def max(a, b):
"""Takes the elementwise maximum of two blobs or a blob and a scalar value.
"""
if type(a) is Blob:
if a.size == 0:
raise ValueError("The blob shouldn't be empty.")
return Blob(PythonWrapper.blob_max(a._internal, float(b)))
elif type(b) is Blob:
if b.size == 0:
raise ValueError("The blob shouldn't be empty.")
return Blob(PythonWrapper.blob_max(float(a), b._internal))
raise ValueError('At least one of `a` and `b` should be neoml.Blob.')
def exp(a):
"""Takes the exponential of each blob element.
"""
if not type(a) is Blob:
raise ValueError('`a` should be neoml.Blob.')
if a.size == 0:
raise ValueError("The blob shouldn't be empty.")
return Blob(PythonWrapper.blob_exp(a._internal))
def abs(a):
"""Takes absolute value of each blob element.
"""
if not type(a) is Blob:
raise ValueError('`a` should be neoml.Blob.')
if a.size == 0:
raise ValueError("The blob shouldn't be empty.")
return Blob(PythonWrapper.blob_abs(a._internal))
def sum(a):
"""Calculates the total sum of blob elements.
"""
if not type(a) is Blob:
raise ValueError('`a` should be neoml.Blob.')
if a.size == 0:
raise ValueError("The blob shouldn't be empty.")
return Blob(PythonWrapper.blob_sum(a._internal))
def get_output(self, layer_name):
"""Returns last blobs of `layer_name` for all models.
:param layer_name: The name of the layer for which last output will be returned.
`layer_name` should correspond to neoml.Sink.
:type layer_name: str
:return: The list of output blobs for all models. Default cpu math engine is used for blobs.
:rtype: *list of size model_count*
"""
return [Blob(blob) for blob in self._get_output(str(layer_name))]
def clip(blob, min_value, max_value):
"""Clips each element of the blob so that it fits between the specified limits.
"""
if not type(blob) is Blob:
raise ValueError('`blob` must be neoml.Blob.')
if blob.size == 0:
raise ValueError("The blobs mustn't be empty.")
return Blob(
PythonWrapper.blob_clip(blob._internal, float(min_value),
float(max_value)))
def const(math_engine, shape, data):
"""Creates a blob of the specified shape filled with `data` value.
"""
if not isinstance(math_engine, MathEngine):
raise ValueError('The `math_engine` should be neoml.MathEngine.')
np_shape = numpy.array(shape, dtype=numpy.int32, copy=False)
if len(np_shape.shape) > 7:
raise ValueError('The `shape` should have not more than 7 dimensions.')
if numpy.isscalar(data):
return Blob(
PythonWrapper.blob_const(math_engine._internal, np_shape,
float(data)))
np_data = numpy.array(data, copy=False, order='C')
if len(np_data.shape) > 7:
raise ValueError('The `shape` should have not more than 7 dimensions.')
return Blob(
PythonWrapper.blob_const(math_engine._internal, np_shape, np_data))
def top_k(a, k=1):
"""Finds values of the k largest elements in the blob.
The result is a blob of size k.
"""
if not type(a) is Blob:
raise ValueError('`a` should be neoml.Blob.')
if int(k) <= 0:
raise ValueError('`k` should be > 0.')
if a.size == 0:
raise ValueError("The blob shouldn't be empty.")
return Blob(PythonWrapper.blob_top_k(a._internal, int(k)))
def binary_cross_entropy(labels, preds, fromLogits):
"""Calculates binary cross-entropy for two blobs: the first one contains labels, the second one contains predictions.
Blobs should be of the same shape.
:math:`result = (1 - labels) * x + log(1 + exp(-x))`
if `fromLogits` then `x = preds`, else :math:`x = log( clippedPreds / (1 - clippedPreds))`
"""
if not type(labels) is Blob:
raise ValueError('`labels` should be neoml.Blob.')
if not type(preds) is Blob:
raise ValueError('`preds` should be neoml.Blob.')
if labels.shape != preds.shape:
raise ValueError("The blobs should be of the same shape.")
if labels.size == 0:
raise ValueError("The blobs shouldn't be empty.")
return Blob(
PythonWrapper.blob_binary_cross_entropy(labels._internal,
preds._internal,
bool(fromLogits)))