def stack_and_shared(_input):
"""
This will take a list of input variables, turn them into theano shared variables, and return them stacked
in a single tensor.
:param _input: list of input variables
:type _input: list, object, or none
:return: symbolic tensor of the input variables stacked, or none
:rtype: Tensor or None
"""
if _input is None:
return None
elif isinstance(_input, list):
shared_ins = []
for _in in _input:
try:
shared_ins.append(theano.shared(_in))
except TypeError as _:
shared_ins.append(_in)
return T.stack(shared_ins)
else:
try:
_output = [theano.shared(_input)]
except TypeError as _:
_output = [_input]
return T.stack(_output)
def _step(x_, h_, c_, pred_, prob_):
h_a = []
c_a = []
for it in range(self.n_levels):
preact = T.dot(h_[it], self.U[it])
preact += T.dot(x_, self.W[it]) + self.b[it]
i = T.nnet.sigmoid(_slice(preact, 0, self.n_dim))
f = T.nnet.sigmoid(_slice(preact, 1, self.n_dim))
o = T.nnet.sigmoid(_slice(preact, 2, self.n_dim))
c = T.tanh(_slice(preact, 3, self.n_dim))
c = f * c_[it] + i * c
h = o * T.tanh(c)
h_a.append(h)
c_a.append(c)
x_ = h
q = T.dot(h, self.L) + self.b0
prob = T.nnet.softmax(q)
pred = T.argmax(prob, axis=1)
return T.stack(h_a).squeeze(), T.stack(c_a).squeeze(), pred, prob
def predict_K(self, x, z, params):
# s_mean, s_x for computing mean from s_x
Ks = []
Ks_new = []
offset = 0
for kern, slice_k in zip(self.kernels, self.slices):
params_k = params[offset: offset + kern.n_params]
K_k, K_new_k = kern.predict_K(
x[:, slice_k], z[:, slice_k], params_k)
Ks.append(K_k)
Ks_new.append(K_new_k)
offset += kern.n_params
log_weights = TT.concatenate((np.asarray([0]),
params[offset:offset + self.n_my_params]))
weights = TT.exp(log_weights) / TT.exp(log_weights).sum()
if len(self.kernels) == 1:
return Ks[0], Ks_new[0]
else:
# XXX: log_K, should be logadd here (#11)
wK = TT.sum(
weights[:, None, None] * TT.stack(*Ks), axis=0)
wK_new = TT.sum(
weights[:, None, None] * TT.stack(*Ks_new), axis=0)
return wK, wK_new
def _for_step(self,
xi_t, xf_t, xo_t, xc_t, mask_t,
h_tm1, c_tm1,
context, context_mask, context_att_trans,
hist_h, hist_h_att_trans,
b_u):
# context: (batch_size, context_size, context_dim)
# (batch_size, att_layer1_dim)
h_tm1_att_trans = T.dot(h_tm1, self.att_h_W1)
# (batch_size, context_size, att_layer1_dim)
att_hidden = T.tanh(context_att_trans + h_tm1_att_trans[:, None, :])
# (batch_size, context_size, 1)
att_raw = T.dot(att_hidden, self.att_W2) + self.att_b2
# (batch_size, context_size)
ctx_att = T.exp(att_raw).reshape((att_raw.shape[0], att_raw.shape[1]))
if context_mask:
ctx_att = ctx_att * context_mask
ctx_att = ctx_att / T.sum(ctx_att, axis=-1, keepdims=True)
# (batch_size, context_dim)
ctx_vec = T.sum(context * ctx_att[:, :, None], axis=1)
##### attention over history #####
if hist_h:
hist_h = T.stack(hist_h).dimshuffle((1, 0, 2))
hist_h_att_trans = T.stack(hist_h_att_trans).dimshuffle((1, 0, 2))
h_tm1_hatt_trans = T.dot(h_tm1, self.hatt_h_W1)
hatt_hidden = T.tanh(hist_h_att_trans + h_tm1_hatt_trans[:, None, :])
hatt_raw = T.dot(hatt_hidden, self.hatt_W2) + self.hatt_b2
hatt_raw = hatt_raw.flatten(2)
h_att_weights = T.nnet.softmax(hatt_raw)
# (batch_size, output_dim)
h_ctx_vec = T.sum(hist_h * h_att_weights[:, :, None], axis=1)
else:
h_ctx_vec = T.zeros_like(h_tm1)
##### attention over history #####
i_t = self.inner_activation(xi_t + T.dot(h_tm1 * b_u[0], self.U_i) + T.dot(ctx_vec, self.C_i) + T.dot(h_ctx_vec, self.H_i))
f_t = self.inner_activation(xf_t + T.dot(h_tm1 * b_u[1], self.U_f) + T.dot(ctx_vec, self.C_f) + T.dot(h_ctx_vec, self.H_f))
c_t = f_t * c_tm1 + i_t * self.activation(xc_t + T.dot(h_tm1 * b_u[2], self.U_c) + T.dot(ctx_vec, self.C_c) + T.dot(h_ctx_vec, self.H_c))
o_t = self.inner_activation(xo_t + T.dot(h_tm1 * b_u[3], self.U_o) + T.dot(ctx_vec, self.C_o) + T.dot(h_ctx_vec, self.H_o))
h_t = o_t * self.activation(c_t)
h_t = (1 - mask_t) * h_tm1 + mask_t * h_t
c_t = (1 - mask_t) * c_tm1 + mask_t * c_t
# ctx_vec = theano.printing.Print('ctx_vec')(ctx_vec)
return h_t, c_t, ctx_vec
def stack_and_shared(input):
"""
This will take a list of input variables, turn them into theano shared variables, and return them stacked
in a single tensor.
Parameters
----------
input : list or object
List of input variables to stack into a single shared tensor.
Returns
-------
tensor
Symbolic tensor of the input variables stacked, or None if input was None.
"""
if input is None:
return None
elif isinstance(input, list):
shared_ins = []
for _in in input:
try:
shared_ins.append(theano.shared(_in))
except TypeError as _:
shared_ins.append(_in)
return T.stack(shared_ins)
else:
try:
_output = [theano.shared(input)]
except TypeError as _:
_output = [input]
return T.stack(_output)
def forward_prop_step_stack(x_t, masks, h_prevs, c_prevs, stack_prevs, ptrs_to_top_prevs):
# determine, for all layers, if this input was a push/pop
is_push, is_pop = map_push_pop(x_t, self.PUSH, self.POP)
is_null = get_is_null(x_t, self.NULL)
nonsymbolic_hs = []
nonsymbolic_cs = []
nonsymbolic_stacks = []
nonsymbolic_ptrs_to_tops = []
h = x_t
for i,layer in enumerate(self.layers):
h, c, stack, ptrs_to_top = layer.forward_prop_stack(h, h_prevs[i,:,:], c_prevs[i,:,:], stack_prevs[i,:,:,:], ptrs_to_top_prevs[i,:,:,:], is_push, is_pop, is_null)
h = h*masks[:,:,i] / self.dropout # inverted dropout for scaling
nonsymbolic_hs.append(h)
nonsymbolic_cs.append(c)
nonsymbolic_stacks.append(stack)
nonsymbolic_ptrs_to_tops.append(ptrs_to_top)
h_s = T.stack(nonsymbolic_hs)
c_s = T.stack(nonsymbolic_cs)
stack_s = T.stack(nonsymbolic_stacks)
ptrs_to_top_s = T.stack(nonsymbolic_ptrs_to_tops)
o_t = self.W_hy.dot(h)
return o_t, h_s, c_s, stack_s, ptrs_to_top_s
def tangent2ambient(self, X, Z):
U = tensor.stack((X.U.dot(Z.M) + Z.Up, X.U), 0).reshape((-1, X.U.shape[1]))
#U = np.hstack((X.U.dot(Z.M) + Z.Up, X.U))
S = tensor.eye(2*self._k)
V = tensor.stack((X.V, Z.Vp), 1).reshape((X.V.shape[0], -1))
#V = np.vstack((X.V, Z.Vp))
return ManifoldElementShared.from_vars((U, S, V), shape=(self._m, self._n), r=self._k)
def tangent2ambient(self, X, Z):
U = tensor.stack((X.U.dot(Z.M) + Z.Up, X.U), 0).reshape((-1, X.U.shape[1]))
#U = np.hstack((X.U.dot(Z.M) + Z.Up, X.U))
S = tensor.eye(2*self._k)
V = tensor.stack((X.V, Z.Vp), 1).reshape((X.V.shape[0], -1))
#V = np.vstack((X.V, Z.Vp))
return (U, S, V)
def retr(self, X, Z, t=None):
U, S, V = X
Up, M, Vp = Z
if t is None:
t = 1.0
Qu, Ru = tensor.nlinalg.qr(Up)
# we need rq decomposition here
Qv, Rv = tensor.nlinalg.qr(Vp[::-1].T)
Rv = Rv.T[::-1]
Rv = Rv[:, ::-1]
Qv = Qv.T[::-1]
# now we have rq decomposition (Rv @ Qv = Z.Vp)
#Rv, Qv = rq(Z.Vp, mode='economic')
zero_block = tensor.zeros((Ru.shape[0], Rv.shape[1]))
block_mat = tensor.stack(
(
tensor.stack((S + t * M, t * Rv), 1).reshape((Rv.shape[0], -1)),
tensor.stack((t * Ru, zero_block), 1).reshape((Ru.shape[0], -1))
)
).reshape((-1, Ru.shape[1] + Rv.shape[1]))
Ut, St, Vt = tensor.nlinalg.svd(block_mat, full_matrices=False)
U_res = tensor.stack((U, Qu), 1).reshape((Qu.shape[0], -1)).dot(Ut[:, :self._k])
V_res = Vt[:self._k, :].dot(tensor.stack((V, Qv), 0).reshape((-1, Qv.shape[1])))
# add some machinery eps to get a slightly perturbed element of a manifold
# even if we have some zeros in S
S_res = tensor.diag(St[:self._k]) + tensor.diag(np.spacing(1) * tensor.ones(self._k))
return (U_res, S_res, V_res)
def generate(self, h_, c_, x_):
h_a = []
c_a = []
for it in range(self.n_levels):
preact = T.dot(x_, self.W[it])
preact += T.dot(h_[it], self.U[it]) + self.b[it]
i = T.nnet.sigmoid(self.slice(preact, 0, self.n_dim))
f = T.nnet.sigmoid(self.slice(preact, 1, self.n_dim))
o = T.nnet.sigmoid(self.slice(preact, 2, self.n_dim))
c = T.tanh(self.slice(preact, 3, self.n_dim))
c = f * c_[it] + i * c
h = o * T.tanh(c)
h_a.append(h)
c_a.append(c)
x_ = h
q = T.dot(h, self.L) + self.b0
# mask = T.concatenate([T.alloc(np_floatX(1.), q.shape[0] - 1), T.alloc(np_floatX(0.), 1)])
prob = T.nnet.softmax(q / 1)
return prob, T.stack(h_a).squeeze(), T.stack(c_a)[0].squeeze()
def func(chol_vec, delta):
chol = tt.stack([
tt.stack([tt.exp(0.1 * chol_vec[0]), 0]),
tt.stack([chol_vec[1], 2 * tt.exp(chol_vec[2])]),
])
cov = tt.dot(chol, chol.T)
return MvNormalLogp()(cov, delta)
def finetune_cost_updates(self, center, mu, learning_rate):
""" This function computes the cost and the updates ."""
# note : we sum over the size of a datapoint; if we are using
# minibatches, L will be a vector, withd one entry per
# example in minibatch
network_output = self.get_output()
temp = T.pow(center - network_output, 2)
L = T.sum(temp, axis=1)
# Add the network reconstruction error
z = self.get_network_reconst()
reconst_err = T.sum(T.pow(self.x - z, 2), axis = 1)
L = self.beta*L + self.lbd*reconst_err
cost1 = T.mean(L)
cost2 = self.lbd*T.mean(reconst_err)
cost3 = cost1 - cost2
# compute the gradients of the cost of the `dA` with respect
# to its parameters
gparams = T.grad(cost1, self.params)
# generate the list of updates
updates = []
grad_values = []
param_norm = []
for param, delta, gparam in zip(self.params, self.delta, gparams):
updates.append( (delta, mu*delta - learning_rate * gparam) )
updates.append( (param, param + mu*mu*delta - (1+mu)*learning_rate*gparam ))
grad_values.append(gparam.norm(L=2))
param_norm.append(param.norm(L=2))
grad_ = T.stack(*grad_values)
param_ = T.stack(*param_norm)
return ((cost1, cost2, cost3, grad_, param_), updates)
def _setOutputs(self) : inps = [] for l in self.network.inConnections[self] : inps.append(l.outputs) self.outputs = tt.stack(inps).reshape((-1, self.nbChannels, self.height, self.width)) self.testOutputs = tt.stack(inps).reshape((-1, self.nbChannels, self.height, self.width))
def _batch_vectorization(self,**args):
fun_in = args["fun"]
symbolic_X_list = args["symbolic_X_list"]
if "symbolic_c_inp_list" in args and "t" in args:
t = args["t"]
symbolic_c_inp_list = args["symbolic_c_inp_list"]
fun = lambda x,y: fun_in(x,y,t)
elif "symbolic_c_inp_list" in args and "t" not in args:
symbolic_c_inp_list = args["symbolic_c_inp_list"]
fun = fun_in
elif "symbolic_c_inp_list" not in args and "t" in args:
t = args["t"]
symbolic_c_inp_list = []
fun = lambda x,y: fun_in(x,t)
fun_list = []
for i in np.arange(self.number_of_rollouts):
symbolic_X_list_i = [a[i] for a in symbolic_X_list]
symbolic_c_inp_list_i = [a[i] for a in symbolic_c_inp_list]
out_list = fun(symbolic_X_list_i,symbolic_c_inp_list)
fun_list.append(out_list)
if type(fun_list[0]) != list:
return T.stack(fun_list,axis = 0)
else:
ziped_list = [list(a) for a in zip(*fun_list)]
return [T.stack(a,axis = 0) for a in ziped_list]
def retr(self, X, Z, t=None):
if t is None:
t = 1.0
Qu, Ru = tensor.nlinalg.QRFull(Z.Up)
# we need rq decomposition here
Qv, Rv = tensor.nlinalg.QRFull(Z.Vp[::-1].T)
Rv = Rv.T[::-1]
Rv[:, :] = Rv[:, ::-1]
Qv = Qv.T[::-1]
# now we have rq decomposition (Rv @ Qv = Z.Vp)
#Rv, Qv = rq(Z.Vp, mode='economic')
zero_block = tensor.zeros((Ru.shape[0], Rv.shape[1]))
block_mat = tensor.stack(
(
tensor.stack((X.S + t * Z.M, t * Rv), 1).reshape((Rv.shape[0], -1)),
tensor.stack((t * Ru, zero_block), 1).reshape((Ru.shape[0], -1))
)
).reshape((-1, Ru.shape[1] + Rv.shape[1]))
Ut, St, Vt = tensor.nlinalg.svd(block_mat, full_matrices=False)
U = tensor.stack((X.U, Qu), 1).reshape((Qu.shape[0], -1)).dot(Ut[:, :self._k])
V = Vt[:self._k, :].dot(tensor.stack((X.V, Qv), 0).reshape((-1, Qv.shape[1])))
# add some machinery eps to get a slightly perturbed element of a manifold
# even if we have some zeros in S
S = tensor.diag(St[:self._k]) + tensor.diag(np.spacing(1) * tensor.ones(self._k))
return ManifoldElementShared.from_vars((U, S, V), shape=(self._m, self._n), r=self._k)
def local_gpu_sum(node):
if isinstance(node.op, tensor.elemwise.CAReduce):
if node.op.scalar_op == scal.add:
x, = node.inputs
if x.owner and x.owner.op == host_from_gpu:
if node.op.axis is None:
reduce_mask = [1] * x.type.ndim
else:
reduce_mask = [0] * x.type.ndim
for a in node.op.axis:
assert reduce_mask[a] == 0
reduce_mask[a] = 1
gsum = GpuSum(reduce_mask)
pattern = "".join(str(i) for i in reduce_mask)
if hasattr(gsum, "c_code_reduce_%s" % pattern):
rval = host_from_gpu(gsum(gpu_from_host(x)))
if rval.type == node.outputs[0].type:
return [rval]
else:
print >>sys.stderr, "WARNING: local_gpu_sum got type wrong"
return None
else:
# Try to make a simpler pattern based on reshaping
# The principle is that if two adjacent dimensions have the same value in
# the reduce_mask, then we can reshape to make them a single dimension, do
# the sum, and then reshape to get them back.
shape_of = node.env.shape_feature.shape_of
x_shape = shape_of[x]
new_in_shp = [x_shape[0]]
new_mask = [reduce_mask[0]]
for i in range(1, x.type.ndim):
if reduce_mask[i] == reduce_mask[i - 1]:
new_in_shp[-1] *= x_shape[i]
else:
new_mask.append(reduce_mask[i])
new_in_shp.append(x_shape[i])
pattern = "".join(str(i) for i in new_mask)
new_gsum = GpuSum(new_mask)
if hasattr(new_gsum, "c_code_reduce_%s" % pattern):
reshaped_x = x.reshape(tensor.stack(*new_in_shp))
sum_reshaped_x = host_from_gpu(new_gsum(gpu_from_host(reshaped_x)))
if sum_reshaped_x.ndim != node.outputs[0].ndim:
unreshaped_sum = sum_reshaped_x.reshape(tensor.stack(*shape_of[node.outputs[0]]))
else:
unreshaped_sum = sum_reshaped_x
if unreshaped_sum.type == node.outputs[0].type:
return [unreshaped_sum]
else:
print >>sys.stderr, "WARNING: local_gpu_sum got type wrong"
return None
raise Exception("GpuSum don't have implemented the pattern", pattern)
return False
def new_attn_step(self,c_t,g_tm1,m_im1,q):
cWq = T.stack([T.dot(T.dot(c_t, self.Wb), q)])
cWm = T.stack([T.dot(T.dot(c_t, self.Wb), m_im1)])
z = T.concatenate([c_t,m_im1,q,c_t*q,c_t*m_im1,T.abs_(c_t-q),T.abs_(c_t-m_im1),cWq,cWm],axis=0)
l_1 = T.dot(self.W1, z) + self.b1
l_1 = T.tanh(l_1)
l_2 = T.dot(self.W2,l_1) + self.b2
return l_2[0]
def new_attention_step(self, ct, prev_g, mem, q_q):
cWq = T.stack([T.dot(T.dot(ct, self.W_b), q_q)])
cWm = T.stack([T.dot(T.dot(ct, self.W_b), mem)])
z = T.concatenate([ct, mem, q_q, ct * q_q, ct * mem, (ct - q_q) ** 2, (ct - mem) ** 2, cWq, cWm])
l_1 = T.dot(self.W_1, z) + self.b_1
l_1 = T.tanh(l_1)
l_2 = T.dot(self.W_2, l_1) + self.b_2
G = T.nnet.sigmoid(l_2)[0]
return G
def max_pool(images, imgshp, maxpoolshp):
"""Implements a max pooling layer
Takes as input a 2D tensor of shape batch_size x img_size and
performs max pooling. Max pooling downsamples by taking the max
value in a given area, here defined by maxpoolshp. Outputs a 2D
tensor of shape batch_size x output_size.
:param images: 2D tensor containing images on which to apply convolution.
Assumed to be of shape batch_size x img_size
:param imgshp: tuple containing image dimensions
:param maxpoolshp: tuple containing shape of area to max pool over
:return: out1, symbolic result (2D tensor)
:return: out2, logical shape of the output
"""
N = numpy
poolsize = N.int64(N.prod(maxpoolshp))
# imgshp contains either 2 entries (height,width) or 3 (nfeatures,h,w)
# in the first case, default nfeatures to 1
if N.size(imgshp) == 2:
imgshp = (1,) + imgshp
# construct indices and index pointers for sparse matrix, which,
# when multiplied with input images will generate a stack of image
# patches
indices, indptr, spmat_shape, sptype, outshp = \
convolution_indices.conv_eval(imgshp, maxpoolshp,
maxpoolshp, mode='valid')
# print 'XXXXXXXXXXXXXXXX MAX POOLING LAYER XXXXXXXXXXXXXXXXXXXX'
# print 'imgshp = ', imgshp
# print 'maxpoolshp = ', maxpoolshp
# print 'outshp = ', outshp
# build sparse matrix, then generate stack of image patches
csc = theano.sparse.CSM(sptype)(N.ones(indices.size), indices,
indptr, spmat_shape)
patches = sparse.structured_dot(csc, images.T).T
pshape = tensor.stack([images.shape[0] *\
tensor.as_tensor(N.prod(outshp)),
tensor.as_tensor(imgshp[0]),
tensor.as_tensor(poolsize)])
patch_stack = tensor.reshape(patches, pshape, ndim=3)
out1 = tensor.max(patch_stack, axis=2)
pshape = tensor.stack([images.shape[0],
tensor.as_tensor(N.prod(outshp)),
tensor.as_tensor(imgshp[0])])
out2 = tensor.reshape(out1, pshape, ndim=3)
out3 = tensor.DimShuffle(out2.broadcastable, (0, 2, 1))(out2)
return tensor.flatten(out3, 2), outshp
def cost(self, readouts, outputs):
# initial state
state = self.frnn_initial_state.apply(self.mlp.apply(readouts))
inputs = outputs
mus = []
sigmas = []
coeffs = []
for i in range(self.number_of_steps):
last_iteration = i == self.number_of_steps - 1
# First generating distribution parameters and sampling.
freq_mu = self.mu.apply(state)
freq_sigma = self.sigma.apply(state) + self.const
freq_coeff = self.coeff2.apply(self.coeff.apply(state), extra_ndim=state.ndim - 2) + self.const
freq_mu = freq_mu.reshape((-1, self.frnn_step_size, self.k))
freq_sigma = freq_sigma.reshape((-1, self.frnn_step_size, self.k))
freq_coeff = freq_coeff.reshape((-1, self.k))
# mu,sigma: shape (-1,fs,k)
# coeff: shape (-1,k)
mus.append(freq_mu)
sigmas.append(freq_sigma)
coeffs.append(freq_coeff)
index = self.frnn_step_size
freq_inputs = inputs[
tuple([slice(0, None)] * (inputs.ndim - 1) + [slice(index, index + self.frnn_step_size)])
]
if not last_iteration:
state = self.frnn_activation.apply(
self.frnn_linear_transition_state.apply(state)
+ self.frnn_linear_transition_input.apply(freq_inputs)
)
mus = tensor.stack(mus, axis=-2)
sigmas = tensor.stack(sigmas, axis=-2)
coeffs = tensor.stack(coeffs, axis=-2)
mus = mus.reshape((-1, self.frnn_step_size * self.number_of_steps, self.k))
sigmas = sigmas.reshape((-1, self.frnn_step_size * self.number_of_steps, self.k))
coeffs = coeffs.repeat(self.frnn_step_size, axis=-2)
mus = mus[tuple([slice(0, None)] * (mus.ndim - 2) + [slice(0, self.frame_size)] + [slice(0, None)])]
sigmas = sigmas[tuple([slice(0, None)] * (sigmas.ndim - 2) + [slice(0, self.frame_size)] + [slice(0, None)])]
coeffs = coeffs[tuple([slice(0, None)] * (coeffs.ndim - 2) + [slice(0, self.frame_size)] + [slice(0, None)])]
# actually prob not necessary
mu = mus.reshape((-1, self.target_size))
sigma = sigmas.reshape((-1, self.target_size))
coeff = coeffs.reshape((-1, self.target_size))
return FRNN_NLL(y=outputs, mu=mu, sig=sigma, coeff=coeff, frame_size=self.frame_size, k=self.k)
def _grad_single(self, ct, s, lnC2, GAMMI2):
lnC = lnC2
GAMMI = GAMMI2
v = self.v#T.as_tensor(self.v)[:,ct:]
v0 = T.as_tensor(v[v[:,0]==0, :])
v1 = T.as_tensor(v[v[:,0]==1, :])
cnp = v.shape[0]
# Gradient of fE wrt the priors over final state
[ofE, oxS], upd_fE_single = th.scan(fn=self._free_energy,
sequences=v,
non_sequences=[s,self.h,lnC,self.b])
ofE0 = ofE[v0].sum()
ofE1 = ofE[v1].sum()
dFE0dlnC = T.jacobian(ofE0, lnC)
dFE1dlnC = T.jacobian(ofE1, lnC)
dFEdlnC = T.jacobian(ofE, lnC)
ofE_ = T.vector()
ofE_.tag.test_value = ofE.tag.test_value
# Gradient of Gamma with respect to its initial condition:
GAMMA, upd_GAMMA = th.scan(fn=self._upd_gamma,
outputs_info=[GAMMI],
non_sequences=[ofE, self.lambd, self.alpha, self.beta, cnp],
n_steps=4)
dGdg = T.grad(GAMMA[-1], GAMMI)
dGdfE = T.jacobian(GAMMA[-1], ofE)
dGdlnC = dGdfE.dot(dFEdlnC)
out1 = ofE0
out2 = ofE1
maxout = T.max([out1, out2])
exp_out1 = T.exp(GAMMA[-1]*(out1 - maxout))
exp_out2 = T.exp(GAMMA[-1]*(out2 - maxout))
norm_const = exp_out1 + exp_out2
# Derivative wrt the second output (gammi):
Jac1_gammi = (-(out1-out2)*dGdg*
T.exp(GAMMA[-1]*(out1+out2 - 2*maxout))/(norm_const**2))
Jac2_gammi = -Jac1_gammi
# dfd1_tZ = Jac1_gammi*dCdf[1][0]+ Jac2_gammi*dCdf[1][1]
# Derivative wrt first input (lnc)
Jac1_lnC = (T.exp(GAMMA[-1]*(out1 + out2 - 2*maxout))/(norm_const**2)*
(-dGdlnC*(out1 - out2) - GAMMA[-1]*(dFE0dlnC - dFE1dlnC)))
Jac2_lnC = -Jac1_lnC
Jac1 = T.concatenate([T.stack(Jac1_gammi), Jac1_lnC])
Jac2 = T.concatenate([T.stack(Jac2_gammi), Jac2_lnC])
self.debug = [Jac1_lnC, Jac2_lnC, Jac2_gammi, Jac1_gammi, dFE0dlnC,
dFE1dlnC, dGdg, out1, out2, v0, v1, v, ct]
return Jac1, Jac2
def underdamped():
Q = self.Q
f = tt.sqrt(tt.maximum(1.0 - 4.0*Q**2, self.eps))
return (
0.5*self.S0*self.w0*Q*tt.stack([1.0+1.0/f, 1.0-1.0/f]),
0.5*self.w0/Q*tt.stack([1.0-f, 1.0+f]),
tt.zeros(0, dtype=self.dtype),
tt.zeros(0, dtype=self.dtype),
tt.zeros(0, dtype=self.dtype),
tt.zeros(0, dtype=self.dtype),
)
def gen_stats(params, infos, what_stats):
if not what_stats:
return []
results = []
for stat in what_stats:
print len(params), len(infos)
res = stat.comp_all(params, infos)
print stat
print res
results.append(T.stack(*res))
return T.stack(*results)
def local_gpua_careduce(node):
if (isinstance(node.op.scalar_op, scalar.basic.Add) or
isinstance(node.op.scalar_op, scalar.basic.Mul)):
x, = node.inputs
greduce = GpuCAReduceCuda(node.op.scalar_op, axis=node.op.axis)
if x.dtype != "float32":
return
gvar = greduce(x)
#We need to have the make node called, otherwise the mask can
#be None
if gvar.owner.op.supports_c_code([gpu_from_host(x)]):
return greduce
else:
# Try to make a simpler pattern based on reshaping
# The principle is that if two adjacent dimensions have
# the same value in the reduce_mask, then we can reshape
# to make them a single dimension, do the reduction, and
# then reshape to get them back.
if node.op.axis is None:
reduce_mask = [1] * x.type.ndim
else:
reduce_mask = [0] * x.type.ndim
for a in node.op.axis:
assert reduce_mask[a] == 0
reduce_mask[a] = 1
shape_of = node.fgraph.shape_feature.shape_of
x_shape = shape_of[x]
new_in_shp = [x_shape[0]]
new_mask = [reduce_mask[0]]
for i in xrange(1, x.type.ndim):
if reduce_mask[i] == reduce_mask[i - 1]:
new_in_shp[-1] *= x_shape[i]
else:
new_mask.append(reduce_mask[i])
new_in_shp.append(x_shape[i])
new_greduce = GpuCAReduceCuda(new_mask, scalar_op)
reshaped_x = x.reshape(tensor.stack(*new_in_shp))
gpu_reshaped_x = gpu_from_host(reshaped_x)
reshaped_gpu_inputs = [gpu_reshaped_x]
if new_greduce.supports_c_code(reshaped_gpu_inputs):
reduce_reshaped_x = host_from_gpu(
new_greduce(gpu_reshaped_x))
if reduce_reshaped_x.ndim != node.outputs[0].ndim:
unreshaped_reduce = reduce_reshaped_x.reshape(
tensor.stack(*shape_of[node.outputs[0]]))
else:
unreshaped_reduce = reduce_reshaped_x
return [unreshaped_reduce]
def test_hessian(self):
chol_vec = tt.vector('chol_vec')
chol_vec.tag.test_value = np.array([0.1, 2, 3])
chol = tt.stack([
tt.stack([tt.exp(0.1 * chol_vec[0]), 0]),
tt.stack([chol_vec[1], 2 * tt.exp(chol_vec[2])]),
])
cov = tt.dot(chol, chol.T)
delta = tt.matrix('delta')
delta.tag.test_value = np.ones((5, 2))
logp = MvNormalLogp()(cov, delta)
g_cov, g_delta = tt.grad(logp, [cov, delta])
tt.grad(g_delta.sum() + g_cov.sum(), [delta, cov])
def get_output_for(self, input, **kwargs):
if input.ndim > 2:
# if the input has more than two dimensions, flatten it into a
# batch of feature vectors.
input = input.flatten(2)
cores = tuple(getattr(self, attr_name) for attr_name in self.attr_names)
unitary_input = tensor.reshape(input, (input.shape[0], 2, self.num_inputs))
IR, II = unitary_input[:, 0, :], unitary_input[:, 1, :]
I = tensor.stack([IR, II], axis=0)
output = comp_wtt_image(I, cores, self.nd, self.ranks)
output = tensor.stack([output[0, ...], output[1, ...]], axis=1)
output = output.reshape((input.shape[0], -1))
return output
def t_mk_pool_ready(t_pool_input, t_pool_shape):
"""
Prepare pooling input
:param t_pool_input: 4D theano tensor batch_sz x channels x height x width
:param t_pool_shape: theano lvector pool_ch x pool_h x pool_w
:return: aux. sizes and input reshaped for pooling
"""
# sizes
# input
t_batch_sz = t_pool_input.shape[0]
t_in_ch = t_pool_input.shape[1]
t_in_h = t_pool_input.shape[2]
t_in_w = t_pool_input.shape[3]
# pooling
t_pool_ch = t_pool_shape[0]
t_pool_h = t_pool_shape[1]
t_pool_w = t_pool_shape[2]
# output
t_out_ch = (t_in_ch + t_pool_ch - 1) // t_pool_ch
t_out_h = (t_in_h + t_pool_h - 1) // t_pool_h
t_out_w = (t_in_w + t_pool_w - 1) // t_pool_w
# we will need to pad input (probably), so here's the padded shape:
t_padded_ch = t_out_ch * t_pool_ch
t_padded_h = t_out_h * t_pool_h
t_padded_w = t_out_w * t_pool_w
t_padded_pool_in_z = T.zeros(T.stack([t_batch_sz, t_padded_ch, t_padded_h, t_padded_w]))
t_padded_pool_in = T.inc_subtensor(t_padded_pool_in_z[:t_batch_sz, :t_in_ch, :t_in_h, :t_in_w], t_pool_input)
# below is all computed
# spatial pooling
t_sp_pooled = images2neibs(t_padded_pool_in, T.stack([t_pool_h, t_pool_w]))
# spatial pooling output shape
# has size (B * C * H/h * W/w) x (h*w)
t_sp_pooled_dims = t_sp_pooled.shape
# lines per channel
# H*W / (h*w)
t_lpc = (t_padded_h * t_padded_w) // (t_pool_h * t_pool_w)
# shape to collect channels
t_ch_pool_prep_dims_1 = T.stack([t_sp_pooled_dims[0] // t_lpc, t_lpc, t_sp_pooled_dims[1]])
# preparing pooling by channels
# reshape to collect channels in a separate dimension
t_ch_pool_prep_1 = T.reshape(t_sp_pooled, t_ch_pool_prep_dims_1)
t_ch_pool_prep_2 = T.shape_padleft(T.transpose(t_ch_pool_prep_1, [1, 0, 2]))
# prepare for channel pooling
t_ch_pool_dims = T.stack([t_pool_ch, t_ch_pool_prep_dims_1[-1]])
t_pool_ready = images2neibs(t_ch_pool_prep_2, t_ch_pool_dims)
return t_batch_sz, t_in_ch, t_in_h, t_in_w, t_out_ch, t_out_h, t_out_w, t_pool_ready
def symbolic_call(self,x,u):
u = TT.clip(u, -self.max_force, self.max_force) #pylint: disable=E1111
dt = self.dt
z = TT.take(x,0,axis=x.ndim-1)
zdot = TT.take(x,1,axis=x.ndim-1)
th = TT.take(x,2,axis=x.ndim-1)
thdot = TT.take(x,3,axis=x.ndim-1)
u0 = TT.take(u,0,axis=u.ndim-1)
th1 = np.pi - th
g = 10.
mc = 1. # mass of cart
mp = .1 # mass of pole
muc = .0005 # coeff friction of cart
mup = .000002 # coeff friction of pole
l = 1. # length of pole
def sign(x):
return TT.switch(x>0, 1, -1)
thddot = -(-g*TT.sin(th1)
+ TT.cos(th1) * (-u0 - mp * l *thdot**2 * TT.sin(th1) + muc*sign(zdot))/(mc+mp)
- mup*thdot / (mp*l)) \
/ (l*(4/3. - mp*TT.cos(th1)**2 / (mc + mp)))
zddot = (u0 + mp*l*(thdot**2 * TT.sin(th1) - thddot * TT.cos(th1)) - muc*sign(zdot)) \
/ (mc+mp)
newzdot = zdot + dt*zddot
newz = z + dt*newzdot
newthdot = thdot + dt*thddot
newth = th + dt*newthdot
done = (z > self.max_cart_pos) | (z < -self.max_cart_pos) | (th > self.max_pole_angle) | (th < -self.max_pole_angle)
ucost = 1e-5*(u**2).sum(axis=u.ndim-1)
xcost = 1-TT.cos(th)
# notdone = TT.neg(done) #pylint: disable=W0612,E1111
notdone = 1-done
costs = TT.stack((done-1)*10., notdone*xcost, notdone*ucost).T #pylint: disable=E1103
newx = TT.stack(newz, newzdot, newth, newthdot).T #pylint: disable=E1103
return [newx,newx,costs,done]
def normout_actfun(input, pool_size, filt_count):
"""Apply (L2) normout over non-overlapping sets of values."""
l_start = filt_count - pool_size
relu_vals = T.stack(\
*[input[:,i:(l_start+i+1):pool_size] for i in range(pool_size)])
pooled_vals = T.sqrt(T.mean(relu_vals**2.0, axis=0))
return pooled_vals
def get_reconstructed_input(self, hidden):
""" Computes the reconstructed input given the values of the hidden layer """
repeated_conv = conv.conv2d(input = hidden, filters = self.W_prime, border_mode='full')
multiple_conv_out = [repeated_conv.flatten()] * numpy.prod(self.poolsize)
stacked_conv_neibs = T.stack(*multiple_conv_out).T
stretch_unpooling_out = neibs2images(stacked_conv_neibs, self.pl, self.x.shape)
return ReLU(stretch_unpooling_out + self.b_prime.dimshuffle('x', 0, 'x', 'x'))
def convolve(kerns,
kshp,
nkern,
images,
imgshp,
step=(1, 1),
bias=None,
mode='valid',
flatten=True):
"""Convolution implementation by sparse matrix multiplication.
:note: For best speed, put the matrix which you expect to be
smaller as the 'kernel' argument
"images" is assumed to be a matrix of shape batch_size x img_size,
where the second dimension represents each image in raster order
If flatten is "False", the output feature map will have shape:
.. code-block:: python
batch_size x number of kernels x output_size
If flatten is "True", the output feature map will have shape:
.. code-block:: python
batch_size x number of kernels * output_size
.. note::
IMPORTANT: note that this means that each feature map (image
generate by each kernel) is contiguous in memory. The memory
layout will therefore be: [ <feature_map_0> <feature_map_1>
... <feature_map_n>], where <feature_map> represents a
"feature map" in raster order
kerns is a 2D tensor of shape nkern x N.prod(kshp)
:param kerns: 2D tensor containing kernels which are applied at every pixel
:param kshp: tuple containing actual dimensions of kernel (not symbolic)
:param nkern: number of kernels/filters to apply.
nkern=1 will apply one common filter to all input pixels
:param images: tensor containing images on which to apply convolution
:param imgshp: tuple containing image dimensions
:param step: determines number of pixels between adjacent receptive fields
(tuple containing dx,dy values)
:param mode: 'full', 'valid' see CSM.evaluate function for details
:param sumdims: dimensions over which to sum for the tensordot operation.
By default ((2,),(1,)) assumes kerns is a nkern x kernsize
matrix and images is a batchsize x imgsize matrix
containing flattened images in raster order
:param flatten: flatten the last 2 dimensions of the output. By default,
instead of generating a batchsize x outsize x nkern tensor,
will flatten to batchsize x outsize*nkern
:return: out1, symbolic result
:return: out2, logical shape of the output img (nkern,heigt,width)
:TODO: test for 1D and think of how to do n-d convolutions
"""
N = numpy
# start by computing output dimensions, size, etc
kern_size = N.int64(N.prod(kshp))
# inshp contains either 2 entries (height,width) or 3 (nfeatures,h,w)
# in the first case, default nfeatures to 1
if N.size(imgshp) == 2:
imgshp = (1, ) + imgshp
# construct indices and index pointers for sparse matrix, which,
# when multiplied with input images will generate a stack of image
# patches
indices, indptr, spmat_shape, sptype, outshp = \
convolution_indices.conv_eval(imgshp, kshp, step, mode)
# build sparse matrix, then generate stack of image patches
csc = theano.sparse.CSM(sptype)(N.ones(indices.size), indices, indptr,
spmat_shape)
patches = (sparse.structured_dot(csc, images.T)).T
# compute output of linear classifier
pshape = tensor.stack(images.shape[0] * tensor.as_tensor(N.prod(outshp)),\
tensor.as_tensor(imgshp[0] * kern_size))
patch_stack = tensor.reshape(patches, pshape, ndim=2)
# kern is of shape: nkern x ksize*number_of_input_features
# output is thus of shape: bsize*outshp x nkern
output = tensor.dot(patch_stack, kerns.T)
# add bias across each feature map (more efficient to do it now)
if bias is not None:
output += bias
# now to have feature maps in raster order ...
# go from bsize*outshp x nkern to bsize x nkern*outshp
newshp = tensor.stack(images.shape[0],\
tensor.as_tensor(N.prod(outshp)),\
tensor.as_tensor(nkern))
tensout = tensor.reshape(output, newshp, ndim=3)
output = tensor.DimShuffle((False, ) * tensout.ndim, (0, 2, 1))(tensout)
if flatten:
output = tensor.flatten(output, 2)
return output, N.hstack((nkern, outshp))
def build_graph(FLAGS):
"""Define training graph.
"""
tparams = OrderedDict()
trng = RandomStreams(
np.random.RandomState(np.random.randint(1024)).randint(
np.iinfo(np.int32).max))
print("Building the computational graph")
# Define bunch of shared variables
init_state = np.zeros((3, 2, FLAGS.batch_size, FLAGS.n_hidden),
dtype=np.float32)
tstate = sharedX(init_state, name='rnn_state')
# Graph input
inp = tensor.matrix('inp', dtype='int64')
inp_mask = tensor.matrix('inp_mask', dtype='float32')
inp.tag.test_value = np.zeros((FLAGS.max_seq_len, FLAGS.batch_size),
dtype='int64')
inp_mask.tag.test_value = np.ones((FLAGS.max_seq_len, FLAGS.batch_size),
dtype='float32')
x, y = inp[:-1], inp[1:]
y_mask = inp_mask[1:]
# Define input embedding layer
_i_embed = LinearCell(FLAGS.n_class,
FLAGS.n_input_embed,
prefix='i_embed',
bias=False,
input_is_int=True)
tparams = merge_dict(tparams, _i_embed._params)
# Call input embedding layer
h_i_emb_3d = _i_embed(x)
# Define the first LSTM module
_rnn_1 = LSTMModule(FLAGS.n_input_embed, FLAGS.n_hidden, prefix='lstm_1')
tparams = merge_dict(tparams, _rnn_1._params)
# Call the first LSTM module
(h_rnn_1_3d, c_rnn_1_3d), last_state_1 = _rnn_1(h_i_emb_3d, tstate[0])
# Define the second LSTM module
_rnn_2 = LSTMModule(FLAGS.n_hidden, FLAGS.n_hidden, prefix='lstm_2')
tparams = merge_dict(tparams, _rnn_1._params)
# Call the second LSTM module
(h_rnn_2_3d, c_rnn_2_3d), last_state_2 = _rnn_2(h_rnn_1_3d, tstate[1])
# Define the third LSTM module
_rnn_3 = LSTMModule(FLAGS.n_hidden, FLAGS.n_hidden, prefix='lstm_3')
tparams = merge_dict(tparams, _rnn_3._params)
# Call the third LSTM module
(h_rnn_3_3d, c_rnn_3_3d), last_state_3 = _rnn_3(h_rnn_2_3d, tstate[2])
# Define output gating layer
_o_gate = LinearCell([FLAGS.n_hidden] * 3,
3,
prefix='o_gate',
activation=tensor.nnet.sigmoid)
tparams = merge_dict(tparams, _o_gate._params)
# Call output gating layer
h_o_gate = _o_gate([h_rnn_1_3d, h_rnn_2_3d, h_rnn_3_3d])
# Define output embedding layer
_o_embed = LinearCell([FLAGS.n_hidden] * 3,
FLAGS.n_output_embed,
prefix='o_embed',
activation=tensor.nnet.relu)
tparams = merge_dict(tparams, _o_embed._params)
# Call output embedding layer
h_o_embed = _o_embed([
h_rnn_1_3d * h_o_gate[:, :, 0][:, :, None],
h_rnn_2_3d * h_o_gate[:, :, 1][:, :, None],
h_rnn_3_3d * h_o_gate[:, :, 2][:, :, None]
])
# Define output layer
_output = LinearCell(FLAGS.n_output_embed, FLAGS.n_class, prefix='output')
tparams = merge_dict(tparams, _output._params)
# Call output layer
h_logit = _output([h_o_embed])
logit_shape = h_logit.shape
logit = h_logit.reshape([logit_shape[0] * logit_shape[1], logit_shape[2]])
logit = logit - logit.max(axis=1).dimshuffle(0, 'x')
probs = logit - tensor.log(
tensor.exp(logit).sum(axis=1).dimshuffle(0, 'x'))
# Compute the cost
y_flat = y.flatten()
y_flat_idx = tensor.arange(y_flat.shape[0]) * FLAGS.n_class + y_flat
cost = -probs.flatten()[y_flat_idx]
cost = cost.reshape([y.shape[0], y.shape[1]])
cost = (cost * y_mask).sum(0)
cost_len = y_mask.sum(0)
last_state = tensor.stack([last_state_1, last_state_2, last_state_3],
axis=0)
f_prop_updates = OrderedDict()
f_prop_updates[tstate] = last_state
states = [tstate]
# Later use for visualization
inps = [inp, inp_mask]
print("Building f_log_prob function")
f_log_prob = theano.function(inps, [cost, cost_len],
updates=f_prop_updates)
cost = cost.mean()
# If the flag is on, apply L2 regularization on weights
if FLAGS.weight_decay > 0.:
weights_norm = 0.
for k, v in tparams.iteritems():
weights_norm += (v**2).sum()
cost += weights_norm * FLAGS.weight_decay
#print("Computing the gradients")
grads = tensor.grad(cost, wrt=itemlist(tparams))
grads = gradient_clipping(grads, tparams, 1.)
# Compile the optimizer, the actual computational graph
learning_rate = tensor.scalar(name='learning_rate')
gshared = [
theano.shared(p.get_value() * 0., name='%s_grad' % k)
for k, p in tparams.iteritems()
]
gsup = OrderedDict(izip(gshared, grads))
print("Building f_prop function")
f_prop = theano.function(inps, [cost],
updates=merge_dict(gsup, f_prop_updates))
opt_updates, opt_tparams = adam(learning_rate, tparams, gshared)
if FLAGS.start_from_ckpt and os.path.exists(opt_file_name):
opt_params = np.load(opt_file_name)
zipp(opt_params, opt_tparams)
print("Building f_update function")
f_update = theano.function([learning_rate], [],
updates=opt_updates,
on_unused_input='ignore')
#print("Building f_debug function")
f_debug = theano.function(inps, [h_rnn_1_3d, h_rnn_2_3d, h_rnn_3_3d],
updates=f_prop_updates,
on_unused_input='ignore')
return f_prop, f_update, f_log_prob, f_debug, tparams, opt_tparams, states, None
def RHNLayer(self, inputs, depth, batch_size, hidden_size, drop_i, drop_s,
init_T_bias, init_H_bias, tied_noise):
"""Variational Recurrent Highway Layer (Theano implementation).
References:
Zilly, J, Srivastava, R, Koutnik, J, Schmidhuber, J., "Recurrent Highway Networks", 2016
Args:
inputs: Theano variable, shape (num_steps, batch_size, hidden_size).
depth: int, the number of RHN inner layers i.e. the number of micro-timesteps per timestep.
drop_i: float, probability of dropout over inputs.
drop_s: float, probability of dropout over recurrent hidden state.
init_T_bias: a valid bias_init argument for linear(), initialization of bias of transform gate T.
init_H_bias: a valid bias_init argument for linear(), initialization of bias of non-linearity H.
tied_noise: boolean, whether to use the same dropout masks when calculating H and when calculating T.
Returns:
y: Theano variable, recurrent hidden states at each timestep. Shape (num_steps, batch_size, hidden_size).
sticky_state_updates: a list of (shared variable, new shared variable value).
"""
# We first compute the linear transformation of the inputs over all timesteps.
# This is done outside of scan() in order to speed up computation.
# The result is then fed into scan()'s step function, one timestep at a time.
noise_i_for_H = self.get_dropout_noise((batch_size, hidden_size),
drop_i)
noise_i_for_T = self.get_dropout_noise(
(batch_size,
hidden_size), drop_i) if not tied_noise else noise_i_for_H
i_for_H = self.apply_dropout(inputs, noise_i_for_H)
i_for_T = self.apply_dropout(inputs, noise_i_for_T)
i_for_H = self.linear(i_for_H,
in_size=hidden_size,
out_size=hidden_size,
bias=True,
bias_init=init_H_bias)
i_for_T = self.linear(i_for_T,
in_size=hidden_size,
out_size=hidden_size,
bias=True,
bias_init=init_T_bias)
# Dropout noise for recurrent hidden state.
noise_s = self.get_dropout_noise((batch_size, hidden_size), drop_s)
if not tied_noise:
noise_s = tt.stack(
noise_s,
self.get_dropout_noise((batch_size, hidden_size), drop_s))
def step_fn(i_for_H_t, i_for_T_t, y_tm1, noise_s):
"""
Args:
Elements of sequences given to scan():
i_for_H_t: linear trans. of inputs for calculating non-linearity H at timestep t. Shape (batch_size, hidden_size).
i_for_T_t: linear trans. of inputs for calculating transform gate T at timestep t. Shape (batch_size, hidden_size).
Result of previous step function invocation (equals the outputs_info given to scan() on first timestep):
y_tm1: Shape (batch_size, hidden_size).
Non-sequences given to scan() (these are the same at all timesteps):
noise_s: (batch_size, hidden_size) or (2, batch_size, hidden_size), depending on value of tied_noise.
"""
tanh, sigm = tt.tanh, tt.nnet.sigmoid
noise_s_for_H = noise_s if tied_noise else noise_s[0]
noise_s_for_T = noise_s if tied_noise else noise_s[1]
s_lm1 = y_tm1
for l in range(depth):
s_lm1_for_H = self.apply_dropout(s_lm1, noise_s_for_H)
s_lm1_for_T = self.apply_dropout(s_lm1, noise_s_for_T)
if l == 0:
# On the first micro-timestep of each timestep we already have bias
# terms summed into i_for_H_t and into i_for_T_t.
H = tanh(i_for_H_t + self.linear(s_lm1_for_H,
in_size=hidden_size,
out_size=hidden_size,
bias=False))
T = sigm(i_for_T_t + self.linear(s_lm1_for_T,
in_size=hidden_size,
out_size=hidden_size,
bias=False))
else:
H = tanh(
self.linear(s_lm1_for_H,
in_size=hidden_size,
out_size=hidden_size,
bias=True,
bias_init=init_H_bias))
T = sigm(
self.linear(s_lm1_for_T,
in_size=hidden_size,
out_size=hidden_size,
bias=True,
bias_init=init_T_bias))
s_l = (H - s_lm1) * T + s_lm1
s_lm1 = s_l
y_t = s_l
return y_t
# The recurrent hidden state of the RHN is sticky (the last hidden state of one batch is carried over to the next batch,
# to be used as an initial hidden state). These states are kept in a shared variable.
y_0 = theano.shared(np.zeros((batch_size, hidden_size), floatX))
self.reset_hidden_state = lambda: y_0.set_value(
np.zeros_like(y_0.get_value())) # invoked before every epoch.
y, _ = theano.scan(step_fn,
sequences=[i_for_H, i_for_T],
outputs_info=[y_0],
non_sequences=[noise_s])
y_last = y[-1]
sticky_state_updates = [(y_0, y_last)]
return y, sticky_state_updates
def init_opt(self):
obs_var = ext.new_tensor(
'obs', ndim=2, dtype=theano.config.floatX) # todo: check the dtype
manager_obs_var = ext.new_tensor('manager_obs',
ndim=2,
dtype=theano.config.floatX)
action_var = self.env.action_space.new_tensor_variable(
'action',
extra_dims=1,
)
# this will have to be the advantage every time the manager makes a decision
manager_advantage_var = ext.new_tensor('manager_advantage',
ndim=1,
dtype=theano.config.floatX)
skill_advantage_var = ext.new_tensor('skill_advantage',
ndim=1,
dtype=theano.config.floatX)
latent_var_sparse = ext.new_tensor('sparse_latent',
ndim=2,
dtype=theano.config.floatX)
latent_var = ext.new_tensor('latents',
ndim=2,
dtype=theano.config.floatX)
assert isinstance(self.policy, HierarchicalPolicy)
#############################################################
### calculating the manager portion of the surrogate loss ###
#############################################################
# i, j should contain the probability of latent j at time step self.period*i
# should be a len(obs)//self.period by len(self.latent) tensor
latent_probs = self.policy.manager.dist_info_sym(
manager_obs_var)['prob']
old_latent_probs = self.old_policy.manager.dist_info_sym(
manager_obs_var)['prob']
actual_latent_probs = TT.sum(latent_probs * latent_var_sparse, axis=1)
old_actual_latent_probs = TT.sum(old_latent_probs * latent_var_sparse,
axis=1)
lr = TT.exp(
TT.log(actual_latent_probs) - TT.log(old_actual_latent_probs))
manager_surr_loss_vector = TT.minimum(
lr * manager_advantage_var,
TT.clip(lr, 1 - self.epsilon, 1 + self.epsilon) *
manager_advantage_var)
manager_surr_loss = -TT.mean(manager_surr_loss_vector)
############################################################
### calculating the skills portion of the surrogate loss ###
############################################################
dist_info_vars = self.policy.low_policy.dist_info_sym_all_latents(
obs_var)
probs = TT.stack([
self.diagonal.log_likelihood_sym(action_var, dist_info)
for dist_info in dist_info_vars
],
axis=1)
actual_action_log_probs = TT.sum(
probs * latent_var,
axis=1) # todo: verify that dist_info_vars is in order
# old policy stuff
old_dist_info_vars = self.old_policy.low_policy.dist_info_sym_all_latents(
obs_var)
old_probs = TT.stack([
self.diagonal.log_likelihood_sym(action_var, dist_info)
for dist_info in old_dist_info_vars
],
axis=1)
old_actual_action_log_probs = TT.sum(old_probs * latent_var, axis=1)
skill_lr = TT.exp(actual_action_log_probs -
old_actual_action_log_probs)
skill_surr_loss_vector = TT.minimum(
skill_lr * skill_advantage_var,
TT.clip(skill_lr, 1 - self.epsilon, 1 + self.epsilon) *
skill_advantage_var)
skill_surr_loss = -TT.mean(skill_surr_loss_vector)
surr_loss = manager_surr_loss / self.average_period + skill_surr_loss
input_list = [
obs_var, manager_obs_var, action_var, manager_advantage_var,
skill_advantage_var, latent_var, latent_var_sparse
]
self.optimizer.update_opt(loss=surr_loss,
target=self.policy,
inputs=input_list)
return dict()
def get_output(self, train):
X = self.get_input(train)
tensors = [T.roll(X, off, axis=self.axis) for off in self.offsets]
return T.stack(tensors, axis=self.offset_axis)
def any_to_tensor_and_labels(x, labels=None):
"""Util for converting input x to tensor trying to
create labels for columns if they are not provided.
Default names for columns are ['x0', 'x1', ...], for mappable
arrays (e.g. pd.DataFrame) their names are treated as labels.
You can override them with `labels` argument.
If you have tensor input you should provide labels as we
cannot get their shape directly
If you pass dict input we cannot rely on labels order thus dict
keys are treated as labels anyway
Parameters
----------
x : np.ndarray | pd.DataFrame | tt.Variable | dict | list
labels : list - names for columns of output tensor
Returns
-------
(x, labels) - tensor and labels for its columns
"""
if isinstance(labels, six.string_types):
labels = [labels]
# pandas.DataFrame
# labels can come from here
# we can override them
if isinstance(x, pd.DataFrame):
if not labels:
labels = x.columns
x = x.as_matrix()
# pandas.Series
# there can still be a label
# we can override labels
elif isinstance(x, pd.Series):
if not labels:
labels = [x.name]
x = x.as_matrix()[:, None]
# dict
# labels are keys,
# cannot override them
elif isinstance(x, dict):
# try to do it via pandas
try:
x = pd.DataFrame.from_dict(x)
labels = x.columns
x = x.as_matrix()
# some types fail there
# another approach is to construct
# variable by hand
except (PandasError, TypeError):
res = []
labels = []
for k, v in x.items():
res.append(v)
labels.append(k)
x = tt.stack(res, axis=1)
if x.ndim == 1:
x = x[:, None]
# case when it can appear to be some
# array like value like lists of lists
# numpy deals with it
elif not isinstance(x, tt.Variable):
x = np.asarray(x)
if x.ndim == 0:
raise ValueError('Cannot use scalars')
elif x.ndim == 1:
x = x[:, None]
# something really strange goes here,
# but user passes labels trusting seems
# to be a good option
elif labels is not None:
x = tt.as_tensor_variable(x)
if x.ndim == 0:
raise ValueError('Cannot use scalars')
elif x.ndim == 1:
x = x[:, None]
else: # trust input
pass
# we should check that we can extract labels
if labels is None and not isinstance(x, tt.Variable):
labels = ['x%d' % i for i in range(x.shape[1])]
# for theano variables we should have labels from user
elif labels is None:
raise ValueError('Please provide labels as '
'we cannot infer shape of input')
else: # trust labels, user knows what he is doing
pass
# it's time to check shapes if we can
if not isinstance(x, tt.Variable):
if not len(labels) == x.shape[1]:
raise ValueError('Please provide full list '
'of labels for coefficients, '
'got len(labels)=%d instead of %d' %
(len(labels), x.shape[1]))
else:
# trust labels, as we raised an
# error in bad case, we have labels
pass
# convert labels to list
if isinstance(labels, pd.RangeIndex):
labels = ['x%d' % i for i in labels]
# maybe it was a tuple ot whatever
elif not isinstance(labels, list):
labels = list(labels)
# as output we need tensor
if not isinstance(x, tt.Variable):
x = tt.as_tensor_variable(x)
# finally check dimensions
if x.ndim == 0:
raise ValueError('Cannot use scalars')
elif x.ndim == 1:
x = x[:, None]
return x, labels
def __init__(self, babi_train_raw, babi_test_raw, word2vec,
word_vector_size, dim, mode, answer_module, input_mask_mode,
memory_hops, l2, normalize_attention, **kwargs):
print "==> not used params in DMN class:", kwargs.keys()
self.vocab = {}
self.ivocab = {}
self.word2vec = word2vec
self.word_vector_size = word_vector_size
self.dim = dim
self.mode = mode
self.answer_module = answer_module
self.input_mask_mode = input_mask_mode
self.memory_hops = memory_hops
self.l2 = l2
self.normalize_attention = normalize_attention
self.train_input, self.train_q, self.train_answer, self.train_input_mask = self._process_input(
babi_train_raw)
self.test_input, self.test_q, self.test_answer, self.test_input_mask = self._process_input(
babi_test_raw)
self.vocab_size = len(self.vocab)
self.input_var = T.matrix('input_var')
self.q_var = T.matrix('question_var')
self.answer_var = T.iscalar('answer_var')
self.input_mask_var = T.ivector('input_mask_var')
print "==> building input module"
self.W_inp_res_in = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.word_vector_size))
self.W_inp_res_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_inp_res = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_inp_upd_in = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.word_vector_size))
self.W_inp_upd_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_inp_upd = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_inp_hid_in = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.word_vector_size))
self.W_inp_hid_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_inp_hid = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
inp_c_history, _ = theano.scan(fn=self.input_gru_step,
sequences=self.input_var,
outputs_info=T.zeros_like(
self.b_inp_hid))
self.inp_c = inp_c_history.take(self.input_mask_var, axis=0)
self.q_q, _ = theano.scan(fn=self.input_gru_step,
sequences=self.q_var,
outputs_info=T.zeros_like(self.b_inp_hid))
self.q_q = self.q_q[-1]
print "==> creating parameters for memory module"
self.W_mem_res_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.W_mem_res_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_mem_res = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_mem_upd_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.W_mem_upd_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_mem_upd = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_mem_hid_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.W_mem_hid_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_mem_hid = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_b = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.W_1 = nn_utils.normal_param(std=0.1,
shape=(self.dim, 7 * self.dim + 0))
self.W_2 = nn_utils.normal_param(std=0.1, shape=(1, self.dim))
self.b_1 = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.b_2 = nn_utils.constant_param(value=0.0, shape=(1, ))
print "==> building episodic memory module (fixed number of steps: %d)" % self.memory_hops
memory = [self.q_q.copy()]
for iter in range(1, self.memory_hops + 1):
current_episode = self.new_episode(memory[iter - 1])
memory.append(
self.GRU_update(memory[iter - 1], current_episode,
self.W_mem_res_in, self.W_mem_res_hid,
self.b_mem_res, self.W_mem_upd_in,
self.W_mem_upd_hid, self.b_mem_upd,
self.W_mem_hid_in, self.W_mem_hid_hid,
self.b_mem_hid))
last_mem = memory[-1]
print "==> building answer module"
self.W_a = nn_utils.normal_param(std=0.1,
shape=(self.vocab_size, self.dim))
if self.answer_module == 'feedforward':
self.prediction = nn_utils.softmax(T.dot(self.W_a, last_mem))
elif self.answer_module == 'recurrent':
self.W_ans_res_in = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.dim + self.vocab_size))
self.W_ans_res_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.dim))
self.b_ans_res = nn_utils.constant_param(value=0.0,
shape=(self.dim, ))
self.W_ans_upd_in = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.dim + self.vocab_size))
self.W_ans_upd_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.dim))
self.b_ans_upd = nn_utils.constant_param(value=0.0,
shape=(self.dim, ))
self.W_ans_hid_in = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.dim + self.vocab_size))
self.W_ans_hid_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.dim))
self.b_ans_hid = nn_utils.constant_param(value=0.0,
shape=(self.dim, ))
def answer_step(prev_a, prev_y):
a = self.GRU_update(prev_a, T.concatenate([prev_y, self.q_q]),
self.W_ans_res_in, self.W_ans_res_hid,
self.b_ans_res, self.W_ans_upd_in,
self.W_ans_upd_hid, self.b_ans_upd,
self.W_ans_hid_in, self.W_ans_hid_hid,
self.b_ans_hid)
y = nn_utils.softmax(T.dot(self.W_a, a))
return [a, y]
# TODO: add conditional ending
dummy = theano.shared(np.zeros((self.vocab_size, ), dtype=floatX))
results, updates = theano.scan(
fn=answer_step,
outputs_info=[last_mem, T.zeros_like(dummy)],
n_steps=1)
self.prediction = results[1][-1]
else:
raise Exception("invalid answer_module")
print "==> collecting all parameters"
self.params = [
self.W_inp_res_in,
self.W_inp_res_hid,
self.b_inp_res,
self.W_inp_upd_in,
self.W_inp_upd_hid,
self.b_inp_upd,
self.W_inp_hid_in,
self.W_inp_hid_hid,
self.b_inp_hid,
self.W_mem_res_in,
self.W_mem_res_hid,
self.b_mem_res,
self.W_mem_upd_in,
self.W_mem_upd_hid,
self.b_mem_upd,
self.W_mem_hid_in,
self.W_mem_hid_hid,
self.b_mem_hid, #self.W_b
self.W_1,
self.W_2,
self.b_1,
self.b_2,
self.W_a
]
if self.answer_module == 'recurrent':
self.params = self.params + [
self.W_ans_res_in, self.W_ans_res_hid, self.b_ans_res,
self.W_ans_upd_in, self.W_ans_upd_hid, self.b_ans_upd,
self.W_ans_hid_in, self.W_ans_hid_hid, self.b_ans_hid
]
print "==> building loss layer and computing updates"
self.loss_ce = T.nnet.categorical_crossentropy(
self.prediction.dimshuffle('x', 0), T.stack([self.answer_var]))[0]
if self.l2 > 0:
self.loss_l2 = self.l2 * nn_utils.l2_reg(self.params)
else:
self.loss_l2 = 0
self.loss = self.loss_ce + self.loss_l2
updates = lasagne.updates.adadelta(self.loss, self.params)
if self.mode == 'train':
print "==> compiling train_fn"
self.train_fn = theano.function(
inputs=[
self.input_var, self.q_var, self.answer_var,
self.input_mask_var
],
outputs=[self.prediction, self.loss],
updates=updates)
print "==> compiling test_fn"
self.test_fn = theano.function(inputs=[
self.input_var, self.q_var, self.answer_var, self.input_mask_var
],
outputs=[self.prediction, self.loss])
def __init__(self,
babi_train_raw,
babi_test_raw,
word2vec,
word_vector_size,
dim,
mode,
answer_module,
input_mask_mode,
memory_hops,
l2,
normalize_attention,
answer_vec,
debug,
sentEmbdLoadState,
sentEmbdType="basic",
**kwargs):
self.vocab = {}
self.ivocab = {}
self.debug = debug
self.word2vec = word2vec
self.word_vector_size = word_vector_size
self.dim = dim
self.mode = mode
self.answer_module = answer_module
self.input_mask_mode = input_mask_mode
self.memory_hops = memory_hops
self.l2 = l2
self.normalize_attention = normalize_attention
self.answer_vec = answer_vec
self.sentEmbdType = sentEmbdType
if (self.mode != 'deploy'):
self.train_input, self.train_q, self.train_answer, self.train_input_mask = self._process_input(
babi_train_raw)
self.test_input, self.test_q, self.test_answer, self.test_input_mask = self._process_input(
babi_test_raw)
self.vocab_size = len(self.vocab)
print(self.vocab_size)
elif self.mode == 'deploy':
self.train_input, self.train_q, self.train_answer, self.train_input_mask = self._process_input(
babi_train_raw)
self.vocab_size = len(self.vocab)
print(self.vocab_size)
# print(self.train_input.shape)
# print(self.train_q.shape)
# print(self.train_input_mask.shape)
#Setting up pre-trained Sentence Embedder for question and input module:
if self.mode != 'deploy':
print("==> Setting up pre-trained Sentence Embedder")
if self.sentEmbdType == "basic":
self.sent_embd = SentEmbd.SentEmbd_basic(self.word_vector_size,
self.dim)
else:
dep_tags = utils.load_dep_tags
self.sent_embd = SentEmbd.SentEmbd_syntactic(
50, hid_dim, len(dep_tags)) #TODO: Dependency Tags
self.sent_embd.load_params(sentEmbdLoadState)
self.input_var = T.matrix('input_var')
self.q_var = T.vector('question_var')
if self.answer_vec == 'word2vec':
self.answer_var = T.vector('answer_var')
else:
self.answer_var = T.iscalar('answer_var')
self.input_mask_var = T.ivector('input_mask_var')
if self.answer_vec == 'one_hot' or self.answer_vec == 'index':
self.answer_size = self.vocab_size
elif self.answer_vec == 'word2vec':
self.answer_size = self.word_vector_size
else:
raise Exception("Invalid answer_vec type")
#Setting up Untrained Memory module
if self.mode != 'deploy':
print("==> Creating parameters for memory module")
self.W_mem_res_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.W_mem_res_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_mem_res = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_mem_upd_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.W_mem_upd_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_mem_upd = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_mem_hid_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.W_mem_hid_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_mem_hid = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_b = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.W_1 = nn_utils.normal_param(std=0.1,
shape=(self.dim, 7 * self.dim + 2))
self.W_2 = nn_utils.normal_param(std=0.1, shape=(1, self.dim))
self.b_1 = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.b_2 = nn_utils.constant_param(value=0.0, shape=(1, ))
if self.mode != 'deploy':
print(
"==> Building episodic memory module (fixed number of steps: %d)"
% self.memory_hops)
memory = [self.q_var.copy()]
for iter in range(1, self.memory_hops + 1):
current_episode = self.new_episode(memory[iter - 1])
memory.append(
self.GRU_update(memory[iter - 1], current_episode,
self.W_mem_res_in, self.W_mem_res_hid,
self.b_mem_res, self.W_mem_upd_in,
self.W_mem_upd_hid, self.b_mem_upd,
self.W_mem_hid_in, self.W_mem_hid_hid,
self.b_mem_hid))
last_mem = memory[-1]
if self.mode != 'deploy': print("==> Building answer module")
self.W_a = nn_utils.normal_param(std=0.1,
shape=(self.answer_size, self.dim))
if self.answer_module == 'feedforward':
self.prediction = nn_utils.softmax(T.dot(self.W_a, last_mem))
# elif self.answer_module == 'recurrent':
# self.W_ans_res_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim + self.answer_size))
# self.W_ans_res_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
# self.b_ans_res = nn_utils.constant_param(value=0.0, shape=(self.dim,))
# self.W_ans_upd_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim + self.answer_size))
# self.W_ans_upd_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
# self.b_ans_upd = nn_utils.constant_param(value=0.0, shape=(self.dim,))
# self.W_ans_hid_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim + self.answer_size))
# self.W_ans_hid_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
# self.b_ans_hid = nn_utils.constant_param(value=0.0, shape=(self.dim,))
# def answer_step(prev_a, prev_y):
# a = self.GRU_update(prev_a, T.concatenate([prev_y, self.q_q]),
# self.W_ans_res_in, self.W_ans_res_hid, self.b_ans_res,
# self.W_ans_upd_in, self.W_ans_upd_hid, self.b_ans_upd,
# self.W_ans_hid_in, self.W_ans_hid_hid, self.b_ans_hid)
# y = T.dot(self.W_a, a)
# if self.answer_vec == 'one_hot' or self.answer_vec == 'index':
# y = nn_utils.softmax(y)
# return [a, y]
# # TODO: add conditional ending
# dummy = theano.shared(np.zeros((self.answer_size, ), dtype=floatX))
# results, updates = theano.scan(fn=answer_step,
# outputs_info=[last_mem, T.zeros_like(dummy)],
# n_steps=1)
# self.prediction = results[1][-1]
else:
raise Exception("invalid answer_module")
if self.mode != 'deploy':
print("==> Collecting all parameters to be trained")
self.params = [
self.W_mem_res_in, self.W_mem_res_hid, self.b_mem_res,
self.W_mem_upd_in, self.W_mem_upd_hid, self.b_mem_upd,
self.W_mem_hid_in, self.W_mem_hid_hid, self.b_mem_hid, self.W_b,
self.W_1, self.W_2, self.b_1, self.b_2, self.W_a
]
# if self.answer_module == 'recurrent':
# self.params = self.params + [self.W_ans_res_in, self.W_ans_res_hid, self.b_ans_res,
# self.W_ans_upd_in, self.W_ans_upd_hid, self.b_ans_upd,
# self.W_ans_hid_in, self.W_ans_hid_hid, self.b_ans_hid]
if self.mode != 'deploy':
print("==> Building loss layer and computing updates")
if debug:
print('Prediction dim:', self.prediction.dimshuffle('x', 0).ndim)
print('Answer dim:', self.answer_var.ndim)
if self.answer_vec == 'word2vec':
self.loss_ce = nn_utils.cosine_proximity_loss(
self.prediction.dimshuffle('x', 0),
T.stack([self.answer_var]))[0][0]
else:
self.loss_ce = T.nnet.categorical_crossentropy(
self.prediction.dimshuffle('x', 0),
T.stack([self.answer_var]))[0]
if self.l2 > 0:
self.loss_l2 = self.l2 * nn_utils.l2_reg(self.params)
else:
self.loss_l2 = 0
self.loss = self.loss_ce + self.loss_l2
if debug: print(self.loss.ndim)
# if self.debug: print(self.loss.eval({self.input_var:self.train_input,self.q_var:self.train_q,self.answer_var:self.train_answer,self.input_mask_var:self.train_input_mask}))
updates = lasagne.updates.adadelta(self.loss, self.params)
if self.mode == 'deploy':
self.deploy_fn = theano.function(
inputs=[self.input_var, self.q_var], outputs=[self.prediction])
else:
if self.mode == 'train':
print("==> compiling train_fn")
self.train_fn = theano.function(
inputs=[self.input_var, self.q_var, self.answer_var],
outputs=[self.prediction, self.loss],
updates=updates)
print("==> compiling test_fn")
self.test_fn = theano.function(
inputs=[self.input_var, self.q_var, self.answer_var],
outputs=[
self.prediction, self.loss, self.input_var, self.q_var,
last_mem
])
if self.mode == 'train':
print("==> computing gradients (for debugging)")
gradient = T.grad(self.loss, self.params)
self.get_gradient_fn = theano.function(
inputs=[self.input_var, self.q_var, self.answer_var],
outputs=gradient)
def get_output(self, input_, mask_, hidden_init):
"""
This function overrides the parents' one.
Creates symbolic function to compute output from an input and output (hidden).
Math Expression
-------------------
Y[t] = out_activation(dot(X[t], W) + dot(Y[t-1], U) + b)
if precompute True, compute dot(X[t],W) for all steps first.
if mask exist and 1, Y[t] = Y[t-1]
Parameters
----------
input_: TensorVariable
mask_: TensorVariable
hidden_init: TensorVariable
Returns
-------
TensorVariable
"""
# input_ are (n_batch, n_timesteps, n_features)
# change to (n_timesteps, n_batch, n_features)
input_ = input_.dimshuffle(1, 0, 2)
# mask_ are (n_batch, n_timesteps)
masks = masks.dimshuffle(1, 0, 'x')
sequence_length = input_.shape[0]
batch_num = input_.shape[1]
# precompute input
if self.precompute:
additional_dims = tuple(
input.shape[k] for k in range(2, input.ndim)) # (output_dim,)
input = T.reshape(input, (sequence_length * batch_num, ) +
additional_dims)
input = T.dot(input, self.W)
additional_dims = tuple(
input.shape[k] for k in range(1, input.ndim)) # (output_dim,)
input = T.reshape(input, (
sequence_length,
batch_num,
) + additional_dims)
# step function
def step(input_, hidden):
if self.precompute:
return self.out_activation.get_output(input_ +
T.dot(hidden, self.U) +
self.b)
else:
return self.out_activation.get_output(
T.dot(input_, self.W) + T.dot(hidden, self.U) + self.b)
# step function, with mask
def step_masked(input_, mask_, hidden):
hidden_computed = step(input_, hidden)
return T.switch(mask_, hidden_computed, hidden)
# main operation
if self.unroll:
counter = range(self.gradient_steps)
if self.backward:
counter = counter[::-1] # reversed index
iter_output = []
outputs_info = [hidden_init]
for index in counter:
step_input = [input_[index], mask_[index]] + outputs_info
step_output = step_masked(*step_input)
iter_output.append(step_output)
outputs_info = [iter_output[-1]]
hidden_output = T.stack(iter_output, axis=0)
else:
hidden_output = theano.scan(
fn=step_masked,
sequences=[input_, mask_],
outputs_info=[hidden_init],
go_backwards=self.backward,
n_steps=None,
truncate_gradient=self.gradient_steps)[
0] # only need outputs, not updates
# computed output are (n_timesteps, n_batch, n_features)
# select only required
if self.output_return_index is None:
hidden_output_return = hidden_output
else:
hidden_output_return = hidden_output[self.output_return_index]
# change to (n_batch, n_timesteps, n_features)
hidden_output_return = hidden_output_return.dimshuffle(
1, 0, *range(2, hidden_output_return.ndim))
# backward order straight
if self.backward:
hidden_output_return = hidden_output_return[:, ::-1]
return hidden_output_return
def neibs2images(neibs, neib_shape, original_shape, mode='valid'):
"""
Function :func:`neibs2images <theano.sandbox.neighbours.neibs2images>`
performs the inverse operation of
:func:`images2neibs <theano.sandbox.neigbours.neibs2images>`. It inputs
the output of :func:`images2neibs <theano.sandbox.neigbours.neibs2images>`
and reconstructs its input.
:param neibs: matrix like the one obtained by
:func:`images2neibs <theano.sandbox.neigbours.neibs2images>`
:param neib_shape: `neib_shape` that was used in
:func:`images2neibs <theano.sandbox.neigbours.neibs2images>`
:param original_shape: original shape of the 4d tensor given to
:func:`images2neibs <theano.sandbox.neigbours.neibs2images>`
:return: Reconstructs the input of
:func:`images2neibs <theano.sandbox.neigbours.neibs2images>`,
a 4d tensor of shape `original_shape`.
.. note:: Currently, the function doesn't support tensors created with
`neib_step` different from default value. This means that it may be
impossible to compute the gradient of a variable gained by
:func:`images2neibs <theano.sandbox.neigbours.neibs2images>` w.r.t.
its inputs in this case, because it uses
:func:`images2neibs <theano.sandbox.neigbours.neibs2images>` for
gradient computation.
Example, which uses a tensor gained in example for
:func:`images2neibs <theano.sandbox.neigbours.neibs2images>`:
.. code-block:: python
im_new = neibs2images(neibs, (5, 5), im_val.shape)
# Theano function definition
inv_window = theano.function([neibs], im_new)
# Function application
im_new_val = inv_window(neibs_val)
.. note:: The code will output the initial image array.
"""
neibs = T.as_tensor_variable(neibs)
neib_shape = T.as_tensor_variable(neib_shape)
original_shape = T.as_tensor_variable(original_shape)
new_neib_shape = T.stack(original_shape[-1] // neib_shape[1],
neib_shape[1])
output_2d = images2neibs(neibs.dimshuffle('x', 'x', 0, 1),
new_neib_shape, mode=mode)
if mode == 'ignore_borders':
valid_shape = list(original_shape)
valid_shape[2] = (valid_shape[2] // neib_shape[0]) * neib_shape[0]
valid_shape[3] = (valid_shape[3] // neib_shape[1]) * neib_shape[1]
output_4d = output_2d.reshape(valid_shape)
# padding the borders with zeros
for d in [2, 3]:
pad_shape = list(output_4d.shape)
pad_shape[d] = original_shape[d] - valid_shape[d]
output_4d = T.concatenate([output_4d, T.zeros(pad_shape)], axis=d)
elif mode == 'valid':
# TODO: we do not implement all mode with this code.
# Add a check for the good cases.
output_4d = output_2d.reshape(original_shape)
else:
raise NotImplementedError("neibs2images do not support mode=%s" % mode)
return output_4d
def __init__(self,
cooccurrence,
z_k,
opt,
initializer,
initial_pz_weight=None,
initial_b=None,
pz_regularizer=None,
tao0=5.,
tao_min=0.25,
tao_decay=1e-6,
eps=1e-9):
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
# 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")
if initial_pz_weight is None:
initial_pz_weight = initializer((x_k, z_k))
pz_weight = K.variable(initial_pz_weight)
pz = softmax_nd(pz_weight)
srng = RandomStreams(123)
rnd = srng.uniform(low=0., high=1., dtype='float32', size=(x_k, z_k))
gumbel = -T.log(eps + T.nnet.relu(-T.log(eps + rnd)))
iteration = K.variable(0, dtype='int32')
temp = T.max(T.stack((tao_min, tao0 / (1. + (tao_decay * iteration)))))
z = softmax_nd((pz_weight + gumbel) / (eps + temp))
# z = pz
w = K.variable(initializer((z_k, x_k)))
if initial_b is None:
initial_b = initializer((x_k,))
b = K.variable(initial_b)
y = softmax_nd(T.dot(z, w) + b)
self.params = [pz_weight, w, b]
nll_loss = -T.sum(co * T.log(eps + y), axis=None)
reg_loss = T.constant(0.)
if pz_regularizer:
reg_loss = pz_regularizer(pz)
total_loss = nll_loss + reg_loss
decay_updates = [(iteration, iteration + 1)]
encoding = T.argmax(pz, axis=1)
one_hot_encoding = tensor_one_hot(encoding, z_k) # (x_k, z_k)
pb = T.dot(T.transpose(one_hot_encoding, (1, 0)), co)
m = T.sum(pb, axis=1, keepdims=True)
c = pb / (m + eps)
validation_nll = -T.sum(pb * T.log(eps + c), axis=None)
utilization = T.sum(T.gt(T.sum(one_hot_encoding, axis=0), 0), axis=0)
updates = opt.get_updates(loss=total_loss, params=self.params)
self.val_fun = theano.function([], validation_nll)
self.encodings_fun = theano.function([], encoding)
self.train_fun = theano.function([], [reg_loss, nll_loss, utilization, temp],
updates=updates + decay_updates)
self.weights = self.params + opt.weights + [iteration]
def _infer_ndim_bcast(ndim, shape, *args):
"""
Infer the number of dimensions from the shape or the other arguments.
:rtype: (int, variable, tuple) triple, where the variable is an integer
vector, and the tuple contains Booleans.
:returns: the first element returned is the inferred number of dimensions.
The second element is the shape inferred (combining symbolic and constant
informations from shape and args).
The third element is a broadcasting pattern corresponding to that shape.
"""
# Find the minimum value of ndim required by the *args
if args:
args_ndim = max(arg.ndim for arg in args)
else:
args_ndim = 0
if isinstance(shape, (tuple, list)):
# there is a convention that -1 means the corresponding shape of a
# potentially-broadcasted symbolic arg
#
# This case combines together symbolic and non-symbolic shape
# information
shape_ndim = len(shape)
if ndim is None:
ndim = shape_ndim
else:
if shape_ndim != ndim:
raise ValueError(
'ndim should be equal to len(shape), but\n',
'ndim = %s, len(shape) = %s, shape = %s' %
(ndim, shape_ndim, shape))
bcast = []
pre_v_shape = []
for i, s in enumerate(shape):
if hasattr(s, 'type'): # s is symbolic
bcast.append(False) # todo - introspect further
pre_v_shape.append(s)
else:
if s >= 0:
pre_v_shape.append(tensor.as_tensor_variable(s))
bcast.append((s == 1))
elif s == -1:
n_a_i = 0
for a in args:
# ndim: _ _ _ _ _ _
# ashp: s0 s1 s2 s3
# i
if i >= ndim - a.ndim:
n_a_i += 1
a_i = i + a.ndim - ndim
if not a.broadcastable[a_i]:
pre_v_shape.append(a.shape[a_i])
bcast.append(False)
break
else:
if n_a_i == 0:
raise ValueError(
('Auto-shape of -1 must overlap'
'with the shape of one of the broadcastable'
'inputs'))
else:
pre_v_shape.append(tensor.as_tensor_variable(1))
bcast.append(True)
else:
ValueError('negative shape', s)
# post-condition: shape may still contain both symbolic and
# non-symbolic things
if len(pre_v_shape) == 0:
v_shape = tensor.constant([], dtype='int32')
else:
v_shape = tensor.stack(*pre_v_shape)
elif shape is None:
# The number of drawn samples will be determined automatically,
# but we need to know ndim
if not args:
raise TypeError(('_infer_ndim_bcast cannot infer shape without'
' either shape or args'))
template = reduce(lambda a, b: a + b, args)
v_shape = template.shape
bcast = template.broadcastable
ndim = template.ndim
else:
v_shape = tensor.as_tensor_variable(shape)
if ndim is None:
ndim = tensor.get_vector_length(v_shape)
bcast = [False] * ndim
if (not (v_shape.dtype.startswith('int')
or v_shape.dtype.startswith('uint'))):
raise TypeError('shape must be an integer vector or list',
v_shape.dtype)
if args_ndim > ndim:
raise ValueError(
'ndim should be at least as big as required by args value',
(ndim, args_ndim), args)
assert ndim == len(bcast)
return ndim, tensor.cast(v_shape, 'int32'), tuple(bcast)
def __init__(self, babi_train_raw, babi_test_raw, word2vec,
word_vector_size, dim, mode, answer_module, input_mask_mode,
memory_hops, l2, normalize_attention, **kwargs):
print "==> not used params in DMN class:", kwargs.keys()
self.vocab = {}
self.ivocab = {}
self.word2vec = word2vec
self.word_vector_size = word_vector_size
self.dim = dim
self.mode = mode
self.answer_module = answer_module
self.input_mask_mode = input_mask_mode
self.memory_hops = memory_hops
#self.batch_size = 1
self.l2 = l2
self.normalize_attention = normalize_attention
self.train_input, self.train_q, self.train_answer, self.train_choices, self.train_input_mask = self._process_input(
babi_train_raw)
self.test_input, self.test_q, self.test_answer, self.test_choices, self.test_input_mask = self._process_input(
babi_test_raw)
self.vocab_size = 2 # number of answer choices
self.inp_var = T.matrix('input_var')
self.q_var = T.matrix('question_var')
self.ca_var = T.matrix('ca_var')
self.cb_var = T.matrix('cb_var')
#self.cc_var = T.matrix('cc_var')
#self.cd_var = T.matrix('cd_var')
self.ans_var = T.iscalar('answer_var')
self.input_mask_var = T.ivector('input_mask_var')
print "==> building input module"
self.W_inp_res_in = theano.shared(lasagne.init.Normal(0.1).sample(
(self.dim, self.word_vector_size)),
borrow=True)
self.W_inp_res_hid = theano.shared(lasagne.init.Normal(0.1).sample(
(self.dim, self.dim)),
borrow=True)
self.b_inp_res = theano.shared(lasagne.init.Constant(0.0).sample(
(self.dim, )),
borrow=True)
self.W_inp_upd_in = theano.shared(lasagne.init.Normal(0.1).sample(
(self.dim, self.word_vector_size)),
borrow=True)
self.W_inp_upd_hid = theano.shared(lasagne.init.Normal(0.1).sample(
(self.dim, self.dim)),
borrow=True)
self.b_inp_upd = theano.shared(lasagne.init.Constant(0.0).sample(
(self.dim, )),
borrow=True)
self.W_inp_hid_in = theano.shared(lasagne.init.Normal(0.1).sample(
(self.dim, self.word_vector_size)),
borrow=True)
self.W_inp_hid_hid = theano.shared(lasagne.init.Normal(0.1).sample(
(self.dim, self.dim)),
borrow=True)
self.b_inp_hid = theano.shared(lasagne.init.Constant(0.0).sample(
(self.dim, )),
borrow=True)
inp_c_history, _ = theano.scan(fn=self.input_gru_step,
sequences=self.inp_var,
outputs_info=T.zeros_like(
self.b_inp_hid))
self.inp_c = inp_c_history.take(self.input_mask_var, axis=0)
self.q_q, _ = theano.scan(fn=self.input_gru_step,
sequences=self.q_var,
outputs_info=T.zeros_like(self.b_inp_hid))
self.q_q = self.q_q[-1]
self.c_vecs = []
#for choice in [self.ca_var, self.cb_var, self.cc_var, self.cd_var]:
for choice in [self.ca_var, self.cb_var]:
history, _ = theano.scan(fn=self.input_gru_step,
sequences=choice,
outputs_info=T.zeros_like(self.b_inp_hid))
self.c_vecs.append(history[-1])
self.c_vecs = T.stack(self.c_vecs).transpose((1, 0)) # (dim, 4)
self.inp_c = T.stack([self.inp_c] * 2).transpose(
(1, 2, 0)) # (fact_cnt, dim, 4)
self.q_q = T.stack([self.q_q] * 2).transpose((1, 0)) # (dim, 4)
print "==> creating parameters for memory module"
self.W_mem_res_in = theano.shared(lasagne.init.Normal(0.1).sample(
(self.dim, self.dim)),
borrow=True)
self.W_mem_res_hid = theano.shared(lasagne.init.Normal(0.1).sample(
(self.dim, self.dim)),
borrow=True)
self.b_mem_res = theano.shared(lasagne.init.Constant(0.0).sample(
(self.dim, )),
borrow=True)
self.W_mem_upd_in = theano.shared(lasagne.init.Normal(0.1).sample(
(self.dim, self.dim)),
borrow=True)
self.W_mem_upd_hid = theano.shared(lasagne.init.Normal(0.1).sample(
(self.dim, self.dim)),
borrow=True)
self.b_mem_upd = theano.shared(lasagne.init.Constant(0.0).sample(
(self.dim, )),
borrow=True)
self.W_mem_hid_in = theano.shared(lasagne.init.Normal(0.1).sample(
(self.dim, self.dim)),
borrow=True)
self.W_mem_hid_hid = theano.shared(lasagne.init.Normal(0.1).sample(
(self.dim, self.dim)),
borrow=True)
self.b_mem_hid = theano.shared(lasagne.init.Constant(0.0).sample(
(self.dim, )),
borrow=True)
self.W_b = theano.shared(lasagne.init.Normal(0.1).sample(
(self.dim, self.dim)),
borrow=True)
self.W_1 = theano.shared(lasagne.init.Normal(0.1).sample(
(self.dim, 10 * self.dim + 3)),
borrow=True)
self.W_2 = theano.shared(lasagne.init.Normal(0.1).sample(
(1, self.dim)),
borrow=True)
self.b_1 = theano.shared(lasagne.init.Constant(0.0).sample(
(self.dim, )),
borrow=True)
self.b_2 = theano.shared(lasagne.init.Constant(0.0).sample((1, )),
borrow=True)
print "==> building episodic memory module (fixed number of steps: %d)" % self.memory_hops
memory = [self.q_q.copy()] # (dim, 4)
for iter in range(1, self.memory_hops + 1):
current_episode = self.new_episode(memory[iter - 1])
memory.append(
self.GRU_update_batch(memory[iter - 1], current_episode,
self.W_mem_res_in, self.W_mem_res_hid,
self.b_mem_res, self.W_mem_upd_in,
self.W_mem_upd_hid, self.b_mem_upd,
self.W_mem_hid_in, self.W_mem_hid_hid,
self.b_mem_hid))
last_mem = memory[-1].flatten()
print "==> building answer module"
self.W_a = theano.shared(lasagne.init.Normal(0.1).sample(
(self.vocab_size, 2 * self.dim)),
borrow=True)
if self.answer_module == 'feedforward':
self.prediction = nn_utils.softmax(T.dot(self.W_a, last_mem))
elif self.answer_module == 'recurrent':
self.W_ans_res_in = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.dim + self.vocab_size))
self.W_ans_res_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.dim))
self.b_ans_res = nn_utils.constant_param(value=0.0,
shape=(self.dim, ))
self.W_ans_upd_in = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.dim + self.vocab_size))
self.W_ans_upd_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.dim))
self.b_ans_upd = nn_utils.constant_param(value=0.0,
shape=(self.dim, ))
self.W_ans_hid_in = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.dim + self.vocab_size))
self.W_ans_hid_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.dim))
self.b_ans_hid = nn_utils.constant_param(value=0.0,
shape=(self.dim, ))
def answer_step(prev_a, prev_y):
a = self.GRU_update(prev_a, T.concatenate([prev_y, self.q_q]),
self.W_ans_res_in, self.W_ans_res_hid,
self.b_ans_res, self.W_ans_upd_in,
self.W_ans_upd_hid, self.b_ans_upd,
self.W_ans_hid_in, self.W_ans_hid_hid,
self.b_ans_hid)
y = nn_utils.softmax(T.dot(self.W_a, a))
return [a, y]
# TODO: add conditional ending
dummy = theano.shared(np.zeros((self.vocab_size, ), dtype=floatX))
results, updates = theano.scan(
fn=answer_step,
outputs_info=[last_mem, T.zeros_like(dummy)],
n_steps=1)
self.prediction = results[1][-1]
print "==> collecting all parameters"
self.params = [
self.W_inp_res_in, self.W_inp_res_hid, self.b_inp_res,
self.W_inp_upd_in, self.W_inp_upd_hid, self.b_inp_upd,
self.W_inp_hid_in, self.W_inp_hid_hid, self.b_inp_hid,
self.W_mem_res_in, self.W_mem_res_hid, self.b_mem_res,
self.W_mem_upd_in, self.W_mem_upd_hid, self.b_mem_upd,
self.W_mem_hid_in, self.W_mem_hid_hid, self.b_mem_hid, self.W_b,
self.W_1, self.W_2, self.b_1, self.b_2, self.W_a
]
if self.answer_module == 'recurrent':
self.params = self.params + [
self.W_ans_res_in, self.W_ans_res_hid, self.b_ans_res,
self.W_ans_upd_in, self.W_ans_upd_hid, self.b_ans_upd,
self.W_ans_hid_in, self.W_ans_hid_hid, self.b_ans_hid
]
print "==> building loss layer and computing updates"
self.loss_ce = T.nnet.categorical_crossentropy(
self.prediction.dimshuffle('x', 0), T.stack([self.ans_var]))[0]
if self.l2 > 0:
self.loss_l2 = self.l2 * nn_utils.l2_reg(self.params)
else:
self.loss_l2 = 0
self.loss = self.loss_ce + self.loss_l2
updates = lasagne.updates.adadelta(self.loss, self.params)
if self.mode == 'train':
print "==> compiling train_fn"
self.train_fn = theano.function(
inputs=[
self.inp_var,
self.q_var,
self.ans_var,
self.ca_var,
self.cb_var, # self.cc_var, self.cd_var,
self.input_mask_var
],
outputs=[self.prediction, self.loss],
updates=updates)
print "==> compiling test_fn"
self.test_fn = theano.function(
inputs=[
self.inp_var,
self.q_var,
self.ans_var,
self.ca_var,
self.cb_var, # self.cc_var, self.cd_var,
self.input_mask_var
],
outputs=[
self.prediction, self.loss, self.inp_c, self.q_q, last_mem
])
if self.mode == 'train':
print "==> computing gradients (for debugging)"
gradient = T.grad(self.loss, self.params)
self.get_gradient_fn = theano.function(
inputs=[
self.inp_var,
self.q_var,
self.ans_var,
self.ca_var,
self.cb_var, # self.cc_var, self.cd_var,
self.input_mask_var
],
outputs=gradient)
def generate_hierarchical_model_parameters(parameter, n_subjects, design,
mu_lower, mu_upper, sd_lower,
sd_upper, bound_lower, bound_upper,
val, testval):
if (design['conditions'] is not None):
if val is None:
mu = tt.stack([
pm.Uniform('{}_{}_mu'.format(parameter, condition),
mu_lower,
mu_upper,
testval=testval)
for condition in design['conditions']
])
sd = tt.stack([
pm.Uniform('{}_{}_sd'.format(parameter, condition),
sd_lower,
sd_upper,
testval=testval)
for condition in design['conditions']
])
bounded = pm.Bound(pm.Normal, bound_lower, bound_upper)
parms = []
n_subjects_per_condition = []
for c, condition in enumerate(design['conditions']):
n_subjects_in_condition = np.unique(design['subject_index'][
design['condition_index'] == c]).size
n_subjects_per_condition.append(n_subjects_in_condition)
parms_tmp = bounded('{}_{}'.format(parameter, condition),
mu=mu[c],
sd=sd[c],
shape=(n_subjects_in_condition))
parms_tmp = tt.concatenate([tt.zeros(1), parms_tmp])
parms.append(parms_tmp[design['D'][:, c]][:, None])
parms = tt.concatenate(parms, axis=1)
else:
parms = []
n_subjects_per_condition = []
for c, condition in enumerate(design['conditions']):
n_subjects_in_condition = np.unique(design['subject_index'][
design['condition_index'] == c]).size
n_subjects_per_condition.append(n_subjects_in_condition)
if len(val) == len(design['conditions']):
parms.append(
pm.Deterministic(
'{}_{}'.format(parameter, condition),
tt.ones(n_subjects_in_condition, 1) * val[c]))
else:
raise ValueError(
'Number of values in {}_val does not match the number of specified {}-conditions.'
.format(parameter, parameter))
# make sure all elements in parms have same size
for set_i, parm_set in enumerate(parms):
if n_subjects_per_condition[set_i] < n_subjects:
parms[set_i] = tt.concatenate([
parm_set,
tt.zeros(
(n_subjects - n_subjects_per_condition[set_i], 1))
],
axis=0)
parms = tt.concatenate(parms, axis=1)
else:
if val is None:
mu = pm.Uniform('{}_mu'.format(parameter),
mu_lower,
mu_upper,
testval=testval)
sd = pm.Uniform('{}_sd'.format(parameter),
sd_lower,
sd_upper,
testval=testval)
bounded = pm.Bound(pm.Normal, bound_lower, bound_upper)
parms = bounded(parameter, mu=mu, sd=sd, shape=(n_subjects, 1))
else:
parms = pm.Deterministic(parameter, tt.ones((n_subjects, 1)) * val)
return parms
# The number of spindles per epoch:
num_spindles_per_epoch = pm.Categorical('num_spindles_per_epoch',
p=pm.Dirichlet(
'spindle_num_prior',
a=spindle_number_prior),
testval=1)
# ----Tracking is a raters spindle marker is real or contaminate-----
# if the number of spindles in an epoch (z) is greater than 0, then use conf to determine if a spindle is real or not
#spindle_chance = data['conf'] # pm.math.switch(num_spindles_per_epoch[data['epoch_i']] > 0, data['conf'], 0)
spindle_chance_prior = pm.Beta('spindle_chance_prior', alpha=2, beta=1)
marker_is_from_real_spindle = pm.Bernoulli('marker_is_from_real_spindle',
p=spindle_chance_prior,
shape=n_data)
marker_is_from_real_spindle_stacked = tt.stack(
[marker_is_from_real_spindle, 1 - marker_is_from_real_spindle],
axis=1) # stack theta for use in mixture model
# ----Mapping between rater's spindles and real spindles (w)----
## Handy matrix to compare z too
compare = np.arange(0, max_spindles_per_epoch + 1) # [0 1 2 3 4 5]*epochs
# Acutual prior for "mapping_marker_to_true_spindle"
# shape=[n_epochs, max_spindles_per_epoch],
# e.g. mapping_marker_to_true_spindle_prior for a single epoch will be like [1 1 1 0 0 0 0],
# and therefore the mapping_marker_to_true_spindle's can only be from [0-2]-1 = [-1, 0, 1], where -1=no mapping
mapping_marker_to_true_spindle_prior = pm.math.where(
compare - num_spindles_per_epoch <= 0, 1, 0)
# no_spindles_prior = np.zeros((n_data, 6))
# no_spindles_prior[:, 0] = 1
# mapping_prior = tt.switch(marker_is_from_real_spindle.reshape((n_data, 1)), mapping_marker_to_true_spindle_prior, no_spindles_prior)
def __init__(self, model, algo='fisher', c_lambd_inv=1e-3, rate=1.05,
over_sampling=1, rescale='momentum'):
""" Init self.
Args:
model,
algo,
c_lambd_inv: Start value of \lambda regularizer (used in matrix
inversion and in F*v computation).
rate: Change per iteration for \lambda.
over_sampling: For Fisher-like methods, use multiple random
vectors per one sample from dataset.
rescale: Can be either False, True or 'momentum'.
Implemented algos:
'gn' - Gauss-Newton matrix,
'fisher' - Fisher matrix,
'kr' - Khatri-Rao matrix,
'kr_diag' - block-diagonal KR matrix.
"""
self.model = model
self.algo = algo
self.x = self.model.x
self.y = T.ivector('y')
self.outc = T.matrix('outc')
# due to theano bugs
self.x_d = shared_empty(2)
self.y_d = shared_empty(1, dtype='int32')
self.outc_d = shared_empty(2)
self.rand_outc_d = shared_empty(3)
# ---
self.rand_outc = T.tensor3('rand_outc')
self.lambd_inv = T.scalar('lambd_inv')
self.c_lambd_inv = c_lambd_inv
self.rate = rate
self.over_sampling = over_sampling
self.rescale = rescale
# -- target def --
self.f_loss = 0
self.f_loss_samples = 0
for i in range(self.over_sampling):
self.f_loss += get_loss(self.model.a, self.rand_outc[i] + my_consider_constant(self.model.a)) * scalar_floatX(self.model.a.shape[0])
self.f_loss_samples += get_loss_samples(self.model.a, self.rand_outc[i] + my_consider_constant(self.model.a))
self.loss = get_loss(self.model.a, self.outc)
self.err = get_error(get_pred(self.model.a), self.y)
self.updates = OrderedDict()
self.grad = sum(([T.grad(self.loss, p)] for p in self.model.params), [])
self.grad_vec = T.concatenate([g.flatten() for g in self.grad])
def get_fisher_mat():
grad2d = []
for p in self.model.params:
grad2d += [T.jacobian(self.f_loss_samples, p)]
if grad2d[-1].ndim == 2:
grad2d[-1] = grad2d[-1].dimshuffle(0, 1, 'x')
grad2d_vec = T.concatenate([g.flatten(2).T for g in grad2d]).T
# tensor wise: F_p,i,j = sum_k grad2d[p,i,k]*grad2d[p,k,j]
# just a slow reference implementation of what is below
# F = T.mean(T.batched_dot(grad2d_vec.dimshuffle(0, 1, 'x'), grad2d_vec.dimshuffle(0, 'x', 1)), 0)/self.over_sampling
F = T.dot(grad2d_vec.T, grad2d_vec)/T.cast(grad2d_vec.shape[0], theano.config.floatX)/self.over_sampling
return F
if self.algo == 'fisher':
self.grad2d = []
for p in self.model.params:
self.grad2d += [T.jacobian(self.f_loss_samples, p)]
if self.grad2d[-1].ndim == 2:
self.grad2d[-1] = self.grad2d[-1].dimshuffle(0, 1, 'x')
self.grad2d_vec = T.concatenate([g.flatten(2).T for g in self.grad2d]).T
# tensor wise: F_p,i,j = sum_k grad2d[p,i,k]*grad2d[p,k,j]
# just a slow reference implementation of what is below
# F = T.mean(T.batched_dot(grad2d_vec.dimshuffle(0, 1, 'x'), grad2d_vec.dimshuffle(0, 'x', 1)), 0)/self.over_sampling
self.F = T.dot(self.grad2d_vec.T, self.grad2d_vec)/T.cast(self.grad2d_vec.shape[0], theano.config.floatX)/self.over_sampling
elif self.algo == 'gn':
self.grad2d = []
for p in self.model.params:
self.grad2d += [T.jacobian(self.model.a.flatten(), p)]
new_shape = (self.model.a.shape[0], self.model.a.shape[1], -1)
self.grad2d[-1] = self.grad2d[-1].reshape(new_shape)
self.grad2d_vec = T.concatenate([g.flatten(3) for g in self.grad2d], 2)
# just a slow reference implementation of what is below
# self.F = T.mean(T.batched_dot(self.grad2d_vec.dimshuffle(0, 2, 1),
# self.grad2d_vec.dimshuffle(0, 1, 2)), axis=0)
self.F = T.tensordot(self.grad2d_vec.dimshuffle(0, 2, 1),
self.grad2d_vec.dimshuffle(0, 1, 2), [(0, 2), (0, 1)])/T.cast(self.grad2d_vec.shape[0], theano.config.floatX)
elif self.algo.startswith('kr'):
self.grads = []
# self.acts = [T.concatenate([self.model.x, T.ones((self.model.x.shape[0], 1))], axis=1)]
self.acts = [self.model.x]
for l in self.model.layers:
cg = T.grad(self.f_loss, l.s)
self.grads.append(cg)
# self.acts.append(T.concatenate([l.a, T.ones((l.a.shape[0], 1))], axis=1))
self.acts.append(l.a)
self.G = []
self.A = []
self.F_block = []
self.F = []
cnt = T.cast(self.grads[0].shape[0], theano.config.floatX)
for i in range(len(self.grads)):
self.G += [[]]
self.A += [[]]
for j in range(len(self.grads)):
# self.G[-1] += [T.mean(T.batched_dot(self.grads[i].dimshuffle(0, 1, 'x'), self.grads[j].dimshuffle(0, 'x', 1)), 0).dimshuffle('x', 0, 1)]
# self.A[-1] += [T.mean(T.batched_dot(self.acts[i].dimshuffle(0, 1, 'x'), self.acts[j].dimshuffle(0, 'x', 1)), 0).dimshuffle('x', 0, 1)]
# self.G[-1] += [T.batched_dot(self.grads[i].dimshuffle(0, 1, 'x'), self.grads[j].dimshuffle(0, 'x', 1))]
# self.A[-1] += [T.batched_dot(self.acts[i].dimshuffle(0, 1, 'x'), self.acts[j].dimshuffle(0, 'x', 1))]
self.G[-1] += [self.grads[i].T.dot(self.grads[j]).dimshuffle('x', 0, 1)/cnt]
self.A[-1] += [self.acts[i].T.dot(self.acts[j]).dimshuffle('x', 0, 1)/cnt]
if self.algo.endswith('diag'):
self.G[-1][-1] *= float(i==j)
self.A[-1][-1] *= float(i==j)
for i in range(len(self.grads)):
self.F_block += [[]]
for j in range(len(self.grads)):
# depends on whether you want to compute the real fisher with this or the kr approximation
# since numpy-base fast_kron somehow computes 3d tensors faster than theano
# cblock = fast_kron(self.A[i][j], self.G[i][j])
cblock = native_kron(self.A[i][j], self.G[i][j])
cblock = cblock.reshape(cblock.shape[1:], ndim=2)
self.F_block[i] += [cblock]
self.F.append(T.concatenate(self.F_block[-1], axis=1))
self.F = T.concatenate(self.F, axis=0)
self.F = (self.F+self.F.T)/2
self.Fdamp = self.F+T.identity_like(self.F)*self.lambd_inv
# There're 3+ different ways of computing F^-1*v in theano,
# and it seems like solve_sym_pos is quite neutral in terms
# of performance + it throws an exception if the provided matrix
# is singular.
# self.new_grad_vec = theano.tensor.slinalg.solve(self.Fdamp, self.grad_vec.dimshuffle(0, 'x'))
self.new_grad_vec = solve_sym_pos(self.Fdamp, self.grad_vec)
# self.new_grad_vec = gpu_solve(self.Fdamp, self.grad_vec.dimshuffle(0, 'x'))
pcount = sum(p.get_value().size for p in self.model.params)
self.ch_history = theano.shared(np.zeros((pcount,), dtype=theano.config.floatX))
if self.rescale == 'momentum':
self.real_fish = get_fisher_mat() + T.identity_like(self.F)*self.lambd_inv
FT = self.real_fish.dot(self.new_grad_vec)
FM = self.real_fish.dot(self.ch_history)
TFT = self.new_grad_vec.T.dot(FT)
MFT = self.ch_history.T.dot(FT)
MFM = self.ch_history.T.dot(FM)
GT = self.grad_vec.T.dot(self.new_grad_vec)
GM = self.grad_vec.T.dot(self.ch_history)
tmp1 = T.stack([TFT.reshape(()), MFT.reshape(())], 0).dimshuffle('x', 0)
tmp2 = T.stack([MFT.reshape(()), MFM.reshape(())], 0).dimshuffle('x', 0)
A = T.concatenate([tmp1, tmp2], 0)
A_pinv = T.nlinalg.MatrixPinv()(A)
b = T.stack([GT.reshape(()), GM.reshape(())], 0).dimshuffle(0, 'x')
res = A_pinv.dot(b).flatten()
alpha = res[0]
beta = res[1]
self.new_grad_vec = self.new_grad_vec * alpha.reshape(()) + self.ch_history * beta.reshape(())
self.F = self.real_fish
self.updates[self.ch_history] = self.new_grad_vec
elif self.rescale:
self.real_fish = get_fisher_mat() + T.identity_like(self.F)*self.lambd_inv
lin_fac = self.grad_vec.T.dot(self.new_grad_vec)
quad_fac = self.new_grad_vec.T.dot(self.real_fish.dot(self.new_grad_vec))
alpha = lin_fac/quad_fac
beta = 0 * alpha
self.new_grad_vec *= alpha.reshape(())
self.F = self.real_fish
# self.Fdamp = self.F+T.identity_like(self.F)*self.lambd_inv
# alpha = T.as_tensor_variable(1)
def _apply_gradient_vec(params, new_grad_vec, updates):
new_grad = []
offset = 0
for p in params:
pval = p.get_value()
new_grad += [new_grad_vec[offset:offset+pval.size].reshape(pval.shape)]
offset += pval.size
updates[p] = p - new_grad[-1]
return new_grad
self.new_grad = _apply_gradient_vec(self.model.params, self.new_grad_vec, self.updates)
self.get_params = theano.function(
inputs=[],
outputs=self.model.params,
on_unused_input='warn'
)
self.quad_est_loss = self.new_grad_vec.T.dot(self.F.dot(self.new_grad_vec))/2
self.est_loss = self.quad_est_loss + self.grad_vec.dot(self.new_grad_vec)
self.print_pls = {}
self.print_pls.update({'shape': self.F.shape[0], 'rank': rank(self.F*10000)})
self.print_pls.update({'grad_mean': T.mean(self.grad_vec**2)**0.5})
self.print_pls.update({'alpha': alpha, 'beta': beta})
# self.print_pls += [self.F]
# self.print_pls += [self.real_fish]
self.train = theano.function(
inputs=[self.lambd_inv],
outputs=[self.est_loss, self.loss, self.err] + list(self.print_pls.values()),
updates=self.updates,
givens={
self.x: self.x_d,
self.y: self.y_d,
self.outc: self.outc_d,
self.rand_outc: self.rand_outc_d
},
on_unused_input='warn',
allow_input_downcast=True,
# profile=True
)
self.eva = theano.function(
inputs=[],
outputs=[self.loss, self.err],
givens={
self.x: self.x_d,
self.y: self.y_d,
self.outc: self.outc_d
},
on_unused_input='warn',
allow_input_downcast=True
)
def step(self, X, y, outc):
"""Perform single train iteration.
Args:
X: input vectors
y: target labels.
outc: target vectors.
Returns:
Dict consisting of 'loss', 'err', 'est_loss', 'rho', 'delta_ll' and
parameters from self.print_pls.
"""
self.x_d.set_value(X)
self.y_d.set_value(y)
self.outc_d.set_value(outc)
self.rand_outc_d.set_value(floatX(nprng.randn(self.over_sampling, *outc.shape)))
old_params = self.get_params()
while True:
# reset params to saved
for op, p in zip(old_params, self.model.params):
p.set_value(op)
try:
t_r = self.train(self.c_lambd_inv)
print_pls_vals = t_r[-len(self.print_pls):]
self.print_pls_res = {k: v for k, v in zip(self.print_pls.keys(), print_pls_vals)}
except numpy.linalg.linalg.LinAlgError:
t_r = [1e20, 1e10, 10] + [None] * len(self.print_pls)
self.print_pls_res = {k: None for k in self.print_pls.keys()}
e_v = self.eva()
delta_ll = t_r[1] - e_v[0]
rho = delta_ll/float(t_r[0])
print()
print('lambda:', round(self.c_lambd_inv, 7), 'rho:', round(rho, 2), 'old loss:', t_r[1], 'new loss:', e_v[0])
if rho < 0:
self.c_lambd_inv *= self.rate * 2
continue
elif rho < 0.5:
self.c_lambd_inv *= self.rate
# self.c_lambd_inv = min(self.c_lambd_inv, 0.02)
elif rho > 0.5:
self.c_lambd_inv /= self.rate
else:
pass
break
# self.train.profiler.print_summary()
res = {'rho': rho, 'est_loss': t_r[0], 'loss': t_r[1], 'err': t_r[2], 'delta_ll': delta_ll}
res.update(self.print_pls_res)
return res
def evaluate(X_test, y_test, outc_test):
"""Return loss and error for provided dataset.
Args:
X_test: input vectors,
y_test: target labels,
outc_test: target vectors.
Returns:
Dict consisting of 'test_loss', 'test_err'.
"""
self.x_d.set_value(X_test)
self.y_d.set_value(y_test)
self.outc_d.set_value(outc_test)
te_v = self.eva()
test_loss = te_v[0]
test_err = te_v[1]
return {'test_loss': test_loss, 'test_err': test_err}
def _check_gv_matrix_correctness(self):
v = T.vector('v')
get_Fv = theano.function(
inputs=[v],
outputs=[self.F.dot(v)],
givens={
self.x: self.x_d,
self.outc: self.outc_d
},
allow_input_downcast=True
)
grad_at = theano.function(
inputs=[],
outputs=sum(([T.grad(self.loss, p)] for p in self.model.params), []),
givens={
self.x: self.x_d,
self.outc: self.outc_d
},
allow_input_downcast=True
)
grads0 = grad_at()
vec = []
EPS = 1e-5
for p in self.model.params:
vec += [nprng.randn(*p.get_value().shape).astype(theano.config.floatX)]
p.set_value(p.get_value()+vec[-1]*EPS)
grads1 = grad_at()
vec_vec = np.concatenate([p.flatten() for p in vec])
F_vec = get_Fv(vec_vec)
F_vec_vec = np.concatenate([f.flatten() for f in F_vec])
grads0_vec = np.concatenate([p.flatten() for p in grads0])
grads1_vec = np.concatenate([p.flatten() for p in grads1])
F_vec_emp = (grads1_vec-grads0_vec)/EPS
print(np.mean(F_vec_emp**2)**0.5, np.mean(F_vec_vec**2)**0.5)
print(np.max(np.abs(F_vec_emp-F_vec_vec)))
exit(0)
def conv2d(
input,
filters,
image_shape=None,
filter_shape=None,
border_mode="valid",
subsample=(1, 1),
**kargs,
):
"""
signal.conv.conv2d performs a basic 2D convolution of the input with the
given filters. The input parameter can be a single 2D image or a 3D tensor,
containing a set of images. Similarly, filters can be a single 2D filter or
a 3D tensor, corresponding to a set of 2D filters.
Shape parameters are optional and will result in faster execution.
Parameters
----------
input : Symbolic theano tensor for images to be filtered.
Dimensions: ([num_images], image height, image width)
filters : Symbolic theano tensor for convolution filter(s).
Dimensions: ([num_filters], filter height, filter width)
border_mode: {'valid', 'full'}
See scipy.signal.convolve2d.
subsample
Factor by which to subsample output.
image_shape : tuple of length 2 or 3
([num_images,] image height, image width).
filter_shape : tuple of length 2 or 3
([num_filters,] filter height, filter width).
kwargs
See theano.tensor.nnet.conv.conv2d.
Returns
-------
symbolic 2D,3D or 4D tensor
Tensor of filtered images, with shape
([number images,] [number filters,] image height, image width).
"""
assert input.ndim in (2, 3)
assert filters.ndim in (2, 3)
# use shape information if it is given to us ###
if filter_shape and image_shape:
if input.ndim == 3:
bsize = image_shape[0]
else:
bsize = 1
imshp = (1, ) + tuple(image_shape[-2:])
if filters.ndim == 3:
nkern = filter_shape[0]
else:
nkern = 1
kshp = filter_shape[-2:]
else:
nkern, kshp = None, None
bsize, imshp = None, None
# reshape tensors to 4D, for compatibility with ConvOp ###
if input.ndim == 3:
sym_bsize = input.shape[0]
else:
sym_bsize = 1
if filters.ndim == 3:
sym_nkern = filters.shape[0]
else:
sym_nkern = 1
new_input_shape = tensor.join(0, tensor.stack([sym_bsize, 1]),
input.shape[-2:])
input4D = tensor.reshape(input, new_input_shape, ndim=4)
new_filter_shape = tensor.join(0, tensor.stack([sym_nkern, 1]),
filters.shape[-2:])
filters4D = tensor.reshape(filters, new_filter_shape, ndim=4)
# perform actual convolution ###
op = conv.ConvOp(
output_mode=border_mode,
dx=subsample[0],
dy=subsample[1],
imshp=imshp,
kshp=kshp,
nkern=nkern,
bsize=bsize,
**kargs,
)
output = op(input4D, filters4D)
# flatten to 3D tensor if convolving with single filter or single image
if input.ndim == 2 and filters.ndim == 2:
if config.warn__signal_conv2d_interface:
warnings.warn(
"theano.tensor.signal.conv2d() now outputs a 2d tensor when both"
" inputs are 2d. To disable this warning, set the Theano flag"
" warn__signal_conv2d_interface to False",
stacklevel=3,
)
output = tensor.flatten(output.T, ndim=2).T
elif input.ndim == 2 or filters.ndim == 2:
output = tensor.flatten(output.T, ndim=3).T
return output
def rnn(step_function,
inputs,
initial_states,
go_backwards=False,
mask=None,
constants=None,
unroll=False,
input_length=None):
'''Iterates over the time dimension of a tensor.
# Arguments
inputs: tensor of temporal data of shape (samples, time, ...)
(at least 3D).
step_function:
Parameters:
input: tensor with shape (samples, ...) (no time dimension),
representing input for the batch of samples at a certain
time step.
states: list of tensors.
Returns:
output: tensor with shape (samples, ...) (no time dimension),
new_states: list of tensors, same length and shapes
as 'states'.
initial_states: tensor with shape (samples, ...) (no time dimension),
containing the initial values for the states used in
the step function.
go_backwards: boolean. If True, do the iteration over
the time dimension in reverse order.
mask: binary tensor with shape (samples, time),
with a zero for every element that is masked.
constants: a list of constant values passed at each step.
unroll: whether to unroll the RNN or to use a symbolic loop (`scan`).
input_length: must be specified if using `unroll`.
# Returns
A tuple (last_output, outputs, new_states).
last_output: the latest output of the rnn, of shape (samples, ...)
outputs: tensor with shape (samples, time, ...) where each
entry outputs[s, t] is the output of the step function
at time t for sample s.
new_states: list of tensors, latest states returned by
the step function, of shape (samples, ...).
'''
ndim = inputs.ndim
assert ndim >= 3, 'Input should be at least 3D.'
if unroll:
if input_length is None:
raise Exception('When specifying `unroll=True`, an `input_length` '
'must be provided to `rnn`.')
axes = [1, 0] + list(range(2, ndim))
inputs = inputs.dimshuffle(axes)
if constants is None:
constants = []
if mask is not None:
if mask.ndim == ndim - 1:
mask = expand_dims(mask)
assert mask.ndim == ndim
mask = mask.dimshuffle(axes)
if unroll:
indices = list(range(input_length))
if go_backwards:
indices = indices[::-1]
successive_outputs = []
successive_states = []
states = initial_states
for i in indices:
output, new_states = step_function(inputs[i],
states + constants)
if len(successive_outputs) == 0:
prev_output = zeros_like(output)
else:
prev_output = successive_outputs[-1]
output = T.switch(mask[i], output, prev_output)
kept_states = []
for state, new_state in zip(states, new_states):
kept_states.append(T.switch(mask[i], new_state, state))
states = kept_states
successive_outputs.append(output)
successive_states.append(states)
outputs = T.stack(*successive_outputs)
states = []
for i in range(len(successive_states[-1])):
states.append(
T.stack(*[
states_at_step[i]
for states_at_step in successive_states
]))
else:
# build an all-zero tensor of shape (samples, output_dim)
initial_output = step_function(inputs[0],
initial_states + constants)[0] * 0
# Theano gets confused by broadcasting patterns in the scan op
initial_output = T.unbroadcast(initial_output, 0, 1)
def _step(input, mask, output_tm1, *states):
output, new_states = step_function(input, states)
# output previous output if masked.
output = T.switch(mask, output, output_tm1)
return_states = []
for state, new_state in zip(states, new_states):
return_states.append(T.switch(mask, new_state, state))
return [output] + return_states
results, _ = theano.scan(_step,
sequences=[inputs, mask],
outputs_info=[initial_output] +
initial_states,
non_sequences=constants,
go_backwards=go_backwards)
# deal with Theano API inconsistency
if type(results) is list:
outputs = results[0]
states = results[1:]
else:
outputs = results
states = []
else:
if unroll:
indices = list(range(input_length))
if go_backwards:
indices = indices[::-1]
successive_outputs = []
successive_states = []
states = initial_states
for i in indices:
output, states = step_function(inputs[i], states + constants)
successive_outputs.append(output)
successive_states.append(states)
outputs = T.stack(*successive_outputs)
states = []
for i in range(len(successive_states[-1])):
states.append(
T.stack(*[
states_at_step[i]
for states_at_step in successive_states
]))
else:
def _step(input, *states):
output, new_states = step_function(input, states)
return [output] + new_states
results, _ = theano.scan(_step,
sequences=inputs,
outputs_info=[None] + initial_states,
non_sequences=constants,
go_backwards=go_backwards)
# deal with Theano API inconsistency
if type(results) is list:
outputs = results[0]
states = results[1:]
else:
outputs = results
states = []
outputs = T.squeeze(outputs)
last_output = outputs[-1]
axes = [1, 0] + list(range(2, outputs.ndim))
outputs = outputs.dimshuffle(axes)
states = [T.squeeze(state[-1]) for state in states]
return last_output, outputs, states
def get_output_mask(self, train=False):
X = self.get_input_mask(train)
if X is None:
return None
tensors = [T.roll(X, off, axis=self.axis) for off in self.offsets]
return T.stack(tensors, axis=self.offset_axis)
def identity(n):
return tensor.stack([tensor.eye(n), tensor.zeros((n, n))], axis=0)
def __init__(self, train_raw, test_raw, dim, mode, l2, l1,
batch_norm, dropout, batch_size,
ihm_C, los_C, ph_C, decomp_C,
partition, nbins, **kwargs):
print "==> not used params in network class:", kwargs.keys()
self.train_raw = train_raw
self.test_raw = test_raw
self.dim = dim
self.mode = mode
self.l2 = l2
self.l1 = l1
self.batch_norm = batch_norm
self.dropout = dropout
self.batch_size = batch_size
self.ihm_C = ihm_C
self.los_C = los_C
self.ph_C = ph_C
self.decomp_C = decomp_C
self.nbins = nbins
if (partition == 'log'):
self.get_bin = metrics.get_bin_log
self.get_estimate = metrics.get_estimate_log
else:
assert self.nbins == 10
self.get_bin = metrics.get_bin_custom
self.get_estimate = metrics.get_estimate_custom
self.train_batch_gen = self.get_batch_gen(self.train_raw)
self.test_batch_gen = self.get_batch_gen(self.test_raw)
self.input_var = T.tensor3('X')
self.input_lens = T.ivector('L')
self.ihm_pos = T.ivector('ihm_pos')
self.ihm_mask = T.ivector('ihm_mask')
self.ihm_label = T.ivector('ihm_label')
self.los_mask = T.imatrix('los_mask')
self.los_label = T.matrix('los_label') # for regression
#self.los_label = T.imatrix('los_label')
self.ph_label = T.imatrix('ph_label')
self.decomp_mask = T.imatrix('decomp_mask')
self.decomp_label = T.imatrix('decomp_label')
print "==> Building neural network"
# common network
network = layers.InputLayer((None, None, self.train_raw[0][0].shape[1]),
input_var=self.input_var)
if (self.dropout > 0):
network = layers.DropoutLayer(network, p=self.dropout)
network = layers.LSTMLayer(incoming=network, num_units=dim,
only_return_final=False,
grad_clipping=10,
ingate=lasagne.layers.Gate(
W_in=Orthogonal(),
W_hid=Orthogonal(),
W_cell=Normal(0.1)),
forgetgate=lasagne.layers.Gate(
W_in=Orthogonal(),
W_hid=Orthogonal(),
W_cell=Normal(0.1)),
cell=lasagne.layers.Gate(W_cell=None,
nonlinearity=lasagne.nonlinearities.tanh,
W_in=Orthogonal(),
W_hid=Orthogonal()),
outgate=lasagne.layers.Gate(
W_in=Orthogonal(),
W_hid=Orthogonal(),
W_cell=Normal(0.1)))
if (self.dropout > 0):
network = layers.DropoutLayer(network, p=self.dropout)
lstm_output = layers.get_output(network)
self.params = layers.get_all_params(network, trainable=True)
self.reg_params = layers.get_all_params(network, regularizable=True)
# for each example in minibatch take the last output
last_outputs = []
for index in range(self.batch_size):
last_outputs.append(lstm_output[index, self.input_lens[index]-1, :])
last_outputs = T.stack(last_outputs)
# take 48h outputs for fixed mortality task
mid_outputs = []
for index in range(self.batch_size):
mid_outputs.append(lstm_output[index, self.ihm_pos[index], :])
mid_outputs = T.stack(mid_outputs)
# in-hospital mortality related network
ihm_network = layers.InputLayer((None, dim), input_var=mid_outputs)
ihm_network = layers.DenseLayer(incoming=ihm_network, num_units=2,
nonlinearity=softmax)
self.ihm_prediction = layers.get_output(ihm_network)
self.ihm_det_prediction = layers.get_output(ihm_network, deterministic=True)
self.params += layers.get_all_params(ihm_network, trainable=True)
self.reg_params += layers.get_all_params(ihm_network, regularizable=True)
self.ihm_loss = (self.ihm_mask * categorical_crossentropy(self.ihm_prediction,
self.ihm_label)).mean()
# length of stay related network
# Regression
los_network = layers.InputLayer((None, None, dim), input_var=lstm_output)
los_network = layers.ReshapeLayer(los_network, (-1, dim))
los_network = layers.DenseLayer(incoming=los_network, num_units=1,
nonlinearity=rectify)
los_network = layers.ReshapeLayer(los_network, (lstm_output.shape[0], -1))
self.los_prediction = layers.get_output(los_network)
self.los_det_prediction = layers.get_output(los_network, deterministic=True)
self.params += layers.get_all_params(los_network, trainable=True)
self.reg_params += layers.get_all_params(los_network, regularizable=True)
self.los_loss = (self.los_mask * squared_error(self.los_prediction,
self.los_label)).mean(axis=1).mean(axis=0)
# phenotype related network
ph_network = layers.InputLayer((None, dim), input_var=last_outputs)
ph_network = layers.DenseLayer(incoming=ph_network, num_units=25,
nonlinearity=sigmoid)
self.ph_prediction = layers.get_output(ph_network)
self.ph_det_prediction = layers.get_output(ph_network, deterministic=True)
self.params += layers.get_all_params(ph_network, trainable=True)
self.reg_params += layers.get_all_params(ph_network, regularizable=True)
self.ph_loss = nn_utils.multilabel_loss(self.ph_prediction, self.ph_label)
# decompensation related network
decomp_network = layers.InputLayer((None, None, dim), input_var=lstm_output)
decomp_network = layers.ReshapeLayer(decomp_network, (-1, dim))
decomp_network = layers.DenseLayer(incoming=decomp_network, num_units=2,
nonlinearity=softmax)
decomp_network = layers.ReshapeLayer(decomp_network, (lstm_output.shape[0], -1, 2))
self.decomp_prediction = layers.get_output(decomp_network)[:, :, 1]
self.decomp_det_prediction = layers.get_output(decomp_network, deterministic=True)[:, :, 1]
self.params += layers.get_all_params(decomp_network, trainable=True)
self.reg_params += layers.get_all_params(decomp_network, regularizable=True)
self.decomp_loss = nn_utils.multilabel_loss_with_mask(self.decomp_prediction,
self.decomp_label,
self.decomp_mask)
"""
data = next(self.train_batch_gen)
print max(data[1])
print lstm_output.eval({self.input_var:data[0]}).shape
exit()
"""
if self.l2 > 0:
self.loss_l2 = self.l2 * nn_utils.l2_reg(self.reg_params)
else:
self.loss_l2 = T.constant(0)
if self.l1 > 0:
self.loss_l1 = self.l1 * nn_utils.l1_reg(self.reg_params)
else:
self.loss_l1 = T.constant(0)
self.reg_loss = self.loss_l1 + self.loss_l2
self.loss = (ihm_C * self.ihm_loss + los_C * self.los_loss +
ph_C * self.ph_loss + decomp_C * self.decomp_loss +
self.reg_loss)
#updates = lasagne.updates.adadelta(self.loss, self.params,
# learning_rate=0.001)
#updates = lasagne.updates.momentum(self.loss, self.params,
# learning_rate=0.00003)
#updates = lasagne.updates.adam(self.loss, self.params)
updates = lasagne.updates.adam(self.loss, self.params, beta1=0.5,
learning_rate=0.0001) # from DCGAN paper
#updates = lasagne.updates.nesterov_momentum(loss, params, momentum=0.9,
# learning_rate=0.001,
all_inputs = [self.input_var, self.input_lens,
self.ihm_pos, self.ihm_mask, self.ihm_label,
self.los_mask, self.los_label,
self.ph_label,
self.decomp_mask, self.decomp_label]
train_outputs = [self.ihm_prediction, self.los_prediction,
self.ph_prediction, self.decomp_prediction,
self.loss,
self.ihm_loss, self.los_loss,
self.ph_loss, self.decomp_loss,
self.reg_loss]
test_outputs = [self.ihm_det_prediction, self.los_det_prediction,
self.ph_det_prediction, self.decomp_det_prediction,
self.loss,
self.ihm_loss, self.los_loss,
self.ph_loss, self.decomp_loss,
self.reg_loss]
## compiling theano functions
if self.mode == 'train':
print "==> compiling train_fn"
self.train_fn = theano.function(inputs=all_inputs,
outputs=train_outputs,
updates=updates)
print "==> compiling test_fn"
self.test_fn = theano.function(inputs=all_inputs,
outputs=test_outputs)
def __init__(self,
n_out,
collapse_output=False,
directions=4,
projection='average',
base=None,
**kwargs):
if base is None:
base = []
super(TwoDLSTMLayer, self).__init__(n_out, **kwargs)
assert len(self.sources) == 1
source = self.sources[0]
n_in = source.attrs['n_out']
X = source.output
assert X.ndim == 4
sizes = source.output_sizes
self.output_sizes = sizes
assert directions in [1, 2,
4], "only 1, 2 or 4 directions are supported"
assert projection in ['average', 'concat'], "invalid projection"
if base:
self.b1 = self.add_param(base[0].b1)
self.b2 = self.add_param(base[0].b2)
if directions >= 1:
self.b3 = self.add_param(base[0].b3)
self.b4 = self.add_param(base[0].b4)
self.W1, self.V_h1, self.V_v1 = self.add_param(
base[0].W1), self.add_param(base[0].V_h1), self.add_param(
base[0].V_v1)
self.W2, self.V_h2, self.V_v2 = self.add_param(
base[0].W2), self.add_param(base[0].V_h2), self.add_param(
base[0].V_v2)
if directions >= 1:
self.W3, self.V_h3, self.V_v3 = self.add_param(
base[0].W3), self.add_param(base[0].V_h3), self.add_param(
base[0].V_v3)
self.W4, self.V_h4, self.V_v4 = self.add_param(
base[0].W4), self.add_param(base[0].V_h4), self.add_param(
base[0].V_v4)
#self.mass = base[0].mass
#self.masks = base[0].masks
#self.b1 = base[0].b1
#self.b2 = base[0].b2
#if directions >= 1:
# self.b3 = base[0].b3
# self.b4 = base[0].b4
#self.W1, self.V_h1, self.V_v1 = base[0].W1, base[0].V_h1, base[0].V_v1
#self.W2, self.V_h2, self.V_v2 = base[0].W2, base[0].V_h2, base[0].V_v2
#if directions >= 1:
# self.W3, self.V_h3, self.V_v3 = base[0].W3, base[0].V_h3, base[0].V_v3
# self.W4, self.V_h4, self.V_v4 = base[0].W4, base[0].V_h4, base[0].V_v4
self.mass = base[0].mass
self.masks = base[0].masks
else:
self.b1 = self.create_and_add_bias(n_out, "1")
self.b2 = self.create_and_add_bias(n_out, "2")
if directions >= 1:
self.b3 = self.create_and_add_bias(n_out, "3")
self.b4 = self.create_and_add_bias(n_out, "4")
self.W1, self.V_h1, self.V_v1 = self.create_and_add_2d_lstm_weights(
n_in, n_out, "1")
self.W2, self.V_h2, self.V_v2 = self.create_and_add_2d_lstm_weights(
n_in, n_out, "2")
if directions >= 1:
self.W3, self.V_h3, self.V_v3 = self.create_and_add_2d_lstm_weights(
n_in, n_out, "3")
self.W4, self.V_h4, self.V_v4 = self.create_and_add_2d_lstm_weights(
n_in, n_out, "4")
# dropout
assert len(self.masks) == 1
mask = self.masks[0]
if mask is not None:
X = self.mass * mask * X
if str(theano.config.device).startswith('cpu'):
Y = T.zeros_like(X)
if projection == 'concat':
Y = Y.repeat(directions, axis=-1)
n_out *= directions
else:
if directions <= 2:
Y = BidirectionalTwoDLSTMOpInstance(X, self.W1, self.W2,
self.V_h1, self.V_h2,
self.V_v1, self.V_v2,
self.b1, self.b2, sizes)
else:
Y = MultiDirectionalTwoDLSTMOpInstance(
X, self.W1, self.W2, self.W3, self.W4, self.V_h1,
self.V_h2, self.V_h3, self.V_h4, self.V_v1, self.V_v2,
self.V_v3, self.V_v4, self.b1, self.b2, self.b3, self.b4,
sizes)
if directions > 1:
Y = T.stack(Y[:directions], axis=-1)
if projection == 'average':
Y = Y.mean(axis=-1)
elif projection == 'concat':
Y = Y.reshape((Y.shape[0], Y.shape[1], Y.shape[2],
Y.shape[3] * Y.shape[4]))
n_out *= directions
else:
Y = Y[0]
Y.name = 'Y'
self.set_attr('n_out', n_out)
self.set_attr('collapse_output', collapse_output)
self.set_attr('directions', directions)
self.set_attr('projection', projection)
#index handling
def index_fn(index, size):
return T.set_subtensor(index[:size], numpy.cast['int8'](1))
index_init = T.zeros((Y.shape[2], Y.shape[1]), dtype='int8')
self.index, _ = theano.scan(
index_fn, [index_init, T.cast(sizes[:, 1], "int32")])
self.index = self.index.dimshuffle(1, 0)
if collapse_output == 'sum' or collapse_output == True:
Y = Y.sum(axis=0)
elif collapse_output == 'mean':
Y = Y.mean(axis=0)
elif collapse_output == 'conv':
from TheanoUtil import circular_convolution
Y, _ = theano.scan(lambda x_i, x_p: circular_convolution(x_i, x_p),
Y, Y[0])
Y = Y[-1]
elif collapse_output == 'flatten':
self.index = T.ones((Y.shape[0] * Y.shape[1], Y.shape[2]),
dtype='int8')
Y = Y.reshape((Y.shape[0] * Y.shape[1], Y.shape[2], Y.shape[3]))
elif str(collapse_output).startswith('pad_'):
pad = numpy.int32(collapse_output.split('_')[-1])
Y = ifelse(
T.lt(Y.shape[0], pad),
T.concatenate([
Y,
T.zeros(
(pad - Y.shape[0], Y.shape[1], Y.shape[2], Y.shape[3]),
'float32')
],
axis=0), ifelse(T.gt(Y.shape[0], pad), Y[:pad],
Y))
Y = Y.dimshuffle(1, 2, 3, 0).reshape(
(Y.shape[1], Y.shape[2], Y.shape[3] * Y.shape[0]))
self.attrs['n_out'] *= pad
elif collapse_output != False:
assert False, "invalid collapse mode"
if self.attrs['batch_norm']:
Y = self.batch_norm(
Y,
self.attrs['n_out'],
index=sizes if not collapse_output else self.index,
force_sample=False)
self.output = Y
def __init__(self, config):
self._params = []
self._np_rng = np.random.RandomState(config.seed // 2 + 123)
self._theano_rng = RandomStreams(
config.seed // 2 + 321) # generates random numbers directly on GPU
self._init_scale = config.init_scale
self._is_training = tt.iscalar('is_training')
self._lr = theano.shared(cast_floatX(config.learning_rate), 'lr')
input_data = tt.imatrix('input_data') # (batch_size, num_steps)
targets = tt.imatrix('targets') # (batch_size, num_steps)
noise_x = tt.matrix('noise_x') # (batch_size, num_steps)
# Embed input words and apply variational dropout (for each sample, the embedding of
# a dropped word-type consists of all zeros at all occurrences of word-type in sample).
embedding = self.make_param((config.vocab_size, config.hidden_size),
'uniform')
inputs = embedding[
input_data.T] # (num_steps, batch_size, hidden_size)
inputs = self.apply_dropout(inputs, tt.shape_padright(noise_x.T))
rhn_updates = []
for _ in range(config.num_layers):
# y shape: (num_steps, batch_size, hidden_size)
y, sticky_state_updates = self.RHNLayer(
inputs, config.depth, config.batch_size, config.hidden_size,
config.drop_i, config.drop_s, config.init_T_bias,
config.init_other_bias, config.tied_noise)
rhn_updates += sticky_state_updates
inputs = y
noise_o = self.get_dropout_noise(
(config.batch_size, config.hidden_size), config.drop_o)
outputs = self.apply_dropout(
y,
tt.shape_padleft(noise_o)) # (num_steps, batch_size, hidden_size)
# logits
softmax_w = embedding.T if config.tied_embeddings else self.make_param(
(config.hidden_size, config.vocab_size), 'uniform')
softmax_b = self.make_param((config.vocab_size, ),
config.init_other_bias)
logits = tt.dot(
outputs,
softmax_w) + softmax_b # (num_steps, batch_size, vocab_size)
# probabilities and prediction loss
flat_logits = logits.reshape(
(config.batch_size * config.num_steps, config.vocab_size))
flat_probs = tt.nnet.softmax(flat_logits)
flat_targets = targets.T.flatten() # (batch_size * num_steps,)
xentropies = tt.nnet.categorical_crossentropy(
flat_probs, flat_targets) # (batch_size * num_steps,)
pred_loss = xentropies.sum() / config.batch_size
# weight decay
l2_loss = 0.5 * tt.sum(tt.stack([tt.sum(p**2) for p in self._params]))
loss = pred_loss + config.weight_decay * l2_loss
grads = theano.grad(loss, self._params)
# gradient clipping
global_grad_norm = tt.sqrt(
tt.sum(tt.stack([tt.sum(g**2) for g in grads])))
clip_factor = ifelse(
global_grad_norm < config.max_grad_norm, cast_floatX(1),
tt.cast(config.max_grad_norm / global_grad_norm, floatX))
param_updates = [(p, p - self._lr * clip_factor * g)
for p, g in zip(self._params, grads)]
self.train = theano.function([input_data, targets, noise_x],
loss,
givens={self._is_training: np.int32(1)},
updates=rhn_updates + param_updates)
self.evaluate = theano.function(
[input_data, targets],
loss,
# Note that noise_x is unused in computation graph of this function since _is_training is false.
givens={
self._is_training: np.int32(0),
noise_x: tt.zeros((config.batch_size, config.num_steps))
},
updates=rhn_updates)
self._num_params = np.sum(
[param.get_value().size for param in self._params])
if config.load_model:
self.load(config.load_model)
def e_hat(y, X, *es):
e = tt.stack(es[:-2])
return y - tt.dot(X, es[-2]) - tt.dot(e, es[-1])
def Unskew(padded):
"""
input.shape: (batch size, HEIGHT, 2*WIDTH - 1, dim)
"""
return T.stack([padded[:, i, i:i + WIDTH, :] for i in range(HEIGHT)],
axis=1)
def local_gpua_careduce(node, context_name):
if isinstance(node.op.scalar_op, (scalar.Add, scalar.Mul,
scalar.Maximum, scalar.Minimum)):
ctx = get_context(context_name)
if ctx.kind == b'opencl':
op = GpuCAReduceCPY
if node.op.scalar_op not in [scalar.add, scalar.mul]:
# We don't support yet all reduction with cpy code.
return
elif ctx.kind == b'cuda':
op = GpuCAReduceCuda
else:
return False
x, = node.inputs
greduce = op(
node.op.scalar_op, axis=node.op.axis,
dtype=getattr(node.op, 'dtype', None),
acc_dtype=getattr(node.op, 'acc_dtype', None))
gvar = greduce(x)
# We need to have the make node called, otherwise the mask can
# be None
if (op is GpuCAReduceCPY or
gvar.owner.op.supports_c_code([
as_gpuarray_variable(x, context_name)])):
return greduce
else:
# Try to make a simpler pattern based on reshaping
# The principle is that if two adjacent dimensions have
# the same value in the reduce_mask, then we can reshape
# to make them a single dimension, do the reduction, and
# then reshape to get them back.
if node.op.axis is None:
reduce_mask = [1] * x.type.ndim
else:
reduce_mask = [0] * x.type.ndim
for a in node.op.axis:
assert reduce_mask[a] == 0
reduce_mask[a] = 1
shape_of = node.fgraph.shape_feature.shape_of
x_shape = shape_of[x]
new_in_shp = [x_shape[0]]
new_mask = [reduce_mask[0]]
for i in xrange(1, x.type.ndim):
if reduce_mask[i] == reduce_mask[i - 1]:
new_in_shp[-1] *= x_shape[i]
else:
new_mask.append(reduce_mask[i])
new_in_shp.append(x_shape[i])
new_axis = []
for idx, m in enumerate(new_mask):
if m == 1:
new_axis.append(idx)
greduce = op(
node.op.scalar_op,
axis=new_axis, reduce_mask=new_mask,
dtype=getattr(node.op, 'dtype', None),
acc_dtype=getattr(node.op, 'acc_dtype', None))
reshaped_x = x.reshape(tensor.stack(new_in_shp))
gpu_reshaped_x = as_gpuarray_variable(reshaped_x, context_name)
gvar = greduce(gpu_reshaped_x)
# We need to have the make node called, otherwise the mask can
# be None
reshaped_gpu_inputs = [gpu_reshaped_x]
if greduce.supports_c_code(reshaped_gpu_inputs):
reduce_reshaped_x = host_from_gpu(
greduce(gpu_reshaped_x))
if reduce_reshaped_x.ndim != node.outputs[0].ndim:
unreshaped_reduce = reduce_reshaped_x.reshape(
tensor.stack(shape_of[node.outputs[0]]))
else:
unreshaped_reduce = reduce_reshaped_x
return [unreshaped_reduce]
def pack(x):
return T.stack(*x)
def build_model(prepared_data, clamp_L0=0.4, eeg_column_i=None, **kwargs):
# ##########
# STEP1: order the data properly so that we can read from it sequentially
# when training the model
subject_x, skill_x, correct_y, start_x, eeg_x, eeg_table, stim_pairs, train_idx, valid_idx = prepared_data
N = len(correct_y)
train_mask = idx_to_mask(train_idx, N)
valid_mask = idx_to_mask(valid_idx, N)
# sort data by subject and skill
sorted_i = sorted(xrange(N),
key=lambda i: (subject_x[i], skill_x[i], start_x[i]))
skill_x = skill_x[sorted_i]
subject_x = subject_x[sorted_i]
correct_y = correct_y[sorted_i]
start_x = start_x[sorted_i]
train_mask = train_mask[sorted_i]
valid_mask = valid_mask[sorted_i]
train_idx = np.nonzero(train_mask)[0]
valid_idx = np.nonzero(valid_mask)[0]
n_skills = np.max(skill_x) + 1
n_subjects = np.max(subject_x) + 1
# binarize eeg
eeg_single_x = np.zeros(N)
if eeg_column_i is not None:
eeg_column = eeg_table[eeg_x, eeg_column_i]
above_median = np.greater(eeg_column, np.median(eeg_column))
eeg_single_x[above_median] = 1
# prepare parameters
p_T = 0.5
p_G = 0.1
p_S = 0.2
p_L0 = 0.7
if clamp_L0 is None:
p_L0 = 0.7
else:
p_L0 = clamp_L0
# eeg_single_x = np.zeros(N)
parameter_base = np.ones(n_skills)
tp_L0, t_L0 = make_probability(parameter_base * p_L0, name='L0')
tp_T, t_T = make_probability(np.ones((n_skills, 2)) * p_T, name='p(T)')
tp_G, t_G = make_probability(p_G, name='p(G)')
tp_S, t_S = make_probability(p_S, name='p(S)')
# declare and prepare variables for theano
i = T.ivector('i')
dummy_float = make_shared(0, name='dummy')
skill_i, subject_i = T.iscalars('skill_i', 'subject_i')
correct_y = make_shared(correct_y, to_int=True)
eeg_single_x = make_shared(eeg_single_x, to_int=True)
def step(correct_i, eeg, prev_L, prev_p_C, P_T, P_S, P_G):
Ln = prev_L + (1 - prev_L) * P_T[eeg]
p_C = prev_L * (1 - P_S) + (1 - prev_L) * P_G
return Ln, p_C
# set up theano functions
((results, p_C),
updates) = theano.scan(fn=step,
sequences=[correct_y[i], eeg_single_x[i]],
outputs_info=[tp_L0[skill_i], dummy_float],
non_sequences=[tp_T[skill_i], tp_G, tp_S])
p_y = T.stack(1 - p_C, p_C)
loss = neg_log_loss(p_y, correct_y[i])
learning_rate = T.fscalar('learning_rate')
if clamp_L0 is None:
params = [t_T, t_L0]
else:
params = [t_T]
update_parameters = [(param, param - learning_rate * T.grad(loss, param))
for param in params]
tf_train = theano.function(inputs=[i, skill_i, learning_rate],
updates=update_parameters,
outputs=[loss, results, i],
allow_input_downcast=True)
tf_valid = theano.function(inputs=[i, skill_i],
outputs=[loss, results, i],
allow_input_downcast=True)
def f_train((i, (subject_i, skill_i)), learning_rate):
return tf_train(i, skill_i, learning_rate)
u0 = T.dscalar("u0")
u1 = T.dscalar("u1")
u2 = T.dscalar("u2")
x_inputs = [x0_v, x1_v, x0_h, x1_h, u0, u1]
u_inputs = [u2]
# Discrete dynamics model definition.
f = T.stack([
x0_v + (x1_v * dt),
x1_v + (((U0 / (T0**2)) * alpha_v * u2 +
(U0 / T0) * beta_v * u1 + gamma_v * U0 * u0 - phi_v *
(X0 / T0) * x1_v - xi_v * X0 * x0_v) / (X0 / (T0**2))) * dt,
# x2_v + ,
x0_h + (x1_h * dt),
x1_h + (((U0 / (T0**2)) * alpha_h * u2 +
(U0 / T0) * beta_h * u1 + gamma_h * U0 * u0 - phi_h *
(X0 / T0) * x1_h - xi_h * X0 * x0_h) / (X0 / (T0**2))) * dt,
# x2_h + ,
u0 + (u1 * dt),
u1 + (u2 * dt)
])
dynamics = AutoDiffDynamics(f, x_inputs, u_inputs)
# dynamics = FiniteDiffDynamics(f, 6, 1)
# dynamics = BatchAutoDiffDynamics(f, state_size, action_size)
# Q = np.eye(dynamics.state_size)#state error
# cost = transpose(x) * Q * x + transpose(u) * R * u
def neibs2images(neibs, neib_shape, original_shape, mode="valid"):
"""
Function :func:`neibs2images <theano.sandbox.neighbours.neibs2images>`
performs the inverse operation of
:func:`images2neibs <theano.sandbox.neigbours.neibs2images>`. It inputs
the output of :func:`images2neibs <theano.sandbox.neigbours.neibs2images>`
and reconstructs its input.
Parameters
----------
neibs : 2d tensor
Like the one obtained by
:func:`images2neibs <theano.sandbox.neigbours.neibs2images>`.
neib_shape
`neib_shape` that was used in
:func:`images2neibs <theano.sandbox.neigbours.neibs2images>`.
original_shape
Original shape of the 4d tensor given to
:func:`images2neibs <theano.sandbox.neigbours.neibs2images>`
Returns
-------
object
Reconstructs the input of
:func:`images2neibs <theano.sandbox.neigbours.neibs2images>`,
a 4d tensor of shape `original_shape`.
Notes
-----
Currently, the function doesn't support tensors created with
`neib_step` different from default value. This means that it may be
impossible to compute the gradient of a variable gained by
:func:`images2neibs <theano.sandbox.neigbours.neibs2images>` w.r.t.
its inputs in this case, because it uses
:func:`images2neibs <theano.sandbox.neigbours.neibs2images>` for
gradient computation.
Examples
--------
Example, which uses a tensor gained in example for
:func:`images2neibs <theano.sandbox.neigbours.neibs2images>`:
.. code-block:: python
im_new = neibs2images(neibs, (5, 5), im_val.shape)
# Theano function definition
inv_window = theano.function([neibs], im_new)
# Function application
im_new_val = inv_window(neibs_val)
.. note:: The code will output the initial image array.
"""
neibs = tt.as_tensor_variable(neibs)
neib_shape = tt.as_tensor_variable(neib_shape)
original_shape = tt.as_tensor_variable(original_shape)
new_neib_shape = tt.stack(
[original_shape[-1] // neib_shape[1], neib_shape[1]])
output_2d = images2neibs(neibs.dimshuffle("x", "x", 0, 1),
new_neib_shape,
mode=mode)
if mode == "ignore_borders":
# We use set_subtensor to accept original_shape we can't infer
# the shape and still raise error when it don't have the right
# shape.
valid_shape = original_shape
valid_shape = tt.set_subtensor(
valid_shape[2], (valid_shape[2] // neib_shape[0]) * neib_shape[0])
valid_shape = tt.set_subtensor(
valid_shape[3], (valid_shape[3] // neib_shape[1]) * neib_shape[1])
output_4d = output_2d.reshape(valid_shape, ndim=4)
# padding the borders with zeros
for d in [2, 3]:
pad_shape = list(output_4d.shape)
pad_shape[d] = original_shape[d] - valid_shape[d]
output_4d = tt.concatenate(
[output_4d, tt.zeros(pad_shape)], axis=d)
elif mode == "valid":
# TODO: we do not implement all mode with this code.
# Add a check for the good cases.
output_4d = output_2d.reshape(original_shape, ndim=4)
else:
raise NotImplementedError(f"neibs2images do not support mode={mode}")
return output_4d
