/external/tensorflow/tensorflow/contrib/distributions/python/ops/bijectors/ |
H A D | softmax_centered.py | 89 if input_shape.ndims is None: 91 if input_shape.ndims != self._static_event_ndims: 93 (input_shape.ndims, self._static_event_ndims)) 94 if input_shape.ndims == 0: 96 if input_shape.ndims == 1: 99 raise ValueError("event_ndims = %d must be 0 or 1" % input_shape.ndims) 102 ndims = array_ops.shape(input_shape) 106 ndims, 0 if self._static_event_ndims == 0 else 1, 108 ndims = control_flow_ops.with_dependencies([is_zero_or_one], ndims) [all...] |
H A D | reshape.py | 143 ndims = array_ops.rank(shape) 144 ndims_ = tensor_util.constant_value(ndims) 150 ndims, 1, message="`{}` rank should be <= 1.".format(shape.op.name))) 184 x_ndims_, x_ndims = x.shape.ndims, array_ops.rank(x) 219 # have known ndims. We can assume that shape Tensors always have 220 # ndims==1 (this assumption is verified inside of 239 ndims = (x.shape.ndims if x.shape.ndims is not None 242 :(ndims [all...] |
/external/tensorflow/tensorflow/core/kernels/ |
H A D | tile_functor_gpu.cu.cc | 32 const int32 ndims, T* dst) { 34 const int32* out_strides = buf + ndims; 35 const int32* in_dim_sizes = buf + ndims * 2; 39 for (int i = 0; i < ndims; ++i) { 55 const int32 ndims = in.dims(); local 56 gtl::InlinedVector<int32, 24> host_buf(ndims * 3); 59 for (int i = 0; i < ndims; ++i) { 61 host_buf[ndims + i] = out_strides[i]; 62 host_buf[ndims * 2 + i] = in.dim_size(i); 77 ndims, 31 TileKernel(int nthreads, const T* src, const int32* buf, const int32 ndims, T* dst) argument [all...] |
H A D | ops_util.h | 91 const int ndims = shape.dims(); local 92 gtl::InlinedVector<T, 8> strides(ndims); 94 for (int i = ndims - 1; i >= 0; --i) { 104 const int ndims = shape.rank(); local 105 gtl::InlinedVector<T, 8> strides(ndims); 107 for (int i = ndims - 1; i >= 0; --i) {
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H A D | cholesky_op.cc | 102 const int ndims = input.dims(); variable 103 const int64 n = input.dim_size(ndims - 1); 106 context, ndims >= 2, 107 errors::InvalidArgument("Input must have rank >= 2, got ", ndims), 110 context, input.dim_size(ndims - 2) == n, 112 input.dim_size(ndims - 2), " != ", n),
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H A D | self_adjoint_eig_v2_op_gpu.cc | 50 const int ndims = input.dims(); variable 52 context, ndims >= 2, 53 errors::InvalidArgument("Input must have rank >= 2, got ", ndims), 55 const int64 n = input.dim_size(ndims - 1); 57 context, input.dim_size(ndims - 2) == n, 59 input.dim_size(ndims - 2), " != ", n),
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H A D | matrix_solve_op.cc | 131 const int ndims = input.dims(); variable 132 const int64 n = input.dim_size(ndims - 1); 133 const int64 nrhs = rhs.dim_size(ndims - 1); 136 context, ndims >= 2, 137 errors::InvalidArgument("Input must have rank >= 2, got ", ndims), 139 OP_REQUIRES_ASYNC(context, rhs.dims() == ndims, 142 ndims, " != ", rhs.dims()), 145 context, input.dim_size(ndims - 2) == n, 147 input.dim_size(ndims - 2), " != ", n), 149 OP_REQUIRES_ASYNC(context, rhs.dim_size(ndims [all...] |
H A D | qr_op_impl.h | 138 const int ndims = input.dims(); variable 139 const int64 m = input.dim_size(ndims - 2); 140 const int64 n = input.dim_size(ndims - 1); 147 context, ndims >= 2, 148 errors::InvalidArgument("Input must have rank >= 2, got ", ndims), 156 q_shape.set_dim(ndims - 1, full_matrices_ ? m : min_size); 161 r_shape.set_dim(ndims - 2, full_matrices_ ? m : min_size); 176 transposed_shape.set_dim(ndims - 2, input.dim_size(ndims - 1)); 177 transposed_shape.set_dim(ndims [all...] |
H A D | determinant_op.cc | 135 const int ndims = input.dims(); variable 136 const int64 n = input.dim_size(ndims - 1); 139 context, ndims >= 2, 140 errors::InvalidArgument("Input must have rank >= 2, got ", ndims), 143 context, input.dim_size(ndims - 2) == n, 145 input.dim_size(ndims - 2), " != ", n), 150 for (int dim = 0; dim < ndims - 2; ++dim) { 275 const int ndims = input.dims(); variable 276 const int64 n = input.dim_size(ndims - 1); 279 context, ndims > [all...] |
H A D | transpose_functor_gpu.cu.cc | 36 const int32 ndims, T* dst) { 38 const int32* out_strides = buf + ndims; 39 const int32* perm = buf + ndims * 2; 43 for (int32 i = 0; i < ndims; ++i) { 63 const int32 ndims = in.dims(); local 64 gtl::InlinedVector<int32, 24> host_buf(ndims * 3); 68 for (int i = 0; i < ndims; ++i) { 70 host_buf[ndims + i] = out_strides[i]; 71 host_buf[ndims * 2 + i] = perm[i]; 86 ndims, 35 TransposeKernel(int nthreads, const T* src, const int32* buf, const int32 ndims, T* dst) argument [all...] |
H A D | mkl_batch_matmul_op.cc | 61 errors::InvalidArgument("lhs and rhs has different ndims: ", 64 const int ndims = lhs.dims(); variable 66 ctx, ndims >= 2, 67 errors::InvalidArgument("lhs and rhs ndims must be >= 2: ", ndims)); 69 for (int i = 0; i < ndims - 2; ++i) { 77 auto batch_size = (ndims == 2) ? 1 : out_shape.num_elements(); 78 auto lhs_rows = lhs.dim_size(ndims - 2); 79 auto lhs_cols = lhs.dim_size(ndims - 1); 80 auto rhs_rows = rhs.dim_size(ndims [all...] |
H A D | reduction_ops_common.h | 98 int ndims() const { return data_reshape_.size(); } function in class:tensorflow::ReductionHelper 156 CHECK_GE(helper.ndims(), 0); 158 if (helper.ndims() == 0 || 159 (helper.ndims() == 1 && !helper.reduce_first_axis())) { 195 } else if ((helper.ndims() == 1) && helper.reduce_first_axis()) { 199 } else if ((helper.ndims() == 2) && helper.reduce_first_axis()) { 203 } else if ((helper.ndims() == 2) && !helper.reduce_first_axis()) { 207 } else if ((helper.ndims() == 3) && helper.reduce_first_axis()) { 212 } else if ((helper.ndims() == 3) && !helper.reduce_first_axis()) {
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/external/tensorflow/tensorflow/compiler/tf2xla/lib/ |
H A D | batch_dot.cc | 45 const int ndims = xla::ShapeUtil::Rank(*x_shape); local 46 if (ndims < 2) { 48 "Arguments to BatchedDot must have rank >= 2: ", ndims); 54 for (int i = 0; i < ndims - 2; ++i) { 64 int x_inner_dim = transpose_x ? (ndims - 2) : (ndims - 1); 65 int y_inner_dim = transpose_y ? (ndims - 1) : (ndims - 2); 82 int x_outer_dim = transpose_x ? (ndims - 1) : (ndims [all...] |
/external/tensorflow/tensorflow/contrib/distributions/python/ops/ |
H A D | shape.py | 38 - `ndims`: size of `shape`; number of `Tensor` dimensions, 57 This class partitions `Tensor` notions of `shape`, `ndims`, and `dims` into 240 ndims: Scalar number of dimensions associated with a `Tensor`. 244 ndims = x.get_shape().ndims 245 if ndims is None: 246 return array_ops.rank(x, name="ndims") 247 return ops.convert_to_tensor(ndims, dtype=dtypes.int32, name="ndims") 263 ndims [all...] |
H A D | sample_stats.py | 371 This function returns the number of dimensions "ndims" of x, as a Python int. 373 The optional expect arguments are used to check the ndims of x, but this is 374 only done if the static ndims of x is not None. 378 expect_static: Expect `x` to have statically defined `ndims`. 387 ndims: A Python integer. 392 ndims = x.get_shape().ndims 393 if ndims is None: 396 ndims = shape_const.ndim 398 if ndims i [all...] |
H A D | independent.py | 166 or batch_shape.ndims is None): 168 d = batch_shape.ndims - self._static_reinterpreted_batch_ndims 185 or batch_shape.ndims is None): 187 d = batch_shape.ndims - self._static_reinterpreted_batch_ndims 223 batch_ndims = distribution.batch_shape.ndims 250 ndims = distribution.batch_shape.ndims 251 if ndims is None: 253 ndims = array_ops.shape(distribution.batch_shape_tensor())[0] 256 return which_maximum(0, ndims [all...] |
H A D | mixture_same_family.py | 149 if (mixture_distribution.event_shape.ndims is not None 150 and mixture_distribution.event_shape.ndims != 0): 162 if mdbs.ndims != 0 and mdbs != cdbs: 244 self._event_shape().ndims) # [n, B, k, [1]*e] 261 self._event_shape().ndims) # [B, k, [1]*e] 279 self._event_shape().ndims) # [B, k, [1]*e] 291 static_event_ndims = self.event_shape.ndims 301 self._event_shape().ndims), 303 self._event_shape().ndims) # [B, k, 1, 1] 316 ndims [all...] |
/external/tensorflow/tensorflow/python/framework/ |
H A D | tensor_shape.py | 464 if self.ndims is None: 466 elif self.ndims == 1: 477 def ndims(self): member in class:TensorShape 544 return unknown_shape(ndims=stop - start) 621 if self.ndims is not None and other.ndims is not None: 622 if self.ndims != other.ndims: 635 if self.ndims not in (None, rank): 654 return self.merge_with(unknown_shape(ndims [all...] |
/external/tensorflow/tensorflow/go/ |
H A D | operation.go | 89 ndims := C.TF_GraphGetTensorNumDims(p.Op.g.c, port, status.c) 96 if ndims < 0 { 99 if ndims == 0 { 102 dims := make([]C.int64_t, ndims) 103 C.TF_GraphGetTensorShape(p.Op.g.c, port, &dims[0], ndims, status.c) 108 ret := Shape{dims: make([]int64, ndims)} 109 for i := 0; i < int(ndims); i++ {
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/external/tensorflow/tensorflow/contrib/tpu/python/tpu/ |
H A D | tpu_sharding.py | 173 ndims = shape.ndims 174 if ndims is None: 176 if ndims <= self._shard_dimension: 209 ndims = shape.ndims 210 if ndims is None: 212 if ndims <= self._shard_dimension:
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/external/tensorflow/tensorflow/contrib/gan/python/features/python/ |
H A D | virtual_batchnorm_impl.py | 84 if reference_batch.shape.ndims is None: 87 ndims = reference_batch.shape.ndims 89 used_axis = ndims + axis 92 if used_axis < 0 or used_axis >= ndims: 94 ' is out of range for input with rank ' + str(ndims)) 198 ndims = input_shape.ndims 199 reduction_axes = list(range(ndims)) 205 self._example_reduction_axes = list(range(ndims)) [all...] |
/external/tensorflow/tensorflow/python/ops/distributions/ |
H A D | distribution.py | 1124 ndims = x.get_shape().ndims # != sample_ndims 1125 if ndims is None: 1127 ndims = array_ops.rank(x) 1129 math_ops.equal(ndims, 0), 1132 elif ndims == 0: 1140 elif ndims != 1: 1151 ndims = x.get_shape().ndims 1152 sample_ndims = sample_shape.ndims [all...] |
/external/tensorflow/tensorflow/python/ops/ |
H A D | image_ops_impl.py | 206 if image.get_shape().ndims is None: 333 if shape.ndims == 3 or shape.ndims is None: 335 elif shape.ndims == 4: 363 if shape.ndims == 3 or shape.ndims is None: 365 elif shape.ndims == 4: 395 if shape.ndims == 3 or shape.ndims is None: 397 elif shape.ndims [all...] |
/external/tensorflow/tensorflow/contrib/kernel_methods/python/ |
H A D | losses.py | 76 logits_rank = logits.get_shape().ndims 92 labels_rank = labels.get_shape().ndims 103 weights_rank = weights.get_shape().ndims
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/external/tensorflow/tensorflow/contrib/losses/python/losses/ |
H A D | loss_ops.py | 60 start_index = max(0, weights.get_shape().ndims) 61 reduction_indices = list(range(start_index, losses.get_shape().ndims)) 132 if losses.get_shape().ndims is None: 133 raise ValueError("losses.get_shape().ndims cannot be None") 135 if weights_shape.ndims is None: 136 raise ValueError("weights.get_shape().ndims cannot be None") 138 if weights_shape.ndims > 1 and weights_shape.dims[-1].is_compatible_with(1): 172 if weights.get_shape().ndims == 0: 185 if weights.get_shape().ndims >= 1: 186 reduction_indices = list(range(1, weights.get_shape().ndims)) [all...] |