| /external/tensorflow/tensorflow/core/kernels/ |
| D | ctc_decoder_ops.cc | 66 const TensorShape& inputs_shape = (*inputs)->shape(); in ValidateInputsGenerateOutputs() local
68 if (inputs_shape.dims() != 3) { in ValidateInputsGenerateOutputs()
72 const int64 max_time = inputs_shape.dim_size(0); in ValidateInputsGenerateOutputs()
73 const int64 batch_size = inputs_shape.dim_size(1); in ValidateInputsGenerateOutputs()
190 const TensorShape& inputs_shape = inputs->shape(); in Compute() local
193 const int64 max_time = inputs_shape.dim_size(0); in Compute()
194 const int64 batch_size = inputs_shape.dim_size(1); in Compute()
195 const int64 num_classes_raw = inputs_shape.dim_size(2); in Compute()
282 const TensorShape& inputs_shape = inputs->shape(); in Compute() local
284 const int64 max_time = inputs_shape.dim_size(0); in Compute()
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| D | ctc_loss_op.cc | 68 const TensorShape& inputs_shape = inputs->shape(); in Compute() local
69 const int64 max_time = inputs_shape.dim_size(0); in Compute()
70 const int64 batch_size = inputs_shape.dim_size(1); in Compute()
71 const int64 num_classes_raw = inputs_shape.dim_size(2); in Compute()
144 ctx->allocate_output("gradient", inputs_shape, &gradient)); in Compute()
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| /external/tensorflow/tensorflow/lite/experimental/examples/lstm/ |
| D | rnn_cell.py | 94 def build(self, inputs_shape): argument
103 if inputs_shape[-1] is None:
105 (inputs_shape,))
107 input_depth = inputs_shape[-1]
284 def build(self, inputs_shape): argument
294 if len(inputs_shape) != 2:
296 "inputs_shape must be 2-dimensional, saw shape: %s" % inputs_shape)
298 inputs_shape[1]
299 if isinstance(inputs_shape[1], int) else inputs_shape[1].value)
301 raise ValueError("Invalid inputs_shape, saw shape: %s" % inputs_shape)
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| /external/tensorflow/tensorflow/contrib/rnn/python/ops/ |
| D | lstm_ops.py | 399 def build(self, inputs_shape): argument
400 if not inputs_shape.dims[1].value:
402 "Expecting inputs_shape[1] to be set: %s" % str(inputs_shape))
403 input_size = inputs_shape.dims[1].value
516 inputs_shape = inputs.get_shape().with_rank(3)
517 if not inputs_shape[2]:
518 raise ValueError("Expecting inputs_shape[2] to be set: %s" % inputs_shape)
519 batch_size = inputs_shape.dims[1].value
522 time_len = inputs_shape.dims[0].value
681 inputs_shape = inputs.get_shape().with_rank(3)
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| D | rnn_cell.py | 1518 def build(self, inputs_shape): argument
1520 tensor_shape.TensorShape(inputs_shape).with_rank(2)[1])
2780 def build(self, inputs_shape): argument
2781 if tensor_shape.dimension_value(inputs_shape[1]) is None:
2783 "Expected inputs.shape[-1] to be known, saw shape: %s" % inputs_shape)
2785 input_depth = tensor_shape.dimension_value(inputs_shape[1])
3120 def build(self, inputs_shape): argument
3121 if tensor_shape.dimension_value(inputs_shape[1]) is None:
3123 "Expected inputs.shape[-1] to be known, saw shape: %s" % inputs_shape)
3125 input_depth = tensor_shape.dimension_value(inputs_shape[1])
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| /external/tensorflow/tensorflow/contrib/model_pruning/python/layers/ |
| D | rnn_cells.py | 91 def build(self, inputs_shape): argument
93 super(MaskedBasicLSTMCell, self).build(inputs_shape)
97 input_depth = inputs_shape.dims[1].value
240 def build(self, inputs_shape): argument
242 super(MaskedLSTMCell, self).build(inputs_shape)
246 input_depth = inputs_shape.dims[1].value
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| /external/tensorflow/tensorflow/contrib/specs/python/ |
| D | summaries_test.py | 38 inputs_shape = (1, 18, 19, 5)
39 inputs = constant_op.constant(_rand(*inputs_shape))
47 spec, input_shape=inputs_shape),
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| /external/tensorflow/tensorflow/contrib/layers/python/layers/ |
| D | normalization.py | 94 inputs_shape = inputs.shape
110 [1, inputs_shape[1].value] + [1 for _ in range(2, inputs_rank)])
117 params_shape = inputs_shape[reduction_axis:reduction_axis + 1]
309 inputs_shape = (axes_before_channels + [groups, channels // groups] +
311 inputs = array_ops.reshape(inputs, inputs_shape)
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| D | layers.py | 285 inputs_shape = inputs.get_shape()
287 params_shape = inputs_shape[-1:]
289 params_shape = inputs_shape[1:2]
421 outputs.set_shape(inputs_shape)
677 inputs_shape = inputs.get_shape()
678 inputs_rank = inputs_shape.ndims
684 inputs_shape[0:1].assert_is_compatible_with(batch_weights.get_shape())
691 params_shape = inputs_shape[1:2]
696 [1, inputs_shape.dims[1].value] + [1 for _ in range(2, inputs_rank)])
699 params_shape = inputs_shape[-1:]
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| /external/tensorflow/tensorflow/python/ops/ |
| D | rnn_cell_impl.py | 444 def build(self, inputs_shape): argument
445 if inputs_shape[-1] is None:
447 % str(inputs_shape))
450 input_depth = inputs_shape[-1]
545 def build(self, inputs_shape): argument
546 if inputs_shape[-1] is None:
548 % str(inputs_shape))
550 input_depth = inputs_shape[-1]
724 def build(self, inputs_shape): argument
725 if inputs_shape[-1] is None:
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| /external/tensorflow/tensorflow/python/keras/layers/ |
| D | convolutional.py | 779 inputs_shape = array_ops.shape(inputs)
780 batch_size = inputs_shape[0]
786 height, width = inputs_shape[h_axis], inputs_shape[w_axis]
1052 inputs_shape = array_ops.shape(inputs)
1053 batch_size = inputs_shape[0]
1059 depth = inputs_shape[d_axis]
1060 height = inputs_shape[h_axis]
1061 width = inputs_shape[w_axis]
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| D | recurrent_test.py | 1412 def build(self, inputs_shape): argument
1415 input_1 = inputs_shape.t1[1]
1416 input_2, input_3 = inputs_shape.t2[1:]
1418 input_1 = inputs_shape[0][1]
1419 input_2, input_3 = inputs_shape[1][1:]
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| /external/tensorflow/tensorflow/contrib/cudnn_rnn/python/ops/ |
| D | cudnn_rnn_ops.py | 118 def build(self, inputs_shape): argument
119 if inputs_shape[1].value is None:
121 % inputs_shape)
123 input_depth = inputs_shape[1].value
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| /external/tensorflow/tensorflow/core/grappler/optimizers/ |
| D | arithmetic_optimizer_test.cc | 897 Output inputs_shape = ops::Shape(s, inputs); in TEST_F() local
900 Output batch_size = ops::Slice(s, inputs_shape, ops::Const(s, {0}, {1}), in TEST_F()
932 Output inputs_shape = ops::Shape(s, inputs); in TEST_F() local
935 Output batch_size = ops::Slice(s, inputs_shape, ops::Const(s, {0}, {1}), in TEST_F()
937 Output height = ops::Slice(s, inputs_shape, ops::Const(s, {2}, {1}), in TEST_F()
939 Output width = ops::Slice(s, inputs_shape, ops::Const(s, {3}, {1}), in TEST_F()
1359 Output inputs_shape = in TEST_F() local
1362 ops::RandomUniform(s.WithOpName("inputs"), inputs_shape, DT_FLOAT); in TEST_F()
1392 Output inputs_shape = in TEST_F() local
1469 Output inputs_shape = in TEST_F() local
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| /external/tensorflow/tensorflow/tools/api/golden/v2/ |
| D | tensorflow.nn.-r-n-n-cell-dropout-wrapper.pbtxt | 132 argspec: "args=[\'self\', \'inputs_shape\'], varargs=None, keywords=None, defaults=None"
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| D | tensorflow.nn.-r-n-n-cell-device-wrapper.pbtxt | 128 argspec: "args=[\'self\', \'inputs_shape\'], varargs=None, keywords=None, defaults=None"
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| D | tensorflow.nn.-r-n-n-cell-residual-wrapper.pbtxt | 128 argspec: "args=[\'self\', \'inputs_shape\'], varargs=None, keywords=None, defaults=None"
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| D | tensorflow.lite.experimental.nn.-tf-lite-r-n-n-cell.pbtxt | 136 argspec: "args=[\'self\', \'inputs_shape\'], varargs=None, keywords=None, defaults=None"
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| D | tensorflow.lite.experimental.nn.-t-f-lite-l-s-t-m-cell.pbtxt | 136 argspec: "args=[\'self\', \'inputs_shape\'], varargs=None, keywords=None, defaults=None"
|
| /external/tensorflow/tensorflow/tools/api/golden/v1/ |
| D | tensorflow.lite.experimental.nn.-tf-lite-r-n-n-cell.pbtxt | 136 argspec: "args=[\'self\', \'inputs_shape\'], varargs=None, keywords=None, defaults=None"
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| D | tensorflow.lite.experimental.nn.-t-f-lite-l-s-t-m-cell.pbtxt | 136 argspec: "args=[\'self\', \'inputs_shape\'], varargs=None, keywords=None, defaults=None"
|