def gibbs(self, sample, countStep, function_mode, h_lid_type = 0):
# templates of Varibles for calculate h_lid by previous value
calc_h_lid = lambda h_lid_old, sample: T.nnet.sigmoid(T.dot(sample, self.W) + self.hBiasbase) #+ T.dot(h_lid_old, self.W2.T)
calc_hBiases = lambda h_lid: self.hBiasbase + T.dot(h_lid, self.W2.T)
calc_vBiases = lambda h_lid: self.vBiasbase + T.dot(h_lid, self.W1.T)
# Parameter: countGibbsStep
def gibbsSamplingForAllTime(sample, start_h_lid):
def gibbsSamplingForOneStepTime(sample, h_lid):
vBias = calc_vBiases(h_lid)
hBias = calc_hBiases(h_lid)
res, updates = self.bm.gibbs(sample, self.W, vBias, hBias, countStep, function_mode)
#res = res[-1]
if h_lid_type == 0:
return [res, calc_h_lid(h_lid, sample), vBias, hBias], updates
else:
return [res, calc_h_lid(h_lid, res), vBias, hBias], updates
[sample_res, hLids, vBiases, hBiases], updates = theano.scan(gibbsSamplingForOneStepTime, sequences=sample, outputs_info=[None, start_h_lid, None, None])
return sample_res, hLids, vBiases, hBiases, updates
# usual gibbs-sampling
if len(sample.broadcastable) == 2:
# matrix! it is one object
res, hLids, vBiases, hBiases, updates = gibbsSamplingForAllTime([sample], self.h_lid_0)
hLids = T.concatenate([[self.h_lid_0], hLids[0:-1]])
return res, hLids, updates, vBiases, hBiases
else:
new_dim = T.cast(sample.shape[0], 'int32');
my_sample = T.transpose(sample, (1, 0, 2))
h_lids_start = T.reshape(T.repeat(self.h_lid_0, new_dim), (self.hidden, new_dim)).T
res, hLids, vBiases, hBiases, updates = gibbsSamplingForAllTime(my_sample, h_lids_start)
res = T.transpose(res, (1, 0, 2))
hLids = T.concatenate([[h_lids_start], hLids[0:-1]])
hLids = T.transpose(hLids, (1, 0, 2))
vBiases = T.transpose(vBiases, (1, 0, 2))
hBiases = T.transpose(hBiases, (1, 0, 2))
return res, hLids, updates, vBiases, hBiases
def sgru3(X, h, W, U, b, t):
t = 0
z_t = T.tanh(T.dot(X,W[t*2+0]) + b[t*2+0])
r_t = (T.dot(h,U[t*2+0]) + b[t*2+1])
z_t2 = (T.dot(X,W[t*2+1]) + b[t*2+2])
r_t2 = T.tanh(T.dot(h,U[t*2+1]) + b[t*2+3])
return T.tanh(T.dot(z_t*r_t,T.transpose(U[t*2+2])) + T.dot(z_t2*r_t2,T.transpose(U[t*2+3])))
def full(self, X, Xs=None):
X, Xc, Xs = self._common(X, Xs)
if Xs is None:
return tt.dot(Xc, tt.transpose(Xc))
else:
Xsc = tt.sub(Xs, self.c)
return tt.dot(Xc, tt.transpose(Xsc))
def forward_batch_step(x_t, H_mask, H_tm1):
H = TT.dot(W_rec,H_tm1) + W_in[:,x_t]
H_t = TT.nnet.sigmoid(H)
Y_t = TT.nnet.softmax(TT.transpose(TT.dot(W_out, H_t)))
Y_t = -TT.log2(Y_t)
Y_t = TT.dot(TT.transpose(Y_t), TT.diag(H_mask))
return [H_t, Y_t]
def bbprop(self):
self.lin_bbprop = self.p_y_given_x - self.p_y_given_x * self.p_y_given_x
self.lin_bbprop /= T.shape(self.p_y_given_x)[0]
self.dict_bbprop = {}
self.dict_bbprop.update({self.b_upmask: T.sum(self.lin_bbprop, 0)})
self.dict_bbprop.update({self.W_upmask: T.dot(T.transpose(self.inp * self.inp), self.lin_bbprop)})
return T.dot(self.lin_bbprop, T.transpose(self.W * self.W)), self.dict_bbprop
def T_l2_cost_conv(x,a,A,imshp,kshp,mask=True):
"""
xsz*ysz*nchannels, nimages = x.shape
xsz*ysz*nfeat, nimages = a.shape
xsz*ysz*nchannels, nfeat = A.shape
"""
#imshp = num images, channels, szy, szx
#kshp = features, channels, szy, szx
#featshp = num images, features, szy, szx
featshp = (imshp[0],kshp[0],imshp[2] - kshp[2] + 1,imshp[3] - kshp[3] + 1) # num images, features, szy, szx
image = T.reshape(T.transpose(x),imshp)
kernel = T.reshape(T.transpose(A),kshp)
features = T.reshape(T.transpose(a),featshp)
# Need to transpose first two dimensions of kernel, and reverse index kernel image dims (for correlation)
kernel_rotated = T.transpose(kernel[:,:,::-1,::-1],axes=[1,0,2,3])
image_estimate = conv2d(features,kernel_rotated,border_mode='full')
if mask:
image_error_temp = image - image_estimate
image_error = T.zeros_like(image_error_temp)
image_error = T.set_subtensor(image_error[:,:,(kshp[2]-1):(imshp[2]-kshp[2]+1),(kshp[3]-1):(imshp[3]-kshp[3]+1)],
image_error_temp[:,:,(kshp[2]-1):(imshp[2]-kshp[2]+1),(kshp[3]-1):(imshp[3]-kshp[3]+1)])
else:
image_error = image - image_estimate
return .5*T.sum(image_error **2)
def nin(X, param):
w1, w2, w3, b1, b2, b3 = param
X = X.dimshuffle(0, 1, 'x', 2, 3) # (n,32,1,r,c)
w1 = w1.dimshuffle(0, 1, 2, 'x', 3, 4) # (64,32,16,1,3,3)
w2 = w2.dimshuffle(0, 1, 'x', 2, 'x', 'x') # (64,32,1,16,1,1)
w3 = w3.dimshuffle(0, 1, 2, 'x', 'x') # (64,2,32,1,1)
b1 = b1.dimshuffle(0, 1, 'x', 2, 'x', 'x') # (64,32,1,16,1,1)
b2 = b2.dimshuffle(0, 1, 'x', 2, 'x', 'x') # (64,32,1,1,1,1)
b3 = b3.dimshuffle(0, 'x', 1, 'x', 'x') # (64,1,2,1,1)
indexi = T.arange(w1.shape[0], dtype='int32') # (0:64)
indexi = T.repeat(indexi, w1.shape[1], axis=0)
indexj = T.arange(w1.shape[1], dtype='int32') # (0:64)
indexj = T.tile(indexj, w1.shape[0])
results, updates = scan(fn=metaOp1,
sequences=[indexi, indexj],
outputs_info=None,
non_sequences=[X, w1, w2, b1, b2],
strict=True) # (64*32,n,1,r,c)
metaShape1 = results.shape[-4], results.shape[-2], results.shape[-1]
reshaped1 = results.reshape((w1.shape[0], w1.shape[1]) + metaShape1) # (64,32,n,r,c)
permuted1 = T.transpose(reshaped1, axes=(0, 2, 1, 3, 4)) # (64,n,32,r,c)
indexi = T.arange(w1.shape[0], dtype='int32') # (0:64)
results, updates = scan(fn=metaOp2,
sequences=[indexi],
outputs_info=None,
non_sequences=[permuted1, w3, b3],
strict=True) # (64,n,2,r,c)
permuted2 = T.transpose(results, axes=(1, 0, 2, 3, 4)) # (n,64,2,r,c)
metaShape2 = permuted2.shape[-2], permuted2.shape[-1]
reshaped2 = permuted2.reshape((permuted2.shape[0], -1) + metaShape2) # (n,128,r,c)
return reshaped2
def theano_kernel_derivative(imshp,kshp,featshp,stride=1):
features = T.tensor4(dtype=theano.config.floatX)
kernel = T.tensor4(dtype=theano.config.floatX)
image = T.tensor4(dtype=theano.config.floatX)
# Need to transpose first two dimensions of kernel, and reverse index kernel image dims (for correlation)
kernel_rotated = T.transpose(kernel[:,:,::-1,::-1],axes=[1,0,2,3])
featshp_logical = (featshp[0],featshp[1],featshp[2]*stride,featshp[3]*stride)
kshp_rotated = (kshp[1], kshp[0], kshp[2], kshp[3])
image_estimate = conv2d(features,kernel_rotated,border_mode='full',
image_shape=featshp,filter_shape=kshp_rotated,
imshp_logical=featshp_logical[1:],kshp_logical=kshp[2:])
image_error = image - image_estimate
image_error_rot = T.transpose(image_error,[1,0,2,3])[:,:,::-1,::-1]
imshp_rot = (imshp[1],imshp[0],imshp[2],imshp[3])
featshp_rot = (featshp[1],featshp[0],featshp[2],featshp[3])
features_rot = T.transpose(features,[1,0,2,3])
featshp_rot_logical = (featshp_rot[0],featshp_rot[1],featshp_rot[2]*stride,featshp_rot[3]*stride)
kernel_grad_rot = -conv2d(image_error_rot,features_rot,
image_shape=imshp_rot,filter_shape=featshp_rot,
imshp_logical=imshp_rot[1:],kshp_logical=featshp_rot_logical[2:])
kernel_grad = T.transpose(kernel_grad_rot,[1,0,2,3])
return function(inputs=[image,features,kernel],outputs=kernel_grad)
def __init__(self, rng, input, n_feature_maps, n_in, n_out, b_size=5, read_file=False, W=None, b=None): # input dim should be: batch_size x n_feature_maps x 504 # n_in and n_out should be 504 and 40 respectively input = T.transpose(input, (1, 0, 2)) self.input = input if read_file==False: W_values = np.asarray( rng.uniform( low=-np.sqrt(6./(n_in+n_out)), high=np.sqrt(6./(n_in+n_out)), size=(n_in, n_out) ), dtype=theano.config.floatX ) W = theano.shared(value=W_values, name='W', borrow=True) b_values = np.zeros((n_out,), dtype=theano.config.floatX) b = theano.shared(value=b_values, name='b', borrow=True) self.W = W self.b = b embedding_list = [] for i in range(n_feature_maps): embedding_list.append(T.tanh(T.dot(input[i], self.W) + self.b)) self.output = T.concatenate(embedding_list, axis=0) self.output = T.reshape(self.output, (n_feature_maps, b_size, n_out)) self.params = [self.W, self.b] self.input = T.transpose(self.input, (1, 0, 2)) self.output = T.transpose(self.output, (1, 0, 2))
def categorical_crossentropy_segm(prediction_proba, targets):
'''
MODIFICATIONS:
- reshape from image-size to array and back
'''
shape = T.shape(prediction_proba)
pred_mod1 = T.transpose(prediction_proba, (0,2,3,1))
pred_mod = T.reshape(pred_mod1, (-1,shape[1]))
if prediction_proba.ndim == targets.ndim:
targ_mod1 = T.transpose(targets,(0,2,3,1))
targ_mod = T.reshape(targ_mod1,(-1,shape[1]))
else:
targ_mod = T.reshape(targets, (-1,))
results = categorical_crossentropy(pred_mod, targ_mod)
results = T.reshape(results, (shape[0],shape[2],shape[3]))
# QUICK IMPLEMENTATION FOR TWO SPECIFIC CLASSES. NEEDS GENERALIZATION
# Weights depending on class occurency:
weights = (1.02275, 44.9647)
cars_indx, not_cars_indx = T.nonzero(targets), T.nonzero(T.eq(targets,0))
T.set_subtensor(results[cars_indx], results[cars_indx]*float32(weights[1]) )
T.set_subtensor(results[not_cars_indx], results[not_cars_indx]*float32(weights[0]) )
return T.sum(results, axis=(1,2))
def nn_param(params,input): from theano import tensor as T from matplotlib import pyplot as plt layers=len(params) if(layers==1): lnum=0 p=T.nnet.sigmoid(T.dot(input,params[lnum][0][1])+params[lnum][1][1]) y=T.nnet.sigmoid(T.dot(p,T.transpose(params[lnum][0][1]))+params[lnum][2][1]) yval=y.eval() return yval for lnum in range(layers): if (lnum==0): p=T.nnet.sigmoid(T.dot(input,params[lnum][0][1])+params[lnum][1][1]) y=T.nnet.sigmoid(T.dot(p,T.transpose(params[lnum][0][1]))+params[lnum][2][1]) yval=y.eval() plt.plot(yval,label='%d'%lnum) else: p=T.nnet.sigmoid(T.dot(yval,params[lnum][0][1])+params[lnum][1][1]) y=T.nnet.sigmoid(T.dot(p,T.transpose(params[lnum][0][1]))+params[lnum][2][1]) yval=y.eval() plt.plot(yval) plt.legend() plt.show() return yval
def __init():
dataset = T.matrix("dataset", dtype=config.globalFloatType())
trans_dataset = T.transpose(dataset)
dot_mul = T.dot(dataset, trans_dataset)
l2 = T.sqrt(T.sum(T.square(dataset), axis=1))
# p =printing.Print("l2")
# l2 = p(l2)
l2_inv2 = T.inv(l2).dimshuffle(['x', 0])
# p =printing.Print("l2_inv2")
# l2_inv2 = p(l2_inv2)
l2_inv1 = T.transpose(l2_inv2)
# p =printing.Print("l2_inv1")
# l2_inv1 = p(l2_inv1)
l2_inv = T.dot(l2_inv1, l2_inv2)
# p =printing.Print("l2_inv")
# l2_inv = p(l2_inv)
affinty = (T.mul(dot_mul, l2_inv) + 1) / 2
globals()['__affinty_fun'] = theano.function(
[dataset],
[affinty],
allow_input_downcast=True
)
def _build_conditional(self, Xnew, pred_noise, diag, X, Xu, y, sigma, cov_total, mean_total):
sigma2 = tt.square(sigma)
Kuu = cov_total(Xu)
Kuf = cov_total(Xu, X)
Luu = cholesky(stabilize(Kuu))
A = solve_lower(Luu, Kuf)
Qffd = tt.sum(A * A, 0)
if self.approx == "FITC":
Kffd = cov_total(X, diag=True)
Lamd = tt.clip(Kffd - Qffd, 0.0, np.inf) + sigma2
else: # VFE or DTC
Lamd = tt.ones_like(Qffd) * sigma2
A_l = A / Lamd
L_B = cholesky(tt.eye(Xu.shape[0]) + tt.dot(A_l, tt.transpose(A)))
r = y - mean_total(X)
r_l = r / Lamd
c = solve_lower(L_B, tt.dot(A, r_l))
Kus = self.cov_func(Xu, Xnew)
As = solve_lower(Luu, Kus)
mu = self.mean_func(Xnew) + tt.dot(tt.transpose(As), solve_upper(tt.transpose(L_B), c))
C = solve_lower(L_B, As)
if diag:
Kss = self.cov_func(Xnew, diag=True)
var = Kss - tt.sum(tt.square(As), 0) + tt.sum(tt.square(C), 0)
if pred_noise:
var += sigma2
return mu, var
else:
cov = (self.cov_func(Xnew) - tt.dot(tt.transpose(As), As) +
tt.dot(tt.transpose(C), C))
if pred_noise:
cov += sigma2 * tt.identity_like(cov)
return mu, stabilize(cov)
def kmaxpooling_output(input):
'''
实现 k-max pooling
1. 先排序
2. 再分别取出前k个值
:param k: k top higiest value
:type k: int
:return:
'''
input = T.transpose(input, axes=(0, 1, 3, 2))
sorted_values = T.argsort(input, axis=3)
topmax_indexes = sorted_values[:, :, :, -k:]
# sort indexes so that we keep the correct order within the sentence
topmax_indexes_sorted = T.sort(topmax_indexes)
# given that topmax only gives the index of the third dimension, we need to generate the other 3 dimensions
dim0 = T.arange(0, input.shape[0]).repeat(input.shape[1] * input.shape[2] * k)
dim1 = T.arange(0, input.shape[1]).repeat(k * input.shape[2]).reshape((1, -1)).repeat(input.shape[0],
axis=0).flatten()
dim2 = T.arange(0, input.shape[2]).repeat(k).reshape((1, -1)).repeat(input.shape[0] * input.shape[1],
axis=0).flatten()
dim3 = topmax_indexes_sorted.flatten()
return T.transpose(
input[dim0, dim1, dim2, dim3].reshape((input.shape[0], input.shape[1], input.shape[2], k)),
axes=(0, 1, 3, 2))
def T_subspacel1_slow_shrinkage_conv(a, L, lam_sparse, lam_slow, imshp,kshp,featshp,stride=(1,1),small_value=.001):
featshp = (imshp[0],kshp[0],featshp[2],featshp[3]) # num images, features, szy, szx
features = T.reshape(T.transpose(a),featshp,ndim=4)
amp = T.sqrt(features[:,::2,:,:]**2 + features[:,1::2,:,:]**2 + small_value)
#damp = amp[:,1:] - amp[:,:-1]
# compose slow shrinkage with subspace l1 shrinkage
# slow shrinkage
div = T.zeros_like(amp)
d1 = amp[1:,:,:,:] - amp[:-1,:,:,:]
d2 = d1[1:,:,:,:] - d1[:-1,:,:,:]
div = T.set_subtensor(div[1:-1,:,:,:], -d2)
div = T.set_subtensor(div[0,:,:,:], -d1[0,:,:,:])
div = T.set_subtensor(div[-1,:,:,:], d1[-1,:,:,:])
slow_amp_shrinkage = 1 - (lam_slow / L) * (div / amp)
slow_amp_value = T.switch(T.gt(slow_amp_shrinkage, 0), slow_amp_shrinkage, 0)
slow_shrinkage_prox_a = slow_amp_value * features[:, ::2, :,:]
slow_shrinkage_prox_b = slow_amp_value * features[:,1::2, :,:]
# subspace l1 shrinkage
amp_slow_shrinkage_prox = T.sqrt(slow_shrinkage_prox_a ** 2 + slow_shrinkage_prox_b ** 2)
#amp_shrinkage = 1. - (lam_slow*lam_sparse/L)*amp_slow_shrinkage_prox
amp_shrinkage = 1. - (lam_sparse / L) / amp_slow_shrinkage_prox
amp_value = T.switch(T.gt(amp_shrinkage, 0.), amp_shrinkage, 0.)
subspacel1_prox = T.zeros_like(features)
subspacel1_prox = T.set_subtensor(subspacel1_prox[:, ::2, :,:], amp_value * slow_shrinkage_prox_a)
subspacel1_prox = T.set_subtensor(subspacel1_prox[:,1::2, :,:], amp_value * slow_shrinkage_prox_b)
reshape_subspacel1_prox = T.transpose(T.reshape(subspacel1_prox,(featshp[0],featshp[1]*featshp[2]*featshp[3]),ndim=2))
return reshape_subspacel1_prox
def _pooling_function(self, inputs, pool_size, strides, border_mode, dim_ordering):
if pool_size[0]<-1:
# k-max pooling
input_layer = T.transpose(inputs, axes=(0, 1, 3, 2))
sorted_values = T.argsort(input_layer, axis=3)
topmax_indexes = sorted_values[:, :, :, -self.k:]
# sort indexes so that we keep the correct order within the sentence
topmax_indexes_sorted = T.sort(topmax_indexes)
# given that topmax only gives the index of the third dimension, we need to generate the other 3 dimensions
dim0 = T.arange(0, input_layer.shape[0]).repeat(input_layer.shape[1] * input_layer.shape[2] * self.k)
dim1 = T.arange(0, input_layer.shape[1]).repeat(self.k * input_layer.shape[2]).reshape((1, -1)).repeat(
input_layer.shape[0],
axis=0).flatten()
dim2 = T.arange(0, input_layer.shape[2]).repeat(self.k).reshape((1, -1)).repeat(
input_layer.shape[0] * input_layer.shape[1],
axis=0).flatten()
dim3 = topmax_indexes_sorted.flatten()
x = T.transpose(
input_layer[dim0, dim1, dim2, dim3].reshape(
(input_layer.shape[0], input_layer.shape[1], input_layer.shape[2], self.k)),
axes=(0, 1, 3, 2))
return x
else:
return super(MaxPooling2DWrapper, self)._pooling_function(inputs, pool_size, strides, border_mode, dim_ordering)
def T_l2_cost_conv_dA(x,a,A,imshp,kshp,featshp,stride=(1,1),mask=True):
image_error, kernel, features = helper_T_l2_cost_conv(x=x,a=a,A=A,imshp=imshp,kshp=kshp,featshp=featshp,stride=stride,mask=mask)
if stride == (1,1):
image_error_rot = T.transpose(image_error,[1,0,2,3])[:,:,::-1,::-1]
imshp_rot = (imshp[1],imshp[0],imshp[2],imshp[3])
featshp_rot = (featshp[1],featshp[0],featshp[2],featshp[3])
features_rot = T.transpose(features,[1,0,2,3])
featshp_rot_logical = (featshp_rot[0],
featshp_rot[1],
imshp[2] - kshp[2] + 1,
imshp[3] - kshp[3] + 1)
kernel_grad_rot = -1.*conv2d(image_error_rot,features_rot,
image_shape=imshp_rot,filter_shape=featshp_rot,
imshp_logical=imshp_rot[1:],kshp_logical=featshp_rot_logical[2:])
kernel_grad = T.transpose(kernel_grad_rot,[1,0,2,3])
reshape_kernel_grad = T.transpose(T.reshape(kernel_grad,(kshp[0],kshp[1]*kshp[2]*kshp[3]),ndim=2))
return reshape_kernel_grad
else:
my_conv = MyConv_view(strides=stride,kshp=kshp)
kernel_grad = my_conv(image_error,features)
reshape_kernel_grad = T.transpose(T.reshape(kernel_grad, (kshp[0], kshp[1] * kshp[2] * kshp[3]), ndim=2))
return reshape_kernel_grad
def get_output_for(self, input, **kwargs):
'''
Computes 2D FFT. Input layer must have dimension [n, 2, nx, ny]
'''
if self.is_3d:
n, nc, nx, ny, nt = self.data_shape
lin = T.transpose(input, axes=(0, 4, 1, 2, 3))
lin = lin.reshape((-1, nc, nx, ny))
lout, updates = theano.scan(self.transform, sequences=lin)
lout = lout.reshape((-1, nt, nc, nx, ny))
out = T.transpose(lout, axes=(0, 2, 3, 4, 1))
return out
# def loop_over_n(i, arr):
# out, updates = theano.scan(self.transform,
# sequences=arr[:, :, i])[0]
# return out
# nt = self.data_shape[-1]
# out, updates = theano.scan(loop_over_n,
# non_sequences=input,
# sequences=xrange(nt))
# return out
out, updates = theano.scan(self.transform, sequences=input)
return out
def full(self, X, Z=None):
X, Xc, Z = self._common(X, Z)
if Z is None:
return tt.dot(Xc, tt.transpose(Xc))
else:
Zc = tt.sub(Z, self.c)
return tt.dot(Xc, tt.transpose(Zc))
def kmeans(train_set_x):
if train_set_x is None:
train_set_x = T.matrix('train_set_x')
########################
# Normalize the inputs #
########################
epsilon_norm = 10
epsilon_zca = 0.015
K = 500
train_set_x = train_set_x - T.mean(train_set_x, axis=0) / T.sqrt(T.var(train_set_x, axis=0) + epsilon_norm)
#####################
# Whiten the inputs #
#####################
# a simple choice of whitening transform is the ZCA whitening transform
# epsilon_zca is small constant
# for contrast-normalizaed data, setting epsilon_zca to 0.01 for 16-by-16 pixel patches,
# or to 0.1 for 8-by-8 pixel patches
# is good starting point
cov = T.dot(train_set_x, T.transpose(train_set_x)) / train_set_x.shape[1]
U, S, V = linalg.svd(cov)
tmp = T.dot(U, T.diag(1/T.sqrt(S + epsilon_zca)))
tmp = T.dot(tmp, T.transpose(U))
whitened_x = T.dot(tmp, train_set_x)
######################
# Training the Model #
######################
# Initialization
dimension_size = whitened_x.shape[0]
num_samples = whitened_x.shape[1]
srng = RandomStreams(seed=234)
D = srng.normal(size=(dimension_size, K))
D = D / T.sqrt(T.sum(T.sqr(D), axis=0))
# typically 10 iterations is enough
num_iteration = 15
# compute new centroids, D_new
for i in xrange(num_iteration):
dx = T.dot(D.T, whitened_x)
arg_max_dx = T.argmax(dx, axis=0)
s = dx[arg_max_dx, T.arange(num_samples)]
S = T.zeros((K, num_samples))
S = T.set_subtensor(S[arg_max_dx, T.arange(num_samples)], s)
D = T.dot(whitened_x, T.transpose(S)) + D
D = D / T.sqrt(T.sum(T.sqr(D), axis=0))
return D
def train(self, n_epochs=100, mini_batch_size=1, learning_rate=0.1):
index = T.lscalar()
x=T.matrix('x')
is_dropout = T.dscalar('is_dropout')
params = [self.W, self.b1, self.b2]
hidden = self.activation_function(T.dot(x, self.W)+self.b1)
arr_n = self.get_mask(self.b1,0.5)
hidden_tilde = hidden
hidden_tilde = arr_n * hidden
output_without_drop = T.dot(hidden,T.transpose(self.W))+self.b2
output_without_drop = self.output_function(output_without_drop)
output_dropout = T.dot(hidden_tilde,T.transpose(self.W))+self.b2
output_dropout = self.output_function(output_dropout)
#x_printed = theano.printing.Print('this is a very important value')(arr_n)
#Use cross-entropy loss.
L = -T.sum(x*T.log(output_dropout) + (1-x)*T.log(1-output_dropout), axis=1)
cost=L.mean()
L_without_drops = -T.sum(x*T.log(output_without_drop) + (1-x)*T.log(1-output_without_drop), axis=1)
cost2 = L_without_drops.mean()
updates=[]
#Return gradient with respect to W, b1, b2.
gparams = T.grad(cost,params)
gparams_shared = theano.shared(gparams,'gparams_shared')
gparams[0] = gparams[0] * arr_n
gparams[1] = gparams[1] * arr_n
#x_printed1 = theano.printing.Print('this is a very important value')(gparams[0])
#x_printed2 = theano.printing.Print('this is a very important value')(gparams[1])
#gparams_shared = gparams_shared*mask
#Create a list of 2 tuples for updates.
for param, gparam in zip(params, gparams):
updates.append((param, param-learning_rate*gparam))
#Train given a mini-batch of the data.
train = th.function(inputs=[index], outputs=[cost], updates=updates,
givens={x:self.X[index:index+mini_batch_size,:]})
valid = th.function(inputs=[index], outputs=[cost2],
givens={x:self.Y[index:index+mini_batch_size,:]})
import time
start_time = time.clock()
for epoch in xrange(n_epochs):
print "Epoch:",epoch
cost_train = 0
cost_valid = 0
for row in xrange(0,self.m, mini_batch_size):
cost_train= cost_train + train(row)[0]
for row in xrange(0,self.Y_m, mini_batch_size):
cost_valid = cost_valid + valid(row)[0]
global_valid_cost.append((cost_valid/self.Y_m))
global_train_cost.append((cost_train/self.m))
end_time = time.clock()
print "Average time per epoch=", (end_time-start_time)/n_epochs
def weighted_binary_cross_entropy_4(pred, target, class_normalization):
# Mix of 0 and 2
# From theano
DIM = pred.shape[1]
BATCH_SIZE = pred.shape[0]
N_on_per_batch = (T.transpose(T.tile(target.sum(axis=1), (DIM, 1))) + 1)
N_off_per_batch = (T.transpose(T.tile((1-target).sum(axis=1), (DIM, 1))) + 1)
class_norm_tile = T.tile(class_normalization, (BATCH_SIZE, 1))
return -(class_norm_tile * target * T.log(pred) / N_on_per_batch + (1.0 - target) * T.log(1.0 - pred) / N_off_per_batch)
def Kmeans(X_train=None, K=300, epsilon_whitening=0.015):
if X_train is None:
X_train = T.matrix("X_train")
########################
# Normalize the inputs #
########################
# A constant added to the variance to avoid division by zero
epsilon_norm = 10
# We subtract from each training sample (each column in X_train) its mean
X_train = X_train - T.mean(X_train, axis=0) / T.sqrt(T.var(X_train, axis=0) + epsilon_norm)
#####################
# Whiten the inputs #
#####################
sigma = T.dot(X_train, T.transpose(X_train)) / X_train.shape[1]
U, s, V = linalg.svd(sigma, full_matrices=False)
tmp = T.dot(U, T.diag(1 / T.sqrt(s + epsilon_whitening)))
tmp = T.dot(tmp, T.transpose(U))
X_Whitened = T.dot(tmp, X_train)
######################
# Training the Model #
######################
# Initialization
dimensions = X_Whitened.shape[0]
samples = X_Whitened.shape[1]
srng = RandomStreams(seed=234)
# We initialize the centroids by sampling them from a normal
# distribution, and then normalizing them to unit length
# D \in R^{n \times k}
D = srng.normal(size=(dimensions, K))
D = D / T.sqrt(T.sum(T.sqr(D), axis=0))
iterations = 30
for i in xrange(iterations):
# Initialize new point representations
# for every pass of the algorithm
S = T.zeros((K, samples))
tmp = T.dot(D.T, X_Whitened)
res = T.argmax(tmp, axis=0)
max_values = tmp[res, T.arange(samples)]
S = T.set_subtensor(S[res, T.arange(samples)], max_values)
D = T.dot(X_Whitened, T.transpose(S))
D = D / T.sqrt(T.sum(T.sqr(D), axis=0))
return D
def forward_prop_step(x_t, h_t_prev, c_t_prev):
h_t_prev.tag.test_value = np.random.uniform(0,1, (300,1)).astype('float64')
c_t_prev.tag.test_value = np.random.uniform(0,1, (300,1)).astype('float64')
argm_xt = T.argmax(x_t, axis=0)[0]
argm_push = T.argmax(self.PUSH, axis=0)[0]
argm_pop = T.argmax(self.POP, axis=0)[0]
is_push = T.eq(argm_xt, argm_push)
is_pop = T.eq(argm_xt, argm_pop)
#candidate_to_push = W_h_push.dot(h_t_prev)
candidate_to_push = h_t_prev
pushed_stack = T.set_subtensor(self.stack[:,:,self.ptr_to_top+1], candidate_to_push)
top_of_stack = self.stack[:,:,self.ptr_to_top]
candidate_to_pop = T.tanh( W_h_prev_pop.dot(h_t_prev) + W_h_stack_pop.dot(top_of_stack) )
self.stack = ifelse( is_push,
pushed_stack,
ifelse( is_pop,
self.stack,
self.stack
)
)
self.ptr_to_top = ifelse( is_push,
self.ptr_to_top+1,
ifelse( is_pop,
self.ptr_to_top-1,
self.ptr_to_top
)
)
h_prime = ifelse( is_push,
h_t_prev,
ifelse( is_pop,
candidate_to_pop,
h_t_prev
)
)
i = T.nnet.hard_sigmoid( W_x_i.dot(x_t) + W_h_i.dot(h_prime) )
o = T.nnet.hard_sigmoid( W_x_o.dot(x_t) + W_h_o.dot(h_prime) )
f = T.nnet.hard_sigmoid( W_x_f.dot(x_t) + W_h_f.dot(h_prime) )
g = T.tanh( W_x_g.dot(x_t) + W_h_g.dot(h_prime) )
c_t = f*c_t_prev + i*g
h_t = o*T.tanh(c_t)
o_t = T.transpose( T.nnet.softmax( T.transpose(W_hy.dot(h_t)) ) )
#theano.printing.debugprint(o_t)
return [o_t, h_t, c_t]
def forward_prop_step(x_t, h_t_prev, h_t_2_prev, c_t_2_prev, c_t_prev):
# h_t_prev.tag.test_value = np.random.uniform(0,1, (self.hidden_dim,self.minibatch_size)).astype('float64')
# c_t_prev.tag.test_value = np.random.uniform(0,1, (self.hidden_dim,self.minibatch_size)).astype('float64')
# Map input to {push,pop,internal}
argm_xt = T.argmax(x_t, axis=0)[0]
argm_push = T.argmax(self.PUSH, axis=0)[0]
argm_pop = T.argmax(self.POP, axis=0)[0]
is_push = T.eq(argm_xt, argm_push)
is_pop = T.eq(argm_xt, argm_pop)
# Layer 1
candidate_to_push = h_t_prev
pushed_stack = T.set_subtensor(self.stack[:,:,self.ptr_to_top+1], candidate_to_push)
top_of_stack = self.stack[:,:,self.ptr_to_top]
candidate_to_pop = T.tanh( self.W_h_prev_pop.dot(h_t_prev) + self.W_h_stack_pop.dot(top_of_stack) )
self.stack = ifelse(is_push, pushed_stack, ifelse( is_pop,self.stack,self.stack))
self.ptr_to_top = ifelse(is_push, self.ptr_to_top+1, ifelse( is_pop, self.ptr_to_top-1, self.ptr_to_top))
h_prime = ifelse(is_push,h_t_prev, ifelse( is_pop, candidate_to_pop, h_t_prev))
i = T.nnet.hard_sigmoid( self.W_x_i.dot(x_t) + self.W_h_i.dot(h_prime) )
o = T.nnet.hard_sigmoid( self.W_x_o.dot(x_t) + self.W_h_o.dot(h_prime) )
f = T.nnet.hard_sigmoid( self.W_x_f.dot(x_t) + self.W_h_f.dot(h_prime) )
g = T.tanh( self.W_x_g.dot(x_t) + self.W_h_g.dot(h_prime) )
c_t = f*c_t_prev + i*g
h_t = o*T.tanh(c_t)
# Layer 2
candidate_to_push_2 = h_t_2_prev
pushed_stack_2 = T.set_subtensor(self.stack_2[:,:,self.ptr_to_top_2+1], candidate_to_push_2)
top_of_stack_2 = self.stack_2[:,:,self.ptr_to_top_2]
candidate_to_pop_2 = T.tanh( self.W_h_prev_pop_2.dot(h_t_2_prev) + self.W_h_stack_pop_2.dot(top_of_stack_2) )
self.stack_2 = ifelse(is_push, pushed_stack_2, ifelse(is_pop, self.stack_2, self.stack_2))
self.ptr_to_top_2 = ifelse(is_push, self.ptr_to_top_2+1, ifelse(is_pop, self.ptr_to_top_2-1, self.ptr_to_top_2))
h_prime_2 = ifelse(is_push, h_t_2_prev, ifelse(is_pop, candidate_to_pop_2, h_t_2_prev))
i_2 = T.nnet.hard_sigmoid( self.W_x_i_2.dot(h_t) + self.W_h_i_2.dot(h_prime_2) )
o_2 = T.nnet.hard_sigmoid( self.W_x_o_2.dot(h_t) + self.W_h_o_2.dot(h_prime_2) )
f_2 = T.nnet.hard_sigmoid( self.W_x_f_2.dot(h_t) + self.W_h_f_2.dot(h_prime_2) )
g_2 = T.tanh( self.W_x_g_2.dot(h_t) + self.W_h_g_2.dot(h_prime_2) )
c_t_2 = f_2*c_t_2_prev + i_2*g_2
h_t_2 = o_2*T.tanh(c_t_2)
# Output
o_t = T.transpose( T.nnet.softmax( T.transpose(self.W_hy.dot(h_t_2)) ) )
return [o_t, h_t, h_t_2, c_t, c_t_2]
def _build_conditional(self, Xnew, X, f, cov_total, mean_total):
Kxx = cov_total(X)
Kxs = self.cov_func(X, Xnew)
L = cholesky(stabilize(Kxx))
A = solve_lower(L, Kxs)
v = solve_lower(L, f - mean_total(X))
mu = self.mean_func(Xnew) + tt.dot(tt.transpose(A), v)
Kss = self.cov_func(Xnew)
cov = Kss - tt.dot(tt.transpose(A), A)
return mu, cov
def square_dist(self, X, Xs):
X2 = tt.sum(tt.square(X), 1)
if Xs is None:
sqd = (-2.0 * tt.dot(X, tt.transpose(X))
+ (tt.reshape(X2, (-1, 1)) + tt.reshape(X2, (1, -1))))
else:
Xs2 = tt.sum(tt.square(Xs), 1)
sqd = (-2.0 * tt.dot(X, tt.transpose(Xs))
+ (tt.reshape(Xs2, (-1, 1)) + tt.reshape(Xs2, (1, -1))))
return tt.clip(sqd, 0.0, np.inf)
def __init__(self, input, iNeuronNum, oNeuronNum, activateType, train):
self._input = input
self._weight = theano.shared((np.random.randn(oNeuronNum, iNeuronNum) / (iNeuronNum ** 0.5)))
self._bias = theano.shared(np.random.randn(oNeuronNum))
self._activateType = activateType
self._output = T.transpose(T.dot(self._weight, T.transpose(self._input)) + self._bias.dimshuffle(0, "x"))
self._parameter = [self._weight, self._bias]
self._iNeuronNum = iNeuronNum
self._oNeuronNum = oNeuronNum
self._train = train
def softmax_segm(x):
'''
MODIFICATIONS:
- reshape from image-size to array and back
'''
shape = T.shape(x)
x_mod = T.transpose(x, (0,2,3,1))
x_mod = T.reshape(x_mod, (-1,shape[1]))
results = softmax(x_mod)
results = T.reshape(results, (shape[0],shape[2],shape[3],shape[1]))
return T.transpose(results, (0,3,1,2))
def my_siamese_loss(y_true, y_pred):
v_pari= y_pred[0::2]
v_dispari= y_pred[1::2]
y_pari= y_true[0::2]
y_dispari= y_true[1::2]
d=T.square(v_pari-v_dispari)
l=T.sum(d,axis=1)
loss=T.mean(T.transpose(y_pari) * l + T.transpose(1-y_pari)*T.maximum(margin-l,0))
return loss
#===================================================================================
#==========================Theano Function definitions==============================
#===================================================================================
ATemp = T.matrix('ATemp')
BTemp = T.tensor3('BTemp')
UTemp = T.matrix('UTemp')
E1Temp = T.vector('E1Temp')
E2Temp = T.vector('E2Temp')
E1E2Temp = T.vector('E1E2Temp')
ECTemp = T.vector('ECTemp')
E1ECTemp = T.vector('E1ECTemp')
#Calculate scoring function
temp1 = E1Temp.dot(BTemp).dot(T.transpose(E2Temp))
temp2 = ATemp.dot(E1E2Temp)
temp3 = temp1 + temp2
temp4 = T.tanh(temp3)
score = UTemp.dot(temp4)
scoringFunction = theano.function(
[ATemp, BTemp, UTemp, E1Temp, E2Temp, E1E2Temp], score)
#=======================================================================
#Function Name : loadEntityVectors
#Input : relation name
#Output : return NN params (A,B,U) for input relation
#Functionality : Returns NN parameters for specific relation
# Function reads parameters from text files dumped while training
#=======================================================================
def transpose(x):
"""Tensor transpose """
return T.transpose(x)
def __init__(self,
cooccurrence,
z_k,
opt,
initializer,
pz_weight_regularizer=None,
pz_regularizer=None,
initial_pz=None,
initial_b=None,
eps=1e-8):
cooccurrence = cooccurrence.astype(np.float32)
self.cooccurrence = cooccurrence
self.z_k = z_k
self.opt = opt
x_k = cooccurrence.shape[0]
self.x_k = x_k
self.pz_weight_regularizer = pz_weight_regularizer
self.pz_regularizer = pz_regularizer
# cooccurrence matrix
n = np.sum(cooccurrence, axis=None)
_co = cooccurrence / n
co = T.constant(_co, name="co") # (x_k, x_k)
_co_m = np.sum(_co, axis=1, keepdims=True)
co_m = T.constant(_co_m, name="co_m") # (x_k,1)
_co_c = _co / (eps + _co_m)
_co_h = np.sum(_co * -np.log(eps + _co_c), axis=1, keepdims=True) # (x_k, 1)
print "COh: {}".format(np.sum(_co_h))
co_h = T.constant(_co_h, name="co_h")
# parameters
# P(z|x)
if initial_pz is None:
initial_pz = initializer((x_k, z_k))
pz_weight = K.variable(initial_pz, name="pz_weight") # (x_k, z_k)
initial_w = initializer((z_k, x_k))
w = K.variable(initial_w, name="w")
if initial_b is None:
initial_b = initializer((x_k,))
b = K.variable(initial_b, name="b")
params = [pz_weight, w, b]
# loss
p_z = softmax_nd(pz_weight) # (x_k, z_k)
bucketprobs = softmax_nd(w + b) # (z_k, x_k)
bucketnll = -T.log(eps + bucketprobs) # (z_k, x_k)
lossparts = T.dot(co, T.transpose(bucketnll, (1, 0))) # (x_k, z_k)
nll = T.sum(p_z * lossparts)
# val loss
enc = T.argmax(pz_weight, axis=1)
oh = tensor_one_hot(enc, k=z_k) # (x_k, z_k)
p_b = T.dot(T.transpose(oh, (1, 0)), co) # (z_k, x_k)
marg = T.sum(p_b, axis=1, keepdims=True) # (z_k, 1)
cond = p_b / (marg + eps) # (z_k, x_k)
val_nll = T.sum(p_b * -T.log(eps + cond), axis=None) # scalar
# utilization
utilization = T.sum(T.gt(T.sum(oh, axis=0), 0), axis=0) # scalar
reg_loss = T.constant(0.)
self.regularize = False
if pz_weight_regularizer:
reg_loss += pz_weight_regularizer(pz_weight)
self.regularize = True
if pz_regularizer:
reg_loss += pz_regularizer(p_z)
self.regularize = True
total_loss = nll + reg_loss
self.val_fun = theano.function([], [nll, reg_loss, total_loss, val_nll, utilization])
self.encodings_fun = theano.function([], enc)
updates = opt.get_updates(params=params, loss=total_loss)
self.train_fun = theano.function([], [nll, reg_loss, total_loss], updates=updates)
self.weights = params + opt.weights
def __init__(self, context, V, K, num_sub_tags, feature_matrix_values, context_sz, rng):
"""
Initialize the parameters of the language model
"""
# training contexts
self.context = context
# initialize context word embedding matrix R of shape (V, K)
# TODO: parameterize initialization
R_values = np.asarray(rng.uniform(-0.01, 0.01, size=(V, K)),
dtype=theano.config.floatX)
R_values[:,0:2] = np.zeros((V,2))
self.R = theano.shared(value=R_values, name='R', borrow=True)
# initialize target word embedding matrix Q of shape (V, K)
Q_values = np.asarray(rng.uniform(-0.01, 0.01, size=(V, K)),
dtype=theano.config.floatX)
Q_values[:,0:2] = np.zeros((V,2))
self.Q = theano.shared(value=Q_values, name='Q', borrow=True)
# initialize weight tensor C of shape (context_sz, K, K)
C_values = np.asarray(rng.normal(0, math.sqrt(0.1),
size=(context_sz, K, K)),
dtype=theano.config.floatX)
self.C = theano.shared(value=C_values, name='C', borrow=True)
# initialize tag matrix
Tag_values = np.asarray(rng.normal(-0.01,0.01,size=(num_sub_tags,K)),
dtype=theano.config.floatX)
self.Tag = theano.shared(value=Tag_values,name='Tag',borrow=True)
# initialize bias vector
b_values = np.asarray(rng.normal(0, math.sqrt(0.1), size=(V,)),
dtype=theano.config.floatX)
self.b = theano.shared(value=b_values, name='b', borrow=True)
# context word representations
self.r_w = self.R[context]
# predicted word representation for target word
self.q_hat = T.tensordot(self.C, self.r_w, axes=[[0,1], [1,2]])
# similarity score between predicted word and all target words
self.s = T.transpose(T.dot(self.Q, self.q_hat) + T.reshape(self.b, (V,1)))
# softmax activation function
self.p_w_given_h = T.nnet.softmax(self.s)
self.feature_matrix = theano.shared(value=feature_matrix_values,name="feature_matrix",borrow=True)
# activation function for tags
# feature_matrix : Tag Size x Sub Tag Size
# Tag : Sub Tag Size x K
# Q.T : K x V
# s_tag = Tag Size x V
self.s_tag = T.dot(T.dot(self.feature_matrix,self.Tag),T.transpose(self.Q))
#self.s_tag = T.dot((T.dot(self.feature_matrix,self.Tag)),T.transpose(self.Q))
# softmax activation function tag given word distribution
self.p_t_given_w = T.nnet.softmax(self.s_tag)
# parameters of the model
self.params = [self.R, self.Q, self.C, self.b, self.Tag]
def __init__(self,
n_in,
n_hidden,
x=T.tensor3("x"),
xc=T.tensor3("xc"),
mask=T.matrix("mask"),
maskc=T.matrix("maskx"),
prefix=""):
self.params = []
if x is not None:
self.x = x
else:
self.x = T.tensor3("x")
if xc is not None:
self.xc = xc
else:
self.xc = T.tensor3("xc")
if mask is not None:
self.mask = mask
else:
self.mask = T.matrix("mask")
if maskc is not None:
self.maskc = maskc
else:
self.maskc = T.matrix("maskc")
#### 转置 为了进行scan运算 ###
nmask = T.transpose(self.mask, axes=(1, 0))
nx = T.transpose(self.x, axes=(1, 0, 2))
nmaskc = T.transpose(self.maskc, axes=(1, 0))
nxc = T.transpose(self.xc, axes=(1, 0, 2))
wz_x, bz = init_weight(n_in, n_hidden, pre="%s_lstm_f_x_" % prefix)
self.params += [wz_x, bz]
wr_x, br = init_weight(n_in, n_hidden, pre="%s_lstm_i_x_" % prefix)
self.params += [wr_x, br]
wc_x, bc = init_weight(n_in, n_hidden, pre="%s_lstm_c_x_" % prefix)
self.params += [wc_x, bc]
wz_h, b_h = init_weight(n_hidden,
n_hidden,
pre="%s_lstm_f_h_" % prefix)
self.params += [wz_h]
wr_h, b_h = init_weight(n_hidden,
n_hidden,
pre="%s_lstm_i_h_" % prefix)
self.params += [wr_h]
wc_h, b_h = init_weight(n_hidden,
n_hidden,
pre="%s_lstm_c_h_" % prefix)
self.params += [wc_h]
#h_t_0 = T.alloc(np.array(0.,dtype=np.float64), x.shape[0], n_hidden)
#c_t_0 = T.alloc(np.array(0.,dtype=np.float64), x.shape[0], n_hidden)
h_t_0 = T.alloc(0., x.shape[0], n_hidden)
h_t_0_c = T.alloc(0., xc.shape[0], n_hidden)
#h_t_0 = theano.shared(np.zeros(n_hidden, dtype=theano.config.floatX))
#c_t_0 = theano.shared(np.zeros(n_hidden, dtype=theano.config.floatX))
h, r = theano.scan(
self.recurrent_fn,
sequences=[nx, nmask],
outputs_info=[h_t_0],
non_sequences=[wz_x, wz_h, bz, wr_x, wr_h, br, wc_x, wc_h, bc])
hc, rc = theano.scan(
self.recurrent_fn,
sequences=[nxc, nmaskc],
outputs_info=[h_t_0_c],
non_sequences=[wz_x, wz_h, bz, wr_x, wr_h, br, wc_x, wc_h, bc])
self.all_hiddenx = T.transpose(h, axes=(1, 0, 2))
self.nn_outx = h[-1]
self.all_hiddenc = T.transpose(hc, axes=(1, 0, 2))
self.nn_outc = hc[-1]
self.nn_out = h[-1] - hc[-1]
def __init__(self, n_hidden, embedding_dimention=50):
##n_in: sequence lstm 的输入维度
##n_hidden: lstm for candi and zp 的隐层维度
##n_hidden_sequence: sequence lstm的隐层维度 因为要同zp的结合做dot，所以其维度要是n_hidden的2倍
## 即 n_hidden_sequence = 2 * n_hidden
self.params = []
self.zp_x_pre = T.matrix("zp_x_pre")
self.zp_x_post = T.matrix("zp_x_post")
#self.zp_x_pre_dropout = _dropout_from_layer(self.zp_x_pre)
#self.zp_x_post_dropout = _dropout_from_layer(self.zp_x_post)
zp_nn_pre = GRU(embedding_dimention, n_hidden, self.zp_x_pre)
#zp_nn_pre = LSTM(embedding_dimention,n_hidden,self.zp_x_pre_dropout)
self.params += zp_nn_pre.params
zp_nn_post = GRU(embedding_dimention, n_hidden, self.zp_x_post)
#zp_nn_post = LSTM(embedding_dimention,n_hidden,self.zp_x_post_dropout)
self.params += zp_nn_post.params
self.zp_out = T.concatenate((zp_nn_pre.nn_out, zp_nn_post.nn_out))
self.ZP_layer = Layer(n_hidden * 2, n_hidden * 2, self.zp_out, ReLU)
self.zp_out_output = self.ZP_layer.output
#self.zp_out_dropout = _dropout_from_layer(T.concatenate((zp_nn_pre.nn_out,zp_nn_post.nn_out)))
self.get_zp_out = theano.function(
inputs=[self.zp_x_pre, self.zp_x_post],
outputs=[self.ZP_layer.output])
### get sequence output for NP ###
self.np_x = T.tensor3("np_x")
self.np_x_post = T.tensor3("np_x")
self.np_x_pre = T.tensor3("np_x")
#self.np_x_dropout = _dropout_from_layer(self.np_x)
self.mask = T.matrix("mask")
self.mask_pre = T.matrix("mask")
self.mask_post = T.matrix("mask")
self.np_nn_x = RNN_batch(embedding_dimention, n_hidden, self.np_x,
self.mask)
self.params += self.np_nn_x.params
self.np_nn_pre = GRU_batch(embedding_dimention, n_hidden,
self.np_x_pre, self.mask_pre)
self.params += self.np_nn_pre.params
self.np_nn_post = GRU_batch(embedding_dimention, n_hidden,
self.np_x_post, self.mask_post)
self.params += self.np_nn_post.params
#self.np_nn_out = LSTM_batch(embedding_dimention,n_hidden*2,self.np_x,self.mask)
#self.np_nn_out = LSTM_batch(embedding_dimention,n_hidden*2,self.np_x_dropout,self.mask)
#self.params += self.np_nn_out.params
#self.np_out = self.np_nn.nn_out
self.np_nn_x_output = (self.np_nn_x.all_hidden).mean(axis=1)
self.np_nn_post_output = self.np_nn_post.nn_out
self.np_nn_pre_output = self.np_nn_pre.nn_out
self.np_out = T.concatenate(
(self.np_nn_x_output, self.np_nn_post_output,
self.np_nn_pre_output),
axis=1)
self.NP_layer = Layer(n_hidden * 3, n_hidden * 2, self.np_out, ReLU)
self.np_out_output = self.NP_layer.output
self.np_x_head = T.transpose(self.np_x, axes=(1, 0, 2))[-1]
self.get_np_head = theano.function(inputs=[self.np_x],
outputs=[self.np_x_head])
self.get_np = theano.function(inputs=[
self.np_x, self.np_x_pre, self.np_x_post, self.mask, self.mask_pre,
self.mask_post
],
outputs=[self.np_out])
self.get_np_out = theano.function(inputs=[
self.np_x, self.np_x_pre, self.np_x_post, self.mask, self.mask_pre,
self.mask_post
],
outputs=[self.np_out_output])
w_attention_zp, b_attention = init_weight(n_hidden * 2,
1,
pre="attention_hidden",
ones=False)
self.params += [w_attention_zp, b_attention]
w_attention_np, b_u = init_weight(n_hidden * 2,
1,
pre="attention_zp",
ones=False)
self.params += [w_attention_np]
self.calcu_attention = tanh(
T.dot(self.np_out_output, w_attention_np) +
T.dot(self.zp_out_output, w_attention_zp) + b_attention)
self.attention = softmax(T.transpose(self.calcu_attention,
axes=(1, 0)))[0]
self.get_attention = theano.function(inputs=[
self.zp_x_pre, self.zp_x_post, self.np_x, self.np_x_pre,
self.np_x_post, self.mask, self.mask_pre, self.mask_post
],
outputs=[self.attention])
new_zp = T.sum(self.attention[:, None] * self.np_x_head, axis=0)
self.get_new_zp = theano.function(inputs=[
self.zp_x_pre, self.zp_x_post, self.np_x, self.np_x_pre,
self.np_x_post, self.mask, self.mask_pre, self.mask_post
],
outputs=[new_zp])
#### *** HOP *** ####
self.w_hop_zp, self.b_hop_zp = init_weight(n_hidden * 2 +
embedding_dimention,
n_hidden * 2,
pre="hop_")
self.params += [self.w_hop_zp, self.b_hop_zp]
## hop 1 ##
self.zp_hop_1_init = T.concatenate(
(zp_nn_pre.nn_out, zp_nn_post.nn_out, new_zp))
self.zp_hop_1 = ReLU(
T.dot(self.zp_hop_1_init, self.w_hop_zp) + self.b_hop_zp)
self.calcu_attention_hop_1 = tanh(
T.dot(self.np_out_output, w_attention_np) +
T.dot(self.zp_hop_1, w_attention_zp) + b_attention)
self.attention_hop_1 = softmax(
T.transpose(self.calcu_attention_hop_1, axes=(1, 0)))[0]
self.get_attention_hop_1 = theano.function(
inputs=[
self.zp_x_pre, self.zp_x_post, self.np_x, self.np_x_pre,
self.np_x_post, self.mask, self.mask_pre, self.mask_post
],
outputs=[self.attention_hop_1])
self.out = self.attention_hop_1
self.get_out = theano.function(inputs=[
self.zp_x_pre, self.zp_x_post, self.np_x, self.np_x_pre,
self.np_x_post, self.mask, self.mask_pre, self.mask_post
],
outputs=[self.out])
l1_norm_squared = sum([(w**2).sum() for w in self.params])
l2_norm_squared = sum([(abs(w)).sum() for w in self.params])
lmbda_l1 = 0.0
#lmbda_l2 = 0.001
lmbda_l2 = 0.0
t = T.bvector()
cost = -(T.log((self.out * t).sum()))
#cost = -(T.log((self.out_dropout*t).sum()))
#cost = 1-((self.out*t).sum())
lr = T.scalar()
#grads = T.grad(cost, self.params)
#updates = [(param, param-lr*grad)
# for param, grad in zip(self.params, grads)]
#updates = lasagne.updates.sgd(cost, self.params, lr)
updates = lasagne.updates.adadelta(cost, self.params)
self.train_step = theano.function(inputs=[
self.zp_x_pre, self.zp_x_post, self.np_x, self.np_x_pre,
self.np_x_post, self.mask, self.mask_pre, self.mask_post, t, lr
],
outputs=[cost],
on_unused_input='warn',
updates=updates)
def __init__(self, n_hidden, embedding_dimention=50, feature_dimention=61):
##n_in: sequence lstm 的输入维度
##n_hidden: lstm for candi and zp 的隐层维度
#repre_active = ReLU
repre_active = linear
self.params = []
self.zp_x_pre = T.matrix("zp_x_pre")
self.zp_x_post = T.matrix("zp_x_post")
zp_nn_pre = LSTM(embedding_dimention, n_hidden, self.zp_x_pre)
self.params += zp_nn_pre.params
zp_nn_post = LSTM(embedding_dimention, n_hidden, self.zp_x_post)
self.params += zp_nn_post.params
attention_pre_on_post = softmax(
(zp_nn_pre.nn_out * zp_nn_post.all_hidden).sum(axis=1))[0]
attention_post_on_pre = softmax(
(zp_nn_post.nn_out * zp_nn_pre.all_hidden).sum(axis=1))[0]
zp_post = T.sum(attention_pre_on_post[:, None] * zp_nn_post.all_hidden,
axis=0)
zp_pre = T.sum(attention_post_on_pre[:, None] * zp_nn_pre.all_hidden,
axis=0)
#self.zp_out = T.concatenate((zp_nn_pre.nn_out,zp_nn_post.nn_out))
self.zp_out = T.concatenate((zp_post, zp_pre))
self.zp_out_output = self.zp_out
### get sequence output for NP ###
self.np_x_post = T.tensor3("np_x")
self.np_x_postc = T.tensor3("np_x")
self.np_x_pre = T.tensor3("np_x")
self.np_x_prec = T.tensor3("np_x")
self.mask_pre = T.matrix("mask")
self.mask_prec = T.matrix("mask")
self.mask_post = T.matrix("mask")
self.mask_postc = T.matrix("mask")
self.np_nn_pre = sub_LSTM_batch(embedding_dimention, n_hidden,
self.np_x_pre, self.np_x_prec,
self.mask_pre, self.mask_prec)
self.params += self.np_nn_pre.params
self.np_nn_post = sub_LSTM_batch(embedding_dimention, n_hidden,
self.np_x_post, self.np_x_postc,
self.mask_post, self.mask_postc)
self.params += self.np_nn_post.params
self.np_nn_post_output = self.np_nn_post.nn_out
self.np_nn_pre_output = self.np_nn_pre.nn_out
self.np_out = T.concatenate(
(self.np_nn_post_output, self.np_nn_pre_output), axis=1)
#np_nn_f = LSTM(n_hidden*2,n_hidden*2,self.np_out)
#self.params += np_nn_f.params
#np_nn_b = LSTM(n_hidden*2,n_hidden*2,self.np_out[::-1])
#self.params += np_nn_b.params
#self.bi_np_out = T.concatenate((np_nn_f.all_hidden,np_nn_b.all_hidden[::-1]),axis=1)
#self.np_out_output = self.bi_np_out
#self.get_np_out = theano.function(inputs=[self.np_x_pre,self.np_x_prec,self.np_x_post,self.np_x_postc,self.mask_pre,self.mask_prec,self.mask_post,self.mask_postc],outputs=[self.np_out_output])
self.feature = T.matrix("feature")
self.feature_layer = Layer(feature_dimention, n_hidden, self.feature,
repre_active)
self.params += self.feature_layer.params
w_attention_zp, b_attention = init_weight(n_hidden * 2,
1,
pre="attention_zp",
ones=False)
self.params += [w_attention_zp, b_attention]
w_attention_np, b_u = init_weight(n_hidden * 2,
1,
pre="attention_np",
ones=False)
self.params += [w_attention_np]
#w_attention_np_rnn,b_u = init_weight(n_hidden*4,1,pre="attention_np_rnn",ones=False)
#self.params += [w_attention_np_rnn]
w_attention_feature, b_u = init_weight(n_hidden,
1,
pre="attention_feature",
ones=False)
self.params += [w_attention_feature]
self.calcu_attention = tanh(
T.dot(self.zp_out_output, w_attention_zp) +
T.dot(self.np_out, w_attention_np) +
T.dot(self.feature_layer.output, w_attention_feature) +
b_attention)
#self.calcu_attention = tanh(T.dot(self.np_out_output,w_attention_np_rnn) + T.dot(self.zp_out_output,w_attention_zp) + T.dot(self.np_out,w_attention_np) + T.dot(self.feature_layer.output,w_attention_feature) + b_attention)
#self.calcu_attention = tanh(T.dot(self.np_out_output,w_attention_np_rnn) + T.dot(self.zp_out_output,w_attention_zp) + T.dot(self.np_out,w_attention_np) + b_attention)
self.attention = softmax(T.transpose(self.calcu_attention,
axes=(1, 0)))[0]
self.out = self.attention
self.get_out = theano.function(inputs=[
self.zp_x_pre, self.zp_x_post, self.np_x_pre, self.np_x_prec,
self.np_x_post, self.np_x_postc, self.mask_pre, self.mask_prec,
self.mask_post, self.mask_postc, self.feature
],
outputs=[self.out],
on_unused_input='warn')
l1_norm_squared = sum([(w**2).sum() for w in self.params])
l2_norm_squared = sum([(abs(w)).sum() for w in self.params])
lmbda_l1 = 0.0
#lmbda_l2 = 0.001
lmbda_l2 = 0.0
t = T.bvector()
cost = -(T.log((self.out * t).sum()))
lr = T.scalar()
updates = lasagne.updates.sgd(cost, self.params, lr)
#updates = lasagne.updates.adadelta(cost, self.params)
self.train_step = theano.function(inputs=[
self.zp_x_pre, self.zp_x_post, self.np_x_pre, self.np_x_prec,
self.np_x_post, self.np_x_postc, self.mask_pre, self.mask_prec,
self.mask_post, self.mask_postc, self.feature, t, lr
],
outputs=[cost],
on_unused_input='warn',
updates=updates)
def logNormalPDF(X, Mu, XChol):
Lambda = Tla.matrix_inverse(T.dot(XChol, T.transpose(XChol)))
XMu = X - Mu
return (-0.5 * T.dot(XMu, T.dot(Lambda, T.transpose(XMu))) +
0.5 * T.log(Tla.det(Lambda)) -
0.5 * np.log(2 * np.pi) * X.shape[0])
def __init__(self,
n_actions,
replay_memory,
build_network,
updates,
screen_size,
initial_weights_file=None):
self.screen_size = screen_size
self.mood_q = None
self.last_q = 0
self.n_parameter_updates = 0
self.alpha = 0.00025
# update frequency ?
# gradient momentum ? 0.95
# squared gradient momentum ? 0.95
# min squared gradient ? 0.01
self.save_every_n_frames = 100000 # ~ once per hour
self.final_exploration_frame = 1000000
self.replay_start_size = 50000
self.i_action = 0
self.state = None
self.initial_epsilon = 1
self.final_epsilon = 0.1
self.epsilon = self.initial_epsilon
self.gamma = 0.99
self.replay_memory = replay_memory
self.log_frequency = 1
self.minibatch_size = 32
# self.replay_memory_size = 1000000
self.target_network_update_frequency = 10000
s0_var = T.tensor4("s0", dtype=theano.config.floatX)
a0_var = T.bmatrix("a0")
r0_var = T.wcol("r0")
s1_var = T.tensor4("s1", dtype=theano.config.floatX)
future_reward_indicator_var = T.bcol("future_reward_indicator")
self.n_actions = n_actions
self.a_lookup = np.eye(self.n_actions, dtype=np.int8)
self.network = build_network(n_actions=self.n_actions,
input_var=T.cast(s0_var, 'float32') /
np.float32(256),
screen_size=self.screen_size)
print("Compiling forward.")
self.forward = theano.function([s0_var],
lasagne.layers.get_output(
self.network, deterministic=True))
self.network_stale = build_network(
n_actions=self.n_actions,
input_var=T.cast(s1_var, 'float32') / np.float32(256),
screen_size=self.screen_size)
print("Compiling forward_stale.")
self.forward_stale = theano.function([s1_var],
lasagne.layers.get_output(
self.network_stale,
deterministic=True))
if initial_weights_file is not None:
with np.load(initial_weights_file) as initial_weights:
param_values = [
initial_weights['arr_%d' % i]
for i in range(len(initial_weights.files))
]
lasagne.layers.set_all_param_values(self.network, param_values)
self.i_action -= self.replay_start_size
self._update_network_stale()
out = lasagne.layers.get_output(self.network)
out_stale = lasagne.layers.get_output(self.network_stale)
self.loss, self.err, __y, __q = build_loss(
out=out,
out_stale=out_stale,
a0_var=a0_var,
r0_var=r0_var,
future_reward_indicator_var=future_reward_indicator_var,
gamma=self.gamma)
params = lasagne.layers.get_all_params(self.network, trainable=True)
print("Compiling train_fn.")
self.train_fn = theano.function(
[s0_var, a0_var, r0_var, s1_var, future_reward_indicator_var], [
self.loss, self.err,
T.transpose(__y),
T.transpose(__q), out, out_stale
],
updates=updates(self.loss, params))
print("Compiling loss_fn.")
self.loss_fn = theano.function(
[s0_var, a0_var, r0_var, s1_var, future_reward_indicator_var],
self.loss)
self.test_mode = False
def __init__(
self,
numpy_rng,
theano_rng=None,
input1_v=None,
input2_v=None,
input3_v=None,
input1_c=None,
input2_c=None,
input3_c=None,
n_visible1_v=4096,
n_visible2_v=4096,
n_visible1_c=3529,
n_visible2_c=3529,
n_hidden_v=None,
n_hidden_c=None,
W1_c=None,
bhid1_c=None,
bvis1_c=None,
W2_c=None,
bhid2_c=None,
bvis2_c=None,
W1_v=None,
bhid1_v=None,
bvis1_v=None,
W2_v=None,
bhid2_v=None,
bvis2_v=None,
lamda=None,
mu=None,
beta=None,
theta=None,
momentum=0.9
):
self.n_visible1_v = n_visible1_v
self.n_visible2_v = n_visible2_v
self.n_hidden_v = n_hidden_v
self.n_visible1_c = n_visible1_c
self.n_visible2_c = n_visible2_c
self.n_hidden_c = n_hidden_c
self.lamda = lamda
self.mu = mu
self.beta = beta
self.theta = theta
self.momentum = momentum
if not theano_rng:
theano_rng = RandomStreams(numpy_rng.randint(2 ** 30))
self.W1_v = W1_v
self.W2_v = W2_v
self.W1_c = W1_c
self.W2_c = W2_c
self.b1_v = bhid1_v
self.b2_v = bhid2_v
self.b1_c = bhid1_c
self.b2_c = bhid2_c
self.b1_prime_v = bvis1_v
self.b2_prime_v = bvis2_v
self.b1_prime_c = bvis1_c
self.b2_prime_c = bvis2_c
self.W1_prime_v = T.transpose(self.W1_v)
self.W2_prime_v = T.transpose(self.W2_v)
self.W1_prime_c = T.transpose(self.W1_c)
self.W2_prime_c = T.transpose(self.W2_c)
self.theano_rng = theano_rng
self.L2_sqr = (
(self.W1_v ** 2).mean() + (self.W2_v ** 2).mean() + (self.W1_c ** 2).mean() + (self.W2_c ** 2).mean()
+ (self.b1_v ** 2).mean() + (self.b2_v ** 2).mean() + (self.b1_c ** 2).mean() + (self.b2_c ** 2).mean()
+ (self.b1_prime_v ** 2).mean() + (self.b2_prime_v ** 2).mean() + (self.b1_prime_c ** 2).mean() + (self.b2_prime_c ** 2).mean()
)
# if no input is given, generate a variable representing the input
if input1_v is None:
# we use a matrix because we expect a minibatch of several
# examples, each example being a row
self.x1_v = T.dmatrix(name='input1_v',dtype='float32')
self.x2_v = T.dmatrix(name='input2_v',dtype='float32')
self.x3_v = T.dmatrix(name='input3_v',dtype='float32')
self.x1_c = T.dmatrix(name='input1_c',dtype='float32')
self.x2_c = T.dmatrix(name='input2_c',dtype='float32')
self.x3_c = T.dmatrix(name='input3_c',dtype='float32')
else:
self.x1_v = input1_v
self.x2_v = input2_v
self.x3_v = input3_v
self.x1_c = input1_c
self.x2_c = input2_c
self.x3_c = input3_c
self.params = [self.W1_v, self.b1_v, self.b1_prime_v,
self.W2_v, self.b2_v, self.b2_prime_v,
self.W1_c, self.b1_c, self.b1_prime_c,
self.W2_c, self.b2_c, self.b2_prime_c
]
# end-snippet-1
self.output1_v = T.nnet.hard_sigmoid (T.dot(self.x1_v, self.W1_v) + self.b1_v)
self.output2_v = T.nnet.hard_sigmoid (T.dot(self.x2_v, self.W2_v) + self.b2_v)
self.output3_v = T.nnet.hard_sigmoid (T.dot(self.x3_v, self.W2_v) + self.b2_v)
self.output1_c = T.nnet.hard_sigmoid (T.dot(self.x1_c, self.W1_c) + self.b1_c)
self.output2_c = T.nnet.hard_sigmoid (T.dot(self.x2_c, self.W2_c) + self.b2_c)
self.output3_c = T.nnet.hard_sigmoid (T.dot(self.x3_c, self.W2_c) + self.b2_c)
self.output1t_v = T.transpose(self.output1_v)
self.output2t_v = T.transpose(self.output2_v)
self.output3t_v = T.transpose(self.output3_v)
self.output1t_c = T.transpose(self.output1_c)
self.output2t_c = T.transpose(self.output2_c)
self.output3t_c = T.transpose(self.output3_c)
self.rec1_v = T.nnet.hard_sigmoid (T.dot(self.output1_v, self.W1_prime_v) + self.b1_prime_v)
self.rec2_v = T.nnet.hard_sigmoid (T.dot(self.output2_v, self.W2_prime_v) + self.b2_prime_v)
self.rec3_v = T.nnet.hard_sigmoid (T.dot(self.output3_v, self.W2_prime_v) + self.b2_prime_v)
self.rec1_c = T.nnet.hard_sigmoid (T.dot(self.output1_c, self.W1_prime_c) + self.b1_prime_c)
self.rec2_c = T.nnet.hard_sigmoid (T.dot(self.output2_c, self.W2_prime_c) + self.b2_prime_c)
self.rec3_c = T.nnet.hard_sigmoid (T.dot(self.output3_c, self.W2_prime_c) + self.b2_prime_c)
weights(np.random.rand(1), np.random.rand(1), np.random.rand(1),
np.random.rand(1), np.random.rand(1), np.random.rand(1),
np.random.rand(1), np.random.rand(1), np.random.rand(1),
np.random.rand(1), np.random.rand(1), np.random.rand(1),
np.random.rand(1), np.random.rand(1), np.random.rand(1),
np.random.rand(1), np.random.rand(1), np.random.rand(1))
#W1real=T.set_subtensor(W[0:3],W[0:3])
#W2real=T.set_subtensor(W[3:6],W[3:6])
#W3real=T.set_subtensor(W[6:9],W[6:9])
#W1imag=T.set_subtensor(W[9:12],W[9:12])
#W2imag=T.set_subtensor(W[12:15],W[12:15])
#W3imag=T.set_subtensor(W[15:18],W[15:18])
cost = T.sqr(
T.sum(T.transpose(W[0:3]) * xreal) -
T.sum(T.transpose(W[9:12]) * ximag)) + T.sqr(
T.sum(T.transpose(W[0:3]) * ximag) +
T.sum(T.transpose(W[9:12]) * xreal)) + T.sqr(
T.sum(T.transpose(W[3:6]) * xreal) -
T.sum(T.transpose(W[12:15]) * ximag)) + T.sqr(
T.sum(T.transpose(W[3:6]) * ximag) +
T.sum(T.transpose(W[12:15]) * xreal)) + T.sqr(
T.sum(T.transpose(W[6:9]) * ximag) +
T.sum(T.transpose(W[15:18]) * xreal)) + l * T.sqr(
T.sqr(
T.sum(T.transpose(W[6:9]) * xreal) -
T.sum(T.transpose(W[15:18]) * ximag)) - 1)
loss = []
gradients = theano.tensor.grad(cost, [W])
def errors(ypred, ytrue):
shp = ypred.shape
rypred = T.transpose(ypred.reshape((shp[1], shp[0] * shp[2] * shp[3])))
preds = T.argmax(ryped, axis=1)
return T.mean(T.neq(preds, ytrue))
def log_loss(self, y):
return -T.dot(T.log(self.p_y_given_x),
T.transpose(y))[T.arange(y.shape[0]),
T.arange(y.shape[0])]
def __init__(self,
n_in,
n_hidden,
x=T.tensor3("x"),
xc=T.tensor3("xc"),
mask=T.matrix("mask"),
maskc=T.matrix("maskx"),
prefix=""):
self.params = []
if x is not None:
self.x = x
else:
self.x = T.tensor3("x")
if xc is not None:
self.xc = xc
else:
self.xc = T.tensor3("xc")
if mask is not None:
self.mask = mask
else:
self.mask = T.matrix("mask")
if maskc is not None:
self.maskc = maskc
else:
self.maskc = T.matrix("maskc")
#### 转置 为了进行scan运算 ###
nmask = T.transpose(self.mask, axes=(1, 0))
nx = T.transpose(self.x, axes=(1, 0, 2))
nmaskc = T.transpose(self.maskc, axes=(1, 0))
nxc = T.transpose(self.xc, axes=(1, 0, 2))
wf_x, bf = init_weight(n_in, n_hidden, pre="%s_lstm_f_x_" % prefix)
self.params += [wf_x, bf]
wi_x, bi = init_weight(n_in, n_hidden, pre="%s_lstm_i_x_" % prefix)
self.params += [wi_x, bi]
wc_x, bc = init_weight(n_in, n_hidden, pre="%s_lstm_c_x_" % prefix)
self.params += [wc_x, bc]
wo_x, bo = init_weight(n_in, n_hidden, pre="%s_lstm_o_x_" % prefix)
self.params += [wo_x, bo]
wf_h, b_h = init_weight(n_hidden,
n_hidden,
pre="%s_lstm_f_h_" % prefix)
self.params += [wf_h]
wi_h, b_h = init_weight(n_hidden,
n_hidden,
pre="%s_lstm_i_h_" % prefix)
self.params += [wi_h]
wc_h, b_h = init_weight(n_hidden,
n_hidden,
pre="%s_lstm_c_h_" % prefix)
self.params += [wc_h]
wo_h, b_h = init_weight(n_hidden,
n_hidden,
pre="%s_lstm_o_h_" % prefix)
self.params += [wo_h]
#h_t_0 = T.alloc(np.array(0.,dtype=np.float64), x.shape[0], n_hidden)
#c_t_0 = T.alloc(np.array(0.,dtype=np.float64), x.shape[0], n_hidden)
h_t_0 = T.alloc(0., x.shape[0], n_hidden)
c_t_0 = T.alloc(0., x.shape[0], n_hidden)
h_t_0_c = T.alloc(0., xc.shape[0], n_hidden)
c_t_0_c = T.alloc(0., xc.shape[0], n_hidden)
[h, c], r = theano.scan(self.lstm_recurrent_fn,
sequences=[nx, nmask],
outputs_info=[h_t_0, c_t_0],
non_sequences=[
wf_x, wf_h, bf, wi_x, wi_h, bi, wc_h, wc_x,
bc, wo_x, wo_h, bo
])
[hc, cc], rc = theano.scan(self.lstm_recurrent_fn,
sequences=[nxc, nmaskc],
outputs_info=[h_t_0_c, c_t_0_c],
non_sequences=[
wf_x, wf_h, bf, wi_x, wi_h, bi, wc_h,
wc_x, bc, wo_x, wo_h, bo
])
self.all_hiddenx = T.transpose(h, axes=(1, 0, 2))
self.nn_outx = h[-1]
self.all_hiddenc = T.transpose(hc, axes=(1, 0, 2))
self.nn_outc = hc[-1]
self.nn_out = h[-1] - hc[-1]
def nll(ypred, ytrue):
shp = ypred.shape
rypred = T.transpose(ypred.reshape((shp[1], shp[0] * shp[2] * shp[3])))
return -T.mean(T.log(rypred)[T.arange(rypred.shape[0]), ytrue])
def __init__(self, n_hidden, embedding_dimention=50, feature_dimention=61):
##n_in: sequence lstm 的输入维度
##n_hidden: lstm for candi and zp 的隐层维度
self.params = []
self.w_embedding = init_weight_file(args.embedding,
args.embedding_dimention)
self.params.append(self.w_embedding)
self.zp_x_pre_index = T.imatrix("zp_x_pre")
self.zp_x_post_index = T.imatrix("zp_x_post")
zp_x_pre_newshape = (T.shape(self.zp_x_pre_index)[0],
args.embedding_dimention)
self.embedding_sub_zp_pre = self.w_embedding[
self.zp_x_pre_index.flatten()]
self.zp_x_pre = T.reshape(self.embedding_sub_zp_pre, zp_x_pre_newshape)
zp_x_post_newshape = (T.shape(self.zp_x_post_index)[0],
args.embedding_dimention)
self.embedding_sub_zp_post = self.w_embedding[
self.zp_x_post_index.flatten()]
self.zp_x_post = T.reshape(self.embedding_sub_zp_post,
zp_x_post_newshape)
zp_nn_pre = LSTM(embedding_dimention, n_hidden, self.zp_x_pre)
self.params += zp_nn_pre.params
zp_nn_post = LSTM(embedding_dimention, n_hidden, self.zp_x_post)
self.params += zp_nn_post.params
danwei = theano.shared(np.eye(8, dtype=theano.config.floatX))
H_pre = zp_nn_pre.all_hidden
H_post = zp_nn_post.all_hidden
Ws1_pre, heihei = init_weight(n_hidden,
n_hidden,
pre="Ws1_pre_zp",
ones=False)
Ws2_pre, heihei = init_weight(8,
n_hidden,
pre="Ws2_pre_zp",
ones=False)
self.params += [Ws1_pre, Ws2_pre]
A_pre = softmax(T.dot(Ws2_pre, T.dot(Ws1_pre, T.transpose(H_pre))))
P_pre = T.dot(A_pre, T.transpose(A_pre)) - danwei
#norm_pre, _ = theano.scan(lambda i, tmp: T.dot(P_pre[i], P_pre[i]) + tmp,
# sequences = T.arange(P_pre.shape[0]),
# outputs_info = np.asarray(0., dtype=theano.config.floatX))
#f_norm_pre = T.sum(norm_pre[-1])
f_norm_pre = (P_pre**2).sum()
zp_out_pre = T.mean(T.dot(A_pre, H_pre), axis=0)
Ws1_post, heihei = init_weight(n_hidden,
n_hidden,
pre="Ws1_post_zp",
ones=False)
Ws2_post, heihei = init_weight(8,
n_hidden,
pre="Ws2_post_zp",
ones=False)
self.params += [Ws1_post, Ws2_post]
A_post = softmax(T.dot(Ws2_post, T.dot(Ws1_post, T.transpose(H_post))))
P_post = T.dot(A_post, T.transpose(A_post)) - danwei
#norm_post, _ = theano.scan(lambda i, tmp: T.dot(P_post[i], P_post[i]) + tmp,
# sequences = T.arange(P_post.shape[0]),
# outputs_info = np.asarray(0., dtype=theano.config.floatX))
#f_norm_post = T.sum(norm_post[-1])
f_norm_post = (P_post**2).sum()
zp_out_post = T.mean(T.dot(A_post, H_post), axis=0)
f_norm = f_norm_pre + f_norm_post
#self.zp_out = T.concatenate((zp_nn_pre.nn_out,zp_nn_post.nn_out))
self.zp_out = T.concatenate((zp_out_pre, zp_out_post))
self.zp_out_output = self.zp_out
### get sequence output for NP ###
self.np_x_post_index = T.itensor3("np_x")
self.np_x_postc_index = T.itensor3("np_x")
self.np_x_pre_index = T.itensor3("np_x")
self.np_x_prec_index = T.itensor3("np_x")
np_x_post_newshape = (T.shape(self.np_x_post_index)[0],
T.shape(self.np_x_post_index)[1],
args.embedding_dimention)
self.embedding_sub_np_x_post = self.w_embedding[
self.np_x_post_index.flatten()]
self.np_x_post = T.reshape(self.embedding_sub_np_x_post,
np_x_post_newshape)
np_x_postc_newshape = (T.shape(self.np_x_postc_index)[0],
T.shape(self.np_x_postc_index)[1],
args.embedding_dimention)
self.embedding_sub_np_x_postc = self.w_embedding[
self.np_x_postc_index.flatten()]
self.np_x_postc = T.reshape(self.embedding_sub_np_x_postc,
np_x_postc_newshape)
np_x_pre_newshape = (T.shape(self.np_x_pre_index)[0],
T.shape(self.np_x_pre_index)[1],
args.embedding_dimention)
self.embedding_sub_np_x_pre = self.w_embedding[
self.np_x_pre_index.flatten()]
self.np_x_pre = T.reshape(self.embedding_sub_np_x_pre,
np_x_pre_newshape)
np_x_prec_newshape = (T.shape(self.np_x_prec_index)[0],
T.shape(self.np_x_prec_index)[1],
args.embedding_dimention)
self.embedding_sub_np_x_prec = self.w_embedding[
self.np_x_prec_index.flatten()]
self.np_x_prec = T.reshape(self.embedding_sub_np_x_prec,
np_x_prec_newshape)
self.mask_pre = T.matrix("mask")
self.mask_prec = T.matrix("mask")
self.mask_post = T.matrix("mask")
self.mask_postc = T.matrix("mask")
self.np_nn_pre = sub_LSTM_batch(embedding_dimention, n_hidden,
self.np_x_pre, self.np_x_prec,
self.mask_pre, self.mask_prec)
self.params += self.np_nn_pre.params
self.np_nn_post = sub_LSTM_batch(embedding_dimention, n_hidden,
self.np_x_post, self.np_x_postc,
self.mask_post, self.mask_postc)
self.params += self.np_nn_post.params
self.np_nn_post_output = self.np_nn_post.nn_out
self.np_nn_pre_output = self.np_nn_pre.nn_out
self.np_out = T.concatenate(
(self.np_nn_post_output, self.np_nn_pre_output), axis=1)
np_nn_f = LSTM(n_hidden * 2, n_hidden * 2, self.np_out)
self.params += np_nn_f.params
np_nn_b = LSTM(n_hidden * 2, n_hidden * 2, self.np_out[::-1])
self.params += np_nn_b.params
self.bi_np_out = T.concatenate(
(np_nn_f.all_hidden, np_nn_b.all_hidden[::-1]), axis=1)
self.np_out_output = self.bi_np_out
#self.get_np_out = theano.function(inputs=[self.np_x_pre,self.np_x_prec,self.np_x_post,self.np_x_postc,self.mask_pre,self.mask_prec,self.mask_post,self.mask_postc],outputs=[self.np_out_output])
#self.feature = T.matrix("feature")
#self.feature_layer = Layer(feature_dimention,n_hidden,self.feature,repre_active)
#self.params += self.feature_layer.params
w_attention_zp, b_attention = init_weight(n_hidden * 2,
1,
pre="attention_zp",
ones=False)
self.params += [w_attention_zp, b_attention]
w_attention_np, b_u = init_weight(n_hidden * 2,
1,
pre="attention_np",
ones=False)
#self.params += [w_attention_np]
w_attention_np_rnn, b_u = init_weight(n_hidden * 4,
1,
pre="attention_np_rnn",
ones=False)
self.params += [w_attention_np_rnn]
#np_out_dropout = _dropout_from_layer(self.np_out_output)
#zp_out_dropout = _dropout_from_layer(self.zp_out_output)
#np_dropout = _dropout_from_layer(self.np_out)
#self.calcu_attention_dropout = tanh(T.dot(np_out_dropout,w_attention_np_rnn) + T.dot(zp_out_dropout,w_attention_zp) + T.dot(np_dropout,w_attention_np) + b_attention)
#self.calcu_attention = tanh(T.dot(self.np_out_output,w_attention_np_rnn) + T.dot(self.zp_out_output,w_attention_zp) + T.dot(self.np_out,w_attention_np) + b_attention)
self.calcu_attention = tanh(
T.dot(self.np_out_output, w_attention_np_rnn) +
T.dot(self.zp_out_output, w_attention_zp) + b_attention)
self.attention = softmax(T.transpose(self.calcu_attention,
axes=(1, 0)))[0]
#self.attention_dropout = softmax(T.transpose(self.calcu_attention_dropout,axes=(1,0)))[0]
self.out = self.attention
#self.out_dropout = self.attention_dropout
self.get_out = theano.function(inputs=[
self.zp_x_pre_index, self.zp_x_post_index, self.np_x_pre_index,
self.np_x_prec_index, self.np_x_post_index, self.np_x_postc_index,
self.mask_pre, self.mask_prec, self.mask_post, self.mask_postc
],
outputs=[self.out],
on_unused_input='warn')
l1_norm_squared = sum([(w**2).sum() for w in self.params])
l2_norm_squared = sum([(abs(w)).sum() for w in self.params])
lmbda_l1 = 0.0
#lmbda_l2 = 0.001
lmbda_l2 = 0.0
t = T.bvector()
cost = -(T.log((self.out * t).sum())) + f_norm
#cost = -(T.log((self.out_dropout*t).sum()))
lr = T.scalar()
updates = lasagne.updates.sgd(cost, self.params, lr)
#updates = lasagne.updates.adadelta(cost, self.params)
self.train_step = theano.function(inputs=[
self.zp_x_pre_index, self.zp_x_post_index, self.np_x_pre_index,
self.np_x_prec_index, self.np_x_post_index, self.np_x_postc_index,
self.mask_pre, self.mask_prec, self.mask_post, self.mask_postc, t,
lr
],
outputs=[cost],
on_unused_input='warn',
updates=updates)
#==========================Theano Function definitions==============================
#===================================================================================
#variables declaration
ATemp = T.matrix('ATemp')
BTemp = T.tensor3('BTemp')
UTemp = T.matrix('UTemp')
E1Temp = T.vector('E1Temp')
E2Temp = T.vector('E2Temp')
E1E2Temp = T.vector('E1E2Temp')
ECTemp = T.vector('ECTemp')
E1ECTemp = T.vector('E1ECTemp')
#Definition of scoring function
score = UTemp.dot(
T.tanh(E1Temp.dot(BTemp).dot(T.transpose(E2Temp)) + ATemp.dot(E1E2Temp)))
scoringFunction = theano.function(
[ATemp, BTemp, UTemp, E1Temp, E2Temp, E1E2Temp], score)
#Definition of loss function
#calculated score of corrupted triplet to calculate loss
scoreCorrupted = UTemp.dot(
T.tanh(E1Temp.dot(BTemp).dot(T.transpose(ECTemp)) + ATemp.dot(E1ECTemp)))
loss = T.largest(0, (1 - (UTemp.dot(
T.tanh(E1Temp.dot(BTemp).dot(T.transpose(E2Temp)) + ATemp.dot(E1E2Temp))
)) + (UTemp.dot(
T.tanh(E1Temp.dot(BTemp).dot(T.transpose(ECTemp)) + ATemp.dot(E1ECTemp))))
+ regparam *
(T.sum(ATemp**2) + T.sum(BTemp**2) + T.sum(UTemp**2)) /
3))
def __init__(self, phase, config, vocabulary_size=1295, hidden_ndim=512):
# need to be same voca_size and hidde_ndim so as to load same shape params
# self.log_self()
size = 101
# model paras
self.config = config
learning_rate = self.config.items['lr']
self.alpha = np.array(1e-3, dtype=np.float32)
self.eps = np.array(1e-6, dtype=np.float32)
self.learning_rate = theano.shared(np.float32(config.items['lr']))
self.nClasses = vocabulary_size + 1
self.vocabulary_size = vocabulary_size
# variables
image = T.tensor4(
'image') # (2*nb*len, 3, 101, 101) or (2*nb*3, 3, 101, 101)
mask = T.matrix('mask') # (nb, max_hlen)
token = T.imatrix('token') # (nb, max_vlen)
self.nb = mask.shape[0]
self.max_xlen = image.shape[0] / 2 / self.nb
self.max_hlen = mask.shape[1]
net = {}
# RGB modal
net['image'] = InputLayer(shape=(None, 3, size,
size)) # (2*nb*len, 3, 101, 101)
# both hand
net['conv1'] = Conv2DLayer(incoming=net['image'],
num_filters=96,
filter_size=7,
stride=2)
net['norm1'] = LocalResponseNormalization2DLayer(incoming=net['conv1'])
net['pool1'] = MaxPool2DLayer(incoming=net['norm1'], pool_size=3)
net['conv2'] = Conv2DLayer(incoming=net['pool1'],
num_filters=256,
filter_size=5)
net['pool2'] = MaxPool2DLayer(incoming=net['conv2'], pool_size=2)
net['conv3'] = Conv2DLayer(incoming=net['pool2'],
num_filters=512,
filter_size=3,
pad=1)
net['conv4'] = Conv2DLayer(incoming=net['conv3'],
num_filters=512,
filter_size=3,
pad=1)
net['conv5'] = Conv2DLayer(incoming=net['conv4'],
num_filters=512,
filter_size=3,
pad=1)
# modal fusion
net['pool5'] = MaxPool2DLayer(incoming=net['conv5'],
pool_size=3) # (2nb*len, 512, 2, 2)
net['fc6'] = DenseLayer(
incoming=net['pool5'],
num_units=1024) # (2nb*len, 1024) or (nb*3, 1024)
# dropout should be shared among timestep, or triplets
net['drop6'] = ReshapeLayer(incoming=net['fc6'],
shape=(2 * self.nb, -1, 1024))
# net['drop6'] = DropoutLayer(incoming=net['pre_drop6'], p=0.2, shared_axes=(1,))
net['fc7'] = DenseLayer(ReshapeLayer(net['drop6'], shape=(-1, 1024)),
num_units=256,
nonlinearity=identity) # (3*nb, 256)
# encoding network for image features
net['mask'] = InputLayer(shape=(None, None),
name='mask') # (nb, max_hlen)
net['pre_conv1d'] = DimshuffleLayer(net['drop6'],
(0, 2, 1)) # (nb, 1024, max_xlen)
net['conv1d_1'] = Conv1DLayer(net['pre_conv1d'],
num_filters=1024,
filter_size=3,
pad='same')
net['pool1d_1'] = MaxPool1DLayer(net['conv1d_1'],
pool_size=2) #(nb, 1024, max_xlen/2)
net['drop1d_1'] = DropoutLayer(net['pool1d_1'],
p=0.1,
shared_axes=(2, ))
net['conv1d_2'] = Conv1DLayer(net['drop1d_1'],
num_filters=1024,
filter_size=3,
pad='same')
net['pool1d_2'] = MaxPool1DLayer(net['conv1d_2'],
pool_size=2) #(nb, 1024, max_hlen)
net['drop1d_2'] = DropoutLayer(net['pool1d_2'],
p=0.1,
shared_axes=(2, ))
# LSTM, input shape=(nb, max_hlen, 1024)
# two LSTM, one for fusion, one for right hand
net['lstm_input'] = DimshuffleLayer(
net['drop1d_2'], (0, 2, 1)) # (2*nb, max_hlen, 1024)
# right hand lstm
net['lstm_input_right'] = ExpressionLayer(
net['lstm_input'],
function=lambda x: x[:x.shape[0] / 2],
output_shape='auto')
net['lstm_frw_right'] = LSTMLayer(
incoming=net['lstm_input_right'],
mask_input=net['mask'],
forgetgate=Gate(b=lasagne.init.Constant(1.0)),
num_units=hidden_ndim) # (nb, max_hlen, hidden_ndim)
net['lstm_bck_right'] = LSTMLayer(
incoming=net['lstm_input_right'],
mask_input=net['mask'],
forgetgate=Gate(b=lasagne.init.Constant(1.0)),
num_units=hidden_ndim,
backwards=True)
net['lstm_shp_right'] = ReshapeLayer(
ConcatLayer((net['lstm_frw_right'], net['lstm_bck_right']),
axis=2),
shape=(-1, 2 * hidden_ndim)) # (nb*max_hlen, 2*hidden_ndim)
# fusion lstm
net['lstm_input_fusion'] = ExpressionLayer(
net['lstm_input'],
function=lambda x: T.concatenate(
[x[:x.shape[0] / 2], x[x.shape[0] / 2:]], axis=2) / 2.0,
output_shape='auto')
net['lstm_frw_fusion'] = LSTMLayer(
incoming=net['lstm_input_fusion'],
mask_input=net['mask'],
forgetgate=Gate(b=lasagne.init.Constant(1.0)),
num_units=hidden_ndim) # (nb, max_hlen, hidden_ndim)
net['lstm_bck_fusion'] = LSTMLayer(
incoming=net['lstm_input_fusion'],
mask_input=net['mask'],
forgetgate=Gate(b=lasagne.init.Constant(1.0)),
num_units=hidden_ndim,
backwards=True)
net['lstm_shp_fusion'] = ReshapeLayer(
ConcatLayer((net['lstm_frw_fusion'], net['lstm_bck_fusion']),
axis=2),
shape=(-1, 2 * hidden_ndim)) # (nb*max_hlen, 2*hidden_ndim)
net['lstm_shp'] = ConcatLayer(
[net['lstm_shp_right'], net['lstm_shp_fusion']], axis=1)
# net['lstm_shp'] = net['lstm_shp_right']
net['out'] = DenseLayer(
net['lstm_shp'], self.nClasses,
nonlinearity=identity) # (nb*max_hlen, nClasses)
net['out_lin'] = ReshapeLayer(net['out'],
shape=(self.nb, -1, self.nClasses))
self.net = net
# try save load model
dummy_save_file = 'dummy.pkl'
glog.info('try save load dummy model to: %s...' % dummy_save_file)
self.save_model(dummy_save_file)
self.load_model(dummy_save_file)
os.system('rm -rf dummy.pkl')
glog.info(
'dummy save load success, remove it and start calculate outputs...'
)
if phase == 'pretrain':
# for triplet pretrain use
self.params_feat = get_all_params(net['fc7'])
regular_feat = lasagne.regularization.apply_penalty(
self.params_feat, lasagne.regularization.l2) * np.array(
5e-4 / 2, dtype=np.float32)
## triplet train loss
triplet_loss_train = self.get_triplet_loss(image,
opflow,
deterministic=False)
loss_train_feat = triplet_loss_train + regular_feat
## triplet valid loss
triplet_loss_valid = self.get_triplet_loss(image,
opflow,
deterministic=True)
loss_valid_feat = triplet_loss_valid + regular_feat
self.updates = lasagne.updates.momentum(
loss_train_feat,
self.params_feat,
learning_rate=learning_rate,
momentum=0.9)
self.inputs = [image, opflow]
self.train_outputs = [loss_train_feat, triplet_loss_train]
self.valid_outputs = [loss_valid_feat, triplet_loss_valid]
elif phase == 'ctc':
# for ctc loss
self.params_full = lasagne.layers.get_all_params(
self.net['out_lin'], trainable=True)
self.regular_params = lasagne.layers.get_all_params(
self.net['out_lin'], regularizable=True)
regular_full = lasagne.regularization.apply_penalty(
self.regular_params, lasagne.regularization.l2) * np.array(
5e-4 / 2, dtype=np.float32)
# full train loss
ctc_loss_train, pred_train = self.get_ctc_loss(image,
mask,
token,
deteministic=False)
loss_train_full = ctc_loss_train + regular_full
# full valid loss
ctc_loss_valid, pred_valid = self.get_ctc_loss(image,
mask,
token,
deteministic=True)
loss_valid_full = ctc_loss_valid + regular_full
self.updates = lasagne.updates.adam(
loss_train_full,
self.params_full,
learning_rate=self.learning_rate)
self.inputs = [image, mask, token]
self.train_outputs = [loss_train_full, ctc_loss_train, pred_train]
self.valid_outputs = [loss_valid_full, ctc_loss_valid, pred_valid]
elif phase == 'extract_feature':
pass
# # for feature extraction
# fc6 = get_output(self.net['fc6'], data, deterministic = True)
# self.feature_func = theano.function(inputs=[data], outputs=fc6)
elif phase == 'get_prediction':
embeding = get_output(self.net['fusion_2'], {
self.net['image']: image,
self.net['opflow']: opflow,
self.net['coord']: coord
},
deterministic=True) # (nb, 1280, len_m)
output_lin = get_output(
self.net['out_lin'], {
self.net['lstm_input']: T.transpose(embeding, (0, 2, 1)),
self.net['mask']: mask
},
deterministic=True)
output_softmax = Softmax(output_lin) # (nb, max_hlen, nClasses)
output_trans = T.transpose(output_softmax,
(1, 0, 2)) # (max_hlen, nb, nClasses)
best_path_loss, best_path = best_right_path_cost(
output_trans, mask, token)
ctc_loss = ctc_cost(output_trans, T.sum(mask,
axis=1,
dtype='int32'), token)
# (nb, max_hlen, voca_size+1)
self.predict_func = theano.function(
inputs=[data, mask, token],
outputs=[best_path_loss, best_path, ctc_loss])
elif phase == 'top_k_prediction':
embeding = get_output(self.net['fusion_2'], {
self.net['image']: image,
self.net['opflow']: opflow,
self.net['coord']: coord
},
deterministic=True) # (nb, 1280, len_m)
output_lin = get_output(
self.net['out_lin'], {
self.net['lstm_input']: T.transpose(embeding, (0, 2, 1)),
self.net['mask']: mask
},
deterministic=True)
output_softmax = Softmax(output_lin) # (nb, max_hlen, nClasses)
output_trans = T.transpose(output_softmax,
(1, 0, 2)) # (max_hlen, nb, nClasses)
top_k_path_loss, top_k_path = top_k_right_path_cost(
output_trans, mask, token, k=config.items['top_k'])
ctc_loss = ctc_cost(output_trans, T.sum(mask,
axis=1,
dtype='int32'), token)
# (nb, max_hlen, voca_size+1)
self.predict_func = theano.function(
inputs=[data, mask, token],
outputs=[output_lin, top_k_path_loss, top_k_path, ctc_loss])
glog.info('Model built, phase = %s' % phase)
margin = 0.10
lambda_ = 1.0
norm_in = T.sqrt(T.sum(prediction * prediction, axis=1))
norm_tar = T.sqrt(T.sum(target_var * target_var, axis=1))
norm_neg = T.sqrt(T.sum(neg_var * neg_var, axis=1))
#norm_in=input_var.sum(axis=1).reshape((input_var.shape[0], 1))
prod_xy_unnorm = (prediction * target_var)
prod_xneg_unnorm = (prediction * neg_var)
prod_xy_unnorm = prod_xy_unnorm.sum(axis=1)
prod_xneg_unnorm = prod_xneg_unnorm.sum(axis=1)
norm = norm_in * norm_tar + eps
norm_xneg = norm_in * norm_neg + eps
prod_xy = prod_xy_unnorm / (T.transpose(norm))
prod_xneg = prod_xneg_unnorm / (T.transpose(norm_xneg))
rank_loss = margin - prod_xy + prod_xneg
rank_loss = T.maximum(rank_loss, 0)
rank_loss_m = T.mean(rank_loss, axis=0)
dist = 1 - prod_xy
dist_m = T.mean(dist, axis=0)
(lr, mtm) = (0.01, 0.9)
#regularize all layers below dense
params = lasagne.layers.get_all_params(network, trainable=True)
loss = dist_m + lambda_ * rank_loss_m
updates = lasagne.updates.adagrad(loss, params, learning_rate=lr)
train_fn = theano.function([input_var, target_var, neg_var],
[loss, prediction],
def cosine_similarity(A, B):
return T.dot(A, T.transpose(B)) / (T.dot(A, T.transpose(A)) *
T.dot(B, T.transpose(B)))
def get_upds(self, inp):
w_update = T.dot(T.transpose(inp),
self.fprop(inp)) * (1.0 / self.MB_size)
h_update = T.mean(self.fprop(inp), axis=0)
v_update = T.mean(inp, axis=0)
return w_update, h_update, v_update
def get_light_curve(self,
orbit=None,
r=None,
t=None,
texp=None,
return_num_eval=False,
light_delay=False,
**kwargs):
"""Get the light curve for an orbit at a set of times
Args:
orbit: An object with a ``get_relative_position`` method that
takes a tensor of times and returns a list of Cartesian
coordinates of a set of bodies relative to the central source.
This method should return three tensors (one for each
coordinate dimension) and each tensor should have the shape
``append(t.shape, r.shape)`` or ``append(t.shape, oversample,
r.shape)`` when ``texp`` is given. The first two coordinate
dimensions are treated as being in the plane of the sky and the
third coordinate is the line of sight with positive values
pointing *away* from the observer. For an example, take a look
at :class:`orbits.KeplerianOrbit`.
r (tensor): The radius of the transiting body in the same units as
``r_star``. This should have a shape that is consistent with
the coordinates returned by ``orbit``. In general, this means
that it should probably be a scalar or a vector with one entry
for each body in ``orbit``.
t (tensor): The times where the light curve should be evaluated.
texp (Optional[tensor]): The exposure time of each observation.
This can be a scalar or a tensor with the same shape as ``t``.
If ``texp`` is provided, ``t`` is assumed to indicate the
timestamp at the *middle* of an exposure of length ``texp``.
"""
if orbit is None:
raise ValueError("missing required argument 'orbit'")
if r is None:
raise ValueError("missing required argument 'r'")
if t is None:
raise ValueError("missing required argument 't'")
r = tt.as_tensor_variable(r)
r = tt.reshape(r, (r.size, ))
t = tt.as_tensor_variable(t)
def pad(arg):
return arg
# return tt.shape_padleft(arg, t.ndim) + tt.shape_padright(
# tt.zeros_like(t), arg.ndim
# )
rgrid = pad(r)
if texp is None:
coords = orbit.get_relative_position(t, light_delay=light_delay)
b = tt.sqrt(coords[0]**2 + coords[1]**2)
b = tt.reshape(b, rgrid.shape)
los = tt.reshape(coords[2], rgrid.shape)
return limbdark(self.c_norm, b / orbit.r_star,
rgrid / orbit.r_star, los)[0]
n = pad(orbit.n)
sini = pad(orbit.sin_incl)
cosi = pad(orbit.cos_incl)
# texp = tt.as_tensor_variable(texp) + tt.zeros_like(rgrid)
if orbit.ecc is None:
aome2 = pad(-orbit.a)
e = 0.0
sinw = 0.0
cosw = 0.0
kwargs["circular"] = True
else:
aome2 = pad(-orbit.a * (1 - orbit.ecc**2))
e = pad(orbit.ecc)
sinw = pad(orbit.sin_omega)
cosw = pad(orbit.cos_omega)
kwargs["circular"] = False
# Apply the time integrated op
tgrid = tt.transpose(orbit._warp_times(t) - orbit.tref)
texp = tt.as_tensor_variable(texp) + tt.zeros_like(tgrid)
kwargs["Nc"] = kwargs.get("Nc", self.num_cl)
op = IntegratedLimbDarkOp(**kwargs)
res = op(
self.c_norm,
texp,
tgrid,
rgrid / orbit.r_star,
n,
aome2,
sini,
cosi,
e,
sinw,
cosw,
)
if return_num_eval:
return res[0], res[1]
return res[0]
def apply_global_transform(pose_params, positions):
R = angle_axis_to_rotation_matrix(pose_params[0])
s = pose_params[1]
R *= s[np.newaxis, :]
t = pose_params[2]
return T.transpose(T.dot(R, T.transpose(positions))) + t
def compute_OD(idx, zS, zD, zAA, zBB):
OD = T.dot(T.transpose(zS[-idx - 1]), zD[idx])
return OD
def build_decoder(self, query_tokens, query_token_embed, query_token_embed_mask):
# logging.info('building decoder ...')
# (batch_size, decoder_state_dim)
decoder_prev_state = ndim_tensor(2, name='decoder_prev_state')
# (batch_size, decoder_state_dim)
decoder_prev_cell = ndim_tensor(2, name='decoder_prev_cell')
# (batch_size, n_timestep, decoder_state_dim)
hist_h = ndim_tensor(3, name='hist_h')
# (batch_size, decoder_state_dim)
prev_action_embed = ndim_tensor(2, name='prev_action_embed')
# (batch_size)
node_id = T.ivector(name='node_id')
# (batch_size, node_embed_dim)
node_embed = self.node_embedding[node_id]
# (batch_size)
par_rule_id = T.ivector(name='par_rule_id')
# (batch_size, decoder_state_dim)
par_rule_embed = T.switch(par_rule_id[:, None] < 0,
T.alloc(0., 1, config.rule_embed_dim),
self.rule_embedding_W[par_rule_id])
# ([time_step])
time_steps = T.ivector(name='time_steps')
# (batch_size)
parent_t = T.ivector(name='parent_t')
# (batch_size, 1)
parent_t_reshaped = T.shape_padright(parent_t)
query_embed = self.query_encoder_lstm(query_token_embed, mask=query_token_embed_mask,
dropout=config.dropout, train=False)
# (batch_size, 1, decoder_state_dim)
prev_action_embed_reshaped = prev_action_embed.dimshuffle((0, 'x', 1))
# (batch_size, 1, node_embed_dim)
node_embed_reshaped = node_embed.dimshuffle((0, 'x', 1))
# (batch_size, 1, node_embed_dim)
par_rule_embed_reshaped = par_rule_embed.dimshuffle((0, 'x', 1))
if not config.frontier_node_type_feed:
node_embed_reshaped *= 0.
if not config.parent_action_feed:
par_rule_embed_reshaped *= 0.
decoder_input = T.concatenate([prev_action_embed_reshaped, node_embed_reshaped, par_rule_embed_reshaped], axis=-1)
# (batch_size, 1, decoder_state_dim)
# (batch_size, 1, decoder_state_dim)
# (batch_size, 1, field_token_encode_dim)
decoder_next_state_dim3, decoder_next_cell_dim3, ctx_vectors = self.decoder_lstm(decoder_input,
init_state=decoder_prev_state,
init_cell=decoder_prev_cell,
hist_h=hist_h,
context=query_embed,
context_mask=query_token_embed_mask,
parent_t_seq=parent_t_reshaped,
dropout=config.dropout,
train=False,
time_steps=time_steps)
decoder_next_state = decoder_next_state_dim3.flatten(2)
# decoder_output = decoder_next_state * (1 - DECODER_DROPOUT)
decoder_next_cell = decoder_next_cell_dim3.flatten(2)
decoder_next_state_trans_rule = self.decoder_hidden_state_W_rule(decoder_next_state)
decoder_next_state_trans_token = self.decoder_hidden_state_W_token(T.concatenate([decoder_next_state, ctx_vectors.flatten(2)], axis=-1))
rule_prob = softmax(T.dot(decoder_next_state_trans_rule, T.transpose(self.rule_embedding_W)) + self.rule_embedding_b)
gen_action_prob = self.terminal_gen_softmax(decoder_next_state)
vocab_prob = softmax(T.dot(decoder_next_state_trans_token, T.transpose(self.vocab_embedding_W)) + self.vocab_embedding_b)
ptr_net_decoder_state = T.concatenate([decoder_next_state_dim3, ctx_vectors], axis=-1)
copy_prob = self.src_ptr_net(query_embed, query_token_embed_mask, ptr_net_decoder_state)
copy_prob = copy_prob.flatten(2)
inputs = [query_tokens]
outputs = [query_embed, query_token_embed_mask]
self.decoder_func_init = theano.function(inputs, outputs)
inputs = [time_steps, decoder_prev_state, decoder_prev_cell, hist_h, prev_action_embed,
node_id, par_rule_id, parent_t,
query_embed, query_token_embed_mask]
outputs = [decoder_next_state, decoder_next_cell,
rule_prob, gen_action_prob, vocab_prob, copy_prob]
self.decoder_func_next_step = theano.function(inputs, outputs)
def work(mode, data_name, test_dataname, pooling_mode="average_exc_pad"):
print "mode: ", mode
print "data_name: ", data_name
print "pooling_mode: ", pooling_mode
print "Started!"
rng = numpy.random.RandomState(23455)
docSentenceCount = T.ivector("docSentenceCount")
sentenceWordCount = T.ivector("sentenceWordCount")
corpus = T.matrix("corpus")
docLabel = T.ivector('docLabel')
# for list-type data
layer0 = DocEmbeddingNN(corpus, docSentenceCount, sentenceWordCount, rng, \
wordEmbeddingDim=249, \
sentenceLayerNodesNum=50, \
sentenceLayerNodesSize=[5, 249], \
docLayerNodesNum=10, \
docLayerNodesSize=[3, 50],
pooling_mode=pooling_mode)
layer1 = HiddenLayer(rng,
input=layer0.output,
n_in=layer0.outputDimension,
n_out=10,
activation=T.tanh)
layer2 = LogisticRegression(input=layer1.output, n_in=10, n_out=2)
# construct the parameter array.
params = layer2.params + layer1.params + layer0.params
# Load the parameters last time, optionally.
# data_name = "car"
para_path = "data/" + data_name + "/model/multi_input_mergeinput" + pooling_mode + ".model"
traintext = "data/" + data_name + "/train/text"
trainlabel = "data/" + data_name + "/train/label"
testtext = "data/" + test_dataname + "/test/text"
testlabel = "data/" + test_dataname + "/test/label"
loadParamsVal(para_path, params)
if (mode == "train" or mode == "test"):
learning_rate = 0.1
error = layer2.errors(docLabel)
cost = layer2.negative_log_likelihood(docLabel)
grads = T.grad(cost, params)
updates = [(param_i, param_i - learning_rate * grad_i)
for param_i, grad_i in zip(params, grads)]
print "Loading test data."
cr_test = CorpusReader(minDocSentenceNum=5,
minSentenceWordNum=5,
dataset=testtext,
labelset=testlabel)
validDocMatrixes, validDocSentenceNums, validSentenceWordNums, validIds, validLabels, _, validPosList = cr_test.getCorpus(
[0, 1000])
# print "Right answer: "
# print zip(validIds, validLabels)
validDocMatrixes = numpy.column_stack((validDocMatrixes, validPosList))
validDocMatrixes = transToTensor(validDocMatrixes,
theano.config.floatX)
# validPosList = transToTensor(validPosList, theano.config.floatX)
validDocSentenceNums = transToTensor(validDocSentenceNums, numpy.int32)
validSentenceWordNums = transToTensor(validSentenceWordNums,
numpy.int32)
validLabels = transToTensor(validLabels, numpy.int32)
print "Data loaded."
valid_model = theano.function(
[], [
cost, error, layer2.y_pred, docLabel,
T.transpose(layer2.p_y_given_x)[1]
],
givens={
corpus: validDocMatrixes,
docSentenceCount: validDocSentenceNums,
sentenceWordCount: validSentenceWordNums,
docLabel: validLabels
},
allow_input_downcast=True)
# ####Validate the model####
costNum, errorNum, pred_label, real_label, pred_prob = valid_model()
print "Valid current model:"
print "Cost: ", costNum
print "Error: ", errorNum
# print "Valid Pred: ", pred_label
# print "pred_prob: ", pred_prob
fpr, tpr, _ = roc_curve(real_label, pred_prob)
if mode == "test":
print "tpr_all: ", tpr
print "fpr_all: ", fpr
roc_auc = auc(fpr, tpr)
print "data_name: ", data_name
print "test_dataname: ", test_dataname
print "ROC: ", roc_auc
fpr, tpr, threshold = roc_curve(real_label, pred_label)
index_of_one = list(threshold).index(1)
ar = (tpr[index_of_one] + 1 - fpr[index_of_one]) / 2
print "TPR: ", tpr[index_of_one]
print "FPR: ", fpr[index_of_one]
print "AR: ", ar
print "threshold: ", threshold[index_of_one]
if mode == "test":
valid_model.free()
return errorNum, roc_auc, tpr[index_of_one], fpr[index_of_one], ar
print "Loading train data."
cr_train = CorpusReader(minDocSentenceNum=5,
minSentenceWordNum=5,
dataset=traintext,
labelset=trainlabel)
docMatrixes, docSentenceNums, sentenceWordNums, ids, labels, _, posList = cr_train.getCorpus(
[0, 100000])
# print "Right answer: "
# print zip(ids, labels)
docMatrixes = numpy.column_stack((docMatrixes, posList))
docMatrixes = transToTensor(docMatrixes, theano.config.floatX)
# posList = transToTensor(posList, theano.config.floatX)
docSentenceNums = transToTensor(docSentenceNums, numpy.int32)
sentenceWordNums = transToTensor(sentenceWordNums, numpy.int32)
labels = transToTensor(labels, numpy.int32)
# valid_cr = CorpusReader(minDocSentenceNum=5, minSentenceWordNum=5, dataset="data/valid/split", labelset="data/valid/label.txt")
print
index = T.lscalar("index")
batchSize = 10
n_batches = (len(docSentenceNums.get_value()) - 1 - 1) / batchSize + 1
print
print "Train set size is ", len(docMatrixes.get_value())
print "Validating set size is ", len(validDocMatrixes.get_value())
print "Batch size is ", batchSize
print "Number of training batches is ", n_batches
print "Compiling computing graph."
# for list-type data
train_model = theano.function(
[index], [cost, error, layer2.y_pred, docLabel],
updates=updates,
givens={
corpus:
docMatrixes,
docSentenceCount:
docSentenceNums[index * batchSize:(index + 1) * batchSize + 1],
sentenceWordCount:
sentenceWordNums,
docLabel:
labels[index * batchSize:(index + 1) * batchSize],
},
allow_input_downcast=True)
print "Compiled."
print "Start to train."
epoch = 0
n_epochs = 10
ite = 0
while (epoch < n_epochs):
epoch = epoch + 1
#######################
for i in range(n_batches):
# for list-type data
print ".",
costNum, errorNum, pred_label, real_label = train_model(i)
print ".",
ite = ite + 1
# for padding data
# costNum, errorNum = train_model(docMatrixes, labels)
# del docMatrixes, docSentenceNums, sentenceWordNums, labels
# print ".",
if (ite % 10 == 0):
print
print "@iter: ", ite
print "Cost: ", costNum
print "Error: ", errorNum
# Validate the model
costNum, errorNum, pred_label, real_label, pred_prob = valid_model(
)
print "Valid current model:"
print "Cost: ", costNum
print "Error: ", errorNum
# print "pred_prob: ", pred_prob
# print "Valid Pred: ", pred_label
fpr, tpr, _ = roc_curve(real_label, pred_prob)
roc_auc = auc(fpr, tpr)
print "data_name: ", data_name
print "test_dataname: ", test_dataname
print "ROC: ", roc_auc
fpr, tpr, threshold = roc_curve(real_label, pred_label)
index_of_one = list(threshold).index(1)
print "TPR: ", tpr[index_of_one]
print "FPR: ", fpr[index_of_one]
print "AR: ", (tpr[index_of_one] + 1 - fpr[index_of_one]) / 2
print "threshold: ", threshold[index_of_one]
# Save model
print "Saving parameters."
saveParamsVal(para_path, params)
print "Saved."
valid_model.free()
train_model.free()
elif (mode == "deploy"):
print "Compiling computing graph."
output_model = theano.function(
[corpus, docSentenceCount, sentenceWordCount], [layer2.y_pred])
print "Compiled."
cr = CorpusReader(minDocSentenceNum=5,
minSentenceWordNum=5,
dataset="data/train_valid/split")
count = 21000
while (count <= 21000):
docMatrixes, docSentenceNums, sentenceWordNums, ids = cr.getCorpus(
[count, count + 100])
docMatrixes = numpy.matrix(docMatrixes, dtype=theano.config.floatX)
docSentenceNums = numpy.array(docSentenceNums, dtype=numpy.int32)
sentenceWordNums = numpy.array(sentenceWordNums, dtype=numpy.int32)
print "start to predict."
pred_y = output_model(docMatrixes, docSentenceNums,
sentenceWordNums)
print "End predicting."
print "Writing resfile."
# print zip(ids, pred_y[0])
f = file("data/test/res/res" + str(count), "w")
f.write(str(zip(ids, pred_y[0])))
f.close()
print "Written." + str(count)
count += 100
def build(self):
# (batch_size, max_example_action_num, action_type)
tgt_action_seq = ndim_itensor(3, 'tgt_action_seq')
# (batch_size, max_example_action_num, action_type)
tgt_action_seq_type = ndim_itensor(3, 'tgt_action_seq_type')
# (batch_size, max_example_action_num)
tgt_node_seq = ndim_itensor(2, 'tgt_node_seq')
# (batch_size, max_example_action_num)
tgt_par_rule_seq = ndim_itensor(2, 'tgt_par_rule_seq')
# (batch_size, max_example_action_num)
tgt_par_t_seq = ndim_itensor(2, 'tgt_par_t_seq')
# (batch_size, max_example_action_num, symbol_embed_dim)
# tgt_node_embed = self.node_embedding(tgt_node_seq, mask_zero=False)
tgt_node_embed = self.node_embedding[tgt_node_seq]
# (batch_size, max_query_length)
query_tokens = ndim_itensor(2, 'query_tokens')
# (batch_size, max_query_length, query_token_embed_dim)
# (batch_size, max_query_length)
query_token_embed, query_token_embed_mask = self.query_embedding(query_tokens, mask_zero=True)
# if WORD_DROPOUT > 0:
# logging.info('used word dropout for source, p = %f', WORD_DROPOUT)
# query_token_embed, query_token_embed_intact = WordDropout(WORD_DROPOUT, self.srng)(query_token_embed, False)
batch_size = tgt_action_seq.shape[0]
max_example_action_num = tgt_action_seq.shape[1]
# previous action embeddings
# (batch_size, max_example_action_num, action_embed_dim)
tgt_action_seq_embed = T.switch(T.shape_padright(tgt_action_seq[:, :, 0] > 0),
self.rule_embedding_W[tgt_action_seq[:, :, 0]],
self.vocab_embedding_W[tgt_action_seq[:, :, 1]])
tgt_action_seq_embed_tm1 = tensor_right_shift(tgt_action_seq_embed)
# parent rule application embeddings
tgt_par_rule_embed = T.switch(tgt_par_rule_seq[:, :, None] < 0,
T.alloc(0., 1, config.rule_embed_dim),
self.rule_embedding_W[tgt_par_rule_seq])
if not config.frontier_node_type_feed:
tgt_node_embed *= 0.
if not config.parent_action_feed:
tgt_par_rule_embed *= 0.
# (batch_size, max_example_action_num, action_embed_dim + symbol_embed_dim + action_embed_dim)
decoder_input = T.concatenate([tgt_action_seq_embed_tm1, tgt_node_embed, tgt_par_rule_embed], axis=-1)
# (batch_size, max_query_length, query_embed_dim)
query_embed = self.query_encoder_lstm(query_token_embed, mask=query_token_embed_mask,
dropout=config.dropout, srng=self.srng)
# (batch_size, max_example_action_num)
tgt_action_seq_mask = T.any(tgt_action_seq_type, axis=-1)
# decoder_hidden_states: (batch_size, max_example_action_num, lstm_hidden_state)
# ctx_vectors: (batch_size, max_example_action_num, encoder_hidden_dim)
decoder_hidden_states, _, ctx_vectors = self.decoder_lstm(decoder_input,
context=query_embed,
context_mask=query_token_embed_mask,
mask=tgt_action_seq_mask,
parent_t_seq=tgt_par_t_seq,
dropout=config.dropout,
srng=self.srng)
# if DECODER_DROPOUT > 0:
# logging.info('used dropout for decoder output, p = %f', DECODER_DROPOUT)
# decoder_hidden_states = Dropout(DECODER_DROPOUT, self.srng)(decoder_hidden_states)
# ====================================================
# apply additional non-linearity transformation before
# predicting actions
# ====================================================
decoder_hidden_state_trans_rule = self.decoder_hidden_state_W_rule(decoder_hidden_states)
decoder_hidden_state_trans_token = self.decoder_hidden_state_W_token(T.concatenate([decoder_hidden_states, ctx_vectors], axis=-1))
# (batch_size, max_example_action_num, rule_num)
rule_predict = softmax(T.dot(decoder_hidden_state_trans_rule, T.transpose(self.rule_embedding_W)) + self.rule_embedding_b)
# (batch_size, max_example_action_num, 2)
terminal_gen_action_prob = self.terminal_gen_softmax(decoder_hidden_states)
# (batch_size, max_example_action_num, target_vocab_size)
vocab_predict = softmax(T.dot(decoder_hidden_state_trans_token, T.transpose(self.vocab_embedding_W)) + self.vocab_embedding_b)
# (batch_size, max_example_action_num, lstm_hidden_state + encoder_hidden_dim)
ptr_net_decoder_state = T.concatenate([decoder_hidden_states, ctx_vectors], axis=-1)
# (batch_size, max_example_action_num, max_query_length)
copy_prob = self.src_ptr_net(query_embed, query_token_embed_mask, ptr_net_decoder_state)
# (batch_size, max_example_action_num)
rule_tgt_prob = rule_predict[T.shape_padright(T.arange(batch_size)),
T.shape_padleft(T.arange(max_example_action_num)),
tgt_action_seq[:, :, 0]]
# (batch_size, max_example_action_num)
vocab_tgt_prob = vocab_predict[T.shape_padright(T.arange(batch_size)),
T.shape_padleft(T.arange(max_example_action_num)),
tgt_action_seq[:, :, 1]]
# (batch_size, max_example_action_num)
copy_tgt_prob = copy_prob[T.shape_padright(T.arange(batch_size)),
T.shape_padleft(T.arange(max_example_action_num)),
tgt_action_seq[:, :, 2]]
# (batch_size, max_example_action_num)
tgt_prob = tgt_action_seq_type[:, :, 0] * rule_tgt_prob + \
tgt_action_seq_type[:, :, 1] * terminal_gen_action_prob[:, :, 0] * vocab_tgt_prob + \
tgt_action_seq_type[:, :, 2] * terminal_gen_action_prob[:, :, 1] * copy_tgt_prob
likelihood = T.log(tgt_prob + 1.e-7 * (1 - tgt_action_seq_mask))
loss = - (likelihood * tgt_action_seq_mask).sum(axis=-1) # / tgt_action_seq_mask.sum(axis=-1)
loss = T.mean(loss)
# let's build the function!
train_inputs = [query_tokens, tgt_action_seq, tgt_action_seq_type,
tgt_node_seq, tgt_par_rule_seq, tgt_par_t_seq]
optimizer = optimizers.get(config.optimizer)
optimizer.clip_grad = config.clip_grad
updates, grads = optimizer.get_updates(self.params, loss)
self.train_func = theano.function(train_inputs, [loss],
# [loss, tgt_action_seq_type, tgt_action_seq,
# rule_tgt_prob, vocab_tgt_prob, copy_tgt_prob,
# copy_prob, terminal_gen_action_prob],
updates=updates)
# if WORD_DROPOUT > 0:
# self.build_decoder(query_tokens, query_token_embed_intact, query_token_embed_mask)
# else:
# self.build_decoder(query_tokens, query_token_embed, query_token_embed_mask)
self.build_decoder(query_tokens, query_token_embed, query_token_embed_mask)
def transpose(x):
return T.transpose(x)
f, phi_m = inp.input_var, lb_op.input_var # f - inputs, phi_m - basis # f.shape = Nxl, phi_m.shape = Nxn
f = T.printing.Print('f')(f)
phi_m = T.printing.Print('phi_m')(phi_m)
# compute A - the input coefficients matrix
A = utils_lasagne.desc_coeff(f, phi_m[:, 0:neigen])
A = T.printing.Print('A')(A)
# compute B - the reference coefficients matrix
# B, At, AtA, AtAi, AtB = ldiv(phi_n[:, 0: neigen], f)
B = utils_lasagne.desc_coeff(f, phi_n[:, 0:neigen])
B = T.printing.Print('B')(B)
# compute C using least-squares: argmin_X( ||X*A - B||^2 )
C = T.transpose(utils_lasagne.ldiv(T.transpose(A), T.transpose(B)))
C = T.printing.Print('C')(C)
# apply mapping A*C
Br = T.dot(C, A)
Br = T.printing.Print('Br')(Br)
# compute smoothed mapped functions g
output = T.dot(phi_n[:, 0:neigen], Br)
funcs = dict()
funcs['predict'] = theano.function(
[inp.input_var, lb_op.input_var],
[output, A, B, C, Br], #, At, AtA, AtAi, AtB],
on_unused_input='warn')
# output_, A_, B_, C_, Br_, gr_, At_, AtA_, AtAi_, AtB_ = funcs['predict'](*x_)
output_, A_, B_, C_, Br_, gr_ = funcs['predict'](*x_)
import theano
import theano.tensor as T
from theano import pp
from theano import function
import numpy as np
from ipdb import set_trace
conv5 = T.ftensor4()
sim_map = T.ftensor3()
top_diff = T.ftensor4()
batch_size, c, h, w = conv5.shape
value = T.reshape(conv5, newshape=(batch_size, c, h * w))
value = T.transpose(value, axes=(0, 2, 1))
context = T.batched_dot(sim_map, value)
context = T.transpose(context, axes=(0, 2, 1))
context = T.reshape(context, newshape=(batch_size, c, h, w))
fuse = context + conv5
fuse_sum = T.sum(fuse * top_diff)
forward_theano = theano.function([conv5, sim_map], fuse)
backward_theano = theano.function([conv5, sim_map, top_diff],
T.grad(fuse_sum, conv5))
one = np.ones(shape=(3, 3))
np_conv5 = np.stack([one, one + 1, one + 2, one + 3],
axis=0).astype(np.float32)