_HIDDEN, input_dim=INPUT_DIM, activation='relu'))
279 NUM_HIDDEN, input_dim=INPUT_DIM, activation='relu'))
322 num_hidden=10, num_classes=10, input_dim=100)
372 num_hidden=NUM_HIDDEN, num_classes=NUM_CLASSES, input_dim=INPUT_DIM)
483 num_hidden=NUM_HIDDEN, num_classes=NUM_CLASSES, input_dim=INPUT_DIM)
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| /external/tensorflow/tensorflow/python/kernel_tests/ |
| D | broadcast_to_ops_test.py | 61 for input_dim in range(1, 6):
62 for output_dim in range(input_dim, 6):
64 input_shape = [2] * input_dim
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| /external/tensorflow/tensorflow/python/keras/distribute/ |
| D | keras_embedding_model_correctness_test.py | 42 word_embed = keras.layers.Embedding(input_dim=20, output_dim=10)(word_ids)
103 input_dim=20,
|