def register_conv2d(self, params, strides, padding, inputs, outputs,
approx=APPROX_KRONECKER_NAME):
if approx == APPROX_KRONECKER_NAME:
self.register_block(params,
fb.ConvKFCBasicFB(self, params, inputs, outputs,
strides, padding))
elif approx == APPROX_DIAGONAL_NAME:
self.register_block(params,
fb.ConvDiagonalFB(self, params, inputs, outputs,
strides, padding))
def runFisherBlockOps(self, params, inputs, outputs, output_grads):
"""Run Ops guaranteed by FisherBlock interface.
Args:
params: Tensor or 2-tuple of Tensors. Represents weights or weights and
bias of this layer.
inputs: list of Tensors of shape [batch_size, input_size]. Inputs to
layer.
outputs: list of Tensors of shape [batch_size, output_size].
Preactivations produced by layer.
output_grads: list of Tensors of shape [batch_size, output_size].
Gradient of loss with respect to 'outputs'.
Returns:
multiply_result: Result of FisherBlock.multiply(params)
multiply_inverse_result: Result of FisherBlock.multiply_inverse(params)
"""
with ops.Graph().as_default(), self.test_session() as sess:
inputs = as_tensors(inputs)
outputs = as_tensors(outputs)
output_grads = as_tensors(output_grads)
params = as_tensors(params)
block = fb.ConvDiagonalFB(lc.LayerCollection(),
params,
strides=[1, 1, 1, 1],
padding='SAME')
for (i, o) in zip(inputs, outputs):
block.register_additional_tower(i, o)
block.instantiate_factors((output_grads, ), damping=0.0)
block._factor.instantiate_cov_variables()
sess.run(tf_variables.global_variables_initializer())
sess.run(block._factor.make_covariance_update_op(0.0))
multiply_result = sess.run(block.multiply(params))
multiply_inverse_result = sess.run(block.multiply_inverse(params))
return multiply_result, multiply_inverse_result
