本文整理汇总了Python中theano.tensor.eye函数的典型用法代码示例。如果您正苦于以下问题:Python eye函数的具体用法?Python eye怎么用?Python eye使用的例子?那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了eye函数的20个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于我们的系统推荐出更棒的Python代码示例。
示例1: _plain_ctc
def _plain_ctc(self, ):
labels2 = tt.concatenate((self.labels, [self.blank, self.blank]))
sec_diag = tt.neq(labels2[:-2], labels2[2:]) * tt.eq(labels2[1:-1], self.blank)
# Last two entries of sec_diag do not matter as they multiply zero rows below.
recurrence_relation = \
tt.eye(self.n) + \
tt.eye(self.n, k=1) + \
tt.eye(self.n, k=2) * sec_diag.dimshuffle((0, 'x'))
pred_y = self.inpt[:, self.labels]
fwd_pbblts, _ = th.scan(
lambda curr, accum: curr * tt.dot(accum, recurrence_relation),
sequences=[pred_y],
outputs_info=[tt.eye(self.n)[0]]
)
# TODO: Add probabilites[-1, -2] only if last label is blank.
# liklihood = ifelse(tt.eq(self.n, 1), fwd_pbblts[-1, -1],
# ifelse(tt.neq(self.labels[-1], self.blank), fwd_pbblts[-1, -1],
# fwd_pbblts[-1, -1] + fwd_pbblts[-1, -2]))
liklihood = fwd_pbblts[-1, -1]
self.cost = -tt.log(liklihood)
self.debug = fwd_pbblts.T
开发者ID:ChillarAnand,项目名称:chamanti_ocr,代码行数:25,代码来源:ctc.py
示例2: __init__
def __init__(self, inpt, labels):
'''
Recurrent Relation:
A matrix that specifies allowed transistions in paths.
At any time, one could
0) Stay at the same label (diagonal is identity)
1) Move to the next label (first upper diagonal is identity)
2) Skip to the next to next label if
a) next label is blank and
b) the next to next label is different from the current
(Second upper diagonal is product of conditons a & b)
'''
n_labels = labels.shape[0]
big_I = T.cast(T.eye(n_labels+2), 'float64')
recurrence_relation1 = T.cast(T.eye(n_labels), 'float64') + big_I[2:,1:-1] + big_I[2:,:-2] * T.cast((T.arange(n_labels) % 2), 'float64')
recurrence_relation = T.cast(recurrence_relation1, 'float64')
'''
Forward path probabilities
'''
pred_y = inpt[:, labels]
probabilities, _ = theano.scan(
lambda curr, prev: curr * T.dot(prev, recurrence_relation),
sequences=[pred_y],
outputs_info=[T.cast(T.eye(n_labels)[0], 'float64')]
)
# Final Costs
labels_probab = T.sum(probabilities[-1, -2:])
self.cost = -T.log(labels_probab)
self.params = []
开发者ID:ZhangAustin,项目名称:AutomaticSpeechChunker,代码行数:33,代码来源:chunker.py
示例3: likelihood
def likelihood(f, l, R, mu, eps, sigma2, lambda_1=1e-4):
# The similarity matrix W is a linear combination of the slices in R
W = T.tensordot(R, mu, axes=1)
# The following indices correspond to labeled and unlabeled examples
labeled = T.eq(l, 1).nonzero()
# Calculating the graph Laplacian of W
D = T.diag(W.sum(axis=0))
L = D - W
# The Covariance (or Kernel) matrix is the inverse of the (regularized) Laplacian
epsI = eps * T.eye(L.shape[0])
rL = L + epsI
Sigma = nlinalg.matrix_inverse(rL)
# The marginal density of labeled examples uses Sigma_LL as covariance (sub-)matrix
Sigma_LL = Sigma[labeled][:, labeled][:, 0, :]
# We also consider additive Gaussian noise with variance sigma2
K_L = Sigma_LL + (sigma2 * T.eye(Sigma_LL.shape[0]))
# Calculating the inverse and the determinant of K_L
iK_L = nlinalg.matrix_inverse(K_L)
dK_L = nlinalg.det(K_L)
f_L = f[labeled]
# The (L1-regularized) log-likelihood is given by the summation of the following four terms
term_A = - (1 / 2) * f_L.dot(iK_L.dot(f_L))
term_B = - (1 / 2) * T.log(dK_L)
term_C = - (1 / 2) * T.log(2 * np.pi)
term_D = - lambda_1 * T.sum(abs(mu))
return term_A + term_B + term_C + term_D
开发者ID:pminervini,项目名称:gaussian-processes,代码行数:35,代码来源:propagation.py
示例4: rbm_K
def rbm_K(self, X, effective_batch_size):
D, DH = self.n_visible, self.n_hidden
W, bh, bv = self.W, self.bh, self.bv
#one bit flipped connected states
Y = X.reshape((effective_batch_size, 1, D), 3) * T.ones((1, D, 1)) #tile out data vectors (repeat each one D times)
Y1 = (Y + T.eye(D).reshape((1, D, D), 3))%2 # flip each bit once
# minimal activation connected states
onehots = T.eye(D)
blanks = T.zeros(D)
eX = energy(X, W, bh, bv)
eY = energy(Y1, W, bh, bv)
eO = energy(onehots, W, bh, bv)
eB = energy(blanks, W, bh, bv)
edif = eX.dimshuffle(0, 'x') - eY #- eB #- eO.dimshuffle('x', 0) - eB
Z = T.exp(0.5*edif)
K = T.sum(Z) / effective_batch_size
K.name = 'K'
K = T.cast(K, 'float32')
return K
开发者ID:ebuchman,项目名称:minimum_probability_flow,代码行数:25,代码来源:mpf.py
示例5: recurrence_relation
def recurrence_relation(size):
"""
Based on code from Shawn Tan
"""
eye2 = T.eye(size + 2)
return T.eye(size) + eye2[2:, 1:-1] + eye2[2:, :-2] * (T.arange(size) % 2)
开发者ID:nlkim0817,项目名称:minet,代码行数:7,代码来源:net.py
示例6: recurrence
def recurrence(blanked_label,blank_symbol):
'''
A(s) = alpha(t,s) + alpha(t,s-1) if l_s = blank or l_s = l_{s-2}
= alpha(t,s) + alpha(t,s-1) + alpha(t,s-2) if l_s != l_{s-2}
we can define a L' x L' matrix R to help do this(L' = 2L+1)
A = alpha(t,:) * R
'''
length = blanked_label.shape[1]
blanks = T.zeros((1,2)) + blank_symbol
ybb = T.concatenate((blanked_label, blanks), axis=1)
'''
ybb: 1 x L'+2 L' = 2*L+1
ybb[0,:-2] == blanked_label ybb[0,2:] = blanked_label move along 2 label
T.neq(ybb[:, :-2], ybb[:, 2:]) -> l'=b or l'_u == l'_(u-2) set 0
T.eq(ybb[:, 1:-1], blank_symbol) -> [0,?,0,?,...0,1,0] ? depends on
whether l_s = l_{s-2} result[0,i]==1 means ybb[0,i] != ybb[0,i+2]
'''
setDiagMatrix = T.neq(ybb[:,:-2],ybb[:,2:])*T.eq(ybb[:,1:-1],blank_symbol)
'''
r2: L' x L'
r3: L' x L'
'''
r2 = T.eye(length,k=1)
r3 = T.eye(length,k=2)*(setDiagMatrix.T)
return r2,r3
开发者ID:star013,项目名称:timit,代码行数:25,代码来源:jinctc.py
示例7: vanilla_ctc
def vanilla_ctc(self, ):
my_labels = TT.concatenate((self.labels, [self.blank, self.blank]))
pre_V = TT.neq(my_labels[:-2], my_labels[2:]) * \
TT.eq(my_labels[1:-1], self.blank)
capLambda = \
TT.eye(self.n) + \
TT.eye(self.n, k=1) + \
TT.eye(self.n, k=2) * pre_V.dimshuffle((0, 'x'))
softmax_outputs = self.inpt[:, self.labels]
alphas, _ = theano.scan(
lambda outPuts, old_alpha: outPuts * TT.dot(old_alpha, capLambda),
sequences=[softmax_outputs],
outputs_info=[TT.eye(self.n)[0]]
)
# TODO: This is what we really should use for the initialization.
# Need to debug and make sure there are no errors.
# initial_alphas = TT.zeros(n)
# initial_alphas[0]=inpt[0][-1]
# initial_alphas[1]=inpt[0][labels[1]]
# alphas, _ = theano.scan(
# lambda outPuts, old_alpha: outPuts * TT.dot(old_alpha, capLambda),
# sequences=[softmax_outputs],
# outputs_info=[initial_alphas]
# )
transcript_prob = TT.sum(alphas[-1, -2:])
self.cost = -TT.log(transcript_prob)
self.debug = alphas.T
开发者ID:Neuroschemata,项目名称:Toy-RNN,代码行数:32,代码来源:ctc.py
示例8: each_loss
def each_loss(outpt, inpt):
# y 是填充了blank之后的ans
blank = 26
y_nblank = T.neq(inpt, blank)
n = T.dot(y_nblank, y_nblank) # 真实的字符长度
N = 2 * n + 1 # 填充后的字符长度,去除尾部多余的填充
labels = inpt[:N]
labels2 = T.concatenate((labels, [blank, blank]))
sec_diag = T.neq(labels2[:-2], labels2[2:]) * T.eq(labels2[1:-1], blank)
recurrence_relation = \
T.eye(N) + \
T.eye(N, k=1) + \
T.eye(N, k=2) * sec_diag.dimshuffle((0, 'x'))
pred_y = outpt[:, labels]
fwd_pbblts, _ = theano.scan(
lambda curr, accum: T.switch(T.eq(curr*T.dot(accum, recurrence_relation), 0.0),
T.dot(accum, recurrence_relation)
, curr*T.dot(accum, recurrence_relation)),
sequences=[pred_y],
outputs_info=[T.eye(N)[0]]
)
#return fwd_pbblts
#liklihood = fwd_pbblts[0, 0]
liklihood = fwd_pbblts[-1, -1] + fwd_pbblts[-1, -2]
#liklihood = T.switch(T.lt(liklihood, 1e-35), 1e-35, liklihood)
#loss = -T.log(T.cast(liklihood, "float32"))
#loss = 10 * (liklihood - 1) * (liklihood - 100)
loss = (T.le(liklihood, 1.0)*(10*(liklihood-1)*(liklihood-100)))+(T.gt(liklihood, 1.0)*(-T.log(T.cast(liklihood, "float32"))))
return loss
开发者ID:nightinwhite,项目名称:Theano-NN_Starter,代码行数:31,代码来源:Layer.py
示例9: recurrence_relation
def recurrence_relation(y, y_mask):
# with blank symbol of -1 this falls back to the recurrence that fails
# with repeating symbols!
blank_symbol = 2
n_y = y.shape[0]
blanks = tensor.zeros((2, y.shape[1])) + blank_symbol
ybb = tensor.concatenate((y, blanks), axis=0).T
# ybb = B x (L'+2) L'=2*label_noblank_length+1
# ybb[:,:-2] == y.T ybb[:,2:] = y.T move along 2 label
# see Alex's paper:
# tensor.neq(ybb[:, :-2], ybb[:, 2:]) -> l'=b or l'_u == l'_(u-2) set 0
# tensor.eq(ybb[:, 1:-1], blank_symbol) -> [0,?,0,?,...0,1,0] ? depends on whether l'_u == l'_(u-2) result[0,i]==1 means ybb[0,i] != ybb[0,i+2]
# sec_diag = B x L'
sec_diag = (tensor.neq(ybb[:, :-2], ybb[:, 2:]) *
tensor.eq(ybb[:, 1:-1], blank_symbol) *
y_mask.T)
# r2: L'xL'
# r3: L'xL'xB
r2 = tensor.eye(n_y, k=1)
# tensor.eye(n_y, k=2).dimshuffle(0, 1, 'x') L' x L' x 1
# sec_diag.dimshuffle(1, 'x', 0) L' x 1 x B
r3 = (tensor.eye(n_y, k=2).dimshuffle(0, 1, 'x') *
sec_diag.dimshuffle(1, 'x', 0))
return r2, r3
开发者ID:star013,项目名称:timit,代码行数:25,代码来源:conquer.py
示例10: recurrence_relation
def recurrence_relation(self, y):
def sec_diag_i(yt, ytp1, ytp2):
return T.neq(yt, ytp2) * T.eq(ytp1, self.n_out)
y_extend = T.concatenate((y, [self.n_out, self.n_out]))
sec_diag, _ = theano.scan(sec_diag_i,
sequences={'input':y_extend, 'taps':[0, 1, 2]})
y_sz = y.shape[0]
return T.eye(y_sz) + \
T .eye(y_sz, k=1) + \
T.eye(y_sz, k=2) * sec_diag.dimshuffle((0, 'x'))
开发者ID:ZhangAustin,项目名称:DeepSpeech,代码行数:12,代码来源:rnntest.py
示例11: __init__
def __init__(self, v=None, **kwargs):
super(HouseholderFlow, self).__init__(**kwargs)
v = self.add_param(v, 'v')
self.shared_params = dict(v=v)
if self.batched:
vv = v.dimshuffle(0, 1, 'x') * v.dimshuffle(0, 'x', 1)
I = tt.eye(self.dim).dimshuffle('x', 0, 1)
vvn = (1e-10+(v**2).sum(-1)).dimshuffle(0, 'x', 'x')
else:
vv = tt.outer(v, v)
I = tt.eye(self.dim)
vvn = ((v**2).sum(-1)+1e-10)
self.H = I - 2. * vv / vvn
开发者ID:springcoil,项目名称:pymc3,代码行数:13,代码来源:flows.py
示例12: _mb_normal_ctc
def _mb_normal_ctc(self, network_output, labels, mask):
n_y = labels.shape[1] / 2
y = labels[:,:n_y]
y = y.dimshuffle(1,0)
y_mask = labels[:,n_y:].astype(theano.config.floatX)
# y_row = labels.dimshuffle(1,0)
# n_y = y_row.shape[0] / 2
# y = y_row[:n_y,:]
# y_mask = y_row[n_y:,:].astype(theano.config.floatX)
y_hat = network_output.dimshuffle(0, 2, 1)
pred_y = y_hat[:, y.astype('int32'), T.arange(self.tpo["batch_size"])]
ybb = T.concatenate((y, self.blanks), axis=0).T
sec_diag = (T.neq(ybb[:, :-2], ybb[:, 2:]) *
T.eq(ybb[:, 1:-1], self.tpo["CTC_blank"]) *
y_mask)
# r1: LxL
# r2: LxL
# r3: LxLxB
r2 = T.eye(n_y, k=1)
r3 = (T.eye(n_y, k=2).dimshuffle(0, 1, 'x') *
sec_diag.dimshuffle(1, 'x', 0))
def step(p_curr, p_prev):
# instead of dot product, we * first
# and then sum oven one dimension.
# objective: T.dot((p_prev)BxL, LxLxB)
# solusion: Lx1xB * LxLxB --> LxLxB --> (sumover)xLxB
dotproduct = (p_prev + T.dot(p_prev, r2) +
(p_prev.dimshuffle(1, 'x', 0) * r3).sum(axis=0).T)
return p_curr.T * dotproduct * y_mask # B x L
probabilities, _ = theano.scan(
step,
sequences=[pred_y],
outputs_info=[T.eye(n_y)[0] * T.ones([self.tpo["batch_size"], n_y])])
labels_probab = T.sum(probabilities[-1,:, -2:])
return T.mean(-T.log(labels_probab))
开发者ID:dzungcamlang,项目名称:recnet,代码行数:51,代码来源:loss_function.py
示例13: grad
def grad(self, inputs, cost_grad):
"""
Notes:
1. The gradient is computed under the assumption that perturbations
of the input array respect triangularity, i.e. partial derivatives wrt
triangular region are zero.
2. In contrast with the usual mathematical presentation, in order to
apply theano's 'reshape' function wich implements row-order (i.e. C
order), the differential expressions below have been derived based on
the row-vectorizations of inputs 'a' and 'b'.
See The Matrix Reference Manual,
Copyright 1998-2011 Mike Brookes, Imperial College, London, UK
"""
a, b = inputs
ingrad = cost_grad
ingrad = tensor.as_tensor_variable(ingrad)
shp_a = (tensor.shape(inputs[0])[1],
tensor.shape(inputs[0])[1])
I_M = tensor.eye(*shp_a)
if self.lower:
inv_a = solve_triangular(a, I_M, lower=True)
tri_M = tril(tensor.ones(shp_a))
else:
inv_a = solve_triangular(a, I_M, lower=False)
tri_M = triu(tensor.ones(shp_a))
if b.ndim == 1:
prod_a_b = tensor.tensordot(-b.T, inv_a.T, axes=1)
prod_a_b = tensor.shape_padleft(prod_a_b)
jac_veca = kron(inv_a, prod_a_b)
jac_b = inv_a
outgrad_veca = tensor.tensordot(ingrad, jac_veca, axes=1)
outgrad_a = tensor.reshape(outgrad_veca,
(inputs[0].shape[0], inputs[0].shape[0])) * tri_M
outgrad_b = tensor.tensordot(ingrad, jac_b, axes=1).flatten(ndim=1)
else:
ingrad_vec = ingrad.flatten(ndim=1)
prod_a_b = tensor.tensordot(-b.T, inv_a.T, axes=1)
jac_veca = kron(inv_a, prod_a_b)
I_N = tensor.eye(tensor.shape(inputs[1])[1],
tensor.shape(inputs[1])[1])
jac_vecb = kron(inv_a, I_N)
outgrad_veca = tensor.tensordot(ingrad_vec, jac_veca, axes=1)
outgrad_a = tensor.reshape(outgrad_veca,
(inputs[0].shape[0], inputs[0].shape[0])) * tri_M
outgrad_vecb = tensor.tensordot(ingrad_vec, jac_vecb, axes=1)
outgrad_b = tensor.reshape(outgrad_vecb,
(inputs[1].shape[0], inputs[1].shape[1]))
return [outgrad_a, outgrad_b]
开发者ID:c0g,项目名称:Theano,代码行数:50,代码来源:spatial.py
示例14: compute_log_averaged_ei
def compute_log_averaged_ei(self, x, X, randomness, incumbent):
# We compute the old predictive mean at x
Kzz = compute_kernel(self.lls, self.lsf, self.z, self.z) + T.eye(self.z.shape[ 0 ]) * self.jitter * T.exp(self.lsf)
KzzInv = T.nlinalg.MatrixInversePSD()(Kzz)
LLt = T.dot(self.LParamPost, T.transpose(self.LParamPost))
covCavityInv = KzzInv + LLt * casting(self.n_points - self.set_for_training) / casting(self.n_points)
covCavity = T.nlinalg.MatrixInversePSD()(covCavityInv)
meanCavity = T.dot(covCavity, casting(self.n_points - self.set_for_training) / casting(self.n_points) * self.mParamPost)
KzzInvmeanCavity = T.dot(KzzInv, meanCavity)
Kxz = compute_kernel(self.lls, self.lsf, x, self.z)
m_old_x = T.dot(Kxz, KzzInvmeanCavity)
# We compute the old predictive mean at X
KXz = compute_kernel(self.lls, self.lsf, X, self.z)
m_old_X = T.dot(KXz, KzzInvmeanCavity)
# We compute the required cross covariance matrices
KXX = compute_kernel(self.lls, self.lsf, X, X) - T.dot(T.dot(KXz, KzzInv), KXz.T) + T.eye(X.shape[ 0 ]) * self.jitter * T.exp(self.lsf)
KXXInv = T.nlinalg.MatrixInversePSD()(KXX)
KxX = compute_kernel(self.lls, self.lsf, x, X)
xX = T.concatenate([ x, X ], 0)
KxXz = compute_kernel(self.lls, self.lsf, xX, self.z)
KxX = KxX - T.dot(T.dot(KxXz[ 0 : x.shape[ 0], : ], KzzInv), KxXz[ x.shape[ 0 ] : xX.shape[ 0 ], : ].T)
# We compute the new posterior mean
samples_internal = T.dot(MatrixChol()(KXX), randomness)
new_predictive_mean = T.tile(m_old_x, [ 1, randomness.shape[ 1 ] ]) + T.dot(KxX, T.dot(KXXInv, samples_internal))
# We compute the new posterior variance
z_expanded = T.concatenate([ self.z, X ], 0)
Kxz_expanded = compute_kernel(self.lls, self.lsf, x, z_expanded)
Kzz_expanded = compute_kernel(self.lls, self.lsf, z_expanded, z_expanded) + T.eye(z_expanded.shape[ 0 ]) * self.jitter * T.exp(self.lsf)
Kzz_expandedInv = T.nlinalg.MatrixInversePSD()(Kzz_expanded)
v_out = T.exp(self.lsf) - T.dot(Kxz_expanded * T.dot(Kxz_expanded, Kzz_expandedInv), T.ones_like(z_expanded[ : , 0 : 1 ]))
new_predictive_var = T.tile(v_out, [ 1, randomness.shape[ 1 ] ])
s = (incumbent - new_predictive_mean) / T.sqrt(new_predictive_var)
log_ei = T.log((incumbent - new_predictive_mean) * ratio(s) + T.sqrt(new_predictive_var)) + log_n_pdf(s)
return T.mean(LogSumExp(log_ei, 1), 1)
开发者ID:nair-p,项目名称:sdvae,代码行数:49,代码来源:sparse_gp_theano_internal.py
示例15: recurrence_relation_
def recurrence_relation_(y_, blank_symbol):
y = y_.dimshuffle(0,'x')
n_y = y.shape[0]
blanks = T.zeros((2, y.shape[1])) + blank_symbol
ybb = T.concatenate((y, blanks), axis=0).T
sec_diag = (T.neq(ybb[:, :-2], ybb[:, 2:]) *
T.eq(ybb[:, 1:-1], blank_symbol))
# r1: LxL
# r2: LxL
# r3: LxL
r2 = T.eye(n_y, k=1)
r3 = (T.eye(n_y, k=2) * sec_diag)
return r2, r3
开发者ID:tangkk,项目名称:tangkk-mirex-ace,代码行数:15,代码来源:ctc.py
示例16: recurrence_relation
def recurrence_relation(y, y_mask):
# with blank symbol of -1 this falls back to the recurrence that fails
# with repeating symbols!
blank_symbol = -1
n_y = y.shape[0]
blanks = tensor.zeros((2, y.shape[1])) + blank_symbol
ybb = tensor.concatenate((y, blanks), axis=0).T
sec_diag = tensor.neq(ybb[:, :-2], ybb[:, 2:]) * tensor.eq(ybb[:, 1:-1], blank_symbol) * y_mask.T
# r1: LxL
# r2: LxL
# r3: LxLxB
r2 = tensor.eye(n_y, k=1)
r3 = tensor.eye(n_y, k=2).dimshuffle(0, 1, "x") * sec_diag.dimshuffle(1, "x", 0)
return r2, r3
开发者ID:star013,项目名称:timit,代码行数:15,代码来源:kyle_comment.py
示例17: __init__
def __init__(self, z0=None, dim=None, v=None, jitter=.1):
super(HouseholderFlow, self).__init__(dim=dim, z0=z0, jitter=jitter)
if v is None:
v = self.add_param(dim, 'v')
self.shared_params = dict(v=v)
v = v.dimshuffle(0, 'x')
self.H = tt.eye(dim) - 2. * v.dot(v.T) / ((v**2).sum()+1e-10)
开发者ID:aasensio,项目名称:pymc3,代码行数:7,代码来源:flows.py
示例18: _path_probabs
def _path_probabs(y, y_hat, y_mask, y_hat_mask, blank_symbol):
"""Compute the probabilities of the paths that are compatible with the
sequence y.
This function uses scan to get the forward probabilities (often denoted
with the symbol alpha in the literature).
See _log_path_probabs for a version that works in log domain.
"""
pred_y = _class_batch_to_labeling_batch(y, y_hat, y_hat_mask)
pred_y = pred_y.dimshuffle(0, 2, 1)
n_labels = y.shape[0]
r2, r3 = _recurrence_relation(y, y_mask, blank_symbol)
def step(p_curr, p_prev):
# instead of dot product, we * first
# and then sum oven one dimension.
# objective: T.dot((p_prev)BxL, LxLxB)
# solusion: Lx1xB * LxLxB --> LxLxB --> (sumover)xLxB
dotproduct = p_prev + tensor.dot(p_prev, r2) + (p_prev.dimshuffle(1, "x", 0) * r3).sum(axis=0).T
return p_curr.T * dotproduct * y_mask.T # B x L
probabilities, _ = theano.scan(
step, sequences=[pred_y], outputs_info=[tensor.eye(n_labels)[0] * tensor.ones(y.T.shape)]
)
return probabilities
开发者ID:trungnt13,项目名称:Lasagne,代码行数:28,代码来源:ctc_cost.py
示例19: get_output_for
def get_output_for(self, input, init=False, **kwargs):
if input.ndim > 2:
# if the input has more than two dimensions, flatten it into a
# batch of feature vectors.
input = input.flatten(2)
activation = T.tensordot(input, self.W, [[1], [0]])
abs_dif = (T.sum(abs(activation.dimshuffle(0,1,2,'x') - activation.dimshuffle('x',1,2,0)),axis=2)
+ 1e6 * T.eye(input.shape[0]).dimshuffle(0,'x',1))
if init:
mean_min_abs_dif = 0.5 * T.mean(T.min(abs_dif, axis=2),axis=0)
abs_dif /= mean_min_abs_dif.dimshuffle('x',0,'x')
self.init_updates = [(self.log_weight_scale, self.log_weight_scale-T.log(mean_min_abs_dif).dimshuffle(0,'x'))]
f = T.sum(T.exp(-abs_dif),axis=2)
if init:
mf = T.mean(f,axis=0)
f -= mf.dimshuffle('x',0)
self.init_updates.append((self.b, -mf))
else:
f += self.b.dimshuffle('x',0)
return T.concatenate([input, f], axis=1)
开发者ID:255BITS,项目名称:improved-gan,代码行数:25,代码来源:nn.py
示例20: __init__
def __init__(self, incoming, dimension, params_init=(GlorotUniform(),
GlorotUniform(),
Uniform([0, 0.1])),
addition_parameters=[False], **kwargs):
'''
init parameters
:param incoming: input to the LISTA layer
:param dimension: 2 numbers list.
dimension[0] is dict_size, length of dictionary vector in LISTA. dimension[1] is T a.k.a depth
:param params_init: init value or init method for LISTA
:transposed: = True if the input dictionary D is the transpose matrix of a theano.compile.SharedVariable V.
In that case self.W = D^T = V^T^T = V
:param kwargs: parameters of super class
:return:
'''
super(LISTA, self).__init__(incoming, **kwargs)
self.transposed = addition_parameters[0]
num_inputs = incoming.output_shape[-1]
self.dict_size = dimension[0]
self.T = dimension[1]
self.W = self.add_param(params_init[0], [num_inputs, self.dict_size], name='W',
lista=True, lista_weight_W=True, sparse_dictionary=True, regularizable=True)
# self.S = self.add_param(params_init[1], [self.dict_size, self.dict_size], name='S',
# lista=True, lista_weight_W=True, regularizable=True)
if T > 0:
self.S = T.eye(self.dict_size) - T.dot(self.get_dictionary(), self.get_dictionary().T)
self.S = self.add_param(theano.shared(floatX(self.S.eval())), [self.dict_size, self.dict_size], name='S',
lista=True, lista_weight_S=True, regularizable=True)
self.theta = self.add_param(theano.shared(floatX(0.01 * np.ones([self.dict_size, ]))), [self.dict_size, ],
name='theta',
lista=True, lista_fun_param=True, regularizable=False)
self.eps = 1e-6
self.clipped_theta = T.clip(self.theta, self.eps, 10)
开发者ID:minhnhat93,项目名称:ssl-lasagne,代码行数:33,代码来源:sparse.py
注:本文中的theano.tensor.eye函数示例由纯净天空整理自Github/MSDocs等源码及文档管理平台,相关代码片段筛选自各路编程大神贡献的开源项目,源码版权归原作者所有,传播和使用请参考对应项目的License;未经允许,请勿转载。 |
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