/external/tensorflow/tensorflow/python/keras/engine/ |
D | input_spec.py | 53 ndim=None, argument 60 self.ndim = len(shape) 62 self.ndim = ndim 70 ('ndim=' + str(self.ndim)) if self.ndim else '', 107 if (spec.ndim is not None or 117 if spec.ndim is not None: 118 ndim = x.shape.ndims 119 if ndim != spec.ndim: 122 'expected ndim=' + str(spec.ndim) + ', found ndim=' + 123 str(ndim) + '. Full shape received: ' + [all …]
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/external/python/cpython3/Objects/ |
D | memoryobject.c | 246 (view->suboffsets && view->suboffsets[dest->ndim-1] >= 0) 251 assert(dest->ndim > 0 && src->ndim > 0); in last_dim_is_contiguous() 254 dest->strides[dest->ndim-1] == dest->itemsize && in last_dim_is_contiguous() 255 src->strides[src->ndim-1] == src->itemsize); in last_dim_is_contiguous() 289 if (dest->ndim != src->ndim) in equiv_shape() 292 for (i = 0; i < dest->ndim; i++) { in equiv_shape() 352 copy_rec(const Py_ssize_t *shape, Py_ssize_t ndim, Py_ssize_t itemsize, in copy_rec() argument 359 assert(ndim >= 1); in copy_rec() 361 if (ndim == 1) { in copy_rec() 373 copy_rec(shape+1, ndim-1, itemsize, in copy_rec() [all …]
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/external/python/cpython3/Modules/ |
D | _testbuffer.c | 154 base->ndim = 1; in ndbuf_new() 267 if (ndbuf->base.ndim == 0) in init_flags() 473 copy_rec(const Py_ssize_t *shape, Py_ssize_t ndim, Py_ssize_t itemsize, in copy_rec() argument 480 assert(ndim >= 1); in copy_rec() 482 if (ndim == 1) { in copy_rec() 506 copy_rec(shape+1, ndim-1, itemsize, in copy_rec() 520 dest->ndim != src->ndim) in cmp_structure() 523 for (i = 0; i < dest->ndim; i++) { in cmp_structure() 541 assert(dest->ndim > 0); in copy_buffer() 549 if ((dest->suboffsets && dest->suboffsets[dest->ndim-1] >= 0) || in copy_buffer() [all …]
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/external/tensorflow/tensorflow/python/framework/ |
D | fast_tensor_util.pyx | 10 tensor_proto, np.ndarray[np.uint16_t, ndim=1] nparray): argument 22 tensor_proto, np.ndarray[np.uint16_t, ndim=1] nparray): argument 30 tensor_proto, np.ndarray[np.float32_t, ndim=1] nparray): argument 38 tensor_proto, np.ndarray[np.float64_t, ndim=1] nparray): argument 46 tensor_proto, np.ndarray[np.int32_t, ndim=1] nparray): argument 53 tensor_proto, np.ndarray[np.uint32_t, ndim=1] nparray): argument 60 tensor_proto, np.ndarray[np.int64_t, ndim=1] nparray): argument 67 tensor_proto, np.ndarray[np.uint64_t, ndim=1] nparray): argument 74 tensor_proto, np.ndarray[np.uint8_t, ndim=1] nparray): argument 82 tensor_proto, np.ndarray[np.uint16_t, ndim=1] nparray): argument [all …]
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/external/tensorflow/tensorflow/python/keras/utils/ |
D | conv_utils.py | 28 def convert_data_format(data_format, ndim): argument 30 if ndim == 3: 32 elif ndim == 4: 34 elif ndim == 5: 37 raise ValueError('Input rank not supported:', ndim) 39 if ndim == 3: 41 elif ndim == 4: 43 elif ndim == 5: 46 raise ValueError('Input rank not supported:', ndim) 222 if not 3 <= kernel.ndim <= 5: [all …]
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D | losses_utils.py | 224 ndim = K.ndim(losses) 225 weight_ndim = K.ndim(sample_weight) 226 losses = K.mean(losses, axis=list(range(weight_ndim, ndim)))
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D | io_utils.py | 135 def ndim(self): member in HDF5Matrix 141 return self.data.ndim
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/external/tensorflow/tensorflow/python/layers/ |
D | utils.py | 28 def convert_data_format(data_format, ndim): argument 30 if ndim == 3: 32 elif ndim == 4: 34 elif ndim == 5: 37 raise ValueError('Input rank not supported:', ndim) 39 if ndim == 3: 41 elif ndim == 4: 43 elif ndim == 5: 46 raise ValueError('Input rank not supported:', ndim)
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/external/tensorflow/tensorflow/python/keras/ |
D | activations_test.py | 59 x = keras.backend.placeholder(ndim=2) 68 x = keras.backend.placeholder(ndim=1) 80 x = keras.backend.placeholder(ndim=2) 98 x = keras.backend.placeholder(ndim=2) 109 x = keras.backend.placeholder(ndim=2) 125 x = keras.backend.placeholder(ndim=2) 138 x = keras.backend.placeholder(ndim=2) 146 x = keras.backend.placeholder(ndim=2) 158 x = keras.backend.placeholder(ndim=2) 170 x = keras.backend.placeholder(ndim=2) [all …]
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D | activations.py | 57 ndim = K.ndim(x) 58 if ndim == 2: 60 elif ndim > 2:
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D | backend.py | 854 def placeholder(shape=None, ndim=None, dtype=None, sparse=False, name=None): argument 884 if ndim: 885 shape = tuple([None for _ in range(ndim)]) 974 def ndim(x): function 1452 if ndim(x) is not None and (ndim(x) > 2 or ndim(y) > 2): 1467 y_permute_dim = list(range(ndim(y))) 1535 x_ndim = ndim(x) 1536 y_ndim = ndim(y) 1552 if ndim(x) == 2 and ndim(y) == 2: 1559 adj_x = None if axes[0] == ndim(x) - 1 else True [all …]
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/external/python/cpython2/Objects/ |
D | memoryobject.c | 11 if (buf->ndim == 0) in get_shape0() 23 if (src->ndim == 1 && src->shape != NULL) { in dup_buffer() 27 if (src->ndim == 1 && src->strides != NULL) { in dup_buffer() 181 if (view->ndim > PY_SSIZE_T_MAX / sizeof(Py_ssize_t)) { in _indirect_copy_nd() 186 indices = (Py_ssize_t *)PyMem_Malloc(sizeof(Py_ssize_t)*view->ndim); in _indirect_copy_nd() 191 for (k=0; k<view->ndim;k++) { in _indirect_copy_nd() 196 for (k=0; k<view->ndim; k++) { in _indirect_copy_nd() 206 func(view->ndim, indices, view->shape); in _indirect_copy_nd() 297 _strided_copy_nd(dest, view->buf, view->ndim, view->shape, in PyMemoryView_GetContiguous() 366 return _IntTupleFromSsizet(self->view.ndim, self->view.shape); in memory_shape_get() [all …]
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D | abstract.c | 377 if (view->ndim == 0) return 1; in _IsFortranContiguous() 378 if (view->strides == NULL) return (view->ndim == 1); in _IsFortranContiguous() 381 if (view->ndim == 1) return (view->shape[0] == 1 || in _IsFortranContiguous() 383 for (i=0; i<view->ndim; i++) { in _IsFortranContiguous() 398 if (view->ndim == 0) return 1; in _IsCContiguous() 402 if (view->ndim == 1) return (view->shape[0] == 1 || in _IsCContiguous() 404 for (i=view->ndim-1; i>=0; i--) { in _IsCContiguous() 435 for (i = 0; i < view->ndim; i++) { in PyBuffer_GetPointer() 503 indices = (Py_ssize_t *)PyMem_Malloc(sizeof(Py_ssize_t)*(view->ndim)); in PyBuffer_ToContiguous() 508 for (k=0; k<view->ndim;k++) { in PyBuffer_ToContiguous() [all …]
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/external/python/cpython3/Lib/test/ |
D | test_buffer.py | 256 def strides_from_shape(ndim, shape, itemsize, layout): argument 259 if ndim == 0: 263 for i in range(ndim-2, -1, -1): 267 for i in range(1, ndim): 313 def getindex(ndim, ind, strides): argument 316 for i in range(ndim): 325 ndim = len(shape) 326 sstrides = strides_from_shape(ndim, shape, 1, 'C') 327 dstrides = strides_from_shape(ndim, shape[::-1], 1, 'C') 330 fr = getindex(ndim, ind, sstrides) [all …]
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/external/tensorflow/tensorflow/python/keras/layers/ |
D | merge.py | 124 input_ndims = list(map(K.ndim, inputs)) 131 x_ndim = K.ndim(x) 141 x_ndim = K.ndim(x) 164 y_ndim = K.ndim(y) 431 elif K.ndim(mask_i) < K.ndim(input_i): 512 axes = [self.axes % K.ndim(x1), self.axes % K.ndim(x2)] 519 axes.append(self.axes[i] % K.ndim(inputs[i]))
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D | local.py | 156 self.input_spec = InputSpec(ndim=3) 220 self.input_spec = InputSpec(ndim=3, axes={1: input_dim}) 222 self.input_spec = InputSpec(ndim=3, axes={-1: input_dim}) 431 self.input_spec = InputSpec(ndim=4) 503 self.input_spec = InputSpec(ndim=4, axes={1: input_filter}) 505 self.input_spec = InputSpec(ndim=4, axes={-1: input_filter}) 636 ndims = int(mask.ndim / 2) 699 kernel = make_2d(kernel, split_dim=K.ndim(kernel) // 2)
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D | convolutional.py | 144 self.input_spec = InputSpec(ndim=self.rank + 2) 177 self.input_spec = InputSpec(ndim=self.rank + 2, 754 self.input_spec = InputSpec(ndim=4, axes={channel_axis: input_dim}) 832 data_format=conv_utils.convert_data_format(self.data_format, ndim=4)) 1028 self.input_spec = InputSpec(ndim=5, axes={channel_axis: input_dim}) 1102 data_format=conv_utils.convert_data_format(self.data_format, ndim=5), 1114 data_format=conv_utils.convert_data_format(self.data_format, ndim=4)) 1287 self.input_spec = InputSpec(ndim=self.rank + 2, 1504 data_format=conv_utils.convert_data_format(self.data_format, ndim=4)) 1510 data_format=conv_utils.convert_data_format(self.data_format, ndim=4)) [all …]
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D | pooling.py | 70 self.input_spec = InputSpec(ndim=3) 234 self.input_spec = InputSpec(ndim=4) 421 self.input_spec = InputSpec(ndim=5) 582 self.input_spec = InputSpec(ndim=3) 691 self.input_spec = InputSpec(ndim=4) 782 self.input_spec = InputSpec(ndim=5)
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D | core.py | 211 self.input_spec = InputSpec(ndim=3) 268 self.input_spec = InputSpec(ndim=4) 326 self.input_spec = InputSpec(ndim=5) 513 self.input_spec = InputSpec(ndim=len(self.dims) + 1) 571 and K.ndim(inputs) is not None and K.ndim(inputs) > 1): 574 range(2, K.ndim(inputs))]) 631 self.input_spec = InputSpec(ndim=2)
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/external/tensorflow/tensorflow/contrib/distributions/python/kernel_tests/bijectors/ |
D | affine_test.py | 505 if v.ndim < 2: 508 vt = np.swapaxes(v, axis1=v.ndim - 2, axis2=v.ndim - 1) 549 if x.ndim == scale.ndim - 1: 553 if x.ndim == scale.ndim - 1: 558 if x.ndim == scale.ndim - 1: 567 if (ildj.ndim > 0 and (len(scale_args) == 1 or ( 571 elif ildj.ndim < scale.ndim - 2:
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/external/tensorflow/tensorflow/compiler/xla/service/gpu/ |
D | cusolver_rewriter.cc | 58 int ndim = a_shape.dimensions_size(); in CreateCholesky() local 59 CHECK_GE(ndim, 2); in CreateCholesky() 60 int64 n = a_shape.dimensions(ndim - 1); in CreateCholesky()
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/external/python/cpython3/Doc/c-api/ |
D | buffer.rst | 156 .. c:member:: int ndim 169 An array of :c:type:`Py_ssize_t` of length :c:member:`~Py_buffer.ndim` 171 ``shape[0] * ... * shape[ndim-1] * itemsize`` MUST be equal to 182 An array of :c:type:`Py_ssize_t` of length :c:member:`~Py_buffer.ndim` 194 An array of :c:type:`Py_ssize_t` of length :c:member:`~Py_buffer.ndim`. 235 :c:member:`~Py_buffer.len`, :c:member:`~Py_buffer.itemsize`, :c:member:`~Py_buffer.ndim`. 348 :c:member:`~Py_buffer.ndim`, :c:member:`~Py_buffer.shape` and :c:member:`~Py_buffer.strides`. 350 If ``ndim == 0``, the memory location pointed to by :c:member:`~Py_buffer.buf` is 368 def verify_structure(memlen, itemsize, ndim, shape, strides, offset): 382 if ndim <= 0: [all …]
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/external/tensorflow/tensorflow/compiler/xla/python_api/ |
D | xla_shape.py | 120 layout = range(ndarray.ndim) 124 layout = list(reversed(xrange(ndarray.ndim)))
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/external/tensorflow/tensorflow/core/api_def/base_api/ |
D | api_def_DynamicPartition.pbtxt | 17 For each index tuple `js` of size `partitions.ndim`, the slice `data[js, ...]` 24 outputs[i].shape = [sum(partitions == i)] + data.shape[partitions.ndim:]
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/external/python/cpython3/Modules/_ctypes/ |
D | stgdict.c | 25 self->ndim = 0; in PyCStgDict_init() 60 res += self->ndim * sizeof(Py_ssize_t); in PyCStgDict_sizeof() 101 dst->shape = PyMem_Malloc(sizeof(Py_ssize_t) * src->ndim); in PyCStgDict_clone() 107 sizeof(Py_ssize_t) * src->ndim); in PyCStgDict_clone() 544 dict->ndim, dict->shape, stgdict->format, buf); in PyCStructUnionType_update_stgdict()
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