def create_weights(self):
self.log_network()
if self.next_layer:
self.w_next = gpu.array(
create_uniform_rdm_weight(self.unitcount,
self.next_layer.unitcount))
self.b_next = gpu.zeros((1, self.next_layer.unitcount))
self.m_next = gpu.zeros(
(self.unitcount, self.next_layer.unitcount))
self.w_grad_next = gpu.zeros(
(self.unitcount, self.next_layer.unitcount))
self.b_grad_next = gpu.zeros((1, self.next_layer.unitcount))
self.w_next_sync = gpu.zeros(
(self.unitcount, self.next_layer.unitcount))
if self.next_layer.config['compression'] == '1bit':
self.errors = gpu.zeros_like(self.w_grad_next)
self.posMask = gpu.zeros_like(self.w_grad_next)
self.negMask = gpu.zeros_like(self.w_grad_next)
self.w_grad_with_errors = gpu.zeros_like(self.w_grad_next)
self.posCount = gpu.zeros((self.w_grad_next.shape_tensor[2], ))
self.negCount = gpu.zeros((self.w_grad_next.shape_tensor[2], ))
self.posAvg = gpu.zeros((self.w_grad_next.shape_tensor[2], ))
self.negAvg = gpu.zeros((self.w_grad_next.shape_tensor[2], ))
if self.next_layer.config['compression'] == '8bit':
self.max_value_buffer = gpu.empty_like(self.w_grad_next)
if self.next_layer: self.next_layer.create_weights()
def handle_offsize(self, batch_size):
if self.activation_offsize == None:
split_axis = (2 if self.config['parallelism'] == 'data' else -1)
self.activation_offsize = gpu.empty((batch_size, self.unitcount),
split_axis)
self.out_offsize = gpu.empty((batch_size, self.unitcount),
split_axis)
self.error_offsize = gpu.empty((batch_size, self.unitcount),
split_axis)
self.bias_ones_offsize = gpu.zeros((batch_size, 1), split_axis) + 1
swap(self.activation, self.activation_offsize)
swap(self.out, self.out_offsize)
swap(self.error, self.error_offsize)
swap(self.bias_ones, self.bias_ones_offsize)
elif self.activation_offsize.shape[2] != batch_size:
del self.activation
del self.out
del self.error
del self.bias_ones
self.create_buffers(batch_size)
else:
swap(self.activation, self.activation_offsize)
swap(self.out, self.out_offsize)
swap(self.error, self.error_offsize)
swap(self.bias_ones, self.bias_ones_offsize)
def create_weights(self):
self.log_network()
if self.next_layer:
self.w_next = gpu.array(create_uniform_rdm_weight(self.unitcount,self.next_layer.unitcount))
self.b_next = gpu.zeros((1, self.next_layer.unitcount))
self.m_next = gpu.zeros((self.unitcount, self.next_layer.unitcount))
self.w_grad_next = gpu.zeros((self.unitcount, self.next_layer.unitcount))
self.b_grad_next = gpu.zeros((1, self.next_layer.unitcount))
self.w_next_sync = gpu.zeros((self.unitcount,self.next_layer.unitcount))
if self.next_layer.config['compression'] == '1bit':
self.errors = gpu.zeros_like(self.w_grad_next)
self.posMask = gpu.zeros_like(self.w_grad_next)
self.negMask = gpu.zeros_like(self.w_grad_next)
self.w_grad_with_errors = gpu.zeros_like(self.w_grad_next)
self.posCount = gpu.zeros((self.w_grad_next.shape_tensor[2],))
self.negCount = gpu.zeros((self.w_grad_next.shape_tensor[2],))
self.posAvg = gpu.zeros((self.w_grad_next.shape_tensor[2],))
self.negAvg = gpu.zeros((self.w_grad_next.shape_tensor[2],))
if self.next_layer.config['compression'] == '8bit':
self.max_value_buffer = gpu.empty_like(self.w_grad_next)
if self.next_layer: self.next_layer.create_weights()
def handle_offsize(self, batch_size):
if self.activation_offsize == None:
split_axis = (2 if self.config['parallelism'] == 'data' else -1)
self.activation_offsize = gpu.empty((batch_size,self.unitcount),split_axis)
self.out_offsize = gpu.empty((batch_size,self.unitcount),split_axis)
self.error_offsize = gpu.empty((batch_size,self.unitcount),split_axis)
self.bias_ones_offsize = gpu.zeros((batch_size,1),split_axis)+1
swap(self.activation, self.activation_offsize)
swap(self.out, self.out_offsize)
swap(self.error, self.error_offsize)
swap(self.bias_ones, self.bias_ones_offsize)
elif self.activation_offsize.shape[2] != batch_size:
del self.activation
del self.out
del self.error
del self.bias_ones
self.create_buffers(batch_size)
else:
swap(self.activation, self.activation_offsize)
swap(self.out, self.out_offsize)
swap(self.error, self.error_offsize)
swap(self.bias_ones, self.bias_ones_offsize)