def test_bug_2009_06_02_trac_387():
y = tensor.lvector('y')
f = theano.function([y],
tensor.int_div(
tensor.DimShuffle(y[0].broadcastable, ['x'])(y[0]), 2))
sys.stdout.flush()
print(f(numpy.ones(1, dtype='int64') * 3))
def test_bug_2009_06_02_trac_387():
y = tensor.lvector('y')
f = theano.function([y],
tensor.int_div(
tensor.DimShuffle(y[0].broadcastable, ['x'])(y[0]),
2))
print(f(numpy.ones(1, dtype='int64') * 3))
def test_bug_2009_06_02_trac_387():
y = tensor.lvector("y")
f = theano.function([y],
tensor.int_div(
tensor.DimShuffle(y[0].broadcastable, ["x"])(y[0]),
2))
print(f(np.ones(1, dtype="int64") * 3))
def input_row_from_variables(ori_ip,dest_ip,ori_lat,ori_long,dest_lat,dest_long,ori_type,dest_type,dist):
'''Create an input row for the MLP from the inputs'''
input_row = tensor.zeros([input_size])
offset = 0
ips = [ori_ip,dest_ip]
for ip in ips:
for _ in range(4):
input_row = add_one_shot(input_row, offset, tensor.mod(ip,256))
ip = tensor.int_div(ip,256)
offset += 256
for lat_,long_ in [(ori_lat,ori_long),(dest_lat,dest_long)]:
translated_lat = tensor.iround((coordinate_size-1)*(lat_/180 + 0.5))
input_row = add_thermo(input_row, offset,translated_lat)
offset += coordinate_size
translated_long = tensor.iround((coordinate_size-1)*(long_/360 + 0.5))
input_row = add_thermo(input_row, offset,translated_long)
offset += coordinate_size
for type_ in [ori_type,dest_type]:
add_one_shot(input_row, offset, type_ +1)
offset += type_size
translated_dist = tensor.iround((dest_size-1)*(tensor.minimum(1,dist/max_earth_distance)))
input_row = add_thermo(input_row, offset,translated_dist)
#could be useful if we want to add something
offset +=dest_size
return input_row
def test_bug_2009_06_02_trac_387():
y = tensor.lvector('y')
#f = theano.function([y], tensor.stack(y[0] / 2))
#f = theano.function([y], tensor.join(0,tensor.shape_padleft(y[0] / 2,1)))
f = theano.function([y], tensor.int_div(tensor.DimShuffle(y[0].broadcastable, ['x'])(y[0]), 2))
sys.stdout.flush()
print f(numpy.ones(1, dtype='int64') * 3)
def define_loss_generator(net, generated_img, true_image, loss_type='log'):
assert (loss_type in ['log', 'sqr'])
input = T.concatenate([generated_img, true_image], axis=0)
output = lasagne.layers.get_output(net['out'], inputs=input)
batch_size = T.int_div(input.shape[0], 2)
if loss_type == 'sqr':
loss = (T.sqr(output[:batch_size] - 1)).mean()
else:
loss = -T.log(output[:batch_size] + eps).mean()
return loss
def train_givens(self, batch_index, batch_size):
'''
batch_index is a theano_variable.
'''
# compute the gpu batch index
# these will all be theano variables
solver_batches_per_gpu_batch = T.cast(T.int_div(self.num_GPU_store,batch_size), 'int32')
real_batch_index = T.cast(T.mod(batch_index, solver_batches_per_gpu_batch), 'int32')
givens = {self.X_batch_var:self.GPU_X_train[real_batch_index*batch_size:(real_batch_index+1)*batch_size]}
givens[self.y_batch_var] = self.GPU_y_train[real_batch_index*batch_size:(real_batch_index+1)*batch_size]
return givens
def define_loss_discriminator(net,
generated_img,
input_to_discriminator,
loss_type='log'):
assert (loss_type in ['log', 'sqr'])
input = T.concatenate([generated_img, input_to_discriminator], axis=0)
output = lasagne.layers.get_output(net['out'], inputs=input)
batch_size = T.int_div(input.shape[0], 2)
if loss_type == 'log':
true_loss = -T.log(output[batch_size:] + eps).mean()
generated_loss = -T.log(1 - output[:batch_size] + eps).mean()
else:
true_loss = (T.sqr(output[batch_size:] - 1)).mean()
generated_loss = (T.sqr(output[:batch_size])).mean()
return true_loss + generated_loss
def input_row_from_variables(ori_ip, dest_ip, ori_lat, ori_long, dest_lat,
dest_long, ori_type, dest_type, dist,
latency):
'''Create an input row for the MLP from the inputs'''
input_row = tensor.zeros([input_size])
offset = 0
ips = [ori_ip, dest_ip]
for ip in ips:
for _ in range(4):
input_row = add_one_shot(input_row, offset,
tensor.mod(ip, 256))
ip = tensor.int_div(ip, 256)
offset += 256
for lat_, long_ in [(ori_lat, ori_long), (dest_lat, dest_long)]:
translated_lat = tensor.iround(
(coordinate_size - 1) * (lat_ / 180 + 0.5))
input_row = add_thermo(input_row, offset, translated_lat)
offset += coordinate_size
translated_long = tensor.iround(
(coordinate_size - 1) * (long_ / 360 + 0.5))
input_row = add_thermo(input_row, offset, translated_long)
offset += coordinate_size
for type_ in [ori_type, dest_type]:
input_row = add_one_shot(input_row, offset, type_ + 1)
offset += type_size
translated_dist = tensor.iround(
(dist_size - 1) * (tensor.minimum(1, dist / max_earth_distance)))
input_row = add_thermo(input_row, offset, translated_dist)
offset += dist_size
translated_dist = tensor.iround(
(small_dist_size - 1) *
(tensor.minimum(1, dist / max_earth_distance)))
input_row = add_thermo(input_row, offset, translated_dist)
#could be useful if we want to add something
offset += small_dist_size
return input_row
def train_givens(self, batch_index, batch_size):
'''
batch_index is a theano_variable.
'''
# compute the gpu batch index
# these will all be theano variables
solver_batches_per_gpu_batch = T.cast(
T.int_div(self.num_GPU_store, batch_size), 'int32')
real_batch_index = T.cast(
T.mod(batch_index, solver_batches_per_gpu_batch), 'int32')
givens = {
self.X_batch_var:
self.GPU_X_train[real_batch_index *
batch_size:(real_batch_index + 1) * batch_size]
}
givens[self.y_batch_var] = self.GPU_y_train[real_batch_index *
batch_size:
(real_batch_index + 1) *
batch_size]
return givens
def input_row_from_variables(ip_, lat_, long_, type_):
'''Create an input row for the MLP from the inputs'''
input_row = tensor.zeros([input_size])
offset = 0
for _ in range(4):
input_row = add_one_shot(input_row, offset, tensor.mod(ip_, 256))
ip_ = tensor.int_div(ip_, 256)
offset += 256
translated_lat = tensor.iround((coordinate_size-1) * (lat_/180 + 0.5))
input_row = add_thermo(input_row, offset, translated_lat)
offset += coordinate_size
translated_long = tensor.iround((coordinate_size-1) * (long_/360 + 0.5))
input_row = add_thermo(input_row, offset, translated_long)
offset += coordinate_size
input_row = add_one_shot(input_row, offset, type_ +1)
offset += type_size
return input_row
def GetJacobi(i, B):
y, x = T.int_div(i, N), T.mod(i, N) #divmod(i,N)
B = T.set_subtensor(B[y, N + x], 1)
B2 = self(B)
B = T.set_subtensor(B[y, N + x], 0)
return B2[:N, N:].reshape((N * N, ))