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See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. # coding: utf-8 # pylint: disable=too-many-lines, protected-access # pylint: disable=import-error, no-name-in-module, undefined-variable """NDArray API of MXNet.""" from __future__ import absolute_import from __future__ import division try: from __builtin__ import slice as py_slice except ImportError: from builtins import slice as py_slice from array import array as native_array import ctypes import warnings import operator from functools import reduce # pylint: disable=redefined-builtin import numpy as np from ..base import _LIB, numeric_types, integer_types from ..base import c_array, c_array_buf, c_handle_array, mx_real_t from ..base import mx_uint, NDArrayHandle, check_call from ..base import ctypes2buffer from ..context import Context from . import _internal from . import op from ._internal import NDArrayBase __all__ = ["NDArray", "concatenate", "_DTYPE_NP_TO_MX", "_DTYPE_MX_TO_NP", "_GRAD_REQ_MAP", "ones", "add", "arange", "divide", "equal", "full", "greater", "greater_equal", "imdecode", "lesser", "lesser_equal", "maximum", "minimum", "moveaxis", "modulo", "multiply", "not_equal", "onehot_encode", "power", "subtract", "true_divide", "waitall", "_new_empty_handle"] _STORAGE_TYPE_UNDEFINED = -1 _STORAGE_TYPE_DEFAULT = 0 _STORAGE_TYPE_ROW_SPARSE = 1 _STORAGE_TYPE_CSR = 2 # pylint: disable= no-member _DTYPE_NP_TO_MX = { None: -1, np.float32: 0, np.float64: 1, np.float16: 2, np.uint8: 3, np.int32: 4, np.int8: 5, np.int64: 6, } _DTYPE_MX_TO_NP = { -1: None, 0: np.float32, 1: np.float64, 2: np.float16, 3: np.uint8, 4: np.int32, 5: np.int8, 6: np.int64, } _STORAGE_TYPE_STR_TO_ID = { 'undefined': _STORAGE_TYPE_UNDEFINED, 'default': _STORAGE_TYPE_DEFAULT, 'row_sparse': _STORAGE_TYPE_ROW_SPARSE, 'csr': _STORAGE_TYPE_CSR, } _STORAGE_TYPE_ID_TO_STR = { _STORAGE_TYPE_UNDEFINED: 'undefined', _STORAGE_TYPE_DEFAULT: 'default', _STORAGE_TYPE_ROW_SPARSE: 'row_sparse', _STORAGE_TYPE_CSR: 'csr', } _GRAD_REQ_MAP = { 'null': 0, 'write': 1, 'add': 3 } # pylint: enable= no-member # Return code for dispatching indexing function call _NDARRAY_UNSUPPORTED_INDEXING = -1 _NDARRAY_BASIC_INDEXING = 0 _NDARRAY_ADVANCED_INDEXING = 1 def _new_empty_handle(): """Returns a new empty handle. Empty handle can be used to hold a result. Returns ------- handle A new empty `NDArray` handle. """ hdl = NDArrayHandle() check_call(_LIB.MXNDArrayCreateNone(ctypes.byref(hdl))) return hdl def _new_alloc_handle(shape, ctx, delay_alloc, dtype=mx_real_t): """Return a new handle with specified shape and context. Empty handle is only used to hold results. Returns ------- handle A new empty `NDArray` handle. """ hdl = NDArrayHandle() check_call(_LIB.MXNDArrayCreateEx( c_array_buf(mx_uint, native_array('I', shape)), mx_uint(len(shape)), ctypes.c_int(ctx.device_typeid), ctypes.c_int(ctx.device_id), ctypes.c_int(int(delay_alloc)), ctypes.c_int(int(_DTYPE_NP_TO_MX[np.dtype(dtype).type])), ctypes.byref(hdl))) return hdl def _new_from_shared_mem(shared_pid, shared_id, shape, dtype): hdl = NDArrayHandle() check_call(_LIB.MXNDArrayCreateFromSharedMem( ctypes.c_int(shared_pid), ctypes.c_int(shared_id), c_array(mx_uint, shape), mx_uint(len(shape)), ctypes.c_int(int(_DTYPE_NP_TO_MX[np.dtype(dtype).type])), ctypes.byref(hdl))) return hdl <div class="viewcode-block" id="waitall"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.waitall">[docs]</a>def waitall(): """Wait for all async operations to finish in MXNet. This function is used for benchmarking only. """ check_call(_LIB.MXNDArrayWaitAll())</div> def _storage_type(handle): storage_type = ctypes.c_int(0) check_call(_LIB.MXNDArrayGetStorageType(handle, ctypes.byref(storage_type))) return storage_type.value <div class="viewcode-block" id="NDArray"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray">[docs]</a>class NDArray(NDArrayBase): """An array object representing a multidimensional, homogeneous array of fixed-size items. """ __slots__ = [] # make numpy functions return NDArray instead of numpy object array __array_priority__ = 1000.0 # pylint: disable= no-member, undefined-variable <div class="viewcode-block" id="NDArray.__repr__"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__repr__">[docs]</a> def __repr__(self): """Returns a string representation of the array.""" shape_info = 'x'.join(['%d' % x for x in self.shape]) return '\n%s\n<%s %s @%s>' % (str(self.asnumpy()), self.__class__.__name__, shape_info, self.context)</div> def __reduce__(self): return NDArray, (None,), self.__getstate__() def _to_shared_mem(self): shared_pid = ctypes.c_int() shared_id = ctypes.c_int() check_call(_LIB.MXNDArrayGetSharedMemHandle( self.handle, ctypes.byref(shared_pid), ctypes.byref(shared_id))) return shared_pid.value, shared_id.value, self.shape, self.dtype <div class="viewcode-block" id="NDArray.__add__"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__add__">[docs]</a> def __add__(self, other): """x.__add__(y) <=> x+y <=> mx.nd.add(x, y) """ return add(self, other)</div> <div class="viewcode-block" id="NDArray.__iadd__"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__iadd__">[docs]</a> def __iadd__(self, other): """x.__iadd__(y) <=> x+=y """ if not self.writable: raise ValueError('trying to add to a readonly NDArray') if isinstance(other, NDArray): return op.broadcast_add(self, other, out=self) elif isinstance(other, numeric_types): return _internal._plus_scalar(self, float(other), out=self) else: raise TypeError('type %s not supported' % str(type(other)))</div> def __radd__(self, other): return self.__add__(other) <div class="viewcode-block" id="NDArray.__sub__"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__sub__">[docs]</a> def __sub__(self, other): """x.__sub__(y) <=> x-y <=> mx.nd.subtract(x, y) """ return subtract(self, other)</div> <div class="viewcode-block" id="NDArray.__isub__"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__isub__">[docs]</a> def __isub__(self, other): """x.__isub__(y) <=> x-=y """ if not self.writable: raise ValueError('trying to subtract from a readonly NDArray') if isinstance(other, NDArray): return op.broadcast_sub(self, other, out=self) elif isinstance(other, numeric_types): return _internal._minus_scalar(self, float(other), out=self) else: raise TypeError('type %s not supported' % str(type(other)))</div> <div class="viewcode-block" id="NDArray.__rsub__"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__rsub__">[docs]</a> def __rsub__(self, other): """x.__rsub__(y) <=> y-x <=> mx.nd.subtract(y, x) """ return subtract(other, self)</div> <div class="viewcode-block" id="NDArray.__mul__"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__mul__">[docs]</a> def __mul__(self, other): """x.__mul__(y) <=> x*y <=> mx.nd.multiply(x, y) """ return multiply(self, other)</div> <div class="viewcode-block" id="NDArray.__neg__"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__neg__">[docs]</a> def __neg__(self): """x.__neg__(y) <=> -x """ return _internal._mul_scalar(self, -1.0)</div> <div class="viewcode-block" id="NDArray.__imul__"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__imul__">[docs]</a> def __imul__(self, other): """x.__imul__(y) <=> x*=y """ if not self.writable: raise ValueError('trying to multiply to a readonly NDArray') if isinstance(other, NDArray): return op.broadcast_mul(self, other, out=self) elif isinstance(other, numeric_types): return _internal._mul_scalar(self, float(other), out=self) else: raise TypeError('type %s not supported' % str(type(other)))</div> def __rmul__(self, other): return self.__mul__(other) <div class="viewcode-block" id="NDArray.__div__"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__div__">[docs]</a> def __div__(self, other): """x.__div__(y) <=> x/y <=> mx.nd.divide(x, y) """ return divide(self, other)</div> <div class="viewcode-block" id="NDArray.__rdiv__"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__rdiv__">[docs]</a> def __rdiv__(self, other): """x.__rdiv__(y) <=> y/x <=> mx.nd.divide(y, x) """ return divide(other, self)</div> <div class="viewcode-block" id="NDArray.__idiv__"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__idiv__">[docs]</a> def __idiv__(self, other): """x.__rdiv__(y) <=> x/=y """ if not self.writable: raise ValueError('trying to divide from a readonly NDArray') if isinstance(other, NDArray): return op.broadcast_div(self, other, out=self) elif isinstance(other, numeric_types): return _internal._div_scalar(self, float(other), out=self) else: raise TypeError('type %s not supported' % str(type(other)))</div> def __truediv__(self, other): return divide(self, other) def __rtruediv__(self, other): return divide(other, self) def __itruediv__(self, other): return self.__idiv__(other) <div class="viewcode-block" id="NDArray.__mod__"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__mod__">[docs]</a> def __mod__(self, other): """x.__mod__(y) <=> x%y <=> mx.nd.modulo(x, y) """ return modulo(self, other)</div> <div class="viewcode-block" id="NDArray.__rmod__"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__rmod__">[docs]</a> def __rmod__(self, other): """x.__rmod__(y) <=> y%x <=> mx.nd.modulo(y, x) """ return modulo(other, self)</div> <div class="viewcode-block" id="NDArray.__imod__"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__imod__">[docs]</a> def __imod__(self, other): """x.__rmod__(y) <=> x%=y """ if not self.writable: raise ValueError('trying to take modulo from a readonly NDArray') if isinstance(other, NDArray): return op.broadcast_mod(self, other, out=self) elif isinstance(other, numeric_types): return _internal._mod_scalar(self, float(other), out=self) else: raise TypeError('type %s not supported' % str(type(other)))</div> <div class="viewcode-block" id="NDArray.__pow__"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__pow__">[docs]</a> def __pow__(self, other): """x.__pow__(y) <=> x**y <=> mx.nd.power(x,y) """ return power(self, other)</div> <div class="viewcode-block" id="NDArray.__rpow__"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__rpow__">[docs]</a> def __rpow__(self, other): """x.__pow__(y) <=> y**x <=> mx.nd.power(y,x) """ return power(other, self)</div> <div class="viewcode-block" id="NDArray.__eq__"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__eq__">[docs]</a> def __eq__(self, other): """x.__eq__(y) <=> x==y <=> mx.nd.equal(x, y) """ return equal(self, other)</div> <div class="viewcode-block" id="NDArray.__hash__"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__hash__">[docs]</a> def __hash__(self): """Default hash function.""" return id(self)//16</div> <div class="viewcode-block" id="NDArray.__ne__"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__ne__">[docs]</a> def __ne__(self, other): """x.__ne__(y) <=> x!=y <=> mx.nd.not_equal(x, y) """ return not_equal(self, other)</div> <div class="viewcode-block" id="NDArray.__gt__"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__gt__">[docs]</a> def __gt__(self, other): """x.__gt__(y) <=> x>y <=> mx.nd.greater(x, y) """ return greater(self, other)</div> <div class="viewcode-block" id="NDArray.__ge__"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__ge__">[docs]</a> def __ge__(self, other): """x.__ge__(y) <=> x>=y <=> mx.nd.greater_equal(x, y) """ return greater_equal(self, other)</div> <div class="viewcode-block" id="NDArray.__lt__"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__lt__">[docs]</a> def __lt__(self, other): """x.__lt__(y) <=> x<y <=> mx.nd.lesser(x, y) """ return lesser(self, other)</div> <div class="viewcode-block" id="NDArray.__le__"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__le__">[docs]</a> def __le__(self, other): """x.__le__(y) <=> x<=y <=> mx.nd.less_equal(x, y) """ return lesser_equal(self, other)</div> def __bool__(self): num_elements = reduce(operator.mul, self.shape, 1) if num_elements == 0: return False elif num_elements == 1: return bool(self.asscalar()) else: raise ValueError("The truth value of an NDArray with multiple elements " \ "is ambiguous.") __nonzero__ = __bool__ <div class="viewcode-block" id="NDArray.__len__"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__len__">[docs]</a> def __len__(self): """Number of element along the first axis.""" return self.shape[0]</div> def __getstate__(self): handle = self.handle this = {'handle' : None} if handle is not None: length = ctypes.c_size_t() cptr = ctypes.POINTER(ctypes.c_char)() check_call(_LIB.MXNDArraySaveRawBytes(self.handle, ctypes.byref(length), ctypes.byref(cptr))) this['handle'] = ctypes2buffer(cptr, length.value) return this def __setstate__(self, state): # pylint: disable=assigning-non-slot handle = state['handle'] if handle is not None: buf = handle handle = NDArrayHandle() ptr = (ctypes.c_char * len(buf)).from_buffer(buf) length = ctypes.c_size_t(len(buf)) check_call(_LIB.MXNDArrayLoadFromRawBytes(ptr, length, ctypes.byref(handle))) self.handle = handle else: self.handle = None # pylint: disable=line-too-long <div class="viewcode-block" id="NDArray.__setitem__"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__setitem__">[docs]</a> def __setitem__(self, key, value): """x.__setitem__(i, y) <=> x[i]=y Sets value to self[key]. This functions supports advanced indexing defined in the following reference with some restrictions. https://docs.scipy.org/doc/numpy-1.13.0/reference/arrays.indexing.html#combining-advanced-and-basic-indexing - If key is a list type, only a list of integers is supported, e.g. key=[1, 2] is supported, while not for key=[[1, 2]]. - Ellipsis (...) and np.newaxis are not supported. - Boolean array indexing is not supported. Parameters ---------- key : int, slice, list, np.ndarray, NDArray, or tuple of all previous types The indexing key. value : scalar or array-like object that can be broadcast to the shape of self[key] The value to set. Examples -------- >>> x = mx.nd.zeros((2,3)) >>> x[:] = 1 >>> x.asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> x.asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> x[:,1:2] = 2 >>> x.asnumpy() array([[ 1., 2., 1.], [ 1., 2., 1.]], dtype=float32) >>> x[1:2,1:] = 3 >>> x.asnumpy() array([[ 1., 2., 1.], [ 1., 3., 3.]], dtype=float32) >>> x[1:,0:2] = mx.nd.zeros((1,2)) >>> x.asnumpy() array([[ 1., 2., 1.], [ 0., 0., 3.]], dtype=float32) >>> x[1,2] = 4 >>> x.asnumpy() array([[ 1., 2., 1.], [ 0., 0., 4.]], dtype=float32) >>> x[[0], [1, 2]] = 5 >>> x.asnumpy() array([[ 1., 5., 5.], [ 0., 0., 4.]], dtype=float32) >>> x[::-1, 0:2:2] = [6] >>> x.asnumpy() array([[ 6., 5., 5.], [ 6., 0., 4.]], dtype=float32) """ indexing_dispatch_code = _get_indexing_dispatch_code(key) if indexing_dispatch_code == _NDARRAY_BASIC_INDEXING: self._set_nd_basic_indexing(key, value) elif indexing_dispatch_code == _NDARRAY_ADVANCED_INDEXING: self._set_nd_advanced_indexing(key, value) else: raise ValueError('Indexing NDArray with index=%s and type=%s is not supported' % (str(key), str(type(key))))</div> # pylint: enable=line-too-long # pylint: disable=line-too-long <div class="viewcode-block" id="NDArray.__getitem__"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.__getitem__">[docs]</a> def __getitem__(self, key): """x.__getitem__(i) <=> x[i] Returns a sliced view of this array if the elements fetched are contiguous in memory; otherwise, returns a newly created NDArray. This functions supports advanced indexing defined in the following reference with some restrictions. https://docs.scipy.org/doc/numpy-1.13.0/reference/arrays.indexing.html#combining-advanced-and-basic-indexing - If key is a list type, only a list of integers is supported, e.g. key=[1, 2] is supported, while not for key=[[1, 2]]. - Ellipsis (...) and np.newaxis are not supported. - Boolean array indexing is not supported. Parameters ---------- key : int, slice, list, np.ndarray, NDArray, or tuple of all previous types Indexing key. Examples -------- >>> x = mx.nd.arange(0,6).reshape((2,3)) >>> x.asnumpy() array([[ 0., 1., 2.], [ 3., 4., 5.]], dtype=float32) >>> x[1].asnumpy() array([ 3., 4., 5.], dtype=float32) >>> y = x[0:1] >>> y[:] = 2 >>> x.asnumpy() array([[ 2., 2., 2.], [ 3., 4., 5.]], dtype=float32) >>> x = mx.nd.arange(0, 8, dtype='int32').reshape((2, 2, 2)) >>> x[[0, 1]] [[[0 1] [2 3]] [[4 5] [6 7]]] >>> x[1:, [0, 1]] [[[4 5] [6 7]]] >>> y = np.array([0, 1], dtype='int32') >>> x[1:, y] [[[4 5] [6 7]]] >>> y = mx.nd.array([0, 1], dtype='int32') >>> x[1:, y] [[[4 5] [6 7]]] """ indexing_dispatch_code = _get_indexing_dispatch_code(key) if indexing_dispatch_code == _NDARRAY_BASIC_INDEXING: return self._get_nd_basic_indexing(key) elif indexing_dispatch_code == _NDARRAY_ADVANCED_INDEXING: return self._get_nd_advanced_indexing(key) else: raise ValueError('Indexing NDArray with index=%s and type=%s is not supported' % (str(key), str(type(key))))</div> # pylint: enable=line-too-long def _get_index_nd(self, key): """Returns an index array for use in scatter_nd and gather_nd.""" def _is_advanced_index(index): """The definition of advanced index here includes integers as well, while integers are considered as basic index type when the key contains only slices and integers.""" return not isinstance(index, py_slice) if isinstance(key, (NDArray, np.ndarray, list, integer_types, py_slice)): key = (key,) assert isinstance(key, tuple),\ 'index=%s must be a NDArray, or np.ndarray, or list, or tuple ' \ ' type to use advanced indexing, received type=%s' % (str(key), str(type(key))) assert len(key) > 0, "Cannot slice with empty indices" shape = self.shape assert len(shape) >= len(key),\ "Slicing dimensions exceeds array dimensions, %d vs %d" % (len(key), len(shape)) indices = [] dtype = 'int32' # index data type passed to gather_nd op need_broadcast = (len(key) != 1) advanced_indices = [] # include list, NDArray, np.ndarray, integer basic_indices = [] # include only slices advanced_index_bshape = None # final advanced index shape for i, idx_i in enumerate(key): is_advanced_index = True if isinstance(idx_i, (np.ndarray, list, tuple)): idx_i = array(idx_i, ctx=self.context, dtype=dtype) advanced_indices.append(i) elif isinstance(idx_i, py_slice): start, stop, step = _get_index_range(idx_i.start, idx_i.stop, shape[i], idx_i.step) idx_i = arange(start, stop, step, ctx=self.context, dtype=dtype) basic_indices.append(i) is_advanced_index = False elif isinstance(idx_i, integer_types): start, stop, step = _get_index_range(idx_i, idx_i+1, shape[i], 1) idx_i = arange(start, stop, step, ctx=self.context, dtype=dtype) advanced_indices.append(i) elif isinstance(idx_i, NDArray): if dtype != idx_i.dtype: idx_i = idx_i.astype(dtype) advanced_indices.append(i) else: raise IndexError('Indexing NDArray with index=%s of type=%s is not supported' % (str(key), str(type(key)))) if is_advanced_index: if advanced_index_bshape is None: advanced_index_bshape = idx_i.shape elif advanced_index_bshape != idx_i.shape: need_broadcast = True advanced_index_bshape = _get_broadcast_shape(advanced_index_bshape, idx_i.shape) indices.append(idx_i) # Get final index shape for gather_nd. See the following reference # for determining the output array shape. # https://docs.scipy.org/doc/numpy-1.13.0/reference/arrays.indexing.html#combining-advanced-and-basic-indexing # pylint: disable=line-too-long if len(advanced_indices) == 0: raise ValueError('Advanced index tuple must contain at least one of the following types:' ' list, tuple, NDArray, np.ndarray, integer, received index=%s' % key) # determine the output array's shape by checking whether advanced_indices are all adjacent # or separated by slices advanced_indices_adjacent = True for i in range(0, len(advanced_indices)-1): if advanced_indices[i] + 1 != advanced_indices[i+1]: advanced_indices_adjacent = False break index_bshape_list = [] # index broadcasted shape if advanced_indices_adjacent: for i in range(0, advanced_indices[0]): index_bshape_list.extend(indices[i].shape) if not need_broadcast and indices[i].shape != advanced_index_bshape: need_broadcast = True index_bshape_list.extend(advanced_index_bshape) for i in range(advanced_indices[-1]+1, len(indices)): if not need_broadcast and indices[i].shape != advanced_index_bshape: need_broadcast = True index_bshape_list.extend(indices[i].shape) else: index_bshape_list.extend(advanced_index_bshape) for i in basic_indices: index_bshape_list.extend(indices[i].shape) if not need_broadcast and indices[i].shape != advanced_index_bshape: need_broadcast = True index_bshape = tuple(index_bshape_list) # Need to broadcast all ndarrays in indices to the final shape. # For example, suppose an array has shape=(5, 6, 7, 8) and # key=(slice(1, 5), [[1, 2]], slice(2, 5), [1]). # Since key[1] and key[3] are two advanced indices here and they are # separated by basic indices key[0] and key[2], the output shape # is (1, 2, 4, 3), where the first two elements come from the shape # that key[1] and key[3] should broadcast to, which is (1, 2), and # the last two elements come from the shape of two basic indices. # In order to broadcast all basic and advanced indices to the output shape, # we need to reshape them based on their axis. For example, to broadcast key[0], # with shape=(4,), we first need to reshape it into (1, 1, 4, 1), and then # broadcast the reshaped array to (1, 2, 4, 3); to broadcast key[1], we first # reshape it into (1, 2, 1, 1), then broadcast the reshaped array to (1, 2, 4, 3). if need_broadcast: broadcasted_indices = [] idx_rshape = [1] * len(index_bshape) if advanced_indices_adjacent: advanced_index_bshape_start = advanced_indices[0] # start index of advanced_index_bshape in index_shape advanced_index_bshape_stop = advanced_index_bshape_start + len(advanced_index_bshape) for i, idx in enumerate(key): if _is_advanced_index(idx): k = advanced_index_bshape_stop # find the reshaped shape for indices[i] for dim_size in indices[i].shape[::-1]: k -= 1 idx_rshape[k] = dim_size else: if i < advanced_indices[0]: # slice is on the left side of advanced indices idx_rshape[i] = indices[i].shape[0] elif i > advanced_indices[-1]: # slice is on the right side of advanced indices idx_rshape[i-len(key)] = indices[i].shape[0] else: raise ValueError('basic index i=%d cannot be between advanced index i=%d and i=%d' % (i, advanced_indices[0], advanced_indices[-1])) # broadcast current index to the final shape broadcasted_indices.append(indices[i].reshape(tuple(idx_rshape)).broadcast_to(index_bshape)) # reset idx_rshape to ones for j, _ in enumerate(idx_rshape): idx_rshape[j] = 1 else: basic_index_offset = len(advanced_index_bshape) for i, idx in enumerate(key): if _is_advanced_index(idx): k = len(advanced_index_bshape) for dim_size in indices[i].shape[::-1]: k -= 1 idx_rshape[k] = dim_size else: idx_rshape[basic_index_offset] = indices[i].shape[0] basic_index_offset += 1 # broadcast current index to the final shape broadcasted_indices.append(indices[i].reshape(tuple(idx_rshape)).broadcast_to(index_bshape)) # reset idx_rshape to ones for j, _ in enumerate(idx_rshape): idx_rshape[j] = 1 indices = broadcasted_indices return op.stack(*indices) def _prepare_value_nd(self, value, vshape): """Given value and vshape, create an `NDArray` from value with the same context and dtype as the current one and broadcast it to vshape.""" if isinstance(value, numeric_types): value_nd = full(shape=vshape, val=value, ctx=self.context, dtype=self.dtype) elif isinstance(value, NDArray): value_nd = value.as_in_context(self.context) if value_nd.dtype != self.dtype: value_nd = value_nd.astype(self.dtype) else: try: value_nd = array(value, ctx=self.context, dtype=self.dtype) except: raise TypeError('NDArray does not support assignment with non-array-like' ' object %s of type %s' % (str(value), str(type(value)))) if value_nd.shape != vshape: value_nd = value_nd.broadcast_to(vshape) return value_nd def _set_nd_basic_indexing(self, key, value): """This function is called by __setitem__ when key is a basic index, i.e. an integer, or a slice, or a tuple of integers and slices. No restrictions on the values of slices' steps.""" shape = self.shape if isinstance(key, integer_types): sliced_arr = self._at(key) sliced_arr[:] = value return elif isinstance(key, py_slice): if key.step is None or key.step == 1: # trivial step if key.start is not None or key.stop is not None: sliced_arr = self._slice(key.start, key.stop) sliced_arr[:] = value return # assign value to the whole NDArray # may need to broadcast first if isinstance(value, NDArray): if value.handle is not self.handle: value.copyto(self) elif isinstance(value, numeric_types): _internal._full(shape=shape, ctx=self.context, dtype=self.dtype, value=float(value), out=self) elif isinstance(value, (np.ndarray, np.generic)): if isinstance(value, np.generic) or value.shape != shape: value = np.broadcast_to(value, shape) self._sync_copyfrom(value) else: # value might be a list or a tuple value_nd = self._prepare_value_nd(value, shape) value_nd.copyto(self) return else: # non-trivial step, use _slice_assign or _slice_assign_scalar key = (key,) assert isinstance(key, tuple), "key=%s must be a tuple of slices and integers" % str(key) assert len(key) <= len(shape), "Indexing dimensions exceed array dimensions, %d vs %d"\ % (len(key), len(shape)) begin = [] end = [] steps = [] oshape = [] # output shape of slice using key vshape = [] # value shape of data[key] for i, slice_i in enumerate(key): dim_size = 1 if isinstance(slice_i, py_slice): begin.append(slice_i.start) end.append(slice_i.stop) steps.append(slice_i.step) start, stop, step = _get_index_range(slice_i.start, slice_i.stop, shape[i], slice_i.step) dim_size = _get_dim_size(start, stop, step) vshape.append(dim_size) elif isinstance(slice_i, integer_types): begin.append(slice_i) end.append(slice_i+1) steps.append(1) else: raise ValueError("basic indexing does not support index=%s of type=%s" % (str(slice_i), str(type(slice_i)))) oshape.append(dim_size) oshape.extend(shape[len(key):]) vshape.extend(shape[len(key):]) # if key contains all integers, vshape should be (1,) if len(vshape) == 0: vshape.append(1) oshape = tuple(oshape) vshape = tuple(vshape) if isinstance(value, numeric_types): _internal._slice_assign_scalar(self, out=self, begin=begin, end=end, step=steps, scalar=float(value)) else: value_nd = self._prepare_value_nd(value, vshape) if vshape != oshape: value_nd = value_nd.reshape(oshape) _internal._slice_assign(self, value_nd, begin, end, steps, out=self) def _set_nd_advanced_indexing(self, key, value): """This function is called by __setitem__ when key is an advanced index.""" indices = self._get_index_nd(key) vshape = _get_oshape_of_gather_nd_op(self.shape, indices.shape) value_nd = self._prepare_value_nd(value, vshape) _internal._scatter_set_nd(data=value_nd, indices=indices, shape=self.shape, out=self) def _get_nd_basic_indexing(self, key): """This function is called when key is a slice, or an integer, or a tuple of slices or integers""" shape = self.shape if isinstance(key, integer_types): if key > shape[0] - 1: raise IndexError( 'index {} is out of bounds for axis 0 with size {}'.format( key, shape[0])) return self._at(key) elif isinstance(key, py_slice): if key.step is not None and key.step != 1: if key.step == 0: raise ValueError("slice step cannot be zero") return op.slice(self, begin=(key.start,), end=(key.stop,), step=(key.step,)) elif key.start is not None or key.stop is not None: return self._slice(key.start, key.stop) else: return self if not isinstance(key, tuple): raise ValueError('index=%s must be a slice, or an ineger, or a tuple' ' of slices and integers to use basic indexing, received type=%s' % (str(key), str(type(key)))) assert len(key) != 0, 'basic index cannot be an empty tuple' begin = [] end = [] step = [] kept_axes = [] # axes where slice_i is a slice i = -1 for i, slice_i in enumerate(key): if isinstance(slice_i, integer_types): begin.append(slice_i) end.append(slice_i+1) step.append(1) elif isinstance(slice_i, py_slice): if slice_i.step == 0: raise ValueError('basic index=%s cannot have slice=%s with step = 0' % (str(key), str(slice_i))) begin.append(slice_i.start) end.append(slice_i.stop) step.append(slice_i.step) kept_axes.append(i) else: raise ValueError('basic_indexing does not support slicing with ' 'index=%s of type=%s.' % (str(slice_i), str(type(slice_i)))) kept_axes.extend(range(i+1, len(shape))) sliced_nd = op.slice(self, begin, end, step) if len(kept_axes) == len(shape): return sliced_nd # squeeze sliced_shape to remove the axes indexed by integers oshape = [] sliced_shape = sliced_nd.shape for axis in kept_axes: oshape.append(sliced_shape[axis]) # if key is a tuple of integers, still need to keep 1 dim # while in Numpy, the output will become an value instead of an ndarray if len(oshape) == 0: oshape.append(1) oshape = tuple(oshape) assert np.prod(oshape) == np.prod(sliced_shape), 'oshape=%s has different size'\ ' than sliced_shape=%s'\ % (oshape, sliced_shape) return sliced_nd.reshape(oshape) def _get_nd_advanced_indexing(self, key): """Get item when key is a tuple of any objects of the following types: NDArray, np.ndarray, list, tuple, slice, and integer.""" return op.gather_nd(self, self._get_index_nd(key)) def _sync_copyfrom(self, source_array): """Performs a synchronized copy from the `source_array` to the current array. This is called through ``x[:] = source_array``, where the `source_array` is a `numpy.ndarray` or array-like object. This function blocks until all the pending read/write operations with respect to the current `NDArray` are finished and carry out the copy operation to the current NDArray. Parameters ---------- source_array : array_like The data source we would like to copy from. Example ------- >>> a = mx.nd.array([1, 2]) >>> a.asnumpy() array([ 1., 2.], dtype=float32) >>> a[:] = np.array([3, 4]) >> a.asnumpy() array([ 3., 4.], dtype=float32) """ if not isinstance(source_array, np.ndarray): try: source_array = np.array(source_array, dtype=self.dtype) except: raise TypeError('array must consist of array-like data,' + 'type %s is not supported' % str(type(array))) source_array = np.ascontiguousarray(source_array, dtype=self.dtype) if source_array.shape != self.shape: raise ValueError('Shape inconsistent: expected %s vs got %s'%( str(self.shape), str(source_array.shape))) check_call(_LIB.MXNDArraySyncCopyFromCPU( self.handle, source_array.ctypes.data_as(ctypes.c_void_p), ctypes.c_size_t(source_array.size))) def _slice(self, start, stop): """Returns a sliced NDArray that shares memory with the current one. This is called through ``x[start:stop]``. Parameters ---------- start : int Starting inclusive index of slice in the first dim. stop : int Finishing exclusive index of slice in the first dim. Returns ------- `NDArray` sharing the memory with the current one sliced from start to stop in the first dim. Examples: >>> a = mx.nd.array([[1,2], [3, 4], [5, 6], [7, 8]]) >>> a[1:2].asnumpy() array([[ 3., 4.]], dtype=float32) >>> a[1:1].asnumpy() array([], shape=(0, 2), dtype=float32) """ handle = NDArrayHandle() start, stop, _ = _get_index_range(start, stop, self.shape[0]) check_call(_LIB.MXNDArraySlice( self.handle, mx_uint(start), mx_uint(stop), ctypes.byref(handle))) return NDArray(handle=handle, writable=self.writable) def _at(self, idx): """Returns a view of the array sliced at `idx` in the first dim. This is called through ``x[idx]``. Parameters ---------- idx : int index for slicing the `NDArray` in the first dim. Returns ------- NDArray `NDArray` sharing the memory with the current one sliced at `idx` in the first dim. Examples -------- >>> a = mx.nd.array([[1,2], [3, 4]]) >>> a[1].asnumpy() array([ 3., 4.], dtype=float32) >>> b = mx.nd.array([1, 2, 3, 4]) >>> b[0].asnumpy() array([ 1.], dtype=float32) """ handle = NDArrayHandle() if idx < 0: length = self.shape[0] idx += length if idx < 0: raise IndexError('index %d is out of bounds for axis 0 with size %d' % (idx-length, length)) check_call(_LIB.MXNDArrayAt( self.handle, mx_uint(idx), ctypes.byref(handle))) return NDArray(handle=handle, writable=self.writable) <div class="viewcode-block" id="NDArray.reshape"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.reshape">[docs]</a> def reshape(self, shape): """Returns a **view** of this array with a new shape without altering any data. Parameters ---------- shape : tuple of int The new shape should not change the array size, namely ``np.prod(new_shape)`` should be equal to ``np.prod(self.shape)``. One dimension can be -1. In this case, the value is inferred from the length of the array and remaining dimensions. 0 Dimensions in shape will be copied from original shape, i.e. if x.shape == (3, 4, 5), x.reshape((0, 20)).shape will be (3, 20). Returns ------- NDArray An array with desired shape that shares data with this array. Examples -------- >>> x = mx.nd.arange(0,6).reshape((2,3)) >>> x.asnumpy() array([[ 0., 1., 2.], [ 3., 4., 5.]], dtype=float32) >>> y = x.reshape((3,2)) >>> y.asnumpy() array([[ 0., 1.], [ 2., 3.], [ 4., 5.]], dtype=float32) >>> y = x.reshape((3,-1)) >>> y.asnumpy() array([[ 0., 1.], [ 2., 3.], [ 4., 5.]], dtype=float32) >>> y[:] = -1 >>> x.asnumpy() array([[-1., -1., -1.], [-1., -1., -1.]], dtype=float32) """ handle = NDArrayHandle() # Actual reshape check_call(_LIB.MXNDArrayReshape(self.handle, len(shape), c_array_buf(ctypes.c_int, native_array('i', shape)), ctypes.byref(handle))) return NDArray(handle=handle, writable=self.writable)</div> <div class="viewcode-block" id="NDArray.reshape_like"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.reshape_like">[docs]</a> def reshape_like(self, *args, **kwargs): """Convenience fluent method for :py:func:`reshape_like`. The arguments are the same as for :py:func:`reshape_like`, with this array as data. """ return op.reshape_like(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.zeros_like"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.zeros_like">[docs]</a> def zeros_like(self, *args, **kwargs): """Convenience fluent method for :py:func:`zeros_like`. The arguments are the same as for :py:func:`zeros_like`, with this array as data. """ return op.zeros_like(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.ones_like"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.ones_like">[docs]</a> def ones_like(self, *args, **kwargs): """Convenience fluent method for :py:func:`ones_like`. The arguments are the same as for :py:func:`ones_like`, with this array as data. """ return op.ones_like(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.broadcast_axes"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.broadcast_axes">[docs]</a> def broadcast_axes(self, *args, **kwargs): """Convenience fluent method for :py:func:`broadcast_axes`. The arguments are the same as for :py:func:`broadcast_axes`, with this array as data. """ return op.broadcast_axes(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.repeat"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.repeat">[docs]</a> def repeat(self, *args, **kwargs): """Convenience fluent method for :py:func:`repeat`. The arguments are the same as for :py:func:`repeat`, with this array as data. """ return op.repeat(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.pad"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.pad">[docs]</a> def pad(self, *args, **kwargs): """Convenience fluent method for :py:func:`pad`. The arguments are the same as for :py:func:`pad`, with this array as data. """ return op.pad(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.swapaxes"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.swapaxes">[docs]</a> def swapaxes(self, *args, **kwargs): """Convenience fluent method for :py:func:`swapaxes`. The arguments are the same as for :py:func:`swapaxes`, with this array as data. """ return op.swapaxes(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.split"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.split">[docs]</a> def split(self, *args, **kwargs): """Convenience fluent method for :py:func:`split`. The arguments are the same as for :py:func:`split`, with this array as data. """ return op.split(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.slice"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.slice">[docs]</a> def slice(self, *args, **kwargs): """Convenience fluent method for :py:func:`slice`. The arguments are the same as for :py:func:`slice`, with this array as data. """ return op.slice(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.slice_axis"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.slice_axis">[docs]</a> def slice_axis(self, *args, **kwargs): """Convenience fluent method for :py:func:`slice_axis`. The arguments are the same as for :py:func:`slice_axis`, with this array as data. """ return op.slice_axis(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.take"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.take">[docs]</a> def take(self, *args, **kwargs): """Convenience fluent method for :py:func:`take`. The arguments are the same as for :py:func:`take`, with this array as data. """ return op.take(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.one_hot"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.one_hot">[docs]</a> def one_hot(self, *args, **kwargs): """Convenience fluent method for :py:func:`one_hot`. The arguments are the same as for :py:func:`one_hot`, with this array as data. """ return op.one_hot(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.pick"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.pick">[docs]</a> def pick(self, *args, **kwargs): """Convenience fluent method for :py:func:`pick`. The arguments are the same as for :py:func:`pick`, with this array as data. """ return op.pick(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.sort"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.sort">[docs]</a> def sort(self, *args, **kwargs): """Convenience fluent method for :py:func:`sort`. The arguments are the same as for :py:func:`sort`, with this array as data. """ return op.sort(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.topk"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.topk">[docs]</a> def topk(self, *args, **kwargs): """Convenience fluent method for :py:func:`topk`. The arguments are the same as for :py:func:`topk`, with this array as data. """ return op.topk(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.argsort"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.argsort">[docs]</a> def argsort(self, *args, **kwargs): """Convenience fluent method for :py:func:`argsort`. The arguments are the same as for :py:func:`argsort`, with this array as data. """ return op.argsort(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.argmax"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.argmax">[docs]</a> def argmax(self, *args, **kwargs): """Convenience fluent method for :py:func:`argmax`. The arguments are the same as for :py:func:`argmax`, with this array as data. """ return op.argmax(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.argmax_channel"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.argmax_channel">[docs]</a> def argmax_channel(self, *args, **kwargs): """Convenience fluent method for :py:func:`argmax_channel`. The arguments are the same as for :py:func:`argmax_channel`, with this array as data. """ return op.argmax_channel(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.argmin"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.argmin">[docs]</a> def argmin(self, *args, **kwargs): """Convenience fluent method for :py:func:`argmin`. The arguments are the same as for :py:func:`argmin`, with this array as data. """ return op.argmin(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.clip"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.clip">[docs]</a> def clip(self, *args, **kwargs): """Convenience fluent method for :py:func:`clip`. The arguments are the same as for :py:func:`clip`, with this array as data. """ return op.clip(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.abs"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.abs">[docs]</a> def abs(self, *args, **kwargs): """Convenience fluent method for :py:func:`abs`. The arguments are the same as for :py:func:`abs`, with this array as data. """ return op.abs(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.sign"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.sign">[docs]</a> def sign(self, *args, **kwargs): """Convenience fluent method for :py:func:`sign`. The arguments are the same as for :py:func:`sign`, with this array as data. """ return op.sign(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.flatten"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.flatten">[docs]</a> def flatten(self, *args, **kwargs): """Convenience fluent method for :py:func:`flatten`. The arguments are the same as for :py:func:`flatten`, with this array as data. """ return op.flatten(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.expand_dims"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.expand_dims">[docs]</a> def expand_dims(self, *args, **kwargs): """Convenience fluent method for :py:func:`expand_dims`. The arguments are the same as for :py:func:`expand_dims`, with this array as data. """ return op.expand_dims(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.tile"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.tile">[docs]</a> def tile(self, *args, **kwargs): """Convenience fluent method for :py:func:`tile`. The arguments are the same as for :py:func:`tile`, with this array as data. """ return op.tile(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.transpose"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.transpose">[docs]</a> def transpose(self, *args, **kwargs): """Convenience fluent method for :py:func:`transpose`. The arguments are the same as for :py:func:`transpose`, with this array as data. """ return op.transpose(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.flip"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.flip">[docs]</a> def flip(self, *args, **kwargs): """Convenience fluent method for :py:func:`flip`. The arguments are the same as for :py:func:`flip`, with this array as data. """ return op.flip(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.sum"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.sum">[docs]</a> def sum(self, *args, **kwargs): """Convenience fluent method for :py:func:`sum`. The arguments are the same as for :py:func:`sum`, with this array as data. """ return op.sum(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.nansum"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.nansum">[docs]</a> def nansum(self, *args, **kwargs): """Convenience fluent method for :py:func:`nansum`. The arguments are the same as for :py:func:`nansum`, with this array as data. """ return op.nansum(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.prod"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.prod">[docs]</a> def prod(self, *args, **kwargs): """Convenience fluent method for :py:func:`prod`. The arguments are the same as for :py:func:`prod`, with this array as data. """ return op.prod(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.nanprod"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.nanprod">[docs]</a> def nanprod(self, *args, **kwargs): """Convenience fluent method for :py:func:`nanprod`. The arguments are the same as for :py:func:`nanprod`, with this array as data. """ return op.nanprod(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.mean"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.mean">[docs]</a> def mean(self, *args, **kwargs): """Convenience fluent method for :py:func:`mean`. The arguments are the same as for :py:func:`mean`, with this array as data. """ return op.mean(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.max"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.max">[docs]</a> def max(self, *args, **kwargs): """Convenience fluent method for :py:func:`max`. The arguments are the same as for :py:func:`max`, with this array as data. """ return op.max(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.min"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.min">[docs]</a> def min(self, *args, **kwargs): """Convenience fluent method for :py:func:`min`. The arguments are the same as for :py:func:`min`, with this array as data. """ return op.min(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.norm"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.norm">[docs]</a> def norm(self, *args, **kwargs): """Convenience fluent method for :py:func:`norm`. The arguments are the same as for :py:func:`norm`, with this array as data. """ return op.norm(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.round"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.round">[docs]</a> def round(self, *args, **kwargs): """Convenience fluent method for :py:func:`round`. The arguments are the same as for :py:func:`round`, with this array as data. """ return op.round(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.rint"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.rint">[docs]</a> def rint(self, *args, **kwargs): """Convenience fluent method for :py:func:`rint`. The arguments are the same as for :py:func:`rint`, with this array as data. """ return op.rint(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.fix"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.fix">[docs]</a> def fix(self, *args, **kwargs): """Convenience fluent method for :py:func:`fix`. The arguments are the same as for :py:func:`fix`, with this array as data. """ return op.fix(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.floor"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.floor">[docs]</a> def floor(self, *args, **kwargs): """Convenience fluent method for :py:func:`floor`. The arguments are the same as for :py:func:`floor`, with this array as data. """ return op.floor(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.ceil"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.ceil">[docs]</a> def ceil(self, *args, **kwargs): """Convenience fluent method for :py:func:`ceil`. The arguments are the same as for :py:func:`ceil`, with this array as data. """ return op.ceil(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.trunc"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.trunc">[docs]</a> def trunc(self, *args, **kwargs): """Convenience fluent method for :py:func:`trunc`. The arguments are the same as for :py:func:`trunc`, with this array as data. """ return op.trunc(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.sin"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.sin">[docs]</a> def sin(self, *args, **kwargs): """Convenience fluent method for :py:func:`sin`. The arguments are the same as for :py:func:`sin`, with this array as data. """ return op.sin(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.cos"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.cos">[docs]</a> def cos(self, *args, **kwargs): """Convenience fluent method for :py:func:`cos`. The arguments are the same as for :py:func:`cos`, with this array as data. """ return op.cos(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.tan"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.tan">[docs]</a> def tan(self, *args, **kwargs): """Convenience fluent method for :py:func:`tan`. The arguments are the same as for :py:func:`tan`, with this array as data. """ return op.tan(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.arcsin"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.arcsin">[docs]</a> def arcsin(self, *args, **kwargs): """Convenience fluent method for :py:func:`arcsin`. The arguments are the same as for :py:func:`arcsin`, with this array as data. """ return op.arcsin(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.arccos"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.arccos">[docs]</a> def arccos(self, *args, **kwargs): """Convenience fluent method for :py:func:`arccos`. The arguments are the same as for :py:func:`arccos`, with this array as data. """ return op.arccos(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.arctan"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.arctan">[docs]</a> def arctan(self, *args, **kwargs): """Convenience fluent method for :py:func:`arctan`. The arguments are the same as for :py:func:`arctan`, with this array as data. """ return op.arctan(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.degrees"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.degrees">[docs]</a> def degrees(self, *args, **kwargs): """Convenience fluent method for :py:func:`degrees`. The arguments are the same as for :py:func:`degrees`, with this array as data. """ return op.degrees(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.radians"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.radians">[docs]</a> def radians(self, *args, **kwargs): """Convenience fluent method for :py:func:`radians`. The arguments are the same as for :py:func:`radians`, with this array as data. """ return op.radians(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.sinh"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.sinh">[docs]</a> def sinh(self, *args, **kwargs): """Convenience fluent method for :py:func:`sinh`. The arguments are the same as for :py:func:`sinh`, with this array as data. """ return op.sinh(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.cosh"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.cosh">[docs]</a> def cosh(self, *args, **kwargs): """Convenience fluent method for :py:func:`cosh`. The arguments are the same as for :py:func:`cosh`, with this array as data. """ return op.cosh(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.tanh"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.tanh">[docs]</a> def tanh(self, *args, **kwargs): """Convenience fluent method for :py:func:`tanh`. The arguments are the same as for :py:func:`tanh`, with this array as data. """ return op.tanh(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.arcsinh"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.arcsinh">[docs]</a> def arcsinh(self, *args, **kwargs): """Convenience fluent method for :py:func:`arcsinh`. The arguments are the same as for :py:func:`arcsinh`, with this array as data. """ return op.arcsinh(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.arccosh"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.arccosh">[docs]</a> def arccosh(self, *args, **kwargs): """Convenience fluent method for :py:func:`arccosh`. The arguments are the same as for :py:func:`arccosh`, with this array as data. """ return op.arccosh(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.arctanh"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.arctanh">[docs]</a> def arctanh(self, *args, **kwargs): """Convenience fluent method for :py:func:`arctanh`. The arguments are the same as for :py:func:`arctanh`, with this array as data. """ return op.arctanh(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.exp"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.exp">[docs]</a> def exp(self, *args, **kwargs): """Convenience fluent method for :py:func:`exp`. The arguments are the same as for :py:func:`exp`, with this array as data. """ return op.exp(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.expm1"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.expm1">[docs]</a> def expm1(self, *args, **kwargs): """Convenience fluent method for :py:func:`expm1`. The arguments are the same as for :py:func:`expm1`, with this array as data. """ return op.expm1(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.log"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.log">[docs]</a> def log(self, *args, **kwargs): """Convenience fluent method for :py:func:`log`. The arguments are the same as for :py:func:`log`, with this array as data. """ return op.log(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.log10"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.log10">[docs]</a> def log10(self, *args, **kwargs): """Convenience fluent method for :py:func:`log10`. The arguments are the same as for :py:func:`log10`, with this array as data. """ return op.log10(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.log2"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.log2">[docs]</a> def log2(self, *args, **kwargs): """Convenience fluent method for :py:func:`log2`. The arguments are the same as for :py:func:`log2`, with this array as data. """ return op.log2(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.log1p"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.log1p">[docs]</a> def log1p(self, *args, **kwargs): """Convenience fluent method for :py:func:`log1p`. The arguments are the same as for :py:func:`log1p`, with this array as data. """ return op.log1p(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.sqrt"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.sqrt">[docs]</a> def sqrt(self, *args, **kwargs): """Convenience fluent method for :py:func:`sqrt`. The arguments are the same as for :py:func:`sqrt`, with this array as data. """ return op.sqrt(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.rsqrt"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.rsqrt">[docs]</a> def rsqrt(self, *args, **kwargs): """Convenience fluent method for :py:func:`rsqrt`. The arguments are the same as for :py:func:`rsqrt`, with this array as data. """ return op.rsqrt(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.cbrt"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.cbrt">[docs]</a> def cbrt(self, *args, **kwargs): """Convenience fluent method for :py:func:`cbrt`. The arguments are the same as for :py:func:`cbrt`, with this array as data. """ return op.cbrt(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.rcbrt"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.rcbrt">[docs]</a> def rcbrt(self, *args, **kwargs): """Convenience fluent method for :py:func:`rcbrt`. The arguments are the same as for :py:func:`rcbrt`, with this array as data. """ return op.rcbrt(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.square"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.square">[docs]</a> def square(self, *args, **kwargs): """Convenience fluent method for :py:func:`square`. The arguments are the same as for :py:func:`square`, with this array as data. """ return op.square(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.reciprocal"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.reciprocal">[docs]</a> def reciprocal(self, *args, **kwargs): """Convenience fluent method for :py:func:`reciprocal`. The arguments are the same as for :py:func:`reciprocal`, with this array as data. """ return op.reciprocal(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.relu"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.relu">[docs]</a> def relu(self, *args, **kwargs): """Convenience fluent method for :py:func:`relu`. The arguments are the same as for :py:func:`relu`, with this array as data. """ return op.relu(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.sigmoid"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.sigmoid">[docs]</a> def sigmoid(self, *args, **kwargs): """Convenience fluent method for :py:func:`sigmoid`. The arguments are the same as for :py:func:`sigmoid`, with this array as data. """ return op.sigmoid(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.softmax"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.softmax">[docs]</a> def softmax(self, *args, **kwargs): """Convenience fluent method for :py:func:`softmax`. The arguments are the same as for :py:func:`softmax`, with this array as data. """ return op.softmax(self, *args, **kwargs)</div> <div class="viewcode-block" id="NDArray.log_softmax"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.log_softmax">[docs]</a> def log_softmax(self, *args, **kwargs): """Convenience fluent method for :py:func:`log_softmax`. The arguments are the same as for :py:func:`log_softmax`, with this array as data. """ return op.log_softmax(self, *args, **kwargs)</div> # pylint: disable= undefined-variable <div class="viewcode-block" id="NDArray.broadcast_to"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.broadcast_to">[docs]</a> def broadcast_to(self, shape): """Broadcasts the input array to a new shape. Broadcasting is only allowed on axes with size 1. The new shape cannot change the number of dimensions. For example, you could broadcast from shape (2, 1) to (2, 3), but not from shape (2, 3) to (2, 3, 3). Parameters ---------- shape : tuple of int The shape of the desired array. Returns ------- NDArray A NDArray with the desired shape that is not sharing data with this array, even if the new shape is the same as ``self.shape``. Examples -------- >>> x = mx.nd.arange(0,3).reshape((1,3,1)) >>> x.asnumpy() array([[[ 0.], [ 1.], [ 2.]]], dtype=float32) >>> y = x.broadcast_to((2,3,3)) >>> y.asnumpy() array([[[ 0., 0., 0.], [ 1., 1., 1.], [ 2., 2., 2.]], <BLANKLINE> [[ 0., 0., 0.], [ 1., 1., 1.], [ 2., 2., 2.]]], dtype=float32) """ cur_shape = self.shape err_str = 'operands could not be broadcast together with remapped shapes' \ '[original->remapped]: {} and requested shape {}'.format(cur_shape, shape) if len(shape) < len(cur_shape): raise ValueError(err_str) cur_shape = (1,) * (len(shape) - len(cur_shape)) + cur_shape cur_shape_arr = np.array(cur_shape) broadcasting_axes = np.nonzero(cur_shape_arr != np.array(shape)) if (cur_shape_arr[broadcasting_axes] != 1).any(): raise ValueError(err_str) if cur_shape != self.shape: return op.broadcast_to(self.reshape(cur_shape), shape=shape) else: return op.broadcast_to(self, shape=tuple(shape))</div> # pylint: enable= undefined-variable <div class="viewcode-block" id="NDArray.wait_to_read"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.wait_to_read">[docs]</a> def wait_to_read(self): """Waits until all previous write operations on the current array are finished. This method guarantees that all previous write operations that pushed into the backend engine for execution are actually finished. Examples -------- >>> import time >>> tic = time.time() >>> a = mx.nd.ones((1000,1000)) >>> b = mx.nd.dot(a, a) >>> print(time.time() - tic) # doctest: +SKIP 0.003854036331176758 >>> b.wait_to_read() >>> print(time.time() - tic) # doctest: +SKIP 0.0893700122833252 """ check_call(_LIB.MXNDArrayWaitToRead(self.handle))</div> @property def ndim(self): """Returns the number of dimensions of this array Examples -------- >>> x = mx.nd.array([1, 2, 3, 4]) >>> x.ndim 1 >>> x = mx.nd.array([[1, 2], [3, 4]]) >>> x.ndim 2 """ return len(self.shape) @property def shape(self): """Tuple of array dimensions. Examples -------- >>> x = mx.nd.array([1, 2, 3, 4]) >>> x.shape (4L,) >>> y = mx.nd.zeros((2, 3, 4)) >>> y.shape (2L, 3L, 4L) """ ndim = mx_uint() pdata = ctypes.POINTER(mx_uint)() check_call(_LIB.MXNDArrayGetShape( self.handle, ctypes.byref(ndim), ctypes.byref(pdata))) return tuple(pdata[:ndim.value]) @property def size(self): """Number of elements in the array. Equivalent to the product of the array's dimensions. Examples -------- >>> import numpy as np >>> x = mx.nd.zeros((3, 5, 2)) >>> x.size 30 >>> np.prod(x.shape) 30 """ size = 1 for i in self.shape: size *= i return size @property def context(self): """Device context of the array. Examples -------- >>> x = mx.nd.array([1, 2, 3, 4]) >>> x.context cpu(0) >>> type(x.context) <class 'mxnet.context.Context'> >>> y = mx.nd.zeros((2,3), mx.gpu(0)) >>> y.context gpu(0) """ dev_typeid = ctypes.c_int() dev_id = ctypes.c_int() check_call(_LIB.MXNDArrayGetContext( self.handle, ctypes.byref(dev_typeid), ctypes.byref(dev_id))) return Context(Context.devtype2str[dev_typeid.value], dev_id.value) @property def dtype(self): """Data-type of the array's elements. Returns ------- numpy.dtype This NDArray's data type. Examples -------- >>> x = mx.nd.zeros((2,3)) >>> x.dtype <type 'numpy.float32'> >>> y = mx.nd.zeros((2,3), dtype='int32') >>> y.dtype <type 'numpy.int32'> """ mx_dtype = ctypes.c_int() check_call(_LIB.MXNDArrayGetDType( self.handle, ctypes.byref(mx_dtype))) return _DTYPE_MX_TO_NP[mx_dtype.value] @property def stype(self): """Storage-type of the array. """ return _STORAGE_TYPE_ID_TO_STR[_storage_type(self.handle)] @property # pylint: disable= invalid-name, undefined-variable def T(self): """Returns a copy of the array with axes transposed. Equivalent to ``mx.nd.transpose(self)`` except that self is returned if ``self.ndim < 2``. Unlike ``numpy.ndarray.T``, this function returns a copy rather than a view of the array unless ``self.ndim < 2``. Examples -------- >>> x = mx.nd.arange(0,6).reshape((2,3)) >>> x.asnumpy() array([[ 0., 1., 2.], [ 3., 4., 5.]], dtype=float32) >>> x.T.asnumpy() array([[ 0., 3.], [ 1., 4.], [ 2., 5.]], dtype=float32) """ if len(self.shape) < 2: return self return op.transpose(self) # pylint: enable= invalid-name, undefined-variable @property def _fresh_grad(self): """Whether this array's corresponding gradient array (registered via `autograd.mark_variables`) has been updated by `autograd.backward` since last reset. `_fresh_grad` need to be manually set to False after consuming gradient (usually after updating this array). """ out = ctypes.c_int() check_call(_LIB.MXNDArrayGetGradState(self.handle, ctypes.byref(out))) return out.value @_fresh_grad.setter def _fresh_grad(self, state): check_call(_LIB.MXNDArraySetGradState(self.handle, ctypes.c_int(state))) <div class="viewcode-block" id="NDArray.asnumpy"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.asnumpy">[docs]</a> def asnumpy(self): """Returns a ``numpy.ndarray`` object with value copied from this array. Examples -------- >>> x = mx.nd.ones((2,3)) >>> y = x.asnumpy() >>> type(y) <type 'numpy.ndarray'> >>> y array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> z = mx.nd.ones((2,3), dtype='int32') >>> z.asnumpy() array([[1, 1, 1], [1, 1, 1]], dtype=int32) """ data = np.empty(self.shape, dtype=self.dtype) check_call(_LIB.MXNDArraySyncCopyToCPU( self.handle, data.ctypes.data_as(ctypes.c_void_p), ctypes.c_size_t(data.size))) return data</div> <div class="viewcode-block" id="NDArray.asscalar"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.asscalar">[docs]</a> def asscalar(self): """Returns a scalar whose value is copied from this array. This function is equivalent to ``self.asnumpy()[0]``. This NDArray must have shape (1,). Examples -------- >>> x = mx.nd.ones((1,), dtype='int32') >>> x.asscalar() 1 >>> type(x.asscalar()) <type 'numpy.int32'> """ if self.shape != (1,): raise ValueError("The current array is not a scalar") return self.asnumpy()[0]</div> <div class="viewcode-block" id="NDArray.astype"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.astype">[docs]</a> def astype(self, dtype): """Returns a copy of the array after casting to a specified type. Parameters ---------- dtype : numpy.dtype or str The type of the returned array. Returns ------- NDArray, CSRNDArray or RowSparseNDArray The copied array after casting to the specified type. Examples -------- >>> x = mx.nd.zeros((2,3), dtype='float32') >>> y = x.astype('int32') >>> y.dtype <type 'numpy.int32'> """ res = empty(self.shape, ctx=self.context, dtype=dtype) self.copyto(res) return res</div> <div class="viewcode-block" id="NDArray.copyto"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.copyto">[docs]</a> def copyto(self, other): """Copies the value of this array to another array. If ``other`` is a ``NDArray`` object, then ``other.shape`` and ``self.shape`` should be the same. This function copies the value from ``self`` to ``other``. If ``other`` is a context, a new ``NDArray`` will be first created on the target context, and the value of ``self`` is copied. Parameters ---------- other : NDArray or Context The destination array or context. Returns ------- NDArray, CSRNDArray or RowSparseNDArray The copied array. If ``other`` is an ``NDArray``, then the return value and ``other`` will point to the same ``NDArray``. Examples -------- >>> x = mx.nd.ones((2,3)) >>> y = mx.nd.zeros((2,3), mx.gpu(0)) >>> z = x.copyto(y) >>> z is y True >>> y.asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> y.copyto(mx.gpu(0)) <NDArray 2x3 @gpu(0)> """ if isinstance(other, NDArray): if other.handle is self.handle: warnings.warn('You are attempting to copy an array to itself', RuntimeWarning) return return _internal._copyto(self, out=other) elif isinstance(other, Context): hret = NDArray(_new_alloc_handle(self.shape, other, True, self.dtype)) return _internal._copyto(self, out=hret) else: raise TypeError('copyto does not support type ' + str(type(other)))</div> <div class="viewcode-block" id="NDArray.copy"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.copy">[docs]</a> def copy(self): """Makes a copy of this ``NDArray``, keeping the same context. Returns ------- NDArray, CSRNDArray or RowSparseNDArray The copied array Examples -------- >>> x = mx.nd.ones((2,3)) >>> y = x.copy() >>> y.asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) """ return self.copyto(self.context)</div> <div class="viewcode-block" id="NDArray.as_in_context"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.as_in_context">[docs]</a> def as_in_context(self, context): """Returns an array on the target device with the same value as this array. If the target context is the same as ``self.context``, then ``self`` is returned. Otherwise, a copy is made. Parameters ---------- context : Context The target context. Returns ------- NDArray, CSRNDArray or RowSparseNDArray The target array. Examples -------- >>> x = mx.nd.ones((2,3)) >>> y = x.as_in_context(mx.cpu()) >>> y is x True >>> z = x.as_in_context(mx.gpu(0)) >>> z is x False """ if self.context == context: return self return self.copyto(context)</div> <div class="viewcode-block" id="NDArray.attach_grad"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.attach_grad">[docs]</a> def attach_grad(self, grad_req='write', stype=None): """Attach a gradient buffer to this NDArray, so that `backward` can compute gradient with respect to it. Parameters ---------- grad_req : {'write', 'add', 'null'} How gradient will be accumulated. - 'write': gradient will be overwritten on every backward. - 'add': gradient will be added to existing value on every backward. - 'null': do not compute gradient for this NDArray. stype : str, optional The storage type of the gradient array. Defaults to the same stype of this NDArray. """ from . import zeros as _zeros if stype is not None: grad = _zeros(self.shape, stype=stype) else: grad = op.zeros_like(self) # pylint: disable=undefined-variable grad_req = _GRAD_REQ_MAP[grad_req] check_call(_LIB.MXAutogradMarkVariables( 1, ctypes.pointer(self.handle), ctypes.pointer(mx_uint(grad_req)), ctypes.pointer(grad.handle)))</div> @property def grad(self): """Returns gradient buffer attached to this NDArray.""" from . import _ndarray_cls hdl = NDArrayHandle() check_call(_LIB.MXNDArrayGetGrad(self.handle, ctypes.byref(hdl))) if hdl.value is None: return None return _ndarray_cls(hdl) <div class="viewcode-block" id="NDArray.detach"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.detach">[docs]</a> def detach(self): """Returns a new NDArray, detached from the current graph.""" from . import _ndarray_cls hdl = NDArrayHandle() check_call(_LIB.MXNDArrayDetach(self.handle, ctypes.byref(hdl))) return _ndarray_cls(hdl)</div> <div class="viewcode-block" id="NDArray.backward"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.backward">[docs]</a> def backward(self, out_grad=None, retain_graph=False, train_mode=True): """Compute the gradients of this NDArray w.r.t variables. Parameters ---------- out_grad : NDArray, optional Gradient with respect to head. retain_graph : bool, optional Whether to retain the computaion graph for another backward pass on the same graph. By default the computaion history is cleared. train_mode : bool, optional Whether to compute gradient for training or inference. """ if out_grad is None: ograd_handles = [NDArrayHandle(0)] else: ograd_handles = [out_grad.handle] check_call(_LIB.MXAutogradBackwardEx( 1, c_handle_array([self]), c_array(NDArrayHandle, ograd_handles), 0, ctypes.c_void_p(0), ctypes.c_int(retain_graph), ctypes.c_int(0), ctypes.c_int(train_mode), ctypes.c_void_p(0), ctypes.c_void_p(0)))</div> <div class="viewcode-block" id="NDArray.tostype"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.NDArray.tostype">[docs]</a> def tostype(self, stype): """Return a copy of the array with chosen storage type. See Also ---------- :meth:`mxnet.ndarray.cast_storage`. Returns ------- NDArray, CSRNDArray or RowSparseNDArray A copy of the array with the chosen storage stype """ return op.cast_storage(self, stype=stype)</div></div> def _get_indexing_dispatch_code(key): """Returns a dispatch code for calling basic or advanced indexing functions.""" if isinstance(key, (NDArray, np.ndarray)): return _NDARRAY_ADVANCED_INDEXING elif isinstance(key, list): # TODO(junwu): Add support for nested lists besides integer list for i in key: if not isinstance(i, integer_types): raise TypeError('Indexing NDArray only supports a list of integers as index' ' when key is of list type, received element=%s of type=%s' % (str(i), str(type(i)))) return _NDARRAY_ADVANCED_INDEXING elif isinstance(key, (integer_types, py_slice)): return _NDARRAY_BASIC_INDEXING elif isinstance(key, tuple): for idx in key: if isinstance(idx, (NDArray, np.ndarray, list, tuple)): return _NDARRAY_ADVANCED_INDEXING elif not isinstance(idx, (py_slice, integer_types)): raise ValueError("NDArray does not support slicing with key %s of type %s." % (str(idx), str(type(idx)))) return _NDARRAY_BASIC_INDEXING else: return _NDARRAY_UNSUPPORTED_INDEXING def _get_index_range(start, stop, length, step=1): """Given start, stop, step and array length, return absolute values of start, stop, and step for generating index range. The returned values have been compensated by adding length if they are less than zero for all the cases but slice(None, None, -1). Note that the returned value of stop is not necessarily >= 0, since absolute stop is -1 in the case of slice(None, None, -1).""" if step == 0: raise ValueError('step size cannot be zero') if length < 0: raise ValueError('array length cannot be less than zero') if step is None: step = 1 if start is None: if step > 0: start = 0 else: start = length - 1 elif start < 0: start += length if start < 0: raise IndexError('Slicing start %d exceeds limit of %d' % (start-length, length)) elif start >= length: raise IndexError('Slicing start %d exceeds limit of %d' % (start, length)) if stop is None: if step > 0: stop = length else: # this supports case such as ::-1 # stop = -1 here refers to the element before index 0, # instead of the last element in the array stop = -1 elif stop < 0: stop += length if stop < 0: raise IndexError('Slicing stop %d exceeds limit of %d' % (stop-length, length)) elif stop > length: raise IndexError('Slicing stop %d exceeds limit of %d' % (stop, length)) return start, stop, step def _get_oshape_of_gather_nd_op(dshape, ishape): """Given data and index shapes, get the output `NDArray` shape. This basically implements the infer shape logic of op gather_nd.""" assert len(dshape) > 0 and len(ishape) > 0 oshape = list(ishape[1:]) if ishape[0] < len(dshape): oshape.extend(dshape[ishape[0]:]) return tuple(oshape) def _get_dim_size(start, stop, step): """Given start, stop, and stop, calculate the number of elements of this slice.""" assert step != 0 if step > 0: assert start < stop dim_size = (stop - start - 1) // step + 1 else: assert stop < start dim_size = (start - stop - 1) // (-step) + 1 return dim_size def _get_broadcast_shape(shape1, shape2): """Given two shapes that are not identical, find the shape that both input shapes can broadcast to.""" if shape1 == shape2: return shape1 length1 = len(shape1) length2 = len(shape2) if length1 > length2: shape = list(shape1) else: shape = list(shape2) i = max(length1, length2) - 1 for a, b in zip(shape1[::-1], shape2[::-1]): if a != 1 and b != 1 and a != b: raise ValueError('shape1=%s is not broadcastable to shape2=%s' % (shape1, shape2)) shape[i] = max(a, b) i -= 1 return tuple(shape) <div class="viewcode-block" id="onehot_encode"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.onehot_encode">[docs]</a>def onehot_encode(indices, out): """One-hot encoding indices into matrix out. .. note:: `onehot_encode` is deprecated. Use `one_hot` instead. """ # pylint: disable= no-member, protected-access return _internal._onehot_encode(indices, out, out=out)</div> # pylint: enable= no-member, protected-access <div class="viewcode-block" id="ones"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.ones">[docs]</a>def ones(shape, ctx=None, dtype=None, **kwargs): """Returns a new array filled with all ones, with the given shape and type. Parameters ---------- shape : int or tuple of int or list of int The shape of the empty array. ctx : Context, optional An optional device context. Defaults to the current default context (``mxnet.Context.default_ctx``). dtype : str or numpy.dtype, optional An optional value type (default is `float32`). out : NDArray, optional The output NDArray (default is `None`). Returns ------- NDArray A new array of the specified shape filled with all ones. Examples -------- >>> mx.nd.ones(1).asnumpy() array([ 1.], dtype=float32) >>> mx.nd.ones((1,2), mx.gpu(0)) <NDArray 1x2 @gpu(0)> >>> mx.nd.ones((1,2), dtype='float16').asnumpy() array([[ 1., 1.]], dtype=float16) """ # pylint: disable= unused-argument if ctx is None: ctx = Context.default_ctx dtype = mx_real_t if dtype is None else dtype # pylint: disable= no-member, protected-access return _internal._ones(shape=shape, ctx=ctx, dtype=dtype, **kwargs)</div> # pylint: enable= no-member, protected-access <div class="viewcode-block" id="full"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.full">[docs]</a>def full(shape, val, ctx=None, dtype=mx_real_t, out=None): """Returns a new array of given shape and type, filled with the given value `val`. Parameters -------- shape : int or tuple of int The shape of the new array. val : scalar Fill value. ctx : Context, optional Device context (default is the current default context). dtype : `str` or `numpy.dtype`, optional The data type of the returned `NDArray`. The default datatype is `float32`. out : NDArray, optional The output NDArray (default is `None`). Returns ------- NDArray `NDArray` filled with `val`, with the given shape, ctx, and dtype. Examples -------- >>> mx.nd.full(1, 2.0).asnumpy() array([ 2.], dtype=float32) >>> mx.nd.full((1, 2), 2.0, mx.gpu(0)) <NDArray 1x2 @gpu(0)> >>> mx.nd.full((1, 2), 2.0, dtype='float16').asnumpy() array([[ 2., 2.]], dtype=float16) """ out = empty(shape, ctx, dtype) if out is None else out out[:] = val return out</div> def array(source_array, ctx=None, dtype=None): """Creates an array from any object exposing the array interface. Parameters ---------- source_array : array_like An object exposing the array interface, an object whose `__array__` method returns an array, or any (nested) sequence. ctx : Context, optional Device context (default is the current default context). dtype : str or numpy.dtype, optional The data type of the output array. The default dtype is ``source_array.dtype`` if `source_array` is an `NDArray`, `float32` otherwise. Returns ------- NDArray An `NDArray` with the same contents as the `source_array`. """ if isinstance(source_array, NDArray): dtype = source_array.dtype if dtype is None else dtype else: dtype = mx_real_t if dtype is None else dtype if not isinstance(source_array, np.ndarray): try: source_array = np.array(source_array, dtype=dtype) except: raise TypeError('source_array must be array like object') arr = empty(source_array.shape, ctx, dtype) arr[:] = source_array return arr <div class="viewcode-block" id="moveaxis"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.moveaxis">[docs]</a>def moveaxis(tensor, source, destination): """Moves the `source` axis into the `destination` position while leaving the other axes in their original order Parameters ---------- tensor : mx.nd.array The array which axes should be reordered source : int Original position of the axes to move. destination : int Destination position for each of the original axes. Returns ------- result : mx.nd.array Array with moved axes. Examples -------- >>> X = mx.nd.array([[1, 2, 3], [4, 5, 6]]) >>> mx.nd.moveaxis(X, 0, 1).shape (3L, 2L) """ axes = list(range(tensor.ndim)) try: axes.pop(source) except IndexError: raise ValueError('Source should verify 0 <= source < tensor.ndim' 'Got %d' % source) try: axes.insert(destination, source) except IndexError: raise ValueError('Destination should verify 0 <= destination < tensor.ndim' 'Got %d' % destination) return op.transpose(tensor, axes)</div> # pylint: disable= no-member, protected-access, too-many-arguments, redefined-outer-name <div class="viewcode-block" id="arange"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.arange">[docs]</a>def arange(start, stop=None, step=1.0, repeat=1, ctx=None, dtype=mx_real_t): """Returns evenly spaced values within a given interval. Values are generated within the half-open interval [`start`, `stop`). In other words, the interval includes `start` but excludes `stop`. The function is similar to the built-in Python function `range` and to `numpy.arange`, but returns an `NDArray`. Parameters ---------- start : number, optional Start of interval. The default start value is 0. stop : number End of interval. step : number, optional Spacing between values. The default step size is 1. repeat : int, optional Number of times to repeat each element. The default repeat count is 1. ctx : Context, optional Device context. Default context is the current default context. dtype : str or numpy.dtype, optional The data type of the `NDArray`. The default datatype is `np.float32`. Returns ------- NDArray `NDArray` of evenly spaced values in the specified range. Examples -------- >>> mx.nd.arange(3).asnumpy() array([ 0., 1., 2.], dtype=float32) >>> mx.nd.arange(2, 6).asnumpy() array([ 2., 3., 4., 5.], dtype=float32) >>> mx.nd.arange(2, 6, step=2).asnumpy() array([ 2., 4.], dtype=float32) >>> mx.nd.arange(2, 6, step=1.5, repeat=2).asnumpy() array([ 2. , 2. , 3.5, 3.5, 5. , 5. ], dtype=float32) >>> mx.nd.arange(2, 6, step=2, repeat=3, dtype='int32').asnumpy() array([2, 2, 2, 4, 4, 4], dtype=int32) """ if ctx is None: ctx = Context.default_ctx return _internal._arange(start=start, stop=stop, step=step, repeat=repeat, dtype=dtype, ctx=str(ctx))</div> # pylint: enable= no-member, protected-access, too-many-arguments #pylint: disable= too-many-arguments, no-member, protected-access def _ufunc_helper(lhs, rhs, fn_array, fn_scalar, lfn_scalar, rfn_scalar=None): """ Helper function for element-wise operation. The function will perform numpy-like broadcasting if needed and call different functions. Parameters -------- lhs : NDArray or numeric value Left-hand side operand. rhs : NDArray or numeric value Right-hand operand, fn_array : function Function to be called if both lhs and rhs are of ``NDArray`` type. fn_scalar : function Function to be called if both lhs and rhs are numeric values. lfn_scalar : function Function to be called if lhs is ``NDArray`` while rhs is numeric value rfn_scalar : function Function to be called if lhs is numeric value while rhs is ``NDArray``; if none is provided, then the function is commutative, so rfn_scalar is equal to lfn_scalar Returns -------- NDArray result array """ if isinstance(lhs, numeric_types): if isinstance(rhs, numeric_types): return fn_scalar(lhs, rhs) else: if rfn_scalar is None: # commutative function return lfn_scalar(rhs, float(lhs)) else: return rfn_scalar(rhs, float(lhs)) elif isinstance(rhs, numeric_types): return lfn_scalar(lhs, float(rhs)) elif isinstance(rhs, NDArray): return fn_array(lhs, rhs) else: raise TypeError('type %s not supported' % str(type(rhs))) #pylint: enable= too-many-arguments, no-member, protected-access <div class="viewcode-block" id="add"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.add">[docs]</a>def add(lhs, rhs): """Returns element-wise sum of the input arrays with broadcasting. Equivalent to ``lhs + rhs``, ``mx.nd.broadcast_add(lhs, rhs)`` and ``mx.nd.broadcast_plus(lhs, rhs)``. .. note:: If the corresponding dimensions of two arrays have the same size or one of them has size 1, then the arrays are broadcastable to a common shape. Parameters ---------- lhs : scalar or array First array to be added. rhs : scalar or array Second array to be added. If ``lhs.shape != rhs.shape``, they must be broadcastable to a common shape. Returns ------- NDArray The element-wise sum of the input arrays. Examples -------- >>> x = mx.nd.ones((2,3)) >>> y = mx.nd.arange(2).reshape((2,1)) >>> z = mx.nd.arange(2).reshape((1,2)) >>> x.asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> y.asnumpy() array([[ 0.], [ 1.]], dtype=float32) >>> z.asnumpy() array([[ 0., 1.]], dtype=float32) >>> (x+2).asnumpy() array([[ 3., 3., 3.], [ 3., 3., 3.]], dtype=float32) >>> (x+y).asnumpy() array([[ 1., 1., 1.], [ 2., 2., 2.]], dtype=float32) >>> mx.nd.add(x,y).asnumpy() array([[ 1., 1., 1.], [ 2., 2., 2.]], dtype=float32) >>> (z + y).asnumpy() array([[ 0., 1.], [ 1., 2.]], dtype=float32) """ # pylint: disable= no-member, protected-access return _ufunc_helper( lhs, rhs, op.broadcast_add, operator.add, _internal._plus_scalar, None)</div> # pylint: enable= no-member, protected-access <div class="viewcode-block" id="subtract"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.subtract">[docs]</a>def subtract(lhs, rhs): """Returns element-wise difference of the input arrays with broadcasting. Equivalent to ``lhs - rhs``, ``mx.nd.broadcast_sub(lhs, rhs)`` and ``mx.nd.broadcast_minus(lhs, rhs)``. .. note:: If the corresponding dimensions of two arrays have the same size or one of them has size 1, then the arrays are broadcastable to a common shape. Parameters ---------- lhs : scalar or array First array to be subtracted. rhs : scalar or array Second array to be subtracted. If ``lhs.shape != rhs.shape``, they must be broadcastable to a common shape. Returns ------- NDArray The element-wise difference of the input arrays. Examples -------- >>> x = mx.nd.ones((2,3)) >>> y = mx.nd.arange(2).reshape((2,1)) >>> z = mx.nd.arange(2).reshape((1,2)) >>> x.asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> y.asnumpy() array([[ 0.], [ 1.]], dtype=float32) >>> z.asnumpy() array([[ 0., 1.]], dtype=float32) >>> (x-2).asnumpy() array([[-1., -1., -1.], [-1., -1., -1.]], dtype=float32) >>> (x-y).asnumpy() array([[ 1., 1., 1.], [ 0., 0., 0.]], dtype=float32) >>> mx.nd.subtract(x,y).asnumpy() array([[ 1., 1., 1.], [ 0., 0., 0.]], dtype=float32) >>> (z-y).asnumpy() array([[ 0., 1.], [-1., 0.]], dtype=float32) """ # pylint: disable= no-member, protected-access return _ufunc_helper( lhs, rhs, op.broadcast_sub, operator.sub, _internal._minus_scalar, _internal._rminus_scalar)</div> # pylint: enable= no-member, protected-access <div class="viewcode-block" id="multiply"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.multiply">[docs]</a>def multiply(lhs, rhs): """Returns element-wise product of the input arrays with broadcasting. Equivalent to ``lhs * rhs`` and ``mx.nd.broadcast_mul(lhs, rhs)``. .. note:: If the corresponding dimensions of two arrays have the same size or one of them has size 1, then the arrays are broadcastable to a common shape. Parameters ---------- lhs : scalar or array First array to be multiplied. rhs : scalar or array Second array to be multiplied. If ``lhs.shape != rhs.shape``, they must be broadcastable to a common shape. Returns ------- NDArray The element-wise multiplication of the input arrays. Examples -------- >>> x = mx.nd.ones((2,3)) >>> y = mx.nd.arange(2).reshape((2,1)) >>> z = mx.nd.arange(2).reshape((1,2)) >>> x.asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> y.asnumpy() array([[ 0.], [ 1.]], dtype=float32) >>> z.asnumpy() array([[ 0., 1.]], dtype=float32) >>> (x*2).asnumpy() array([[ 2., 2., 2.], [ 2., 2., 2.]], dtype=float32) >>> (x*y).asnumpy() array([[ 0., 0., 0.], [ 1., 1., 1.]], dtype=float32) >>> mx.nd.multiply(x, y).asnumpy() array([[ 0., 0., 0.], [ 1., 1., 1.]], dtype=float32) >>> (z*y).asnumpy() array([[ 0., 0.], [ 0., 1.]], dtype=float32) """ # pylint: disable= no-member, protected-access return _ufunc_helper( lhs, rhs, op.broadcast_mul, operator.mul, _internal._mul_scalar, None)</div> # pylint: enable= no-member, protected-access <div class="viewcode-block" id="divide"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.divide">[docs]</a>def divide(lhs, rhs): """Returns element-wise division of the input arrays with broadcasting. Equivalent to ``lhs / rhs`` and ``mx.nd.broadcast_div(lhs, rhs)``. .. note:: If the corresponding dimensions of two arrays have the same size or one of them has size 1, then the arrays are broadcastable to a common shape. Parameters ---------- lhs : scalar or array First array in division. rhs : scalar or array Second array in division. The arrays to be divided. If ``lhs.shape != rhs.shape``, they must be broadcastable to a common shape. Returns ------- NDArray The element-wise division of the input arrays. Examples -------- >>> x = mx.nd.ones((2,3))*6 >>> y = mx.nd.ones((2,1))*2 >>> x.asnumpy() array([[ 6., 6., 6.], [ 6., 6., 6.]], dtype=float32) >>> y.asnumpy() array([[ 2.], [ 2.]], dtype=float32) >>> x/2 <NDArray 2x3 @cpu(0)> >>> (x/3).asnumpy() array([[ 2., 2., 2.], [ 2., 2., 2.]], dtype=float32) >>> (x/y).asnumpy() array([[ 3., 3., 3.], [ 3., 3., 3.]], dtype=float32) >>> mx.nd.divide(x,y).asnumpy() array([[ 3., 3., 3.], [ 3., 3., 3.]], dtype=float32) """ # pylint: disable= no-member, protected-access return _ufunc_helper( lhs, rhs, op.broadcast_div, operator.truediv, _internal._div_scalar, _internal._rdiv_scalar)</div> # pylint: enable= no-member, protected-access <div class="viewcode-block" id="modulo"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.modulo">[docs]</a>def modulo(lhs, rhs): """Returns element-wise modulo of the input arrays with broadcasting. Equivalent to ``lhs % rhs`` and ``mx.nd.broadcast_mod(lhs, rhs)``. .. note:: If the corresponding dimensions of two arrays have the same size or one of them has size 1, then the arrays are broadcastable to a common shape. Parameters ---------- lhs : scalar or array First array in modulo. rhs : scalar or array Second array in modulo. The arrays to be taken modulo. If ``lhs.shape != rhs.shape``, they must be broadcastable to a common shape. Returns ------- NDArray The element-wise modulo of the input arrays. Examples -------- >>> x = mx.nd.ones((2,3))*6 >>> y = mx.nd.ones((2,1))*4 >>> x.asnumpy() array([[ 6., 6., 6.], [ 6., 6., 6.]], dtype=float32) >>> y.asnumpy() array([[ 4.], [ 4.]], dtype=float32) >>> x%5 <NDArray 2x3 @cpu(0)> >>> (x%5).asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> (x%y).asnumpy() array([[ 2., 2., 2.], [ 2., 2., 2.]], dtype=float32) >>> mx.nd.modulo(x,y).asnumpy() array([[ 2., 2., 2.], [ 2., 2., 2.]], dtype=float32) """ # pylint: disable= no-member, protected-access return _ufunc_helper( lhs, rhs, op.broadcast_mod, operator.mod, _internal._mod_scalar, _internal._rmod_scalar)</div> # pylint: enable= no-member, protected-access <div class="viewcode-block" id="power"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.power">[docs]</a>def power(base, exp): """Returns result of first array elements raised to powers from second array, element-wise with broadcasting. Equivalent to ``base ** exp`` and ``mx.nd.broadcast_power(lhs, rhs)``. .. note:: If the corresponding dimensions of two arrays have the same size or one of them has size 1, then the arrays are broadcastable to a common shape. Parameters ---------- base : scalar or NDArray The base array exp : scalar or NDArray The exponent array. If ``base.shape != exp.shape``, they must be broadcastable to a common shape. Returns -------- NDArray The bases in x raised to the exponents in y. Examples -------- >>> x = mx.nd.ones((2,3))*2 >>> y = mx.nd.arange(1,3).reshape((2,1)) >>> z = mx.nd.arange(1,3).reshape((2,1)) >>> x.asnumpy() array([[ 2., 2., 2.], [ 2., 2., 2.]], dtype=float32) >>> y.asnumpy() array([[ 1.], [ 2.]], dtype=float32) >>> z.asnumpy() array([[ 1.], [ 2.]], dtype=float32) >>> (x**2).asnumpy() array([[ 4., 4., 4.], [ 4., 4., 4.]], dtype=float32) >>> (x**y).asnumpy() array([[ 2., 2., 2.], [ 4., 4., 4.]], dtype=float32) >>> mx.nd.power(x,y).asnumpy() array([[ 2., 2., 2.], [ 4., 4., 4.]], dtype=float32) >>> (z**y).asnumpy() array([[ 1.], [ 4.]], dtype=float32) """ # pylint: disable= no-member, protected-access return _ufunc_helper( base, exp, op.broadcast_power, operator.pow, _internal._power_scalar, _internal._rpower_scalar)</div> # pylint: enable= no-member, protected-access <div class="viewcode-block" id="maximum"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.maximum">[docs]</a>def maximum(lhs, rhs): """Returns element-wise maximum of the input arrays with broadcasting. Equivalent to ``mx.nd.broadcast_maximum(lhs, rhs)``. .. note:: If the corresponding dimensions of two arrays have the same size or one of them has size 1, then the arrays are broadcastable to a common shape. Parameters ---------- lhs : scalar or array First array to be compared. rhs : scalar or array Second array to be compared. If ``lhs.shape != rhs.shape``, they must be broadcastable to a common shape. Returns ------- NDArray The element-wise maximum of the input arrays. Examples -------- >>> x = mx.nd.ones((2,3)) >>> y = mx.nd.arange(2).reshape((2,1)) >>> z = mx.nd.arange(2).reshape((1,2)) >>> x.asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> y.asnumpy() array([[ 0.], [ 1.]], dtype=float32) >>> z.asnumpy() array([[ 0., 1.]], dtype=float32) >>> mx.nd.maximum(x, 2).asnumpy() array([[ 2., 2., 2.], [ 2., 2., 2.]], dtype=float32) >>> mx.nd.maximum(x, y).asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> mx.nd.maximum(y, z).asnumpy() array([[ 0., 1.], [ 1., 1.]], dtype=float32) """ # pylint: disable= no-member, protected-access return _ufunc_helper( lhs, rhs, op.broadcast_maximum, lambda x, y: x if x > y else y, _internal._maximum_scalar, None)</div> # pylint: enable= no-member, protected-access <div class="viewcode-block" id="minimum"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.minimum">[docs]</a>def minimum(lhs, rhs): """Returns element-wise minimum of the input arrays with broadcasting. Equivalent to ``mx.nd.broadcast_minimum(lhs, rhs)``. .. note:: If the corresponding dimensions of two arrays have the same size or one of them has size 1, then the arrays are broadcastable to a common shape. Parameters ---------- lhs : scalar or array First array to be compared. rhs : scalar or array Second array to be compared. If ``lhs.shape != rhs.shape``, they must be broadcastable to a common shape. Returns ------- NDArray The element-wise minimum of the input arrays. Examples -------- >>> x = mx.nd.ones((2,3)) >>> y = mx.nd.arange(2).reshape((2,1)) >>> z = mx.nd.arange(2).reshape((1,2)) >>> x.asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> y.asnumpy() array([[ 0.], [ 1.]], dtype=float32) >>> z.asnumpy() array([[ 0., 1.]], dtype=float32) >>> mx.nd.minimum(x, 2).asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> mx.nd.minimum(x, y).asnumpy() array([[ 0., 0., 0.], [ 1., 1., 1.]], dtype=float32) >>> mx.nd.minimum(z, y).asnumpy() array([[ 0., 0.], [ 0., 1.]], dtype=float32) """ # pylint: disable= no-member, protected-access return _ufunc_helper( lhs, rhs, op.broadcast_minimum, lambda x, y: x if x < y else y, _internal._minimum_scalar, None)</div> # pylint: enable= no-member, protected-access <div class="viewcode-block" id="equal"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.equal">[docs]</a>def equal(lhs, rhs): """Returns the result of element-wise **equal to** (==) comparison operation with broadcasting. For each element in input arrays, return 1(true) if corresponding elements are same, otherwise return 0(false). Equivalent to ``lhs == rhs`` and ``mx.nd.broadcast_equal(lhs, rhs)``. .. note:: If the corresponding dimensions of two arrays have the same size or one of them has size 1, then the arrays are broadcastable to a common shape. Parameters ---------- lhs : scalar or array First array to be compared. rhs : scalar or array Second array to be compared. If ``lhs.shape != rhs.shape``, they must be broadcastable to a common shape. Returns ------- NDArray Output array of boolean values. Examples -------- >>> x = mx.nd.ones((2,3)) >>> y = mx.nd.arange(2).reshape((2,1)) >>> z = mx.nd.arange(2).reshape((1,2)) >>> x.asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> y.asnumpy() array([[ 0.], [ 1.]], dtype=float32) >>> z.asnumpy() array([[ 0., 1.]], dtype=float32) >>> (x == 1).asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> (x == y).asnumpy() array([[ 0., 0., 0.], [ 1., 1., 1.]], dtype=float32) >>> mx.nd.equal(x,y).asnumpy() array([[ 0., 0., 0.], [ 1., 1., 1.]], dtype=float32) >>> (z == y).asnumpy() array([[ 1., 0.], [ 0., 1.]], dtype=float32) """ # pylint: disable= no-member, protected-access return _ufunc_helper( lhs, rhs, op.broadcast_equal, lambda x, y: 1 if x == y else 0, _internal._equal_scalar, None)</div> # pylint: enable= no-member, protected-access <div class="viewcode-block" id="not_equal"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.not_equal">[docs]</a>def not_equal(lhs, rhs): """Returns the result of element-wise **not equal to** (!=) comparison operation with broadcasting. For each element in input arrays, return 1(true) if corresponding elements are different, otherwise return 0(false). Equivalent to ``lhs != rhs`` and ``mx.nd.broadcast_not_equal(lhs, rhs)``. .. note:: If the corresponding dimensions of two arrays have the same size or one of them has size 1, then the arrays are broadcastable to a common shape. Parameters ---------- lhs : scalar or array First array to be compared. rhs : scalar or array Second array to be compared. If ``lhs.shape != rhs.shape``, they must be broadcastable to a common shape. Returns ------- NDArray Output array of boolean values. Examples -------- >>> x = mx.nd.ones((2,3)) >>> y = mx.nd.arange(2).reshape((2,1)) >>> z = mx.nd.arange(2).reshape((1,2)) >>> x.asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> y.asnumpy() array([[ 0.], [ 1.]], dtype=float32) >>> z.asnumpy() array([[ 0., 1.]], dtype=float32) >>> (z == y).asnumpy() array([[ 1., 0.], [ 0., 1.]], dtype=float32) >>> (x != 1).asnumpy() array([[ 0., 0., 0.], [ 0., 0., 0.]], dtype=float32) >>> (x != y).asnumpy() array([[ 1., 1., 1.], [ 0., 0., 0.]], dtype=float32) >>> mx.nd.not_equal(x, y).asnumpy() array([[ 1., 1., 1.], [ 0., 0., 0.]], dtype=float32) >>> (z != y).asnumpy() array([[ 0., 1.], [ 1., 0.]], dtype=float32) """ # pylint: disable= no-member, protected-access return _ufunc_helper( lhs, rhs, op.broadcast_not_equal, lambda x, y: 1 if x != y else 0, _internal._not_equal_scalar, None)</div> # pylint: enable= no-member, protected-access <div class="viewcode-block" id="greater"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.greater">[docs]</a>def greater(lhs, rhs): """Returns the result of element-wise **greater than** (>) comparison operation with broadcasting. For each element in input arrays, return 1(true) if lhs elements are greater than rhs, otherwise return 0(false). Equivalent to ``lhs > rhs`` and ``mx.nd.broadcast_greater(lhs, rhs)``. .. note:: If the corresponding dimensions of two arrays have the same size or one of them has size 1, then the arrays are broadcastable to a common shape. Parameters ---------- lhs : scalar or array First array to be compared. rhs : scalar or array Second array to be compared. If ``lhs.shape != rhs.shape``, they must be broadcastable to a common shape. Returns ------- NDArray Output array of boolean values. Examples -------- >>> x = mx.nd.ones((2,3)) >>> y = mx.nd.arange(2).reshape((2,1)) >>> z = mx.nd.arange(2).reshape((1,2)) >>> x.asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> y.asnumpy() array([[ 0.], [ 1.]], dtype=float32) >>> z.asnumpy() array([[ 0., 1.]], dtype=float32) >>> (x > 1).asnumpy() array([[ 0., 0., 0.], [ 0., 0., 0.]], dtype=float32) >>> (x > y).asnumpy() array([[ 1., 1., 1.], [ 0., 0., 0.]], dtype=float32) >>> mx.nd.greater(x, y).asnumpy() array([[ 1., 1., 1.], [ 0., 0., 0.]], dtype=float32) >>> (z > y).asnumpy() array([[ 0., 1.], [ 0., 0.]], dtype=float32) """ # pylint: disable= no-member, protected-access return _ufunc_helper( lhs, rhs, op.broadcast_greater, lambda x, y: 1 if x > y else 0, _internal._greater_scalar, _internal._lesser_scalar)</div> # pylint: enable= no-member, protected-access <div class="viewcode-block" id="greater_equal"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.greater_equal">[docs]</a>def greater_equal(lhs, rhs): """Returns the result of element-wise **greater than or equal to** (>=) comparison operation with broadcasting. For each element in input arrays, return 1(true) if lhs elements are greater than equal to rhs, otherwise return 0(false). Equivalent to ``lhs >= rhs`` and ``mx.nd.broadcast_greater_equal(lhs, rhs)``. .. note:: If the corresponding dimensions of two arrays have the same size or one of them has size 1, then the arrays are broadcastable to a common shape. Parameters ---------- lhs : scalar or array First array to be compared. rhs : scalar or array Second array to be compared. If ``lhs.shape != rhs.shape``, they must be broadcastable to a common shape. Returns ------- NDArray Output array of boolean values. Examples -------- >>> x = mx.nd.ones((2,3)) >>> y = mx.nd.arange(2).reshape((2,1)) >>> z = mx.nd.arange(2).reshape((1,2)) >>> x.asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> y.asnumpy() array([[ 0.], [ 1.]], dtype=float32) >>> z.asnumpy() array([[ 0., 1.]], dtype=float32) >>> (x >= 1).asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> (x >= y).asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> mx.nd.greater_equal(x, y).asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> (z >= y).asnumpy() array([[ 1., 1.], [ 0., 1.]], dtype=float32) """ # pylint: disable= no-member, protected-access return _ufunc_helper( lhs, rhs, op.broadcast_greater_equal, lambda x, y: 1 if x >= y else 0, _internal._greater_equal_scalar, _internal._lesser_equal_scalar)</div> # pylint: enable= no-member, protected-access <div class="viewcode-block" id="lesser"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.lesser">[docs]</a>def lesser(lhs, rhs): """Returns the result of element-wise **lesser than** (<) comparison operation with broadcasting. For each element in input arrays, return 1(true) if lhs elements are less than rhs, otherwise return 0(false). Equivalent to ``lhs < rhs`` and ``mx.nd.broadcast_lesser(lhs, rhs)``. .. note:: If the corresponding dimensions of two arrays have the same size or one of them has size 1, then the arrays are broadcastable to a common shape. Parameters ---------- lhs : scalar or array First array to be compared. rhs : scalar or array Second array to be compared. If ``lhs.shape != rhs.shape``, they must be broadcastable to a common shape. Returns ------- NDArray Output array of boolean values. Examples -------- >>> x = mx.nd.ones((2,3)) >>> y = mx.nd.arange(2).reshape((2,1)) >>> z = mx.nd.arange(2).reshape((1,2)) >>> x.asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> y.asnumpy() array([[ 0.], [ 1.]], dtype=float32) >>> z.asnumpy() array([[ 0., 1.]], dtype=float32) >>> (x < 1).asnumpy() array([[ 0., 0., 0.], [ 0., 0., 0.]], dtype=float32) >>> (x < y).asnumpy() array([[ 0., 0., 0.], [ 0., 0., 0.]], dtype=float32) >>> mx.nd.lesser(x, y).asnumpy() array([[ 0., 0., 0.], [ 0., 0., 0.]], dtype=float32) >>> (z < y).asnumpy() array([[ 0., 0.], [ 1., 0.]], dtype=float32) """ # pylint: disable= no-member, protected-access return _ufunc_helper( lhs, rhs, op.broadcast_lesser, lambda x, y: 1 if x < y else 0, _internal._lesser_scalar, _internal._greater_scalar)</div> # pylint: enable= no-member, protected-access <div class="viewcode-block" id="lesser_equal"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.lesser_equal">[docs]</a>def lesser_equal(lhs, rhs): """Returns the result of element-wise **lesser than or equal to** (<=) comparison operation with broadcasting. For each element in input arrays, return 1(true) if lhs elements are lesser than equal to rhs, otherwise return 0(false). Equivalent to ``lhs <= rhs`` and ``mx.nd.broadcast_lesser_equal(lhs, rhs)``. .. note:: If the corresponding dimensions of two arrays have the same size or one of them has size 1, then the arrays are broadcastable to a common shape. Parameters ---------- lhs : scalar or array First array to be compared. rhs : scalar or array Second array to be compared. If ``lhs.shape != rhs.shape``, they must be broadcastable to a common shape. Returns ------- NDArray Output array of boolean values. Examples -------- >>> x = mx.nd.ones((2,3)) >>> y = mx.nd.arange(2).reshape((2,1)) >>> z = mx.nd.arange(2).reshape((1,2)) >>> x.asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> y.asnumpy() array([[ 0.], [ 1.]], dtype=float32) >>> z.asnumpy() array([[ 0., 1.]], dtype=float32) >>> (x <= 1).asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> (x <= y).asnumpy() array([[ 0., 0., 0.], [ 1., 1., 1.]], dtype=float32) >>> mx.nd.lesser_equal(x, y).asnumpy() array([[ 0., 0., 0.], [ 1., 1., 1.]], dtype=float32) >>> (z <= y).asnumpy() array([[ 1., 0.], [ 1., 1.]], dtype=float32) """ # pylint: disable= no-member, protected-access return _ufunc_helper( lhs, rhs, op.broadcast_lesser_equal, lambda x, y: 1 if x <= y else 0, _internal._lesser_equal_scalar, _internal._greater_equal_scalar)</div> # pylint: enable= no-member, protected-access <div class="viewcode-block" id="true_divide"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.true_divide">[docs]</a>def true_divide(lhs, rhs): """This function is similar to :meth:`divide`. """ return divide(lhs, rhs)</div> <div class="viewcode-block" id="concatenate"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.concatenate">[docs]</a>def concatenate(arrays, axis=0, always_copy=True): """DEPRECATED, use ``concat`` instead Parameters ---------- arrays : list of `NDArray` Arrays to be concatenate. They must have identical shape except the first dimension. They also must have the same data type. axis : int The axis along which to concatenate. always_copy : bool Default `True`. When not `True`, if the arrays only contain one `NDArray`, that element will be returned directly, avoid copying. Returns ------- NDArray An `NDArray` that lives on the same context as `arrays[0].context`. """ assert isinstance(arrays, list) assert len(arrays) > 0 assert isinstance(arrays[0], NDArray) if not always_copy and len(arrays) == 1: return arrays[0] shape_axis = arrays[0].shape[axis] shape_rest1 = arrays[0].shape[0:axis] shape_rest2 = arrays[0].shape[axis+1:] dtype = arrays[0].dtype for arr in arrays[1:]: shape_axis += arr.shape[axis] assert shape_rest1 == arr.shape[0:axis] assert shape_rest2 == arr.shape[axis+1:] assert dtype == arr.dtype ret_shape = shape_rest1 + (shape_axis,) + shape_rest2 ret = empty(ret_shape, ctx=arrays[0].context, dtype=dtype) idx = 0 begin = [0 for _ in ret_shape] end = list(ret_shape) for arr in arrays: if axis == 0: ret[idx:idx+arr.shape[0]] = arr else: begin[axis] = idx end[axis] = idx+arr.shape[axis] # pylint: disable=no-member,protected-access _internal._crop_assign(ret, arr, out=ret, begin=tuple(begin), end=tuple(end)) # pylint: enable=no-member,protected-access idx += arr.shape[axis] return ret</div> # pylint: disable=redefined-outer-name <div class="viewcode-block" id="imdecode"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.imdecode">[docs]</a>def imdecode(str_img, clip_rect=(0, 0, 0, 0), out=None, index=0, channels=3, mean=None): """DEPRECATED, use mx.img instead Parameters ---------- str_img : str Binary image data clip_rect : iterable of 4 int Clip decoded image to rectangle (x0, y0, x1, y1). out : NDArray Output buffer. Can be 3 dimensional (c, h, w) or 4 dimensional (n, c, h, w). index : int Output decoded image to i-th slice of 4 dimensional buffer. channels : int Number of channels to output. Decode to grey scale when channels = 1. mean : NDArray Subtract mean from decode image before outputing. """ # pylint: disable= no-member, protected-access, too-many-arguments if mean is None: mean = NDArray(_new_empty_handle()) if out is None: return _internal._imdecode(mean, index, clip_rect[0], clip_rect[1], clip_rect[2], clip_rect[3], channels, len(str_img), str_img=str_img) else: return _internal._imdecode(mean, index, clip_rect[0], clip_rect[1], clip_rect[2], clip_rect[3], channels, len(str_img), str_img=str_img, out=out)</div> def zeros(shape, ctx=None, dtype=None, **kwargs): """Returns a new array filled with all zeros, with the given shape and type. Parameters ---------- shape : int or tuple of int The shape of the empty array. ctx : Context, optional An optional device context (default is the current default context). dtype : str or numpy.dtype, optional An optional value type (default is `float32`). out : NDArray, optional The output NDArray (default is `None`). Returns ------- NDArray A created array Examples -------- >>> mx.nd.zeros(1).asnumpy() array([ 0.], dtype=float32) >>> mx.nd.zeros((1,2), mx.gpu(0)) <NDArray 1x2 @gpu(0)> >>> mx.nd.zeros((1,2), mx.gpu(0), 'float16').asnumpy() array([[ 0., 0.]], dtype=float16) """ # pylint: disable= unused-argument if ctx is None: ctx = Context.default_ctx dtype = mx_real_t if dtype is None else dtype # pylint: disable= no-member, protected-access return _internal._zeros(shape=shape, ctx=ctx, dtype=dtype, **kwargs) # pylint: enable= no-member, protected-access def empty(shape, ctx=None, dtype=None): """Returns a new array of given shape and type, without initializing entries. Parameters ---------- shape : int or tuple of int The shape of the empty array. ctx : Context, optional An optional device context (default is the current default context). dtype : str or numpy.dtype, optional An optional value type (default is `float32`). Returns ------- NDArray A created array. """ if isinstance(shape, int): shape = (shape, ) if ctx is None: ctx = Context.default_ctx if dtype is None: dtype = mx_real_t return NDArray(handle=_new_alloc_handle(shape, ctx, False, dtype)) </pre></div> </div> </div> <div aria-label="main navigation" class="sphinxsidebar rightsidebar" role="navigation"> <div class="sphinxsidebarwrapper"> </div> </div> </div><div class="footer"> <div class="section-disclaimer"> <div class="container"> <div> <img height="60" src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/apache_incubator_logo.png"/> Apache MXNet is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator. 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versions/1.0.0/_modules/mxnet/ndarray/ndarray.html (3,122 lines of code) (raw):