def call(self, x, mask=None):
activ = MaxPooling2D.call(self, x)
updates = self.update_spikevars(activ)
with tf.control_dependencies(updates):
return activ + 0
def call(self, x, mask=None):
"""Layer functionality."""
# print("WARNING: Rate-based spiking MaxPooling layer is not implemented "
# "in TensorFlow backend. Falling back on AveragePooling. Switch "
# "to Theano backend to use MaxPooling.")
# return k.pool2d(x, self.pool_size, self.strides, self.padding,
# pool_mode='avg')
return MaxPooling2D.call(self, x)
def call(self, x, mask=None):
"""Layer functionality."""
# Skip integration of input spikes in membrane potential. Directly
# transmit new spikes. The output psp is nonzero wherever there has
# been an input spike at any time during simulation.
input_psp = MaxPooling2D.call(self, x)
if self.spiketrain is not None:
new_spikes = tf.logical_xor(k.greater(input_psp, 0),
k.greater(self.last_spiketimes, 0))
self.add_update([(self.spiketrain,
self.time * k.cast(new_spikes, k.floatx()))])
psp = self.get_psp(input_psp)
return k.cast(psp, k.floatx())
def call(self, x, mask=None):
"""Layer functionality."""
return MaxPooling2D.call(self, x)