def load_data_wrapper():
"""Return a tuple containing ``(training_data, validation_data,
test_data)``. Based on ``load_data``, but the format is more
convenient for use in our implementation of neural networks.
In particular, ``training_data`` is a list containing 50,000
2-tuples ``(x, y)``. ``x`` is a 784-dimensional numpy.ndarray
containing the input image. ``y`` is a 10-dimensional
numpy.ndarray representing the unit vector corresponding to the
correct digit for ``x``.
``validation_data`` and ``test_data`` are lists containing 10,000
2-tuples ``(x, y)``. In each case, ``x`` is a 784-dimensional
numpy.ndarry containing the input image, and ``y`` is the
corresponding classification, i.e., the digit values (integers)
corresponding to ``x``.
Obviously, this means we're using slightly different formats for
the training data and the validation / test data. These formats
turn out to be the most convenient for use in our neural network
code."""
tr_d, va_d, te_d = load_data()
training_inputs = [cp.reshape(cp.asarray(x), (784, 1)) for x in tr_d[0]]
training_results = [vectorized_result(y) for y in tr_d[1]]
training_data = list(zip(training_inputs, training_results))
validation_inputs = [cp.reshape(cp.asarray(x), (784, 1)) for x in va_d[0]]
validation_data = list(zip(validation_inputs, va_d[1]))
test_inputs = [cp.reshape(cp.asarray(x), (784, 1)) for x in te_d[0]]
test_data = list(zip(test_inputs, te_d[1]))
return (training_data, validation_data, test_data)
def lobe_calc(Left_Lobe, RightLobe, Four_Y, Four_X, FourXY, cutoff):
lobe_shape = Left_Lobe.shape
two_dims_rolled = lobe_shape[2] * lobe_shape[3]
Left_Lobe = cp.reshape(Left_Lobe,
(lobe_shape[0], lobe_shape[1], two_dims_rolled))
RightLobe = cp.reshape(RightLobe,
(lobe_shape[0], lobe_shape[1], two_dims_rolled))
for pp in range(int(two_dims_rolled)):
(ii, jj) = np.unravel_index(pp, (lobe_shape[2], lobe_shape[3]))
xq = Four_X[ii, jj]
yq = Four_Y[ii, jj]
d_plus = (((Four_X + xq)**2) + ((Four_Y + yq)**2))**0.5
d_minu = (((Four_X - xq)**2) + ((Four_Y - yq)**2))**0.5
d_zero = FourXY
ll = Left_Lobe[:, :, pp]
ll[d_plus < cutoff] = 1
ll[d_minu > cutoff] = 1
ll[d_zero < cutoff] = 1
Left_Lobe[:, :, pp] = ll
rr = RightLobe[:, :, pp]
rr[d_plus > cutoff] = 1
rr[d_minu < cutoff] = 1
rr[d_zero < cutoff] = 1
RightLobe[:, :, pp] = rr
Left_Lobe = cp.reshape(Left_Lobe, lobe_shape)
RightLobe = cp.reshape(RightLobe, lobe_shape)
def dot(a, b, out=None):
"""Returns a dot product of two arrays.
For arrays with more than one axis, it computes the dot product along the
last axis of ``a`` and the second-to-last axis of ``b``. This is just a
matrix product if the both arrays are 2-D. For 1-D arrays, it uses their
unique axis as an axis to take dot product over.
Args:
a (cupy.ndarray): The left argument.
b (cupy.ndarray): The right argument.
out (cupy.ndarray): Output array.
Returns:
cupy.ndarray: The dot product of ``a`` and ``b``.
.. seealso:: :func:`numpy.dot`
"""
a_ndim = a.ndim
b_ndim = b.ndim
assert a_ndim > 0 and b_ndim > 0
a_is_vec = a_ndim == 1
b_is_vec = b_ndim == 1
if a_is_vec:
a = cupy.reshape(a, (1, a.size))
a_ndim = 2
if b_is_vec:
b = cupy.reshape(b, (b.size, 1))
b_ndim = 2
a_axis = a_ndim - 1
b_axis = b_ndim - 2
if a.shape[a_axis] != b.shape[b_axis]:
raise ValueError('Axis dimension mismatch')
if a_axis:
a = cupy.rollaxis(a, a_axis, 0)
if b_axis:
b = cupy.rollaxis(b, b_axis, 0)
k = a.shape[0]
m = b.size // k
n = a.size // k
ret_shape = a.shape[1:] + b.shape[1:]
if out is None:
if a_is_vec:
ret_shape = () if b_is_vec else ret_shape[1:]
elif b_is_vec:
ret_shape = ret_shape[:-1]
else:
if out.size != n * m:
raise ValueError('Output array has an invalid size')
if not out.flags.c_contiguous:
raise ValueError('Output array must be C-contiguous')
return _tensordot_core(a, b, out, n, m, k, ret_shape)
def my_sum(S1, sig, varargin=None):
# returns a running sum applied sequentially across a choice of dimensions and bin sizes
# S1 is the matrix to be filtered
# sig is either a scalar or a sequence of scalars, one for each axis to be filtered.
# it's the plus/minus bin length for the summing filter
# varargin can be the dimensions to do filtering, if len(sig) != x.shape
# if sig is scalar and no axes are provided, the default axis is 2
idims = 1
if varargin is not None:
idims = varargin
idims = _make_vect(idims)
if _is_vect(idims) and _is_vect(sig):
sigall = sig
else:
sigall = np.tile(sig, len(idims))
for sig, idim in zip(sigall, idims):
Nd = S1.ndim
S1 = cp.transpose(S1, [idim] + list(range(0, idim)) +
list(range(idim + 1, Nd)))
dsnew = S1.shape
S1 = cp.reshape(S1, (S1.shape[0], -1), order='F')
dsnew2 = S1.shape
S1 = cp.concatenate((cp.full(
(sig, dsnew2[1]), 0), S1, cp.full((sig, dsnew2[1]), 0)),
axis=0)
Smax = S1[:dsnew2[0], :]
for j in range(1, 2 * sig + 1):
Smax = Smax + S1[j:j + dsnew2[0], :]
S1 = cp.reshape(Smax, dsnew, order='F')
S1 = cp.transpose(
S1,
list(range(1, idim + 1)) + [0] + list(range(idim + 1, Nd)))
return S1
def __init__(self, num_stride, padding, num_filter, filter_size,
input_shape):
self.stride = num_stride
self.padding = padding # 'same' or 'valid'
self.num_filter = num_filter
self.filter_size = filter_size # [a,b,c]
self.weights = np.reshape(
np.random.normal(
0, 0.05,
filter_size[0] * filter_size[1] * filter_size[2] * num_filter),
(filter_size[0], filter_size[1], filter_size[2], num_filter))
# height, width, channel, num_filter
self.bias = np.reshape(np.random.normal(0, 0.05, num_filter),
(num_filter))
self.dweights = np.zeros_like(self.weights)
self.dbias = np.zeros_like(self.bias)
self.input_shape = input_shape # batch_size, height, width, channel
self.output_shape = self.get_output_shape(
input_shape) # batch_size, height, width, filter
# self.inputt = np.zeros_like(input_shape)
self.padding_shape = self.get_pad_shape()
def Fisher(self, key, label, batchsize, nb_class, convert_dim, dimension,
affinity):
label = cp.array(label)
if (self.n_shot == 1):
Sw = cp.identity(dimension, dtype='float32')
else:
Sw = self.local_cov_in_class(key.data, label, nb_class, batchsize,
affinity)
#Sw=self.local_cov_in_class_NN(key.data,label,nb_class,batchsize,5)
Sb = self.local_cov_bet_class(key.data, label, nb_class, batchsize,
affinity)
#Sb=self.local_cov_bet_class_NN(key.data,label,nb_class,batchsize,5)
Sb_Sw = Sb - 0.5 * Sw
if (self.n_shot == 1):
Sb_Sw = Sb
lam, v = np.linalg.eigh(Sb_Sw)
lam = cp.asarray(lam)
v = cp.asarray(v)
eigen_id = cp.argsort(lam)[::-1]
eigen_value = lam[eigen_id]
eigen_vector = v[:, eigen_id]
W = eigen_vector[:, :convert_dim]
W = cp.reshape(W, [dimension, convert_dim])
W = W / cp.reshape(cp.linalg.norm(W, axis=0), [1, convert_dim])
W = F.transpose(W)
return W
def forward(self, is_training=True):
inputs = self.input_tensor
# self.input_shape =inputs.shape
gamma, beta = self.variables
N, C, H, W = inputs.shape
self.shape_field = tuple([i for i in range(2, inputs.ndim)])
x_group = cp.reshape(inputs, (N, self.G, C // self.G, H, W))
mean = cp.mean(x_group, axis=self.shape_field, keepdims=True)
var = cp.var(x_group, axis=self.shape_field, keepdims=True)
xgmu = x_group - mean
sqrtvar = cp.sqrt(var + self.epsilon)
x_group_norm = xgmu / sqrtvar
x_norm = cp.reshape(x_group_norm, (N, C, H, W))
outputs = gamma.output_tensor * x_norm + beta.output_tensor
self.cache = (xgmu, sqrtvar, x_norm)
self.output_tensor = outputs
if self.require_grads:
self.grads = cp.zeros_like(self.output_tensor)
super().forward(is_training)
def dot(a, b, out=None):
"""Returns a dot product of two arrays.
For arrays with more than one axis, it computes the dot product along the
last axis of ``a`` and the second-to-last axis of ``b``. This is just a
matrix product if the both arrays are 2-D. For 1-D arrays, it uses their
unique axis as an axis to take dot product over.
Args:
a (cupy.ndarray): The left argument.
b (cupy.ndarray): The right argument.
out (cupy.ndarray): Output array.
Returns:
cupy.ndarray: The dot product of ``a`` and ``b``.
.. seealso:: :func:`numpy.dot`
"""
a_ndim = a.ndim
b_ndim = b.ndim
assert a_ndim > 0 and b_ndim > 0
a_is_vec = a_ndim == 1
b_is_vec = b_ndim == 1
if a_is_vec:
a = cupy.reshape(a, (1, a.size))
a_ndim = 2
if b_is_vec:
b = cupy.reshape(b, (b.size, 1))
b_ndim = 2
a_axis = a_ndim - 1
b_axis = b_ndim - 2
if a.shape[a_axis] != b.shape[b_axis]:
raise ValueError('Axis dimension mismatch')
if a_axis:
a = cupy.rollaxis(a, a_axis, 0)
if b_axis:
b = cupy.rollaxis(b, b_axis, 0)
k = a.shape[0]
m = b.size // k
n = a.size // k
ret_shape = a.shape[1:] + b.shape[1:]
if out is None:
if a_is_vec:
ret_shape = () if b_is_vec else ret_shape[1:]
elif b_is_vec:
ret_shape = ret_shape[:-1]
else:
if out.size != n * m:
raise ValueError('Output array has an invalid size')
if not out.flags.c_contiguous:
raise ValueError('Output array must be C-contiguous')
return _tensordot_core(a, b, out, n, m, k, ret_shape)
def evol(s, B, U, chi, d):
for i_bond in [0, 1]:
ia = np.mod(i_bond - 1, 2)
ib = np.mod(i_bond, 2)
ic = np.mod(i_bond + 1, 2)
chia = B[ib].shape[1]
chic = B[ic].shape[2]
# Construct theta matrix and time evolution #
theta = cp.tensordot(B[ib], B[ic], axes=(2, 1)) # i a j b
theta = cp.tensordot(U, theta, axes=([2, 3], [0, 2])) # ip jp a b
theta = cp.tensordot(cp.diag(s[ia]), theta, axes=([1, 2])) # a ip jp b
theta = cp.reshape(cp.transpose(theta, (1, 0, 2, 3)),
(d * chia, d * chic)) # ip a jp b
# Schmidt decomposition #
X, Y, Z = cp.linalg.svd(theta, full_matrices=0)
chi2 = np.min([cp.sum(Y > 10.**(-10)).get(), chi])
piv = cp.zeros(len(Y), cp.bool)
piv[(cp.argsort(Y)[::-1])[:chi2]] = True
Y = Y[piv]
invsq = cp.sqrt(sum(Y**2))
X = X[:, piv]
Z = Z[piv, :]
# Obtain the new values for B and s #
s[ib] = Y / invsq
X = cp.reshape(X, (d, chia, chi2))
X = cp.transpose(cp.tensordot(cp.diag(s[ia]**(-1)), X, axes=(1, 1)),
(1, 0, 2))
B[ib] = cp.tensordot(X, cp.diag(s[ib]), axes=(2, 0))
B[ic] = cp.transpose(cp.reshape(Z, (chi2, d, chic)), (1, 0, 2))
return s, B
def jitter(raster, window):
ntrials, nbins = raster.shape
# if needed, pad to be divisible by window
if nbins % window:
pad = cp.zeros((ntrials, -nbins % window))
raster = cp.concatenate([raster, pad], axis=1)
nbins_rounded = raster.shape[1]
n_jitter_bins = nbins_rounded // window
# get psth
psth = raster.mean(axis=0)
# bin over window and sum
raster_binned = cp.reshape(raster, (ntrials, window, n_jitter_bins)).sum(axis=1)
psth_binned = cp.reshape(psth, (window, n_jitter_bins)).sum(axis=0)
# determine correction
correction = raster_binned / cp.expand_dims(psth_binned, 0)
correction = cp.tile(cp.expand_dims(correction, 1), [1, window, 1])
correction = cp.reshape(correction, (ntrials, nbins_rounded))
# apply correction
raster_jittered = cp.expand_dims(psth, 0) * correction
# trim off padding
raster_jittered = raster_jittered[:, :nbins]
raster_jittered[cp.isnan(raster_jittered)] = 0
return raster_jittered
def bss_eval_sources_cupy(reference_sources,
estimated_sources,
compute_permutation=True,
nsrc=2):
"""
"""
# make sure the input is of shape (nsrc, nsampl)
nsampl = estimated_sources.shape[-1]
estimated_sources = cp.reshape(
cp.array(estimated_sources, dtype=cp.float64), [nsrc, nsampl])
reference_sources = cp.reshape(
cp.array(reference_sources, dtype=cp.float64), [nsrc, nsampl])
# does user desire permutations?
if compute_permutation:
# compute criteria for all possible pair matches
sdr = cp.empty((nsrc, nsrc))
sir = cp.empty((nsrc, nsrc))
sar = cp.empty((nsrc, nsrc))
for jest in range(nsrc):
for jtrue in range(nsrc):
s_true, e_spat, e_interf, e_artif = \
_bss_decomp_mtifilt_cupy(reference_sources,
estimated_sources[jest],
jtrue, 512, nsrc)
sdr[jest,jtrue], sir[jest,jtrue], sar[jest,jtrue] = \
_bss_source_crit_cupy(s_true, e_spat, e_interf, e_artif)
# select the best ordering
perms = list(itertools.permutations(list(range(nsrc))))
mean_sir = np.empty(len(perms))
dum = np.arange(nsrc)
for (i, perm) in enumerate(perms):
mean_sir[i] = np.mean(cp.asnumpy(sir)[perm, dum])
popt = perms[np.argmax(mean_sir)]
idx = (popt, dum)
return (cp.asnumpy(sdr)[idx], cp.asnumpy(sir)[idx],
cp.asnumpy(sar)[idx], np.asarray(popt))
else:
# compute criteria for only the simple correspondence
# (estimate 1 is estimate corresponding to reference source 1, etc.)
sdr = np.empty(nsrc)
sir = np.empty(nsrc)
sar = np.empty(nsrc)
for j in range(nsrc):
s_true, e_spat, e_interf, e_artif = \
_bss_decomp_mtifilt(reference_sources,
estimated_sources[j],
j, 512)
sdr[j], sir[j], sar[j] = \
_bss_source_crit(s_true, e_spat, e_interf, e_artif)
# return the default permutation for compatibility
popt = np.arange(nsrc)
return (sdr, sir, sar, popt)
def gather_indexes(sequence_tensor, positions):
"""Gathers the vectors at the specific positions over a minibatch."""
batch_size, seq_length, width = sequence_tensor.shape
flat_offsets = np.reshape(np.arange(0, batch_size, dtype=np.int32) * seq_length, [-1, 1])
flat_positions = np.reshape(positions + flat_offsets, [-1])
flat_sequence_tensor = np.reshape(sequence_tensor,
[batch_size * seq_length, width])
output_tensor = flat_sequence_tensor[[flat_positions]]
return output_tensor
def updater(x_row,updated_h,weights,num_features,num_models,learning_rate):
x_row = cp.array(x_row.toarray())
x_row = cp.reshape(x_row,(1,num_features))
#x_row = x_row.reshape(1, num_features)
#x_row = cupyx.scipy.sparse.csr_matrix(x_row)
updated_h = cp.array(updated_h)
updated_h = cp.reshape(updated_h, (num_models, 1))
#update = cupyx.scipy.sparse.csr_matrix.dot(updated_h, x_row) * learning_rate
update = cp.dot(updated_h,x_row) * learning_rate
weights += update
cp.cuda.Stream.null.synchronize()
def cupy_jit_resizer4D(data4D,resized_size,return_numpy=False):
data_size = np.shape(data4D)
flattened_shape = (data_size[0]*data_size[1],data_size[2]*data_size[3])
data4D_flatten = cp.reshape(cp.asarray(data4D),flattened_shape)
flat_res_shape = (data_size[0]*data_size[1],resized_size[0]*resized_size[1])
flatres4D = cp.zeros(flat_res_shape,dtype=data4D.dtype)
cupy_jit_2D_xdim[1,32](data4D_flatten,flat_res_shape[1],flatres4D,flat_res_shape[0]) # call numba.cuda kernel with 1 block and 32 threads in that block
res4D = cp.reshape(flatres4D,(data_size[0],data_size[1],resized_size[0],resized_size[1]))
if return_numpy:
res4D = cp.asnumpy(res4D)
return res4D
def check_eigenvalues(PHI):
a, b, c, d, e, f, g, h, i = cp.reshape(PHI[3:12, :], [9, 1, J])
a0 = -a * e * i + a * f * h + b * d * i - b * f * g - c * d * h + c * e * g
a1 = a * e - b * d + a * i - c * g + e * i - f * h
a2 = -a - e - i
#1
bb1 = (abs(a2 + a0) <= 1 + a1).astype(int)
#2
bb2 = (abs(a2 - 3 * a0) <= (3 - a1)).astype(int)
#3
bb3 = ((a0**2 + a1 - a0 * a2) <= 1).astype(int)
return cp.reshape(((bb1 + bb2 + bb3 != 3).astype(int)), [J])
def visualizeWeights(w1, w2, w3):
fig, (ax1, ax2, ax3) = plt.subplots(nrows=1, ncols=3)
new_w1 = cp.reshape(w1[:-1], (48, 48))
new_w2 = cp.reshape(w2[:-1], (48, 48))
new_w3 = cp.reshape(w3[:-1], (48, 48))
ax1.imshow(new_w1)
ax1.set_title('Part A')
ax2.imshow(new_w2)
ax2.set_title('Part B')
ax3.imshow(new_w3)
ax3.set_title('Part C')
fig.show()
def predict_row(self, x): # x.shape=[N,2]
#pdb.set_trace()
x = self.normX(cp.asarray(x))
dist = cp.tile(self.X, [x.shape[0], 1]) - cp.reshape(
cp.tile(x, [1, self.X.shape[0]]),
[self.X.shape[0] * x.shape[0], 2])
Psi = cp.reshape(
cp.exp(
-cp.sum(self.theta * cp.power(cp.abs(dist), self.pl), axis=1)),
[x.shape[0], self.X.shape[0]]) # 次元方向に和
ccc = Psi.dot(self.bbb)
fff = ccc + self.mu
return cp.asnumpy(self.inversenormy(fff))
def decoder(self, z):
#self.d_out : reconstruction image 28x28
self.z = np.reshape(z, (self.batch_size, self.nz))
self.d_h0_l = self.z.dot(self.d_W0) + self.d_b0
self.d_h0_a = relu(self.d_h0_l)
self.d_h1_l = self.d_h0_a.dot(self.d_W1) + self.d_b1
self.d_h1_a = sigmoid(self.d_h1_l)
self.d_out = np.reshape(self.d_h1_a, (self.batch_size, 28, 28, 1))
return self.d_out
def cupy_resizer4D(data4D, resized_size, return_numpy=False):
data4D = cp.asarray(data4D)
data_size = data4D.shape
flattened_shape = (data_size[0] * data_size[1],
data_size[2] * data_size[3])
data4D = cp.reshape(data4D, flattened_shape)
flat_res_shape = (data_size[0] * data_size[1],
resized_size[0] * resized_size[1])
res4D = cp.zeros(flat_res_shape, dtype=data4D.dtype)
res4D = cupy_xdim_res_loop(data4D, res4D, flat_res_shape[0])
res4D = cp.reshape(
res4D, (data_size[0], data_size[1], resized_size[0], resized_size[1]))
if return_numpy:
res4D = cp.asnumpy(res4D)
return res4D
def hebbian_rule(W,
b,
zeta,
a,
prev_layer,
activation_func,
den_activation,
y,
w=None,
d=None):
dW = {}
dB = {}
delta = None
try:
batch_size = y.shape[1]
except IndexError:
batch_size = 1
y = cp.reshape(y, (y.shape[0], batch_size))
y = cp.argmax(y, axis=0).reshape((1, y.shape[1]))
a['s'] = cp.reshape(a['s'], (a['s'].shape[0], 1, a['s'].shape[1]))
out_in = cp.einsum('nij,nkj->nik', a['s'], a['d'])
out_w = cp.einsum('nik,nij->nkj', a['s'], W['s'])
out_w_out = cp.einsum('nik,nji->njk', out_w, a['s'])
dW['s'] = (1 / batch_size) * (out_in - out_w_out)
out_b = cp.einsum('nik,nij->nkj', a['s'], b['s'])
out_b_out = cp.einsum('nik,nji->njk', out_b, a['s'])
dB['s'] = (1 / batch_size) * cp.sum(y, axis=1)
dB['s'] = cp.reshape(dB['s'], (dB['s'].shape[0], 1, 1))
# prev_layer = cp.reshape(prev_layer,(prev_layer.shape[0],1,prev_layer.shape[1]))
out_in = cp.einsum('nij,kj->nik', a['d'], prev_layer)
out_w = cp.einsum('nik,nij->nkj', a['d'], W['d'])
out_w_out = cp.einsum('nik,nji->njk', out_w, a['d'])
dW['d'] = (1 / batch_size) * (out_in - out_w_out)
out_b = cp.einsum('nik,nij->nkj', a['d'], b['d'])
out_b_out = cp.einsum('nik,nji->njk', out_b, a['d'])
dB['d'] = (out_in - out_b_out)
dB['d'] = (1 / batch_size) * cp.sum(dB['d'], axis=2)
dB['d'] = cp.reshape(dB['d'], (dB['d'].shape[0], dB['d'].shape[1], 1))
return [dW, dB, delta]
def get_atom_dists(self):
if os.path.isfile(self.file):
pdb_file = open(self.file,'r')
else:
raise OSError('File {} does not exist'.format(self.file))
lineno = 0
frames = []
atoms = []
val_frames = []
val_atoms = []
for line in pdb_file:
lineno += 1
if line.startswith('ATOM'):
try:
at_obj = PDBAtom(line)
atoms.append([at_obj.x, at_obj.y, at_obj.z])
val_atoms.append(at_obj.valence_count)
except:
sys.stderr.write('\nProblem parsing line {} in file {}\n'.format(lineno, self.file))
sys.stderr.write(line)
sys.stderr.write('Probably ATOM entry is formatted incorrectly?\n')
sys.stderr.write('Please refer to - http://www.wwpdb.org/documentation/format32/sect9.html#ATOM\n\n')
sys.exit(1)
elif line.startswith('END'):
frames.append(atoms)
atoms = []
val_frames.append(val_atoms)
val_atoms = []
pdb_file.close()
base = cp.zeros((len(framesindices), len(frames[0]), 3))
for i in range(len(framesindices)):
for j in range(len(frames[i])):
for k in range(len(frames[i][j])):
base[i][j][k] = frames[i][j][k]
dists = cp.reshape(base, (len(framesindices), 1, len(frames[0]), 3)) - cp.reshape(base, (len(framesindices), len(frames[0]), 1, 3))
cp.cuda.Stream.null.synchronize()
dists = dists**2
dists = dists.sum(3)
dists = cp.sqrt(dists)
cp.cuda.Stream.null.synchronize()
self.valence_list = val_frames
self.distance_graphs = dists
return self.distance_graphs
def update_path(self, quantile):
w = 1 / self.population * w_quantile(self.population, quantile)
w /= cp.sum(w)
w = w[:, cp.newaxis]
eta_m = 1.0
eta_c = 1.0 / ((self.in_dim * self.out_dim)**2 * cp.sum(w**2))
self.W_mean = cp.array(self.W_mean)
self.W_cov = cp.array(self.W_cov)
self.b_mean = cp.array(self.b_mean)
self.b_cov = cp.array(self.b_cov)
W_mean_ = self.W_mean
b_mean_ = self.b_mean
self.W_mean = self.W_mean + eta_m * cp.sum(
w *
(cp.reshape(self.W, [self.population, self.in_dim * self.out_dim])
- self.W_mean),
axis=0)
self.W_p_sig = (1 - self.W_c_sig) * self.W_p_sig + cp.sqrt(
1 - (1 - self.W_c_sig)**2) * cp.sqrt(1 / np.sum(w**2)) * cp.sqrt(
1 / self.W_cov) * (self.W_mean - W_mean_) / self.W_sigma
self.W_sigma = self.W_sigma * cp.exp(
self.W_c_sig * (cp.linalg.norm(self.W_p_sig) /
(math.sqrt(self.in_dim * self.out_dim) *
(1 - 1 / (4 * self.in_dim * self.out_dim) + 1 /
(21 * ((self.in_dim * self.out_dim)**2)))) - 1))
self.b_mean = self.b_mean + eta_m * cp.sum(w * (self.b - self.b_mean),
axis=0)
self.b_p_sig = (1 - self.b_c_sig) * self.b_p_sig + cp.sqrt(
1 - (1 - self.b_c_sig)**2) * cp.sqrt(1 / np.sum(w**2)) * cp.sqrt(
1 / self.b_cov) * (self.b_mean - b_mean_) / self.b_sigma
self.b_sigma = self.b_sigma * cp.exp(self.b_c_sig *
(cp.linalg.norm(self.b_p_sig) /
(math.sqrt(self.out_dim) *
(1 - 1 /
(4 * self.out_dim) + 1 /
(21 *
(self.out_dim**2)))) - 1))
self.W_cov = self.W_cov + eta_c * cp.sum(w * ((
(cp.reshape(self.W, [self.population, self.in_dim * self.out_dim])
- W_mean_) / self.W_sigma)**2 - self.W_cov),
axis=0)
self.b_cov = self.b_cov + eta_c * cp.sum(
w * (((self.b - b_mean_) / self.b_sigma)**2 - self.b_cov), axis=0)
def kron(matrix1, matrix2):
"""Kronecker product"""
s1, s2 = matrix1.shape
s3, s4 = matrix2.shape
return cp.reshape(
matrix1.reshape((s1, 1, s2, 1))*matrix2.reshape((1, s3, 1, s4)),
(s1*s3, s2*s4))
def _derivativenorm(self):
"""Compute the derivative of the norm
Returns
-------
derivative : numpy array, shape (m_parameters,)
"""
w2 = cp.reshape(self.w,(self.n_features,self.d,self.D,self.D))
derivative = cp.zeros((self.n_features,self.d,self.D,self.D))
tmp=cp.zeros((self.n_features,self.D))
tmp2=cp.zeros((self.n_features,self.D))
tmp[0,:]=cp.sum(cp.square(w2[0,:,0,:]),0)
for i in range(1,self.n_features-1):
tmp[i,:]=cp.dot(tmp[i-1,:],cp.sum(cp.square(w2[i,:,:,:]),0))
tmp[self.n_features-1,:]=cp.inner(tmp[self.n_features-2,:],
cp.sum(cp.square(w2[self.n_features-1,:,:,0]),0))
tmp2[self.n_features-1,:]=cp.sum(cp.square(w2[self.n_features-1,:,:,0]),0)
for i in range(self.n_features-2,-1,-1):
tmp2[i,:]=cp.dot(cp.sum(cp.square(w2[i,:,:,:]),0),tmp2[i+1,:])
tmp2[0,:]=cp.inner(cp.sum(cp.square(w2[0,:,0,:]),0),tmp2[1,:])
for j in range(self.d):
derivative[0,j,0,:]=cp.multiply(tmp2[1,:],2*(w2[0,j,0,:]))
derivative[self.n_features-1,j,:,0]=\
cp.multiply(tmp[self.n_features-2,:],2*(w2[self.n_features-1,j,:,0]))
for i in range(1,self.n_features-1):
temp3=cp.outer(tmp[i-1,:],tmp2[i+1,:])
for j in range(self.d):
derivative[i,j,:,:]=cp.multiply(temp3,2*(w2[i,j,:,:]))
return derivative.reshape(self.m_parameters)
def objective(trial):
seq_len = trial.suggest_int('seq_len', 10, 60)
N=10
datas=[]
for i in range(N):
l=len(data)*i//N
r=len(data)*(i+1)//N
datas.append(data[l:r])
#train_iter = LSTM_Iterator(train, batch_size=10, seq_len=seq_len)
# 学習結果の保存
logs=[]
for i in range(N):
print("phase :" + str(i) )
#test = [v.tolist() for v in datas[i]]
test = datas[i]
p=datas[0:i] + datas[i+1:N]
#p=np.asarray(p)
train = [v.tolist() for v in np.reshape(p, -1)]
train = cp.array(train, dtype=cp.float32)
logs.append(learn(trial, train, test, seq_len))
logsum = {}
for v in logs:
for key, value in v.items():
logsum[key] = logsum.get(key, 0) + value/N
print(logsum)
for key, value in logsum.items():
trial.set_user_attr(key, value)
# 最終的なバリデーションの値を返す
val_err = logsum['validation/main/loss']
return val_err
def inject_error(weight, mask0, mask1, num_bits=32):
if num_bits == 32:
dtype = cp.uint32
ftype = cp.float32
shape = weight.shape
weight_flatten = cp.ravel(weight).view(dtype)
mask0, mask0_bit = mask0
mask1, mask1_bit = mask1
zero = cp.zeros(1, dtype=dtype)
if (mask0.__len__() is not 0) or (mask1.__len__() is not 0):
for b in range(num_bits):
fault = cp.full(weight_flatten.size, 2**b, dtype=dtype)
bit_loc0 = cp.where(mask0_bit == b, mask0, zero).nonzero()[0]
bit_loc1 = cp.where(mask1_bit == b, mask1, zero).nonzero()[0]
uniform0 = cp.zeros(weight_flatten.size, dtype=dtype)
uniform1 = cp.zeros(weight_flatten.size, dtype=dtype)
# Inject bit error
if bit_loc0.__len__() > 0:
cp.put(uniform0, mask0[bit_loc0], fault)
cp.put(uniform1, mask1[bit_loc1], fault)
# Stuck at 0
not_mask0 = cp.invert(uniform0)
weight_flatten = cp.bitwise_and(weight_flatten, not_mask0)
# Stuck at 1
weight_flatten = cp.bitwise_or(weight_flatten, uniform1)
weight_float = weight_flatten.view(ftype)
return cp.reshape(weight_float, shape)
else:
return weight
def my_conv2(x, sig, varargin=None, **kwargs):
# TODO: Fix so output matches my_conv2_cpu
# x is the matrix to be filtered along a choice of axes
# sig is either a scalar or a sequence of scalars, one for each axis to be filtered
# varargin can be the dimensions to do filtering, if len(sig) != x.shape
# if sig is scalar and no axes are provided, the default axis is 2
if sig <= .25:
return x
idims = 1
if varargin is not None:
idims = varargin
idims = _make_vect(idims)
if _is_vect(idims) and _is_vect(sig):
sigall = sig
else:
sigall = np.tile(sig, len(idims))
for sig, idim in zip(sigall, idims):
Nd = x.ndim
x = cp.transpose(x, [idim] + list(range(0, idim)) +
list(range(idim + 1, Nd)))
dsnew = x.shape
x = cp.reshape(x, (x.shape[0], -1), order='F')
tmax = ceil(4 * sig)
dt = cp.arange(-tmax, tmax + 1)
gaus = cp.exp(-dt**2 / (2 * sig**2))
gaus = gaus / cp.sum(gaus)
y = convolve_gpu(x, gaus, **kwargs)
y = y.reshape(dsnew, order='F')
y = cp.transpose(
y,
list(range(1, idim + 1)) + [0] + list(range(idim + 1, Nd)))
return y
def forward_prop(x, local_time, sequence, isFirst, timestamp, satellite_name):
s = cp.empty([local_time, distance_forward, channels_hidden, M, N])
e = cp.empty([local_time, distance_forward])
alpha = cp.empty([local_time, distance_forward])
p = cp.empty([local_time, channels_p, M, N])
# Hidden Unit
h = cp.empty([local_time + 1, channels_hidden, M, N])
h[-1] = cp.zeros([channels_hidden, M, N])
# LSTM FORWARD PROPAGATION
for t in np.arange(local_time):
# Attention Network
for z in range(
timestamp + t - (distance + learning_window), timestamp +
distance_forward + t - (distance + learning_window)):
temp = cp.concatenate(
(cp.asarray(satellite_images[sequence][z]), h[t - 1]), axis=0)
s[t][z - (timestamp + t - (distance + learning_window))] = tanh(
cp.asarray(
F.convolution_2d(temp.reshape(
1, channels_img + channels_hidden, M, N),
e_kernel,
b=None,
pad=pad_constant)[0].data) + bias_e)
s_temp = s[t][z - (timestamp + t -
(distance + learning_window))].reshape(
M * N * channels_hidden)
e[t][z - (timestamp + t - (distance + learning_window))] = cp.dot(
v_connected_weights,
s_temp) + bias_v[z - (timestamp + t -
(distance + learning_window))]
xtemp = satellite_images[sequence][timestamp - distance:timestamp -
distance + distance_forward, 0]
alpha[t] = softmax(e[t])
p[t] = cp.tensordot(alpha[t], cp.asarray(xtemp), axes=1).reshape(
1, M, N) # Sum all x arrays up, weighted array
temporary = cp.concatenate((x[t], p[t], h[t - 1]), axis=0)
temporary = temporary.reshape(
1, channels_img + channels_p + channels_hidden, M, N)
h[t] = tanh(
cp.asarray(
F.convolution_2d(temporary, main_kernel, b=None, pad=2)
[0].data) + bias_h)
# 1 x 1 convolution
output = cp.matmul(connected_weights, h[local_time - 1].reshape(
channels_hidden, M * N)).reshape(M, N) + bias_y[0]
true_output = rect_linear(output)
return true_output, output, cp.reshape(
h[local_time - 1], (channels_hidden, M * N)), p, h, s, e, alpha, xtemp
def _reshape_nd(arr, ndim, dim):
"""Reshape a 1D array to have n dimensions, all singletons but one.
Parameters
----------
arr : array, shape (N,)
Input array
ndim : int
Number of desired dimensions of reshaped array.
dim : int
Which dimension/axis will not be singleton-sized.
Returns
-------
arr_reshaped : array, shape ([1, ...], N, [1,...])
View of `arr` reshaped to the desired shape.
Examples
--------
>>> arr = cp.random.random(7)
>>> _reshape_nd(arr, 2, 0).shape
(7, 1)
>>> _reshape_nd(arr, 3, 1).shape
(1, 7, 1)
>>> _reshape_nd(arr, 4, -1).shape
(1, 1, 1, 7)
"""
kernel_shape = _kernel_shape(ndim, dim)
return cp.reshape(arr, kernel_shape)
def __call__(self, x, inference=False):
if self.mask is None:
self.mask = self.xp.zeros(
(self.n_centroid, x.shape[1], x.shape[2]), dtype=np.float32)
self.mask[:, :, :self.length] = 1
self.centroid.initialize((self.n_centroid, x.shape[1], x.shape[2]))
I = np.broadcast_to(np.arange(x.shape[0]),
(self.n_centroid, x.shape[0])).T
J = np.broadcast_to(np.arange(self.n_centroid), I.shape)
I = np.ravel(I)
J = np.ravel(J)
centers = F.softmax(self.centroid) * self.mask
if inference:
d = edit_distance(x[I], centers.data[J]).astype(np.float32)
d = cupy.reshape(d, (x.shape[0], self.n_centroid))
centroid_indexes = np.argmin(cupy.asnumpy(d), axis=1)
d = d[np.arange(len(centroid_indexes)), centroid_indexes]
return F.mean(d), centroid_indexes
else:
d = soft_edit_distance(x[I], centers[J], self.tau1)
d = F.reshape(d, (x.shape[0], self.n_centroid))
coef = F.softmax(-d * self.tau2)
S = F.broadcast_to(F.sum(coef, axis=0), coef.shape)
d = F.sum(d * coef / S)
return d / self.n_centroid
#d = F.min(d, axis=1)
return F.mean(d)
def compute3Dpositions(item_path, file_path, xp=np):
K = getcamK(file_path, xp=xp)
fx = K[0, 0]
fy = K[1, 1]
u0 = K[0, 2]
v0 = K[1, 2]
u = xp.tile(xp.arange(1, 641), (480, 1))
v = xp.expand_dims(xp.arange(1, 481), axis=1)
v = xp.tile(v, (1, 640))
u_u0_by_fx = (u - u0) / fx
v_v0_by_fy = (v - v0) / fy
with open(item_path, 'r') as f:
lines = f.read()
lines = lines.split()
str2mat = xp.array(lines, dtype=xp.float64)
z = xp.reshape(str2mat, (480, 640))
z = z / xp.sqrt(u_u0_by_fx**2 + v_v0_by_fy**2 + 1)
x = ((u - u0) / fx) * z
y = ((v - v0) / fy) * z
return z
def take(a, indices, axis=None, out=None):
"""Takes elements of an array at specified indices along an axis.
This is an implementation of "fancy indexing" at single axis.
This function does not support ``mode`` option.
Args:
a (cupy.ndarray): Array to extract elements.
indices (int or array-like): Indices of elements that this function
takes.
axis (int): The axis along which to select indices. The flattened input
is used by default.
out (cupy.ndarray): Output array. If provided, it should be of
appropriate shape and dtype.
Returns:
cupy.ndarray: The result of fancy indexing.
.. seealso:: :func:`numpy.take`
"""
if axis is None:
a = a.ravel()
lshape = ()
rshape = ()
else:
if axis >= a.ndim:
raise ValueError('Axis overrun')
lshape = a.shape[:axis]
rshape = a.shape[axis + 1:]
if numpy.isscalar(indices):
a = cupy.rollaxis(a, axis)
if out is None:
return a[indices].copy()
else:
out[:] = a[indices]
return out
elif not isinstance(indices, cupy.ndarray):
indices = cupy.array(indices, dtype=int)
out_shape = lshape + indices.shape + rshape
if out is None:
out = cupy.empty(out_shape, dtype=a.dtype)
else:
if out.dtype != a.dtype:
raise TypeError('Output dtype mismatch')
if out.shape != out_shape:
raise ValueError('Output shape mismatch')
cdim = indices.size
rdim = internal.prod(rshape)
indices = cupy.reshape(
indices, (1,) * len(lshape) + indices.shape + (1,) * len(rshape))
return _take_kernel(a, indices, cdim, rdim, out)
def __call__(self, hs, path_through, test=False):
""" zのフォーマットはshape(1, 512, 2, 2)となっており、512bitが最大値だと思われる """
"""
path_throughのフォーマットはshape(256)なので、全然足りない
4倍して,2,2で切って、代入する
"""
path_through_4 = cp.repeat(path_through, 4).astype('float32')
path_through_2x2 = chainer.cuda.to_gpu(cp.reshape(path_through_4, (1, 256, 2, 2)) )
#print("orig shape.", hs[-1].shape)
#print("tag shape.", path_through_2x2.shape)
#print("tag object.", type(path_through_2x2))
#print("shape, ", type(hs), type(hs[-1]))
hs[-1] = F.concat( (hs[-1], Variable(path_through_2x2)) )
"""
- device: <CUDA Device 0>
- volatile: OFF
- backend: <class 'cupy.core.core.ndarray'>
- shape: (1, 512, 2, 2)
- dtype: float32
"""
#cupy.concat(hs[-1],)
"""
import numpy as np
import cupy as cp
import cupy.manipulation.join as cujoin
from chainer import Variable
cucat = cujoin.concatenate
path_through
for i in range(len(path_through)-4):
p1, p2, p3, p4 = path_through[i:i+4]
sample = cp.array([[[[p1, p2], [p3, p4]]]]).astype('float32')
break
#print( sample.shape )
vsample = Variable(sample)
import chainer.functions.array.concat as cat
hs[-1] = cat.concat([hs[-1], vsample])
"""
#print("hs", hs[-1], hs[-1].__len__(), hs[-1].debug_print())
h = self.c0(hs[-1], test=test)
for i in range(1,8):
h = F.concat([h, hs[-i-1]])
if i<7:
h = self['c%d'%i](h, test=test)
else:
h = self.c7(h)
return h
def _tensordot_core(a, b, out, n, m, k, ret_shape):
ret_dtype = a.dtype.char
if ret_dtype != b.dtype.char:
ret_dtype = numpy.find_common_type((ret_dtype, b.dtype), ()).char
# Cast to float32 or float64
if ret_dtype == 'f' or ret_dtype == 'd':
dtype = ret_dtype
else:
dtype = numpy.find_common_type((ret_dtype, 'f'), ()).char
a = a.astype(dtype, copy=False)
b = b.astype(dtype, copy=False)
if not a.size or not b.size:
if a.size or b.size:
raise ValueError('cannot dot zero-sized and non-zero-sized arrays')
if out is None:
return cupy.zeros(ret_shape, dtype=ret_dtype)
else:
out.fill(0)
return out
if out is None:
out = cupy.empty(ret_shape, dtype)
if dtype == ret_dtype:
ret = out
else:
ret = cupy.empty(ret_shape, ret_dtype)
else:
ret = out
if out.dtype != dtype:
out = cupy.empty(ret_shape, dtype)
# It copies the operands if needed
if a.shape != (k, n):
a = cupy.reshape(a, (k, n))
if b.shape != (k, m):
b = cupy.reshape(b, (k, m))
c = out
if c.shape != (n, m):
c = c.view()
c.shape = (n, m)
# Be careful that cuBLAS uses the FORTRAN-order matrix representation.
if k == 1:
if n == 1:
# Scalar-vector product
cupy.multiply(a, b, c)
elif m == 1:
# Scalar-vector product
cupy.multiply(a.T, b, c)
else:
# Outer product A^T * B
# c is C-contiguous while cuBLAS requires F-contiguous arrays, so
# we compute C^T = B^T * A here.
handle = cuda.Device().cublas_handle
c.fill(0)
a, inca = _to_cublas_vector(a, 1)
b, incb = _to_cublas_vector(b, 1)
if dtype == 'f':
ger = cublas.sger
elif dtype == 'd':
ger = cublas.dger
ger(handle, m, n, 1, b.data.ptr, incb, a.data.ptr, inca,
c.data.ptr, m)
if dtype != ret_dtype:
elementwise.copy(out, ret)
return ret
handle = cuda.Device().cublas_handle
if n == 1:
if m == 1:
# Inner product
a, inca = _to_cublas_vector(a, 0)
b, incb = _to_cublas_vector(b, 0)
mode = cublas.getPointerMode(handle)
cublas.setPointerMode(handle,
cublas.CUBLAS_POINTER_MODE_DEVICE)
if dtype == 'f':
dot = cublas.sdot
elif dtype == 'd':
dot = cublas.ddot
try:
dot(handle, k, a.data.ptr, inca, b.data.ptr, incb, c.data.ptr)
finally:
cublas.setPointerMode(handle, mode)
else:
# Matrix-vector product B^T * A
a, inca = _to_cublas_vector(a, 0)
b, transb, ldb = _mat_to_cublas_contiguous(b, 1)
if transb:
# gemv requires (m, k) as the original matrix dimensions
# rather than the transposed dimensions.
m, k = k, m
if dtype == 'f':
gemv = cublas.sgemv
elif dtype == 'd':
gemv = cublas.dgemv
gemv(handle, transb, m, k, 1, b.data.ptr, ldb, a.data.ptr, inca,
0, c.data.ptr, 1)
elif m == 1:
# Matrix-vector product A^T * B
a, transa, lda = _mat_to_cublas_contiguous(a, 1)
b, incb = _to_cublas_vector(b, 0)
if transa:
# gemv requires (n, k) as the original matrix dimensions rather
# than the transposed dimensions.
n, k = k, n
if dtype == 'f':
gemv = cublas.sgemv
elif dtype == 'd':
gemv = cublas.dgemv
gemv(handle, transa, n, k, 1, a.data.ptr, lda, b.data.ptr, incb, 0,
c.data.ptr, 1)
else:
# Matrix-Matrix product A^T * B
# c is C-contiguous while cuBLAS assumes F-contiguous inputs, so we
# compute C^T = B^T * A here.
a, transa, lda = _mat_to_cublas_contiguous(a, 0)
b, transb, ldb = _mat_to_cublas_contiguous(b, 1)
if dtype == 'f':
gemm = cublas.sgemm
elif dtype == 'd':
gemm = cublas.dgemm
gemm(handle, transb, transa, m, n, k, 1, b.data.ptr, ldb, a.data.ptr,
lda, 0, c.data.ptr, m)
if dtype != ret_dtype:
elementwise.copy(out, ret)
return ret
def choice(self, a, size=None, replace=True, p=None):
"""Returns an array of random values from a given 1-D array.
.. seealso::
:func:`cupy.random.choice` for full document,
:meth:`numpy.random.choice`
"""
if a is None:
raise ValueError('a must be 1-dimensional or an integer')
if isinstance(a, cupy.ndarray) and a.ndim == 0:
raise NotImplementedError
if isinstance(a, six.integer_types):
a_size = a
if a_size <= 0:
raise ValueError('a must be greater than 0')
else:
a = cupy.array(a, copy=False)
if a.ndim != 1:
raise ValueError('a must be 1-dimensional or an integer')
else:
a_size = len(a)
if a_size == 0:
raise ValueError('a must be non-empty')
if p is not None:
p = cupy.array(p)
if p.ndim != 1:
raise ValueError('p must be 1-dimensional')
if len(p) != a_size:
raise ValueError('a and p must have same size')
if not (p >= 0).all():
raise ValueError('probabilities are not non-negative')
p_sum = cupy.sum(p).get()
if not numpy.allclose(p_sum, 1):
raise ValueError('probabilities do not sum to 1')
if not replace:
raise NotImplementedError
if size is None:
raise NotImplementedError
shape = size
size = numpy.prod(shape)
if p is not None:
p = cupy.broadcast_to(p, (size, a_size))
index = cupy.argmax(cupy.log(p) -
cupy.random.gumbel(size=(size, a_size)),
axis=1)
if not isinstance(shape, six.integer_types):
index = cupy.reshape(index, shape)
else:
index = cupy.random.randint(0, a_size, size=shape)
# Align the dtype with NumPy
index = index.astype(cupy.int64, copy=False)
if isinstance(a, six.integer_types):
return index
if index.ndim == 0:
return cupy.array(a[index], dtype=a.dtype)
return a[index]
