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 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_buffers(self, batch_size):
self.activation = gpu.empty((batch_size,self.unitcount))
self.out = gpu.empty((batch_size,self.unitcount))
self.error = gpu.empty((batch_size,self.unitcount))
self.bias_ones = gpu.ones((batch_size,1))
def normal(mean, std, shape):
A = gpu.empty(shape)
randomize_gaussian(A, mean, std)
return A
def randn(d0, d1):
A = gpu.empty((d0, d1))
randomize_gaussian(A, 0, 1)
return A
def rand(d0, d1):
A = gpu.empty((d0, d1))
randomize_uniform(A)
return A
def create_buffers(self, batch_size):
self.activation = gpu.empty((batch_size, self.unitcount))
self.out = gpu.empty((batch_size, self.unitcount))
self.error = gpu.empty((batch_size, self.unitcount))
self.bias_ones = gpu.ones((batch_size, 1))
def normal(mean, std, shape):
A = gpu.empty(shape)
randomize_gaussian(A,mean,std)
return A
def randn(d0, d1):
A = gpu.empty((d0,d1))
randomize_gaussian(A,0,1)
return A
def rand(d0, d1):
A = gpu.empty((d0,d1))
randomize_uniform(A)
return A
