| /external/tensorflow/tensorflow/python/keras/tests/ |
| D | model_subclassing_compiled_test.py | 44 input_dim = 50
54 x = np.ones((num_samples, input_dim))
63 input_dim = 50
73 x1 = np.ones((num_samples, input_dim))
74 x2 = np.ones((num_samples, input_dim))
84 input_dim = 50
94 x = np.ones((num_samples, input_dim), dtype=np.float32)
108 input_dim = 50
113 x1 = np.ones((num_samples, input_dim))
114 x2 = np.ones((num_samples, input_dim))
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| D | model_subclassing_test.py | 135 input_dim = 50
145 model.build(input_shape=tensor_shape.Dimension(input_dim))
211 input_dim = 50
220 model.build(input_shape=(batch_size, input_dim))
224 model(array_ops.ones((32, input_dim)))
228 input_dim = tensor_shape.Dimension(50)
237 model.build(input_shape=(batch_size, input_dim))
241 model(array_ops.ones((32, input_dim)))
314 input_dim = 50
319 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/python/keras/saving/ |
| D | save_weights_test.py | 89 input_dim = 3
105 [np.random.random((output_dim, input_dim, size, 1))],
106 (None, 4, input_dim),
111 [np.random.random((output_dim, input_dim, size, size))],
112 (None, input_dim, 4, 4),
118 [np.random.random((output_dim, input_dim, size, size))],
119 (None, input_dim, 4, 4),
125 [np.random.random((size, size, input_dim, output_dim))],
126 (None, 4, 4, input_dim),
131 [np.random.random((output_dim, input_dim, size, size, size))],
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| D | saving_utils_test.py | 69 input_dim = 5 if testing_utils.get_model_type() == 'functional' else None
70 model = testing_utils.get_small_mlp(10, 3, input_dim)
73 if input_dim is None:
90 input_dim = 5 if testing_utils.get_model_type() == 'functional' else None
91 model = testing_utils.get_small_mlp(10, 3, input_dim)
115 input_dim = 5
118 input_a = keras.layers.Input(shape=(input_dim,), name='input_a')
119 input_b = keras.layers.Input(shape=(input_dim,), name='input_b')
129 input_a_np = np.random.random((10, input_dim)).astype(np.float32)
130 input_b_np = np.random.random((10, input_dim)).astype(np.float32)
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| /external/tensorflow/tensorflow/compiler/tf2xla/kernels/ |
| D | extract_image_patches_op.cc | 78 int input_dim = GetTensorSpatialDimIndex(num_dims, data_format, i); in Compile() local
80 ctx, ksizes_[input_dim] >= 0, in Compile()
83 dilations_[input_dim])); in Compile()
84 OP_REQUIRES(ctx, strides_[input_dim] >= 1, in Compile()
87 dilations_[input_dim])); in Compile()
88 OP_REQUIRES(ctx, dilations_[input_dim] >= 1, in Compile()
91 dilations_[input_dim])); in Compile()
111 int input_dim = GetTensorSpatialDimIndex(num_dims, data_format, i); in Compile() local
112 kernel_shape[i] = ksizes_[input_dim]; in Compile()
113 kernel_size *= ksizes_[input_dim]; in Compile()
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| /external/tensorflow/tensorflow/python/keras/layers/ |
| D | embeddings.py | 92 input_dim, argument
106 if input_dim <= 0 or output_dim <= 0:
109 input_dim, output_dim))
122 self.input_dim = input_dim
143 shape=(self.input_dim, self.output_dim),
151 shape=(self.input_dim, self.output_dim),
205 'input_dim': self.input_dim,
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| D | embeddings_test.py | 84 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)
114 input_dim=3,
138 layer = keras.layers.Embedding(input_dim=5, output_dim=2)
154 layer = keras.layers.Embedding(input_dim=5, output_dim=2)
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| D | kernelized.py | 190 input_dim = input_shape.dims[1].value
193 self.kernel_initializer, shape=(input_dim, self.output_dim))
197 shape=(input_dim, self.output_dim),
211 self.scale = _get_default_scale(self.kernel_initializer, input_dim)
274 def _get_default_scale(initializer, input_dim): argument
277 return np.sqrt(input_dim / 2.0)
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| D | local.py | 161 input_dim, input_length = input_shape[1], input_shape[2]
163 input_dim, input_length = input_shape[2], input_shape[1]
165 if input_dim is None:
175 self.kernel_shape = (self.output_length, self.kernel_size[0] * input_dim,
187 self.kernel_shape = (input_dim, input_length, self.filters,
190 self.kernel_shape = (input_length, input_dim, self.output_length,
210 input_length * input_dim)
218 filters_in=input_dim,
244 self.input_spec = InputSpec(ndim=3, axes={1: input_dim})
246 self.input_spec = InputSpec(ndim=3, axes={-1: input_dim})
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| D | local_test.py | 95 input_dim = 5
118 input_shape=(num_samples, num_steps, input_dim))
125 input_dim = 5
144 layer.build((num_samples, num_steps, input_dim))
148 np.ones((num_samples, num_steps, input_dim))))
161 layer.build((num_samples, num_steps, input_dim))
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| D | convolutional.py | 973 input_dim = int(input_shape[channel_axis])
974 self.input_spec = InputSpec(ndim=3, axes={channel_axis: input_dim})
975 kernel_shape = self.kernel_size + (self.filters, input_dim)
1244 input_dim = int(input_shape[channel_axis])
1245 self.input_spec = InputSpec(ndim=4, axes={channel_axis: input_dim})
1246 kernel_shape = self.kernel_size + (self.filters, input_dim)
1554 input_dim = int(input_shape[channel_axis])
1555 kernel_shape = self.kernel_size + (self.filters, input_dim)
1556 self.input_spec = InputSpec(ndim=5, axes={channel_axis: input_dim})
1818 input_dim = int(input_shape[channel_axis])
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| /external/tensorflow/tensorflow/python/keras/engine/ |
| D | sequential_test.py | 46 model.add(keras.layers.Dense(1, input_dim=2))
74 input_dim = 3
79 num_hidden, num_classes, input_dim)
84 x = np.random.random((batch_size, input_dim))
99 model.add(keras.layers.Dense(num_hidden, input_dim=input_dim))
112 input_dim = 3
128 x = np.random.random((batch_size, input_dim))
137 input_dim = 3
154 x = array_ops.ones((num_samples, input_dim))
173 model = testing_utils.get_small_sequential_mlp(10, 4, input_dim=3)
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| D | training_generator_test.py | 102 num_hidden=3, num_classes=4, input_dim=2)
140 num_hidden=3, num_classes=4, input_dim=2)
168 num_hidden=3, num_classes=4, input_dim=2)
203 num_hidden=3, num_classes=4, input_dim=2)
241 num_hidden=3, num_classes=4, input_dim=2)
284 num_hidden=10, num_classes=1, input_dim=10)
300 num_hidden=3, num_classes=4, input_dim=2)
373 layers_module.Embedding(input_dim=len(vocab) + 1, output_dim=4),
399 num_hidden=3, num_classes=4, input_dim=2)
442 num_hidden=10, num_classes=1, input_dim=10)
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| D | training_dataset_test.py | 57 model = testing_utils.get_small_mlp(1, 4, input_dim=3)
92 model = testing_utils.get_small_mlp(1, 4, input_dim=3)
208 model = testing_utils.get_small_mlp(1, 4, input_dim=3)
258 model = testing_utils.get_small_mlp(1, 4, input_dim=3)
328 model = testing_utils.get_small_functional_mlp(1, 4, input_dim=3)
357 model = testing_utils.get_small_mlp(1, 4, input_dim=3)
378 model = testing_utils.get_small_mlp(1, 4, input_dim=3)
416 model = testing_utils.get_small_mlp(1, 4, input_dim=3)
450 model = testing_utils.get_small_mlp(1, 4, input_dim=3)
545 8, activation='relu', input_dim=4, kernel_initializer='ones'),
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| /external/XNNPACK/src/operators/ |
| D | constant-pad-nd.c | 125 const size_t input_dim = input_shape[num_dims - 1 - i]; in setup_constant_pad_nd() local
130 normalized_input_shape[XNN_MAX_TENSOR_DIMS - 1 - num_squeezed_dims] = input_dim; in setup_constant_pad_nd()
131 …output_shape[XNN_MAX_TENSOR_DIMS - 1 - num_squeezed_dims] = pre_padding + input_dim + post_padding; in setup_constant_pad_nd()
141 normalized_input_shape[XNN_MAX_TENSOR_DIMS - num_squeezed_dims] *= input_dim; in setup_constant_pad_nd()
142 normalized_output_shape[XNN_MAX_TENSOR_DIMS - num_squeezed_dims] *= input_dim; in setup_constant_pad_nd()
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| /external/tensorflow/tensorflow/python/keras/utils/ |
| D | multi_gpu_utils_test.py | 58 input_dim = 10
70 input_shape=(input_dim,)))
73 x = np.random.random((num_samples, input_dim))
157 input_dim = 10
158 shape = (input_dim,)
186 input_shape=(input_dim,)))
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| /external/tensorflow/tensorflow/python/keras/optimizer_v2/ |
| D | optimizer_v2_test.py | 589 input_dim = 1
594 input_shape=(input_dim,),
712 input_dim = 3
717 input_shape=(input_dim,),
723 num_hidden=num_hidden, num_classes=num_classes, input_dim=input_dim)
732 num_hidden=num_hidden, num_classes=num_classes, input_dim=input_dim)
798 input_dim = 3
803 input_shape=(input_dim,),
809 num_hidden=num_hidden, num_classes=num_classes, input_dim=input_dim)
811 num_hidden=num_hidden, num_classes=num_classes, input_dim=input_dim)
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| /external/tensorflow/tensorflow/python/keras/premade/ |
| D | wide_deep_test.py | 44 dnn_model = sequential.Sequential([core.Dense(units=1, input_dim=3)])
86 dnn_model = sequential.Sequential([core.Dense(units=1, input_dim=3)])
123 dnn_model = sequential.Sequential([core.Dense(units=1, input_dim=3)])
162 dnn_model = sequential.Sequential([core.Dense(units=1, input_dim=3)])
165 sequential.Sequential([core.Dense(units=1, input_dim=3)]))
252 dnn_model = sequential.Sequential([core.Dense(units=1, input_dim=3)])
267 dnn_model = sequential.Sequential([core.Dense(units=1, input_dim=3)])
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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/compiler/xla/service/ |
| D | dynamic_padder.cc | 341 HloInstruction* reshape, int64 input_dim, in RewriteDynamicReshapeSplitInput() argument
345 VLOG(2) << "Reshaping input dim " << input_dim << "to " in RewriteDynamicReshapeSplitInput()
352 ShapeUtil::MakeShape(xla::S32, {operand_shape.dimensions(input_dim)}); in RewriteDynamicReshapeSplitInput()
414 dim->set_size(operand_shape.dimensions(input_dim)); in RewriteDynamicReshapeSplitInput()
416 dim->set_padding_low(operand_shape.dimensions(input_dim) - 1); in RewriteDynamicReshapeSplitInput()
434 if (i != input_dim) { in RewriteDynamicReshapeSplitInput()
439 gather_dim_numbers.add_start_index_map(input_dim); in RewriteDynamicReshapeSplitInput()
441 gather_dim_numbers.add_collapsed_slice_dims(input_dim); in RewriteDynamicReshapeSplitInput()
449 LiteralUtil::CreateR0<int32>(operand_shape.dimensions(input_dim)))); in RewriteDynamicReshapeSplitInput()
453 input_dim)); in RewriteDynamicReshapeSplitInput()
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| /external/tensorflow/tensorflow/lite/delegates/xnnpack/ |
| D | reshape_tester.h | 35 for (int32_t input_dim : input_shape) { in InputShape() local
36 EXPECT_GT(input_dim, 0); in InputShape()
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| /external/tensorflow/tensorflow/python/keras/ |
| D | testing_utils.py | 450 def get_small_sequential_mlp(num_hidden, num_classes, input_dim=None): argument
452 if input_dim:
453 model.add(layers.Dense(num_hidden, activation='relu', input_dim=input_dim))
461 def get_small_functional_mlp(num_hidden, num_classes, input_dim): argument
462 inputs = layers.Input(shape=(input_dim,))
522 def get_small_mlp(num_hidden, num_classes, input_dim): argument
530 return get_small_sequential_mlp(num_hidden, num_classes, input_dim)
532 return get_small_functional_mlp(num_hidden, num_classes, input_dim)
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| /external/XNNPACK/test/ |
| D | constant-pad-operator-tester.h | 35 inline size_t input_dim(size_t i) const { in input_dim() function
85 return pre_padding(i) + input_dim(i) + post_padding(i); in output_dim()
123 input_dims[XNN_MAX_TENSOR_DIMS - num_dims() + i] = input_dim(i); in TestX32()
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| /external/tensorflow/tensorflow/python/kernel_tests/ |
| D | broadcast_to_ops_test.py | 57 for input_dim in range(1, 6):
58 for output_dim in range(input_dim, 6):
60 input_shape = [2] * input_dim
|