def eval(node, clean_up=True):
"""
It evaluates a node that has taken a numpy array as input. Note that sequences
are not supported yet by this method
Examples:
Plus with two matrices
>>> print (cntk.eval(cntk.ops.plus([[-30.,40.], [1.,2.]], [[-30.,40.], [1.,2.]])))
# [array([[[-60., 80.], [2., 4.]]])]
Times with broadcast of a scalar over a matrix
>>> print (cntk.eval(cntk.ops.element_times([[-30.,40.], [1.,2.]], 5)))
# [array([[[-150., 200.], [5., 10.]]])]
Args:
node (:class:`cntk.graph.ComputationNode`): the node to evaluate
clean_up (bool): whether the temporary directory should be removed when the context is left
Returns:
NumPy array containing the result
"""
from cntk.context import get_new_context
from cntk.ops import input_numpy, constant
from cntk.graph import ComputationNode, _InputComputationNodeBase
import numpy as np
# call a helper method to get a context
with get_new_context() as ctx:
ctx.clean_up = clean_up
first = True
# The params are passed as arryas, e.g. plus([1,2], [3,4]), and we need to
# wrap them with input and parameter nodes.
if node.params:
for p in node.params:
if p in node.inputs:
val = getattr(node, p)
if not isinstance(val, ComputationNode):
# One param needs to be an Input() node. This will be fixed in
# CNTK soon, so that we can remove this workaround and evaluate a
# network with no inputs.
if first:
ir = input_numpy([val], alias=p, name=p)
setattr(node, p, ir)
first = False
else:
setattr(node, p, constant(getattr(node, p),
name=p))
else:
if isinstance(val,
_InputComputationNodeBase) and first:
first = False
return ctx.eval(node)
def eval(node):
"""
It evaluates a node that has taken a numpy array as input. Note that sequences
are not supported yet by this method
Examples:
Plus with two matrices
>>> print (cntk.eval(cntk.ops.plus([[-30.,40.], [1.,2.]], [[-30.,40.], [1.,2.]])))
# [array([[[-60., 80.], [2., 4.]]])]
Times with broadcast of a scalar over a matrix
>>> print (cntk.eval(cntk.ops.element_times([[-30.,40.], [1.,2.]], 5)))
# [array([[[-150., 200.], [5., 10.]]])]
Args:
node (:class:`cntk.graph.ComputationNode`): the node to evaluate
Returns:
NumPy array containing the result
"""
from cntk.context import get_context
from cntk.ops import input_numpy, constant
from cntk.graph import ComputationNode
# call a helper method to get a context
ctx = get_context()
first = True
# The params are passed as arryas, e.g. plus([1,2], [3,4]), and we need to
# wrap them with input and parameter nodes.
if node.params:
for p in node.params:
if p in node.inputs:
val = getattr(node, p)
if not isinstance(val, ComputationNode):
# One param needs to be an Input() node. This will being fixed in
# CNTK soon, so that we can remove this workaround and evaluate a
# network with no inputs.
if first:
if not isinstance(val, list):
# inputs have the outmost dimension for sequences
val = [val]
ir = input_numpy([val], alias=p, name=p)
setattr(node, p, ir)
first = False
else:
setattr(node, p, constant(getattr(node, p), name=p))
else:
if val.op_name == 'CNTK2.Input' and first:
first = False
return ctx.eval(node)
def eval(node):
"""
It evaluates a node that has taken a numpy array as input. Note that sequences
are not supported yet by this method
Examples:
Plus with two matrices
>>> print (cntk.eval(cntk.ops.plus([[-30.,40.], [1.,2.]], [[-30.,40.], [1.,2.]])))
# [array([[[-60., 80.], [2., 4.]]])]
Times with broadcast of a scalar over a matrix
>>> print (cntk.eval(cntk.ops.element_times([[-30.,40.], [1.,2.]], 5)))
# [array([[[-150., 200.], [5., 10.]]])]
Args:
node (:class:`cntk.graph.ComputationNode`): the node to evaluate
Returns:
NumPy array containing the result
"""
from cntk.context import get_context
from cntk.ops import input_numpy, constant
from cntk.graph import ComputationNode
# call a helper method to get a context
ctx = get_context()
first = True
# The params are passed as arryas, e.g. plus([1,2], [3,4]), and we need to
# wrap them with input and parameter nodes.
if node.params:
for p in node.params:
if p in node.inputs:
val = getattr(node, p)
if not isinstance(val, ComputationNode):
# One param needs to be an Input() node. This will being fixed in
# CNTK soon, so that we can remove this workaround and evaluate a
# network with no inputs.
if first:
if not isinstance(val, list):
# inputs have the outmost dimension for sequences
val = [val]
ir = input_numpy([val], alias=p, name=p)
setattr(node, p, ir)
first = False
else:
setattr(node, p, constant(getattr(node, p), name=p))
else:
if val.op_name == 'CNTK2.Input' and first:
first = False
return ctx.eval(node)
def eval(node, clean_up=True):
"""
It evaluates a node that has taken a numpy array as input. Note that sequences
are not supported yet by this method
Examples:
Plus with two matrices
>>> print (cntk.eval(cntk.ops.plus([[-30.,40.], [1.,2.]], [[-30.,40.], [1.,2.]])))
# [array([[[-60., 80.], [2., 4.]]])]
Times with broadcast of a scalar over a matrix
>>> print (cntk.eval(cntk.ops.element_times([[-30.,40.], [1.,2.]], 5)))
# [array([[[-150., 200.], [5., 10.]]])]
Args:
node (:class:`cntk.graph.ComputationNode`): the node to evaluate
clean_up (bool): whether the temporary directory should be removed when the context is left
Returns:
NumPy array containing the result
"""
from cntk.context import get_new_context
from cntk.ops import input_numpy, constant
from cntk.graph import ComputationNode, _InputComputationNodeBase
import numpy as np
# call a helper method to get a context
with get_new_context() as ctx:
ctx.clean_up = clean_up
first = True
# The params are passed as arryas, e.g. plus([1,2], [3,4]), and we need to
# wrap them with input and parameter nodes.
if node.params:
for p in node.params:
if p in node.inputs:
val = getattr(node, p)
if not isinstance(val, ComputationNode):
# One param needs to be an Input() node. This will be fixed in
# CNTK soon, so that we can remove this workaround and evaluate a
# network with no inputs.
if first:
ir = input_numpy([val], alias=p, name=p)
setattr(node, p, ir)
first = False
else:
setattr(node, p, constant(getattr(node, p), name=p))
else:
if isinstance(val, _InputComputationNodeBase) and first:
first = False
return ctx.eval(node)
def _generate_eval_config(self,
root_nodes,
input_map=None,
node_unit_test=False,
action_name=None):
'''
Generates the configuration file for write action.
Args:
root_nodes (:class:`cntk.graph.ComputationNode` or list thereof): node(s) to start the graph generation from (most likely evaluation and criterion nodes)
node (:class:`cntk.graph.ComputationNode`): the node to evaluate
input_map (:class:`cntk.reader.InputMap`): describes how to map inputs to the data in a data file using a reader
node_unit_test (bool): set to `True` if you want to output the gradient of a node (backward pass)
action_name (str): the name of the action in cntk configuration file
Returns:
configuration string
'''
if input_map is None:
input_map = InputMap()
description, inputs = self._generate_config(root_nodes, input_map)
if len(inputs) == 0:
# add dummy input to keep CNTK happy
# TODO relieve this requirement on CNTK side
#import ipdb;ipdb.set_trace()
from cntk.ops import input_numpy
dummy_input = input_numpy([[[1]]])
dummy_input.name = '_dummy_input'
input_map._add_unmapped(dummy_input)
desc, _inputs = dummy_input._to_config_description(input_map)
description += '\n\n' + desc
g_params = self._generate_global_params(DevideId=self.device_id,
Precision='"{0}"'.format(
self.precision))
tmpl = open(CNTK_EVAL_TEMPLATE_PATH, "r").read()
tmpl_dict = {
'ActionName': action_name,
'NodeUnitTest': node_unit_test,
'OutputFile': self.output_filename_base,
'ModelDescription': description,
'Reader': input_map._to_config_description(self.directory),
}
return "{0}\n{1}".format(g_params, tmpl % tmpl_dict)
def _generate_eval_config(self, root_nodes, input_map=None,
node_unit_test=False, action_name=None):
'''
Generates the configuration file for write action.
Args:
root_nodes (:class:`cntk.graph.ComputationNode` or list thereof): node(s) to start the graph generation from (most likely evaluation and criterion nodes)
node (:class:`cntk.graph.ComputationNode`): the node to evaluate
input_map (:class:`cntk.reader.InputMap`): describes how to map inputs to the data in a data file using a reader
node_unit_test (bool): set to `True` if you want to output the gradient of a node (backward pass)
action_name (str): the name of the action in cntk configuration file
Returns:
configuration string
'''
if input_map is None:
input_map = InputMap()
description, inputs = self._generate_config(root_nodes, input_map)
if len(inputs) == 0:
# add dummy input to keep CNTK happy
# TODO relieve this requirement on CNTK side
#import ipdb;ipdb.set_trace()
from cntk.ops import input_numpy
dummy_input = input_numpy([[[1]]])
dummy_input.name='_dummy_input'
input_map._add_unmapped(dummy_input)
desc, _inputs = dummy_input._to_config_description(input_map)
description += '\n\n' + desc
g_params = self._generate_global_params(DevideId=self.device_id,
Precision='"{0}"'.format(self.precision))
tmpl = open(CNTK_EVAL_TEMPLATE_PATH, "r").read()
tmpl_dict = {
'ActionName': action_name,
'NodeUnitTest': node_unit_test,
'OutputFile': self.output_filename_base,
'ModelDescription': description,
'Reader': input_map._to_config_description(self.directory),
}
return "{0}\n{1}".format(g_params, tmpl % tmpl_dict)
def _generate_eval_config(self, root_nodes, input_map=None, node_unit_test=False, action_name=None):
"""
Generates the configuration file for write action.
Args:
root_nodes (list): the list of root nodes of the model
node (:class:`cntk.graph.ComputationNode`): the node to evaluate
input_map (:class:`cntk.reader.InputMap`): describes how to map inputs to the data in a data file using a reader
node_unit_test (bool): set to True if you want to output the gradient of a node (backward pass)
action_name (str): the name of the action in cntk configuration file
Returns:
configuration string
"""
if input_map is None:
input_map = InputMap()
description, inputs = self._generate_config(root_nodes, input_map)
if len(inputs) == 0:
# add dummy input to keep CNTK happy
# TODO relieve this requirement on CNTK side
# import ipdb;ipdb.set_trace()
from cntk.ops import input_numpy
dummy_input = input_numpy([[[1]]])
dummy_input.name = "_dummy_input"
input_map._add_unmapped(dummy_input)
desc, _inputs = dummy_input._to_config_description(input_map)
description += "\n\n" + desc
tmpl = open(CNTK_EVAL_TEMPLATE_PATH, "r").read()
tmpl_dict = {
"ActionName": action_name,
"DevideId": self.device_id,
"Precision": self.precision,
"NodeUnitTest": node_unit_test,
"OutputFile": self.output_filename_base,
"ModelDescription": description,
"Reader": input_map._to_config_description(),
}
return tmpl % tmpl_dict
