def __call__(self, f_atoms, f_bonds, super_node_x,
atom_label, mask_reagents, mask_reactants_reagents, pair_label, mask_pair_select,
action, step_num,
stop_idx,
sample_index):
atom_label -= 1
mask_reagents -= 2
mask_reactants_reagents -= 2
action -= 1
batch_size = action.shape[0]
# atom
loss_atom, acc_atom, atoms_selected = self.g_atom(f_atoms, cp.copy(f_bonds), super_node_x, atom_label, mask_reagents, mask_reactants_reagents, batch_size)
# pair
loss_pair = self.g_pair(f_atoms, cp.copy(f_bonds), super_node_x, action, pair_label, mask_pair_select, batch_size, atoms_selected)
# action
loss_action, acc_action = self.g_action(f_atoms, cp.copy(f_bonds), super_node_x, action, batch_size)
# stop
loss_stop, acc_stop = self.g_stop(f_atoms, cp.copy(f_bonds), super_node_x, stop_idx, action, batch_size)
reporter.report({
'loss_stop': loss_stop,
'loss_atom': loss_atom,
'loss_pair': loss_pair,
'loss_action': loss_action,
'acc_stop': acc_stop,
'acc_atom': acc_atom,
# 'acc_pair': acc_pair, # acc_pair need to be further extended
'acc_action': acc_action,
}, self)
def HaversineLocal(busMatrix, lineMatrix, haversine=True):
MatrizOnibus = cp.copy(busMatrix)
MatrizLinhas = cp.copy(lineMatrix)
MatrizLinhas = cp.dsplit(MatrizLinhas, 2)
MatrizOnibus = cp.dsplit(MatrizOnibus, 2)
infVector = cp.squeeze(cp.sum(cp.isnan(MatrizLinhas[0]), axis=1), axis=-1)
MatrizLinhas[0] = cp.expand_dims(MatrizLinhas[0], axis=-1)
MatrizLinhas[1] = cp.expand_dims(MatrizLinhas[1], axis=-1)
MatrizOnibus[0] = cp.expand_dims(MatrizOnibus[0], axis=-1)
MatrizOnibus[1] = cp.expand_dims(MatrizOnibus[1], axis=-1)
MatrizOnibus[0] *= cp.pi / 180
MatrizOnibus[1] *= cp.pi / 180
MatrizLinhas[1] = cp.transpose(MatrizLinhas[1], [2, 3, 0, 1]) * cp.pi / 180
MatrizLinhas[0] = cp.transpose(MatrizLinhas[0], [2, 3, 0, 1]) * cp.pi / 180
# Haversine or euclidian, based on <haversine>
if haversine:
results = 1000*2*6371.0088*cp.arcsin(
cp.sqrt(
(cp.sin((MatrizOnibus[0] - MatrizLinhas[0])*0.5)**2 + \
cp.cos(MatrizOnibus[0])* cp.cos(MatrizLinhas[0]) * cp.sin((MatrizOnibus[1] - MatrizLinhas[1])*0.5)**2)
))
else:
results = cp.sqrt((MatrizOnibus[0] - MatrizLinhas[0])**2 +
(MatrizOnibus[1] - MatrizLinhas[1])**2)
return results, infVector
def exec_crossA0001(self) -> None:
"""exec_crossA0001.
:rtype: None
"""
x_population = cp.copy(self.get_population())
x_aux = cp.copy(self.get_population())
index_selection = int(self.get_n_samples() *
self.get_percent_selection())
index_cross = int(self.get_n_samples() * self.get_percent_cross())
cp.random.shuffle(x_population)
if index_cross % 2 == 0:
y_population = self._crossA0001(
x_population[index_selection:, :][0:index_cross, :],
self.get_n_jobs(),
self.get_n_operations(),
index_cross,
self.get_percent_intra_cross(),
)
x_aux[index_selection:, :][-index_cross:, :] = y_population
self.set_population(x_aux)
else:
index_cross -= 1
y_population = self._crossA0001(
x_population[index_selection:, :][0:index_cross, :],
self.get_n_jobs(),
self.get_n_operations(),
self.get_n_samples(),
self.get_percent_intra_cross(),
)
x_aux[index_selection:, :][-index_cross:, :] = y_population
self.set_population(x_aux)
def resetStocks():
"""
reset global variables
"""
global stockPool, hurstPool
stockPool = np.copy(config._stockPool)
hurstPool = np.copy(config._hurstPool)
def train(X, W1, W2, W3, pad=0):
# Conv 1
H1, cache1 = conv.conv_forward(X, W1, pad)
# ReLu 1
H1_relu = np.copy(H1)
H1_relu[H1 < 0] = 0
# cifar10.plotH(H1_relu[:,:,:4])
# Pool
for m in range(15):
for n in range(15):
x_slice = H1_relu[2*m:2*m+2, 2*n:2*n+2]
P1[m, n] = np.max(x_slice, axis=(0, 1))
# Conv 2
H2, cache2 = conv.conv_forward(P1, W2, pad)
# ReLu 2
H2_relu = np.copy(H2)
H2_relu[H2 < 0] = 0
# cifar10.plotH(H2_relu[:,:,:4])
# FC 1
x = H2_relu.flatten()
scores = x.dot(W3)
# Softmax
ex = np.exp(scores)
probs[sample] = ex/np.sum(ex, keepdims=True)
loss = -np.log(probs[sample, Ytr[sample]])
dscores = np.copy(probs)
dscores[sample, Ytr[sample]] -= 1
# Backprop FC 1
dW3 = np.dot(H2_relu.reshape(3042, 1), dscores[sample].reshape(1, 10))
dH2 = np.dot(dscores[sample], W3.T).reshape(13, 13, depth2)
# Backprop ReLu 2
dH2[H2 <= 0] = 0
# Backprop Conv 2
dP1, dW2 = conv.conv_backward(dH2, cache2)
# Backprop Pool
dH1 = np.zeros(H1.shape)
for m in range(15):
for n in range(15):
dH1[2*m:2*m+2, 2*n:2*n+2] = dP1[m, n]
# Backprop ReLu 1
dH1[H1 <= 0] = 0
# Backprop Conv 1
dX, dW1 = conv.conv_backward(dH1, cache1)
return loss, dW1, dW2, dW3
def test_3d_inactive():
n = 30
lx, ly, lz = n, n, n
data, labels = make_3d_syntheticdata(lx, ly, lz)
old_labels = cp.copy(labels)
labels[5:25, 26:29, 26:29] = -1
after_labels = cp.copy(labels)
labels = random_walker(data, labels, mode='cg')
assert (labels.reshape(data.shape)[13:17, 13:17, 13:17] == 2).all()
assert data.shape == labels.shape
return data, labels, old_labels, after_labels
def __estimate_one_step(self, gamma: cp.float64):
"""
Estimation routine for one given gamma parameter of (iterated) Tikhonov method.
:param gamma: Regularization parameter.
:type gamma: float
"""
order = 1
while order <= self.order:
self.__iteration(gamma)
self.previous = cp.copy(self.current)
order += 1
self.__update_solution()
self.previous = cp.copy(self.initial)
def __init__(self,
dataframe,
train_indices,
test_indices,
prediction_model,
cond_ind_test=None,
data_transform=None,
verbosity=0):
# Default value for the mask
mask = dataframe.mask
if mask is None:
mask = np.zeros(dataframe.values.shape, dtype='bool')
# Get the dataframe shape
T = len(dataframe.values)
# Have the default dataframe be the training data frame
train_mask = np.copy(mask)
train_mask[[t for t in range(T) if t not in train_indices]] = True
self.dataframe = DataFrame(dataframe.values,
mask=train_mask,
missing_flag=dataframe.missing_flag)
# Initialize the models baseclass with the training dataframe
Models.__init__(self,
dataframe=self.dataframe,
model=prediction_model,
data_transform=data_transform,
mask_type='y',
verbosity=verbosity)
# Build the testing dataframe as well
self.test_mask = np.copy(mask)
self.test_mask[[t for t in range(T) if t not in test_indices]] = True
# Setup the PCMCI instance
if cond_ind_test is not None:
# Force the masking
cond_ind_test.set_mask_type('y')
cond_ind_test.verbosity = verbosity
PCMCI.__init__(self,
dataframe=self.dataframe,
cond_ind_test=cond_ind_test,
selected_variables=None,
verbosity=verbosity)
# Set the member variables
self.cond_ind_test = cond_ind_test
# Initialize member varialbes that are set outside
self.target_predictors = None
self.selected_targets = None
self.fitted_model = None
self.test_array = None
def load_mc(self, t_ij):
for j, (l, h) in enumerate(
zip(self.observables.lows, self.observables.highs)):
in_bounds = np.logical_and(t_ij[:, j] > l, t_ij[:, j] < h)
t_ij = t_ij[in_bounds]
self.t_ij = cp.asarray(t_ij)
self.w_i = cp.ones(t_ij.shape[0])
if self.bootstrap_binning is not None:
counts, _ = cp.histogramdd(cp.asarray(self.t_ij),
bins=self.bin_edges,
weights=cp.asarray(self.w_i))
self.counts = (cp.asarray(counts).flatten() / self.bin_vol /
cp.sum(cp.asarray(counts))).reshape(counts.shape)
self.sigma_j = cp.std(self.t_ij, axis=0)
self.h_ij = self._adapt_bandwidth()
for j, (l, h, refl) in enumerate(
zip(self.observables.lows, self.observables.highs,
self.reflect_axes)):
if not refl:
continue
if type(refl) == tuple:
low, high = refl
mask = self.t_ij[:, j] < low
t_ij_reflected_low = cp.copy(self.t_ij[mask, :])
h_ij_reflected_low = self.h_ij[mask, :]
w_i_reflected_low = self.w_i[mask, :]
t_ij_reflected_low[:, j] = 2 * l - t_ij_reflected_low[:, j]
mask = self.t_ij[:, j] > high
t_ij_reflected_high = cp.copy(self.t_ij[mask, :])
h_ij_reflected_high = self.h_ij[mask, :]
w_i_reflected_high = self.w_i[mask, :]
t_ij_reflected_high[:, j] = 2 * h - t_ij_reflected_high[:, j]
else:
t_ij_reflected_low = cp.copy(self.t_ij)
h_ij_reflected_low = self.h_ij
w_i_reflected_low = self.w_i
t_ij_reflected_low[:, j] = 2 * l - self.t_ij[:, j]
t_ij_reflected_high = cp.copy(self.t_ij)
h_ij_reflected_high = self.h_ij
w_i_reflected_high = self.w_i
t_ij_reflected_high[:, j] = 2 * h - self.t_ij[:, j]
self.t_ij = cp.concatenate(
[self.t_ij, t_ij_reflected_low, t_ij_reflected_high])
self.h_ij = cp.concatenate(
[self.h_ij, h_ij_reflected_low, h_ij_reflected_high])
self.w_i = cp.concatenate(
[self.w_i, w_i_reflected_low, w_i_reflected_high])
self.t_ij = cp.ascontiguousarray(self.t_ij)
self.h_ij = cp.ascontiguousarray(self.h_ij)
self.w_i = cp.ascontiguousarray(self.w_i)
def _divide_nonzero(array1, array2, cval=1e-10):
"""
Divides two arrays.
Denominator is set to small value where zero to avoid ZeroDivisionError and
return finite float array.
Parameters
----------
array1 : (N, ..., M) ndarray
Array 1 in the enumerator.
array2 : (N, ..., M) ndarray
Array 2 in the denominator.
cval : float, optional
Value used to replace zero entries in the denominator.
Returns
-------
array : (N, ..., M) ndarray
Quotient of the array division.
"""
# Copy denominator
denominator = cp.copy(array2)
# Set zero entries of denominator to small value
denominator[denominator == 0] = cval
# Return quotient
return cp.divide(array1, denominator)
def __call__(self, x, inst):
h = relu(self.flat1_bn(self.flat1(x)))
h = relu(self.down1_bn(self.down1(h)))
h = relu(self.down2_bn(self.down2(h)))
h = relu(self.down3_bn(self.down3(h)))
h = relu(self.down4_bn(self.down4(h)))
h = relu(self.up1_bn(self.up1(h)))
h = relu(self.up2_bn(self.up2(h)))
h = relu(self.up3_bn(self.up3(h)))
h = relu(self.up4_bn(self.up4(h)))
# outputs = tanh(self.flat2(h))
outputs = self.flat2(h)
# instance-wise average pooling
outputs_mean = cp.copy(outputs.data)
inst = inst.data.astype(int)
ids = np.unique(inst)
for id in ids:
indices = list((inst == id).nonzero())
for j in range(self.out_ch):
outputs_ins = outputs_mean[indices[0], j, indices[2], indices[3]]
mean_feat = cp.mean(outputs_ins)
mean_feat = cp.ones(outputs_ins.shape) * mean_feat
outputs_mean[indices[0], j, indices[2], indices[3]] = mean_feat
return Variable(outputs_mean)
def initial_pop(good_data, require_goods, non_require_goods, non_require_values, limit_weight):
#good_data: 原始商品資料集
#require_goods: 必要性的商品清單
#non_require_goods: 不必要性的商品清單
#non_require_values: 不必要性的商品數量
#limit_weight: 最大重量限制
while True:
# temp_good = cp.copy(good_data) #先將原始資料暫時給另外一個變數使用
temp_good = cp.copy(good_data) #先將原始資料暫時給另外一個變數使用
for i in range(len(require_goods)):
get_index = temp_good[(temp_good[:,8] == require_goods[i]) & (temp_good[:, 11] !=1)] #取得符合條件的資料
temp_index = random.randint(0, (len(get_index)-1)) #隨機取得品項
get_index = get_index[temp_index] #取得商品名稱
get_index[11] = 1
temp_good[temp_good[:,0] == get_index[0]] = get_index
selected_require = np.random.choice(non_require_goods, non_require_values, replace=False)
for i in range(len(selected_require)):
get_index = temp_good[(temp_good[:,8] == selected_require[i]) & (temp_good[:, 11] !=1)] #取得符合條件的資料
temp_index = random.randint(0, (len(get_index)-1)) #隨機取得品項
get_index = get_index[temp_index] #取得商品名稱
get_index[11] = 1
temp_good[temp_good[:,0] == get_index[0]] = get_index
selected_good = temp_good[temp_good[:, 11] == 1]
sum_weight = np.sum(selected_good[:,10])
if sum_weight <= limit_weight:
break
return(selected_good) #回傳結果
def _get_psi_k(self, phi, k):
"""Returns the linear causal effect matrices excluding variable k.
Parameters
----------
phi : array-like
Coefficient matrices at different lags.
k : int
Variable index to exclude causal effects through.
Returns
-------
psi_k : array-like, shape (tau_max + 1, N, N)
Matrices of causal effects excluding k.
"""
psi_k = np.zeros((self.tau_max + 1, self.N, self.N))
psi_k[0] = np.identity(self.N)
phi_k = np.copy(phi)
phi_k[1:, k, :] = 0.
for n in range(1, self.tau_max + 1):
psi_k[n] = np.zeros((self.N, self.N))
for s in range(1, n + 1):
psi_k[n] += np.dot(phi_k[s], psi_k[n - s])
return psi_k
def heat3d_cp(alpha, Tcool, Thot, dx, dy, dz, dt, nx, ny, nz, nt, result='time'):
'''
Solves the 3 dimensional heat equation using cupy
alpha -- thermal diffusivity
Tcool -- initial cold temperature
Thot -- initial hot temperature
dx, dy, dz -- spacing in each dimension
dt -- time step
nx, ny, nz -- number of gridpoints in each dimension
nt -- integration time
result -- either 'time', 'field' or 'both', returning either the total time the computation took, the resulting field or both
'''
in_field = Tcool*cp.ones((nx,ny,nz))
in_field[nx//4:3*nx//4, ny//4:3*ny//4, nz//4:3*nz//4] = Thot
out_field = cp.copy(in_field)
# warm up
op_np(in_field, out_field, alpha, Tcool, dx, dy, dz, dt)
# timing
tic = get_time()
op_np(in_field, out_field, alpha, Tcool, dx, dy, dz, dt, nt=nt)
#toc = time.time()
time_cp = get_time() - tic
if result == 'time':
return time_cp
elif result == 'field':
return in_field
elif result == 'both':
return time_cp, in_field
def imgshift_cupy(img_cupy,dxy):
imgout_cupy=cp.copy(img_cupy)
imgout_cupy=cp.roll(imgout_cupy,int(dxy[0]),axis=0)
imgout_cupy=cp.roll(imgout_cupy,int(dxy[1]),axis=1)
return imgout_cupy
def median_blur(image_unbordered, rows, columns=None):
if columns == None:
columns = rows
if (rows == 1 and columns == 1):
raise "Filter to little, increase rows or columns"
if is_even(rows) or is_even(columns):
raise "Rows and columns must be odd numbers"
width_array, height_array = generate_arrays(rows, columns)
#print(width_array, height_array)
padding = max(width_array + height_array)
members = []
#image = cv2.copyMakeBorder(image_unbordered, padding,padding,padding,padding,cv2.BORDER_REFLECT)
image = cp.copy(image_unbordered)
if len(image.shape) == 3:
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
width, height = image.shape
# convolucion
for i in range(padding, width - padding):
for j in range(padding, height - padding):
members = [
image[i + alpha, j + beta] for alpha in width_array
for beta in height_array
]
image_unbordered[i - padding, j - padding] = median(members)
#print(i,j,len(members))
return image_unbordered
"""
def csc_median_axis_0(X):
"""Find the median across axis 0 of a CSC matrix.
It is equivalent to doing np.median(X, axis=0).
Parameters
----------
X : CSC sparse matrix, shape (n_samples, n_features)
Input data.
Returns
-------
median : ndarray, shape (n_features,)
Median.
"""
if not iscsc(X):
raise TypeError("Expected matrix of CSC format, got %s" % X.format)
indptr = X.indptr
n_samples, n_features = X.shape
median = np.zeros(n_features)
for f_ind, (start, end) in enumerate(zip(indptr[:-1], indptr[1:])):
# Prevent modifying X in place
data = np.copy(X.data[start:end])
nz = n_samples - data.size
median[f_ind] = _get_median(data, nz)
return median
def testBasicMatOpr(self):
# ==============Test arrayfire basic operation test==========================
raw_data = np.arange(20).reshape(4, 5)
raw_data_cp = cp.copy(raw_data)
assert np.array_equal(raw_data, raw_data_cp)
def test_permutation_sort_1dim(self, dtype):
cupy_random = self._xp_random(cupy)
a = cupy.arange(10, dtype=dtype)
b = cupy.copy(a)
c = cupy_random.permutation(a)
testing.assert_allclose(a, b)
testing.assert_allclose(b, cupy.sort(c))
def test_permutation_sort_ndim(self, dtype):
cupy_random = self._xp_random(cupy)
a = cupy.arange(15, dtype=dtype).reshape(5, 3)
b = cupy.copy(a)
c = cupy_random.permutation(a)
testing.assert_allclose(a, b)
testing.assert_allclose(b, cupy.sort(c, axis=0))
def test_backward_case2(self):
vertices = [[0.8, 0.8, 1.], [-0.5, -0.8, 1.], [0.8, -0.8, 1.]]
faces = [[0, 1, 2]]
pyi = 40
pxi = 50
renderer = neural_renderer.Renderer()
renderer.image_size = 64
renderer.anti_aliasing = False
renderer.perspective = False
vertices = chainer.Variable(cp.array(vertices, 'float32'))
faces = cp.array(faces, 'int32')
images = renderer.render_silhouettes(vertices[None, :, :],
faces[None, :, :])
loss = cf.sum(cf.absolute(images[:, pyi, pxi]))
loss.backward()
for i in range(3):
for j in range(2):
axis = 'x' if j == 0 else 'y'
vertices2 = cp.copy(vertices.data)
vertices2[i, j] -= 1. / vertices.grad[i, j]
images = renderer.render_silhouettes(vertices2[None, :, :],
faces[None, :, :])
image = np.tile(images[0].data.get()[:, :, None], (1, 1, 3))
image[pyi, pxi] = [1, 0, 0]
ref = scipy.misc.imread(
'./tests/data/rasterize_silhouettes_case2_v%d_%s.png' %
(i, axis))
ref = ref.astype('float32') / 255
chainer.testing.assert_allclose(ref, image)
def exec_sortA0001(self) -> None:
"""exec_sortA0001.
:rtype: None
"""
x_population = cp.copy(self.get_population())
x_sort = cp.copy(self.get_fitness())
y_population, y_sort = self._sortA0001(
x_population,
x_sort,
self.get_n_jobs(),
self.get_n_operations(),
self.get_n_samples(),
)
self.set_population(y_population)
self._set_fitness(y_sort)
def voxelize(faces, size):
faces = cp.copy(faces)
faces *= size
voxels = _voxelize_sub1(faces, size)
voxels = _voxelize_sub4(voxels)
return voxels
def identifyClassFromProb(probs):
if usegpu == True:
probs = np.asarray(probs)
probs_ = np.copy(probs)
probs_[probs_ > 0.5] = 1
probs_[probs_ <= 0.5] = 0
return probs_
def exec_migrationA0001(self) -> None:
"""exec_migrationA0001.
:rtype: None
"""
x_population = cp.copy(self.get_population())
x_aux = cp.copy(self.get_population())
index_selection = int(self.get_n_samples() *
self.get_percent_selection())
index_migration = int(self.get_n_samples() *
self.get_percent_migration())
cp.random.shuffle(x_population)
y_population = self._permutationA0001(self.get_n_jobs(),
self.get_n_operations(),
index_migration)
x_aux[index_selection:, :][-index_migration:, :] = y_population
self.set_population(x_aux)
def test_copy_orders(self, order):
a = cupy.empty((2, 3, 4))
b = cupy.copy(a, order)
a_cpu = numpy.empty((2, 3, 4))
b_cpu = numpy.copy(a_cpu, order)
assert b.strides == b_cpu.strides
def test_copy_orders(self, order):
a = cupy.empty((2, 3, 4))
b = cupy.copy(a, order)
a_cpu = numpy.empty((2, 3, 4))
b_cpu = numpy.copy(a_cpu, order)
self.assertEqual(b.strides, b_cpu.strides)
def sortAC0001() -> cp.core.core.ndarray:
"""sortAC0001.
:rtype: cp.core.core.ndarray
"""
X_AUX = cp.copy(X)
return X_AUX[y.argsort()], cp.sort(y)
def test_copy_orders(self, order):
a = cupy.empty((2, 3, 4))
b = cupy.copy(a, order)
a_cpu = numpy.empty((2, 3, 4))
b_cpu = numpy.copy(a_cpu, order)
self.assertEqual(b.strides, b_cpu.strides)
def test_permutation_seed1(self, dtype):
a = testing.shaped_random((10,), cupy, dtype)
b = cupy.copy(a)
cupy.random.seed(0)
pa = cupy.random.permutation(a)
cupy.random.seed(0)
pb = cupy.random.permutation(b)
testing.assert_allclose(pa, pb)
def test_shuffle_seed1(self, dtype):
a = testing.shaped_random((10,), cupy, dtype)
b = cupy.copy(a)
cupy.random.seed(0)
cupy.random.shuffle(a)
cupy.random.seed(0)
cupy.random.shuffle(b)
testing.assert_allclose(a, b)
def forward(self, state, action, reward, new_state, is_terminal): q = self.get_q(Variable(state)) q_target = self.get_target_q(Variable(new_state)) max_target_q = cp.max(q_target.data, axis=1) target = cp.copy(q.data) for i in xrange(target.shape[0]): curr_action = int(action[i]) if is_terminal[i]: target[i, curr_action] = reward[i] else: target[i, curr_action] = reward[i] + self.gamma * max_target_q[i] loss = F.mean_squared_error(Variable(target), q) return loss, 0.0 #cp.mean(q.data[:, action[i]])
def forward_onestep(self, x_data, y_data, state, train=True):
batchsize = x_data.shape[0]
x = chainer.Variable(x_data, volatile=not train)
# lstm
if y_data is not None:
y = chainer.Variable(self.xp.array(y_data, dtype=self.xp.int32), volatile=not train)
h_in = self.chain.lstm_xh(x) + \
self.chain.lstm_yh(y) + \
self.chain.lstm_rh(state['r']) + \
self.chain.lstm_hh(state['h'])
else:
h_in = self.chain.lstm_xh(x) + \
self.chain.lstm_rh(state['r']) + \
self.chain.lstm_hh(state['h'])
c_t, h_t = F.lstm(state['c'], h_in)
key = F.reshape(self.chain.l_key(h_t), (batchsize, self.nb_reads, self.memory_shape[1]))
add = F.reshape(self.chain.l_add(h_t), (batchsize, self.nb_reads, self.memory_shape[1]))
sigma = self.chain.l_sigma(h_t)
# Compute least used weight (not differentiable)
if self.xp == cp:
wu_tm1_data = cp.copy(state['used_weight'].data)
lu_index = np.argsort(cuda.to_cpu(wu_tm1_data), axis=1)[:,:self.nb_reads]
else:
wu_tm1_data = state['used_weight'].data
lu_index = np.argsort(wu_tm1_data, axis=1)[:,:self.nb_reads]
wlu_tm1_data = self.xp.zeros((batchsize, self.memory_shape[0]),
dtype=self.xp.float32)
for i in range(batchsize):
for j in range(self.nb_reads):
wlu_tm1_data[i,lu_index[i,j]] = 1. # 1 for least used index
wlu_tm1 = chainer.Variable(wlu_tm1_data, volatile=not train)
# write weight
_wlu_tm1 = F.broadcast_to(
F.reshape(wlu_tm1, (batchsize, 1, self.memory_shape[0])),
(batchsize, self.nb_reads, self.memory_shape[0]))
_sigma = F.broadcast_to(F.reshape(sigma, (batchsize, 1, 1)),
(batchsize, self.nb_reads, self.memory_shape[0]))
ww_t = _sigma * state['read_weight'] + (1 - _sigma) * _wlu_tm1
# write to memory
_lu_mask = 1 - F.broadcast_to(
F.reshape(wlu_tm1, (batchsize, self.memory_shape[0], 1)),
(batchsize, self.memory_shape[0], self.memory_shape[1]))
M_t = state['M'] * _lu_mask + F.batch_matmul(ww_t, add, transa=True)
# read from memory
K_t = cosine_similarity(key, M_t)
# read weight, used weight
wr_t = F.reshape(F.softmax(F.reshape(
K_t, (-1, self.memory_shape[0]))),
(batchsize, self.nb_reads, self.memory_shape[0]))
wu_t = self.gamma * state['used_weight'] + F.sum(wr_t, axis=1) + F.sum(ww_t, axis=1)
# read memory
r_t = F.reshape(F.batch_matmul(wr_t, M_t), (batchsize, -1)) # (batchsize, nb_reads * memory_shape[1])
# set to state
state_new = {
'M': M_t,
'c': c_t,
'h': h_t,
'r': r_t,
'read_weight': wr_t,
'used_weight': wu_t,
}
return state_new
