from ..layers.avoidfreeze import AvoidFreeze
import numpy as np
class Metric(AvoidFreeze):
	def __init__(self, node, params=None):
		if params is None:
			params = {}
			
		params['compute_backward'] = False	
		params['same_input_shape'] = True
		params['compute_forward_in_prediction'] = False
		# No produce dependencias ya que no participa en el forward (porque es un nodo final) ni participa en el backward porque es una metrica.
		super(Metric, self).__init__(node, params=params)

	def computeSize(self):
		return tuple([1])

	def forward(self, inputs):
		super(Metric, self).forward(inputs)

	def derivatives(self, doutput=None):
		pass
3e5b6df65e027dbcf5c6700593ad00c080ab62fa
<mask token>


class Metric(AvoidFreeze):

    def __init__(self, node, params=None):
        if params is None:
            params = {}
        params['compute_backward'] = False
        params['same_input_shape'] = True
        params['compute_forward_in_prediction'] = False
        super(Metric, self).__init__(node, params=params)
    <mask token>

    def forward(self, inputs):
        super(Metric, self).forward(inputs)
    <mask token>

<mask token>


class Metric(AvoidFreeze):

    def __init__(self, node, params=None):
        if params is None:
            params = {}
        params['compute_backward'] = False
        params['same_input_shape'] = True
        params['compute_forward_in_prediction'] = False
        super(Metric, self).__init__(node, params=params)

    def computeSize(self):
        return tuple([1])

    def forward(self, inputs):
        super(Metric, self).forward(inputs)
    <mask token>

<mask token>


class Metric(AvoidFreeze):

    def __init__(self, node, params=None):
        if params is None:
            params = {}
        params['compute_backward'] = False
        params['same_input_shape'] = True
        params['compute_forward_in_prediction'] = False
        super(Metric, self).__init__(node, params=params)

    def computeSize(self):
        return tuple([1])

    def forward(self, inputs):
        super(Metric, self).forward(inputs)

    def derivatives(self, doutput=None):
        pass

from ..layers.avoidfreeze import AvoidFreeze
import numpy as np


class Metric(AvoidFreeze):

    def __init__(self, node, params=None):
        if params is None:
            params = {}
        params['compute_backward'] = False
        params['same_input_shape'] = True
        params['compute_forward_in_prediction'] = False
        super(Metric, self).__init__(node, params=params)

    def computeSize(self):
        return tuple([1])

    def forward(self, inputs):
        super(Metric, self).forward(inputs)

    def derivatives(self, doutput=None):
        pass

from ..layers.avoidfreeze import AvoidFreeze
import numpy as np
class Metric(AvoidFreeze):
	def __init__(self, node, params=None):
		if params is None:
			params = {}
			
		params['compute_backward'] = False	
		params['same_input_shape'] = True
		params['compute_forward_in_prediction'] = False
		# No produce dependencias ya que no participa en el forward (porque es un nodo final) ni participa en el backward porque es una metrica.
		super(Metric, self).__init__(node, params=params)

	def computeSize(self):
		return tuple([1])

	def forward(self, inputs):
		super(Metric, self).forward(inputs)

	def derivatives(self, doutput=None):
		pass