def fprop(self, input, output, train=TRAIN):
#util.log_info("%s %s", input.shape, output.shape)
gpu_copy_to(input, output)
if PFout:
#if True:
print_matrix(output, self.name)
def fprop(self, input, output, train=TRAIN):
#util.log_info("%s %s", input.shape, output.shape)
gpu_copy_to(input, output)
if PFout:
#if True:
print_matrix(output, self.name)
def fprop(self, input, output, train=TRAIN):
max = gpuarray.zeros((1, self.batch_size), dtype=np.float32)
col_max_reduce(max, input)
add_vec_to_cols(input, max, output, alpha=-1)
eltwise_exp(output)
sum = gpuarray.zeros(max.shape, dtype=np.float32)
add_col_sum_to_vec(sum, output, alpha=0)
div_vec_to_cols(output, sum)
if PFout:
print_matrix(output, self.name)
def fprop(self, input, output, train=TRAIN):
cudaconv2.localFilterActs(input, self.weight.wt, output, self.img_size, self.outputSize,
self.outputSize, -self.padding, self.stride, self.numColor, 1)
#util.log_info('%s', output.get().mean())
self.tmp = gpuarray.empty((self.numFilter,
self.get_single_img_size() * self.batch_size / self.numFilter),
dtype=np.float32)
add_vec_to_rows(output, self.bias.wt)
if PFout:
print_matrix(output, self.name)
def fprop(self, input, output, train=TRAIN):
cudaconv2.localFilterActs(input, self.weight.wt, output, self.img_size, self.outputSize,
self.outputSize, -self.padding, self.stride, self.numColor, 1)
#util.log_info('%s', output.get().mean())
self.tmp = gpuarray.empty((self.numFilter,
self.get_single_img_size() * self.batch_size / self.numFilter),
dtype=np.float32)
add_vec_to_rows(output, self.bias.wt)
if PFout:
print_matrix(output, self.name)
def fprop(self, input, output, train=TRAIN):
gpu_copy_to(dot(self.weight.wt, input), output)
add_vec_to_rows(output, self.bias.wt)
if train == TEST:
if self.dropRate > 0.0:
output *= (1.0 - self.dropRate)
else:
if self.dropRate > 0.0:
self.dropMask = to_gpu(np.random.uniform(0, 1, output.size).astype(np.float32).reshape(output.shape))
bigger_than_scaler(self.dropMask, self.dropRate)
gpu_copy_to(output * self.dropMask, output)
if PFout:
print_matrix(output, self.name)
def fprop(self, input, output, train=TRAIN):
gpu_copy_to(dot(self.weight.wt, input), output)
add_vec_to_rows(output, self.bias.wt)
if train == TEST:
if self.dropRate > 0.0:
output *= (1.0 - self.dropRate)
else:
if self.dropRate > 0.0:
self.dropMask = to_gpu(np.random.uniform(0, 1, output.size).astype(np.float32).reshape(output.shape))
bigger_than_scaler(self.dropMask, self.dropRate)
gpu_copy_to(output * self.dropMask, output)
if PFout:
print_matrix(output, self.name)
def test_cifar_loader():
data_dir = '/ssd/nn-data/cifar-10.old/'
dp = data.get_by_name('cifar10')(data_dir, [1])
batch_size = 128
data_list = []
for i in range(11000):
batch = dp.get_next_batch(batch_size)
batch = batch.data.get()
data_list.append(batch)
if batch.shape[1] != batch_size:
break
batch = np.concatenate(data_list, axis=1)
print_matrix(batch, 'batch')
def test_cifar_loader():
data_dir = '/ssd/nn-data/cifar-10.old/'
dp = data.get_by_name('cifar10')(data_dir, [1])
batch_size = 128
data_list = []
for i in range(11000):
batch = dp.get_next_batch(batch_size)
batch = batch.data.get()
data_list.append(batch)
if batch.shape[1] != batch_size:
break
batch = np.concatenate(data_list, axis = 1)
print_matrix(batch, 'batch')
def fprop(self, input, output, train=TRAIN):
self.neuron.activate(input, output)
if PFout:
print_matrix(output, self.name)
def fprop(self, input, output, train=TRAIN):
self.denom = gpuarray.zeros_like(input)
cudaconv2.convResponseNormCrossMap(input, self.denom, output, self.numColor, self.size, self.scaler, self.pow, self.blocked)
if PFout:
print_matrix(output, self.name)
def fprop(self, input, output, train=TRAIN):
cudaconv2.convLocalAvgPool(input, output, self.numColor, self.poolSize, self.start, self.stride,
self.outputSize)
if PFout:
print_matrix(output, self.name)
def fprop(self, input, output, train=TRAIN):
self.neuron.activate(input, output)
if PFout:
print_matrix(output, self.name)
def fprop(self, input, output, train=TRAIN):
self.denom = gpuarray.zeros_like(input)
cudaconv2.convResponseNormCrossMap(input, self.denom, output, self.numColor, self.size, self.scaler, self.pow, self.blocked)
if PFout:
print_matrix(output, self.name)
def fprop(self, input, output, train=TRAIN):
cudaconv2.convLocalAvgPool(input, output, self.numColor, self.poolSize, self.start, self.stride,
self.outputSize)
if PFout:
print_matrix(output, self.name)
