| /external/tensorflow/tensorflow/lite/testing/nnapi_tflite_zip_tests/ |
| D | not_supported.txt | 9 arg_min_max/arg_min_max_input_dtype=tf.float32,input_shape=[1,1,1,3],is_arg_max=True,output_type=tf…
10 arg_min_max/arg_min_max_input_dtype=tf.float32,input_shape=[1,1,1,3],is_arg_max=True,output_type=tf…
11 arg_min_max/arg_min_max_input_dtype=tf.int32,input_shape=[1,1,1,3],is_arg_max=True,output_type=tf.i…
12 arg_min_max/arg_min_max_input_dtype=tf.int32,input_shape=[1,1,1,3],is_arg_max=True,output_type=tf.i…
13 arg_min_max/arg_min_max_input_dtype=tf.float32,input_shape=[2,3,4,5],is_arg_max=True,output_type=tf…
14 arg_min_max/arg_min_max_input_dtype=tf.float32,input_shape=[2,3,4,5],is_arg_max=True,output_type=tf…
15 arg_min_max/arg_min_max_input_dtype=tf.int32,input_shape=[2,3,4,5],is_arg_max=True,output_type=tf.i…
16 arg_min_max/arg_min_max_input_dtype=tf.int32,input_shape=[2,3,4,5],is_arg_max=True,output_type=tf.i…
17 arg_min_max/arg_min_max_input_dtype=tf.float32,input_shape=[2,3,3],is_arg_max=True,output_type=tf.i…
18 arg_min_max/arg_min_max_input_dtype=tf.float32,input_shape=[2,3,3],is_arg_max=True,output_type=tf.i…
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| D | test_manifest.txt | 17 avg_pool/avg_pool_data_format='NHWC',input_shape=[1,1,1,1],ksize=[1,1,1,1],padding='SAME',strides=[…
18 avg_pool/avg_pool_data_format='NHWC',input_shape=[1,1,1,1],ksize=[1,1,1,1],padding='SAME',strides=[…
19 avg_pool/avg_pool_data_format='NHWC',input_shape=[1,1,1,1],ksize=[1,1,1,1],padding='SAME',strides=[…
20 avg_pool/avg_pool_data_format='NHWC',input_shape=[1,15,14,1],ksize=[1,1,1,1],padding='SAME',strides…
21 avg_pool/avg_pool_data_format='NHWC',input_shape=[1,15,14,1],ksize=[1,1,1,1],padding='SAME',strides…
22 avg_pool/avg_pool_data_format='NHWC',input_shape=[1,15,14,1],ksize=[1,1,1,1],padding='SAME',strides…
23 avg_pool/avg_pool_data_format='NHWC',input_shape=[3,15,14,3],ksize=[1,1,1,1],padding='SAME',strides…
24 avg_pool/avg_pool_data_format='NHWC',input_shape=[3,15,14,3],ksize=[1,1,1,1],padding='SAME',strides…
25 avg_pool/avg_pool_data_format='NHWC',input_shape=[3,15,14,3],ksize=[1,1,1,1],padding='SAME',strides…
26 avg_pool/avg_pool_data_format='NHWC',input_shape=[1,1,1,1],ksize=[1,1,1,1],padding='VALID',strides=…
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| /external/tensorflow/tensorflow/python/keras/utils/ |
| D | conv_utils_test.py | 30 def _get_const_output_shape(input_shape, dim): argument
31 return tuple([min(d, dim) for d in input_shape])
166 def test_conv_kernel_mask_fc(self, *input_shape): argument
168 kernel_shape = input_shape
169 ndims = len(input_shape)
171 output_shape = _get_const_output_shape(input_shape, dim=1)
172 mask = np.ones(input_shape + output_shape, np.bool)
176 input_shape,
183 def test_conv_kernel_mask_diag(self, *input_shape): argument
184 ndims = len(input_shape)
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| D | tf_utils.py | 181 def convert_shapes(input_shape, to_tuples=True): argument
207 def _is_atomic_shape(input_shape): argument
209 if _is_shape_component(input_shape):
211 if isinstance(input_shape, tensor_shape.TensorShape):
213 if (isinstance(input_shape, (tuple, list)) and
214 all(_is_shape_component(ele) for ele in input_shape)):
218 def _convert_shape(input_shape): argument
219 input_shape = tensor_shape.TensorShape(input_shape)
221 input_shape = tuple(input_shape.as_list())
222 return input_shape
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| /external/tensorflow/tensorflow/python/kernel_tests/ |
| D | pool_test.py | 148 def _test(self, input_shape, **kwargs): argument
152 np.prod(input_shape), dtype=np.float32).reshape(input_shape) - 1
162 input_shape=[1, 1, 10, 1],
173 for input_shape in [[2, 9, 2], [2, 10, 2]]:
178 input_shape=input_shape,
188 input_shape=input_shape,
199 for input_shape in [[2, 9, 10, 2], [2, 10, 9, 2]]:
204 input_shape=input_shape,
214 input_shape=input_shape,
225 for input_shape in [[2, 9, 10, 11, 2], [2, 10, 9, 11, 2]]:
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| /external/tensorflow/tensorflow/python/keras/layers/ |
| D | convolutional.py | 146 def build(self, input_shape): argument
147 input_shape = tensor_shape.TensorShape(input_shape)
152 if input_shape.dims[channel_axis].value is None:
155 input_dim = int(input_shape[channel_axis])
186 input_shape,
213 def compute_output_shape(self, input_shape): argument
214 input_shape = tensor_shape.TensorShape(input_shape).as_list()
216 space = input_shape[1:-1]
226 return tensor_shape.TensorShape([input_shape[0]] + new_space +
229 space = input_shape[2:]
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| D | pooling.py | 83 def compute_output_shape(self, input_shape): argument
84 input_shape = tensor_shape.TensorShape(input_shape).as_list()
86 steps = input_shape[2]
87 features = input_shape[1]
89 steps = input_shape[1]
90 features = input_shape[2]
96 return tensor_shape.TensorShape([input_shape[0], features, length])
98 return tensor_shape.TensorShape([input_shape[0], length, features])
251 def compute_output_shape(self, input_shape): argument
252 input_shape = tensor_shape.TensorShape(input_shape).as_list()
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| D | pooling_test.py | 35 input_shape=(3, 4, 5))
38 input_shape=(3, 4, 5))
40 keras.layers.pooling.GlobalAveragePooling1D, input_shape=(3, 4, 5))
43 input_shape=(3, 4, 5))
48 model.add(keras.layers.Masking(mask_value=0., input_shape=(3, 4)))
62 input_shape=(3, 4, 5, 6))
66 input_shape=(3, 5, 6, 4))
70 input_shape=(3, 4, 5, 6))
74 input_shape=(3, 5, 6, 4))
81 input_shape=(3, 4, 3, 4, 3))
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| D | core.py | 100 def compute_output_shape(self, input_shape): argument
101 return input_shape
164 def compute_output_shape(self, input_shape): argument
165 return input_shape
214 input_shape = array_ops.shape(inputs)
215 noise_shape = (input_shape[0], 1, input_shape[2])
271 input_shape = array_ops.shape(inputs)
273 return (input_shape[0], input_shape[1], 1, 1)
275 return (input_shape[0], 1, 1, input_shape[3])
329 input_shape = array_ops.shape(inputs)
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| D | merge.py | 87 def build(self, input_shape): argument
89 if not isinstance(input_shape, list):
91 if len(input_shape) < 2:
94 'Got ' + str(len(input_shape)) + ' inputs.')
95 batch_sizes = [s[0] for s in input_shape if s is not None]
101 'batch sizes. Got tensors with shapes : ' + str(input_shape))
102 if input_shape[0] is None:
105 output_shape = input_shape[0][1:]
106 for i in range(1, len(input_shape)):
107 if input_shape[i] is None:
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| D | core_test.py | 39 keras.layers.Dropout, kwargs={'rate': 0.5}, input_shape=(3, 2))
45 input_shape=(3, 2))
55 input_shape=(2, 3, 4))
61 input_shape=(2, 3, 4, 5))
66 input_shape=(2, 3, 4, 5))
72 input_shape=(2, 3, 4, 4, 5))
77 input_shape=(2, 3, 4, 4, 5))
87 input_shape=(3, 2))
98 input_shape=(3, 2))
130 def get_output_shape(input_shape): argument
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| D | kernelized.py | 156 def build(self, input_shape): argument
157 input_shape = tensor_shape.TensorShape(input_shape)
160 if input_shape.rank != 2:
163 input_shape.ndims))
164 if input_shape.dims[1].value is None:
169 ndim=2, axes={1: input_shape.dims[1].value})
170 input_dim = input_shape.dims[1].value
200 super(RandomFourierFeatures, self).build(input_shape)
209 def compute_output_shape(self, input_shape): argument
210 input_shape = tensor_shape.TensorShape(input_shape)
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| /external/tensorflow/tensorflow/compiler/tf2xla/kernels/ |
| D | split_op.cc | 39 const TensorShape input_shape = ctx->InputShape(1); in Compile() local
48 int32 split_dim = split_dim_orig < 0 ? split_dim_orig + input_shape.dims() in Compile()
50 OP_REQUIRES(ctx, 0 <= split_dim && split_dim < input_shape.dims(), in Compile()
51 errors::InvalidArgument("-input rank(-", input_shape.dims(), in Compile()
53 input_shape.dims(), "), but got ", in Compile()
62 ctx, input_shape.dim_size(split_dim) % num_split == 0, in Compile()
66 split_dim_orig, " (size = ", input_shape.dim_size(split_dim), ") ", in Compile()
71 const int32 slice_size = input_shape.dim_size(split_dim) / num_split; in Compile()
75 std::vector<int64> begin(input_shape.dims(), 0); in Compile()
76 std::vector<int64> limits(input_shape.dims()); in Compile()
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| D | shape_op.cc | 39 const TensorShape input_shape = ctx->InputShape(0); in Compile() local
40 Tensor shape_constant(out_dtype_, TensorShape({input_shape.dims()})); in Compile()
41 OP_REQUIRES_OK(ctx, TensorShapeToConstant(input_shape, &shape_constant)); in Compile()
59 const TensorShape input_shape = ctx->InputShape(i); in Compile() local
60 Tensor shape_constant(out_dtype_, TensorShape({input_shape.dims()})); in Compile()
61 OP_REQUIRES_OK(ctx, TensorShapeToConstant(input_shape, &shape_constant)); in Compile()
78 const TensorShape input_shape = ctx->InputShape(0); in Compile() local
79 const int rank = input_shape.dims(); in Compile()
94 const TensorShape input_shape = ctx->InputShape(0); in Compile() local
96 FastBoundsCheck(input_shape.num_elements(), in Compile()
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| D | unpack_op.cc | 47 const TensorShape input_shape = ctx->InputShape(0); in Compile() local
50 if (axis < 0) axis += input_shape.dims(); in Compile()
52 OP_REQUIRES(ctx, 0 <= axis && axis < input_shape.dims(), in Compile()
54 -input_shape.dims(), ", ", in Compile()
55 input_shape.dims(), ")")); in Compile()
58 ctx, input_shape.dims() > 0 && input_shape.dim_size(axis) == num, in Compile()
60 ", got shape ", input_shape.DebugString())); in Compile()
62 auto output_shape = input_shape; in Compile()
67 std::vector<int64> start_indices(input_shape.dims(), 0); in Compile()
68 std::vector<int64> limit_indices(input_shape.dims()); in Compile()
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| D | spacetodepth_op.cc | 60 const std::vector<int64>& input_shape = in Compile() local
62 int input_rank = input_shape.size(); in Compile()
81 OP_REQUIRES(ctx, input_shape[1 + i] % block_size_ == 0, in Compile()
83 "input shape[", 1 + i, "]=", input_shape[1 + i], in Compile()
88 reshaped_shape.push_back(input_shape[0]); in Compile()
90 reshaped_shape.push_back(input_shape[1 + i] / block_size_); in Compile()
93 reshaped_shape.push_back(input_shape[feature_dim]); in Compile()
104 output_shape.push_back(input_shape[0]); in Compile()
106 output_shape.push_back(input_shape[1 + i] / block_size_); in Compile()
108 output_shape.push_back(input_shape[feature_dim] * block_elems); in Compile()
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| D | depthtospace_op.cc | 60 const std::vector<int64>& input_shape = in Compile() local
62 int input_rank = input_shape.size(); in Compile()
79 reshaped_shape.push_back(input_shape[0]); in Compile()
81 reshaped_shape.push_back(input_shape[1 + i]); in Compile()
88 reshaped_shape.push_back(input_shape[feature_dim] / block_elems); in Compile()
97 output_shape.push_back(input_shape[0]); in Compile()
99 output_shape.push_back(input_shape[1 + i] * block_size_); in Compile()
101 output_shape.push_back(input_shape[feature_dim] / block_elems); in Compile()
104 reshaped_shape.push_back(input_shape[0]); in Compile()
110 reshaped_shape.push_back(input_shape[feature_dim] / block_elems); in Compile()
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| D | slice_op.cc | 40 const TensorShape input_shape = ctx->InputShape(0); in Compile() local
48 begin_tensor_shape.num_elements() == input_shape.dims() && in Compile()
49 size_tensor_shape.num_elements() == input_shape.dims(), in Compile()
52 input_shape.dims(), ", but got shapes ", in Compile()
56 const int input_dims = input_shape.dims(); in Compile()
66 size[i] = input_shape.dim_size(i) - begin[i]; in Compile()
73 if (input_shape.dim_size(i) == 0) { in Compile()
80 OP_REQUIRES(ctx, 0 <= b && b <= input_shape.dim_size(i), in Compile()
82 input_shape.dim_size(i), in Compile()
84 OP_REQUIRES(ctx, 0 <= s && b + s <= input_shape.dim_size(i), in Compile()
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| /external/tensorflow/tensorflow/python/framework/ |
| D | common_shapes.py | 190 input_shape = op.inputs[0].get_shape().with_rank(4)
200 input_shape = [input_shape[0], input_shape[2], input_shape[3],
201 input_shape[1]]
203 batch_size = input_shape[0]
204 in_rows = input_shape[1]
205 in_cols = input_shape[2]
211 input_shape[3].assert_is_compatible_with(filter_shape[2])
259 input_shape = op.inputs[0].get_shape().with_rank(4)
262 batch_size = input_shape[0]
263 in_rows = input_shape[1]
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| /external/tensorflow/tensorflow/lite/kernels/internal/ |
| D | resize_nearest_neighbor_test.cc | 30 const RuntimeShape& input_shape, const std::vector<T>& input_data, in TestReferenceResizeNearestNeighbor() argument
39 op_params, input_shape, input_data.data(), output_size_shape, in TestReferenceResizeNearestNeighbor()
48 RuntimeShape input_shape = {1, 2, 2, 1}; in TEST() local
54 TestReferenceResizeNearestNeighbor(input_shape, input_data, output_size_data, in TEST()
59 RuntimeShape input_shape = {1, 2, 2, 1}; in TEST() local
65 TestReferenceResizeNearestNeighbor(input_shape, input_data, output_size_data, in TEST()
70 RuntimeShape input_shape = {1, 3, 3, 1}; in TEST() local
76 TestReferenceResizeNearestNeighbor(input_shape, input_data, output_size_data, in TEST()
81 RuntimeShape input_shape = {1, 2, 2, 1}; in TEST() local
87 TestReferenceResizeNearestNeighbor(input_shape, input_data, output_size_data, in TEST()
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| /external/tensorflow/tensorflow/contrib/fused_conv/python/ops/ |
| D | fused_conv2d_bias_activation_benchmark.py | 32 def build_conv_bias_relu_graph(device, input_shape, filter_shape, strides, argument
52 input_shape = [
53 input_shape[0], input_shape[3], input_shape[1], input_shape[2]
56 inp = variables.Variable(random_ops.truncated_normal(input_shape))
77 def build_fused_conv_bias_relu_graph(device, input_shape, filter_shape, strides, argument
97 input_shape = [
98 input_shape[0], input_shape[3], input_shape[1], input_shape[2]
101 inp = variables.Variable(random_ops.truncated_normal(input_shape))
134 def _run_graph(self, device, input_shape, filter_shape, strides, padding, argument
156 outputs = build_fused_conv_bias_relu_graph(device, input_shape,
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| /external/tensorflow/tensorflow/contrib/eager/python/examples/revnet/ |
| D | blocks.py | 45 input_shape, argument
72 input_shape,
81 input_shape = (filters, input_shape[1] // curr_strides[0],
82 input_shape[2] // curr_strides[1])
84 input_shape = (input_shape[0] // curr_strides[0],
85 input_shape[1] // curr_strides[1], filters)
120 input_shape, argument
144 f_input_shape = (input_shape[0] // 2,) + input_shape[1:]
145 g_input_shape = (filters // 2, input_shape[1] // strides[0],
146 input_shape[2] // strides[1])
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| /external/tensorflow/tensorflow/python/ops/ |
| D | concat_benchmark.py | 35 def build_graph(device, input_shape, variable, num_inputs, axis, grad): argument
51 inputs = [array_ops.zeros(input_shape) for _ in range(num_inputs)]
56 input_shape[0],
57 random.randint(max(1, input_shape[1] - 5), input_shape[1] + 5)
63 random.randint(max(1, input_shape[0] - 5), input_shape[0] + 5),
64 input_shape[1]
81 def _run_graph(self, device, input_shape, variable, num_inputs, axis, grad, argument
99 outputs = build_graph(device, input_shape, variable, num_inputs, axis,
112 "GB/sec" % (device, input_shape[0], input_shape[1], variable,
114 num_inputs * input_shape[0] * input_shape[1] * 4 * 2 *
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| /external/tensorflow/tensorflow/core/api_def/base_api/ |
| D | api_def_BatchToSpaceND.pbtxt | 6 N-D with shape `input_shape = [batch] + spatial_shape + remaining_shape`,
23 `crop_start[i] + crop_end[i] <= block_shape[i] * input_shape[i + 1]`.
30 input_shape[1], ..., input_shape[N-1]]
35 input_shape[1], block_shape[0],
37 input_shape[M], block_shape[M-1],
39 input_shape[M+1], ..., input_shape[N-1]]
44 input_shape[1] * block_shape[0],
46 input_shape[M] * block_shape[M-1],
48 input_shape[M+1],
50 input_shape[N-1]]
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| /external/tensorflow/tensorflow/contrib/model_pruning/python/layers/ |
| D | core_layers.py | 125 def build(self, input_shape): argument
126 input_shape = tensor_shape.TensorShape(input_shape)
128 if tensor_shape.dimension_value(input_shape[channel_axis]) is None:
131 input_dim = tensor_shape.dimension_value(input_shape[channel_axis])
214 def compute_output_shape(self, input_shape): argument
215 input_shape = tensor_shape.TensorShape(input_shape).as_list()
217 space = input_shape[1:-1]
227 return tensor_shape.TensorShape([input_shape[0]] + new_space +
230 space = input_shape[2:]
240 return tensor_shape.TensorShape([input_shape[0], self.filters] +
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