multicnet.py
def readout_act(self, stack):
        """
        Gets the best action
        for a given stack of images
        """
        stack = [stack] if hasattr(stack, 'shape') and len(stack.shape) == 3 else stack
        return self.y_conv.eval(feed_dict = {self.input_image: stack})

        Gets the best action
        for a given stack of images
        
Gets the best action
for a given stack of images
Gets the best action for a given stack of images
def readout_act(self, stack):
        stack = [stack] if hasattr(stack, 'shape') and len(stack.shape) == 3 else stack
        return self.y_conv.eval(feed_dict = {self.input_image: stack})
def readout_act ( self , stack ) : stack = [ stack ] if hasattr ( stack , 'shape' ) and len ( stack . shape ) == 3 else stack return self . y_conv . eval ( feed_dict = { self . input_image : stack } )
Gets the best action
for a given stack of images
Gets the best action for a given stack of images
"""
        Gets the best action
        for a given stack of images
        """
self
stack