def fprop(self, input):
# we reduce the precision of parameters for the computations
self.w_comp = apply_format(self.format, self.W, self.comp_precision, self.w_range)
self.b_comp = apply_format(self.format, self.b, self.comp_precision, self.b_range)
input = input.reshape(self.image_shape)
# convolution
input_shuffled = input.dimshuffle(1, 2, 3, 0) # bc01 to c01b
filters_shuffled = self.w_comp.dimshuffle(1, 2, 3, 0) *self.scale # bc01 to c01b
conv_op = FilterActs(stride=self.filter_stride, partial_sum=self.partial_sum,pad = self.zero_pad)
contiguous_input = gpu_contiguous(input_shuffled)
contiguous_filters = gpu_contiguous(filters_shuffled)
conv_out_shuffled = conv_op(contiguous_input, contiguous_filters)
# downsample each feature map individually, using maxpooling
# pooled_out = downsample.max_pool_2d(input=conv_out,
# ds=poolsize, ignore_border=True)
pool_op = MaxPool(ds=self.pool_shape, stride=self.pool_stride)
pooled_out_shuffled = pool_op(conv_out_shuffled)
pooled_out = pooled_out_shuffled.dimshuffle(3, 0, 1, 2) # c01b to bc01
# bias
pooled_out = apply_format(self.format, pooled_out + self.b_comp.dimshuffle('x', 0, 'x', 'x')*self.scale, self.comp_precision, self.z_range)
# activation
pooled_out = self.activation(pooled_out)
pooled_out = apply_format(self.format, pooled_out.flatten(2), self.comp_precision, self.y_range)
return pooled_out
def dropout_fprop(self, input):
# we reduce the precision of parameters for the computations
self.fixed_W = apply_format(self.format, self.W, self.comp_precision,
self.w_range)
self.fixed_b = apply_format(self.format, self.b, self.comp_precision,
self.b_range)
# create the dropout mask
# The cast is important because
# int * float32 = float64 which pulls things off the gpu
srng = T.shared_randomstreams.RandomStreams(self.rng.randint(999999))
self.mask = T.cast(srng.binomial(n=1, p=self.p, size=T.shape(input)),
theano.config.floatX)
# apply the mask
self.fixed_x = input * self.mask
# weighted sum
self.z = T.dot(self.fixed_x, self.fixed_W) + self.fixed_b
self.fixed_z = apply_format(self.format, self.z, self.comp_precision,
self.z_range)
# activation
self.y = self.activation(self.fixed_z)
self.fixed_y = apply_format(self.format, self.y, self.comp_precision,
self.y_range)
# return the output
return self.fixed_y
def parameter_updates(self, LR, M):
# compute updates
new_update_W = apply_format(self.format, M * self.update_W - LR * self.w_LR_scale * self.fixed_dEdW, self.comp_precision, self.update_w_range)
new_update_b = apply_format(self.format, M * self.update_b - LR * self.b_LR_scale * self.fixed_dEdb, self.comp_precision, self.update_b_range)
# compute new parameters. Note that we use a better precision than the other operations
new_W = apply_format(self.format, self.W + new_update_W, self.update_precision, self.w_range)
new_b = apply_format(self.format, self.b + new_update_b, self.update_precision, self.b_range)
# L2 column constraint on W
col_norms = T.sqrt(T.sum(T.sqr(new_W), axis=0))
# col_norms = T.max(new_W, axis=0)
desired_norms = T.clip(col_norms, 0, self.max_col_norm) # clip = saturate below min and beyond max
new_W = apply_format(self.format, new_W * (desired_norms / (1e-7 + col_norms)), self.update_precision, self.w_range)
# for some reason, works better than
# new_W = new_W * (desired_norms / col_norms)
# It may be a kind of regularization
# return the updates of shared variables
updates = []
updates.append((self.W, new_W))
updates.append((self.b, new_b))
updates.append((self.update_W, new_update_W))
updates.append((self.update_b, new_update_b))
return updates
def bprop(self, dEdy):
self.fixed_dEdy = apply_format(self.format, dEdy, self.comp_precision,
self.dEdy_range)
# activation
self.activation_bprop()
# compute gradients of parameters
self.fixed_dEdW = apply_format(
self.format,
T.grad(cost=None,
wrt=[self.fixed_W],
known_grads={self.z: self.fixed_dEdz})[0],
self.comp_precision, self.dEdw_range)
self.fixed_dEdb = apply_format(
self.format,
T.grad(cost=None,
wrt=[self.fixed_b],
known_grads={self.z: self.fixed_dEdz})[0],
self.comp_precision, self.dEdb_range)
# weighted sum
dEdx = T.grad(cost=None,
wrt=[self.fixed_x],
known_grads={self.z: self.fixed_dEdz})[0]
# apply mask
dEdx = self.mask * dEdx
return dEdx
def dropout_fprop(self, input):
# we reduce the precision of parameters for the computations
self.fixed_W = apply_format(self.format, self.W, self.comp_precision, self.w_range)
self.fixed_b = apply_format(self.format, self.b, self.comp_precision, self.b_range)
# create the dropout mask
# The cast is important because
# int * float32 = float64 which pulls things off the gpu
srng = T.shared_randomstreams.RandomStreams(self.rng.randint(999999))
self.mask = T.cast(srng.binomial(n=1, p=self.p, size=T.shape(input)), theano.config.floatX)
# apply the mask
self.fixed_x = input * self.mask
# weighted sum
self.z = T.dot(self.fixed_x, self.fixed_W) + self.fixed_b
self.fixed_z = apply_format(self.format, self.z, self.comp_precision, self.z_range)
# activation
self.y = self.activation(self.fixed_z)
self.fixed_y = apply_format(self.format, self.y, self.comp_precision, self.y_range)
# return the output
return self.fixed_y
def dropout_fprop(self, input):
# we reduce the precision of parameters for the computations
self.fixed_W = apply_format(self.format, self.W, self.comp_precision,
self.w_range)
self.fixed_b = apply_format(self.format, self.b, self.comp_precision,
self.b_range)
# create the dropout mask
# The cast is important because
# int * float32 = float64 which pulls things off the gpu
srng = T.shared_randomstreams.RandomStreams(self.rng.randint(999999))
self.mask = T.cast(srng.binomial(n=1, p=self.p, size=T.shape(input)),
theano.config.floatX)
input = input * self.mask
self.fixed_x = input.reshape(self.image_shape)
# convolution
input_shuffled = self.fixed_x.dimshuffle(1, 2, 3, 0) # bc01 to c01b
filters_shuffled = self.fixed_W.dimshuffle(1, 2, 3, 0) # bc01 to c01b
conv_op = FilterActs(
stride=self.filter_stride,
partial_sum=self.partial_sum,
pad=self.zero_pad
) # augment partial sum -> use less memory but slower
contiguous_input = gpu_contiguous(input_shuffled)
contiguous_filters = gpu_contiguous(filters_shuffled)
conv_out_shuffled = conv_op(contiguous_input, contiguous_filters)
self.z = conv_out_shuffled.dimshuffle(3, 0, 1, 2) # c01b to bc01
self.fixed_z = apply_format(self.format, self.z, self.comp_precision,
self.z_range)
conv_out_shuffled = self.fixed_z.dimshuffle(1, 2, 3, 0) # bc01 to c01b
conv_out_shuffled = gpu_contiguous(conv_out_shuffled)
# downsample each feature map individually, using maxpooling
# pooled_out = downsample.max_pool_2d(input=conv_out,
# ds=poolsize, ignore_border=True)
pool_op = MaxPool(ds=self.pool_shape, stride=self.pool_stride)
pooled_out_shuffled = pool_op(conv_out_shuffled)
pooled_out = pooled_out_shuffled.dimshuffle(3, 0, 1, 2) # c01b to bc01
# bias
self.u = pooled_out + self.fixed_b.dimshuffle('x', 0, 'x', 'x')
self.fixed_u = apply_format(self.format, self.u, self.comp_precision,
self.z_range)
# activation
self.y = self.activation(self.fixed_u).flatten(2)
self.fixed_y = apply_format(self.format, self.y, self.comp_precision,
self.y_range)
return self.fixed_y
def fprop(self, input):
# we reduce the precision of parameters for the computations
self.w_comp = apply_format(self.format, self.W, self.comp_precision, self.w_range)
self.b_comp = apply_format(self.format, self.b, self.comp_precision, self.b_range)
# scaled weighted sum
self.z = apply_format(self.format, T.dot(input, self.w_comp * self.scale) + self.b_comp*self.scale, self.comp_precision, self.z_range)
# activation
self.y = apply_format(self.format, self.activation(self.z), self.comp_precision, self.y_range)
# return the output
return self.y
def bprop(self, dEdy):
self.fixed_dEdy = apply_format(self.format, dEdy.reshape(self.output_shape), self.comp_precision, self.dEdy_range)
fixed_dEdu = apply_format(self.format, T.grad(cost = None, wrt=[self.fixed_u], known_grads={self.y:self.fixed_dEdy})[0], self.comp_precision,self.dEdz_range)
self.fixed_dEdb = apply_format(self.format, T.grad(cost = None, wrt=[self.fixed_b], known_grads={self.u:fixed_dEdu})[0], self.comp_precision,self.dEdb_range)
self.fixed_dEdz = apply_format(self.format, T.grad(cost = None, wrt=[self.fixed_z], known_grads={self.u:fixed_dEdu})[0], self.comp_precision, self.dEdz_range)
self.fixed_dEdW = apply_format(self.format, T.grad(cost = None, wrt=[self.fixed_W], known_grads={self.z:self.fixed_dEdz})[0], self.comp_precision,self.dEdw_range)
dEdx = T.grad(cost = None, wrt=[self.fixed_x], known_grads={self.z:self.fixed_dEdz})[0]
dEdx = T.reshape(self.mask,T.shape(dEdx)) * dEdx
return dEdx
def dropout_fprop(self, input):
# we reduce the precision of parameters for the computations
self.fixed_W = apply_format(self.format, self.W, self.comp_precision, self.w_range)
self.fixed_b = apply_format(self.format, self.b, self.comp_precision, self.b_range)
# create the dropout mask
# The cast is important because
# int * float32 = float64 which pulls things off the gpu
srng = T.shared_randomstreams.RandomStreams(self.rng.randint(999999))
self.mask = T.cast(srng.binomial(n=1, p=self.p, size=T.shape(input)), theano.config.floatX)
input = input * self.mask
self.fixed_x = input.reshape(self.image_shape)
# convolution
input_shuffled = self.fixed_x.dimshuffle(1, 2, 3, 0) # bc01 to c01b
filters_shuffled = self.fixed_W.dimshuffle(1, 2, 3, 0) # bc01 to c01b
conv_op = FilterActs(stride=self.filter_stride, partial_sum=self.partial_sum,pad = self.zero_pad) # augment partial sum -> use less memory but slower
contiguous_input = gpu_contiguous(input_shuffled)
contiguous_filters = gpu_contiguous(filters_shuffled)
conv_out_shuffled = conv_op(contiguous_input, contiguous_filters)
self.z = conv_out_shuffled.dimshuffle(3, 0, 1, 2) # c01b to bc01
self.fixed_z = apply_format(self.format, self.z, self.comp_precision, self.z_range)
conv_out_shuffled = self.fixed_z.dimshuffle(1, 2, 3, 0) # bc01 to c01b
conv_out_shuffled = gpu_contiguous(conv_out_shuffled)
# downsample each feature map individually, using maxpooling
# pooled_out = downsample.max_pool_2d(input=conv_out,
# ds=poolsize, ignore_border=True)
pool_op = MaxPool(ds=self.pool_shape, stride=self.pool_stride)
pooled_out_shuffled = pool_op(conv_out_shuffled)
pooled_out = pooled_out_shuffled.dimshuffle(3, 0, 1, 2) # c01b to bc01
# bias
self.u = pooled_out + self.fixed_b.dimshuffle('x', 0, 'x', 'x')
self.fixed_u = apply_format(self.format, self.u, self.comp_precision, self.z_range)
# activation
self.y = self.activation(self.fixed_u).flatten(2)
self.fixed_y = apply_format(self.format, self.y, self.comp_precision, self.y_range)
return self.fixed_y
def activation_bprop(self):
self.fixed_dEdz = apply_format(
self.format,
T.grad(cost=None,
wrt=[self.fixed_z],
known_grads={self.y: self.fixed_dEdy})[0],
self.comp_precision, self.dEdz_range)
def bprop(self, dEdy):
self.fixed_dEdy = apply_format(self.format, dEdy, self.comp_precision, self.dEdy_range)
# activation
self.activation_bprop()
# compute gradients of parameters
self.fixed_dEdW = apply_format(self.format, T.grad(cost = None, wrt=[self.fixed_W], known_grads={self.z:self.fixed_dEdz})[0], self.comp_precision, self.dEdw_range)
self.fixed_dEdb = apply_format(self.format, T.grad(cost = None, wrt=[self.fixed_b], known_grads={self.z:self.fixed_dEdz})[0], self.comp_precision, self.dEdb_range)
# weighted sum
dEdx = T.grad(cost = None, wrt=[self.fixed_x], known_grads={self.z:self.fixed_dEdz})[0]
# apply mask
dEdx = self.mask * dEdx
return dEdx
def fprop(self, input):
# we reduce the precision of parameters for the computations
self.w_comp = apply_format(self.format, self.W, self.comp_precision,
self.w_range)
self.b_comp = apply_format(self.format, self.b, self.comp_precision,
self.b_range)
# scaled weighted sum
self.z = apply_format(
self.format,
T.dot(input, self.w_comp * self.scale) + self.b_comp * self.scale,
self.comp_precision, self.z_range)
# activation
self.y = apply_format(self.format, self.activation(self.z),
self.comp_precision, self.y_range)
# return the output
return self.y
def bprop(self, dEdy):
self.fixed_dEdy = apply_format(self.format,
dEdy.reshape(self.output_shape),
self.comp_precision, self.dEdy_range)
fixed_dEdu = apply_format(
self.format,
T.grad(cost=None,
wrt=[self.fixed_u],
known_grads={self.y: self.fixed_dEdy})[0],
self.comp_precision, self.dEdz_range)
self.fixed_dEdb = apply_format(
self.format,
T.grad(cost=None,
wrt=[self.fixed_b],
known_grads={self.u: fixed_dEdu})[0], self.comp_precision,
self.dEdb_range)
self.fixed_dEdz = apply_format(
self.format,
T.grad(cost=None,
wrt=[self.fixed_z],
known_grads={self.u: fixed_dEdu})[0], self.comp_precision,
self.dEdz_range)
self.fixed_dEdW = apply_format(
self.format,
T.grad(cost=None,
wrt=[self.fixed_W],
known_grads={self.z: self.fixed_dEdz})[0],
self.comp_precision, self.dEdw_range)
dEdx = T.grad(cost=None,
wrt=[self.fixed_x],
known_grads={self.z: self.fixed_dEdz})[0]
dEdx = T.reshape(self.mask, T.shape(dEdx)) * dEdx
return dEdx
def fprop(self, input):
# we reduce the precision of parameters for the computations
self.w_comp = apply_format(self.format, self.W, self.comp_precision,
self.w_range)
self.b_comp = apply_format(self.format, self.b, self.comp_precision,
self.b_range)
input = input.reshape(self.image_shape)
# convolution
input_shuffled = input.dimshuffle(1, 2, 3, 0) # bc01 to c01b
filters_shuffled = self.w_comp.dimshuffle(
1, 2, 3, 0) * self.scale # bc01 to c01b
conv_op = FilterActs(stride=self.filter_stride,
partial_sum=self.partial_sum,
pad=self.zero_pad)
contiguous_input = gpu_contiguous(input_shuffled)
contiguous_filters = gpu_contiguous(filters_shuffled)
conv_out_shuffled = conv_op(contiguous_input, contiguous_filters)
# downsample each feature map individually, using maxpooling
# pooled_out = downsample.max_pool_2d(input=conv_out,
# ds=poolsize, ignore_border=True)
pool_op = MaxPool(ds=self.pool_shape, stride=self.pool_stride)
pooled_out_shuffled = pool_op(conv_out_shuffled)
pooled_out = pooled_out_shuffled.dimshuffle(3, 0, 1, 2) # c01b to bc01
# bias
pooled_out = apply_format(
self.format,
pooled_out + self.b_comp.dimshuffle('x', 0, 'x', 'x') * self.scale,
self.comp_precision, self.z_range)
# activation
pooled_out = self.activation(pooled_out)
pooled_out = apply_format(self.format, pooled_out.flatten(2),
self.comp_precision, self.y_range)
return pooled_out
def parameter_updates(self, LR, M):
# compute updates
new_update_W = apply_format(
self.format,
M * self.update_W - LR * self.w_LR_scale * self.fixed_dEdW,
self.comp_precision, self.update_w_range)
new_update_b = apply_format(
self.format,
M * self.update_b - LR * self.b_LR_scale * self.fixed_dEdb,
self.comp_precision, self.update_b_range)
# compute new parameters. Note that we use a better precision than the other operations
new_W = apply_format(self.format, self.W + new_update_W,
self.update_precision, self.w_range)
new_b = apply_format(self.format, self.b + new_update_b,
self.update_precision, self.b_range)
# L2 column constraint on W
col_norms = T.sqrt(T.sum(T.sqr(new_W), axis=0))
# col_norms = T.max(new_W, axis=0)
desired_norms = T.clip(
col_norms, 0,
self.max_col_norm) # clip = saturate below min and beyond max
new_W = apply_format(self.format,
new_W * (desired_norms / (1e-7 + col_norms)),
self.update_precision, self.w_range)
# for some reason, works better than
# new_W = new_W * (desired_norms / col_norms)
# It may be a kind of regularization
# return the updates of shared variables
updates = []
updates.append((self.W, new_W))
updates.append((self.b, new_b))
updates.append((self.update_W, new_update_W))
updates.append((self.update_b, new_update_b))
return updates
def activation_bprop(self):
self.fixed_dEdz = apply_format(self.format,
T.grad(cost = None, wrt=[self.fixed_z], known_grads={self.y:self.fixed_dEdy})[0],
self.comp_precision, self.dEdz_range)
def activation_bprop(self):
self.fixed_dEdz = apply_format(self.format, self.fixed_dEdy,
self.comp_precision, self.dEdz_range)
def activation_bprop(self):
self.fixed_dEdz = apply_format(self.format, self.fixed_dEdy,
self.comp_precision, self.dEdz_range)
