def init_count_window_bigrams(self, train_stories, window_size, batch_size):
window = T.matrix('window', dtype='int32')
window.tag.test_value = rng.randint(self.lexicon_size, size=(window_size, 100)).astype('int32')
window.tag.test_value[1, 10] = -1
window.tag.test_value[:, 0] = -1
window.tag.test_value[-1, 1] = -1
words1 = window[0]
words2 = window[1:].T
word_index = T.scalar('word_index', dtype='int32')
word_index.tag.test_value = 0
batch_index = T.scalar('batch_index', dtype='int32')
batch_index.tag.test_value = 0
#select words in sequence and batch
window_ = train_stories[word_index:word_index + window_size, batch_index:batch_index + batch_size]
#filter stories with all empty words from this batch
window_ = window_[:, T.argmin(window_[0] < 0):]
self.count_window_bigrams = theano.function(inputs=[word_index, batch_index],\
outputs=[words1, words2],\
givens={window: window_},\
on_unused_input='ignore',\
allow_input_downcast=True)
def get_output(self, train=False):
X = self.get_input(train)
# mask = self.get_padded_shuffled_mask(train, X, pad=0)
mask = self.get_input_mask(train=train)
ind = T.switch(T.eq(mask[:, -1], 1.), mask.shape[-1], T.argmin(mask, axis=-1)).astype('int32').ravel()
max_time = T.max(ind)
X = X.dimshuffle((1, 0, 2))
Y = T.dot(X, self.W) + self.b
# h0 = T.unbroadcast(alloc_zeros_matrix(X.shape[1], self.output_dim), 1)
h0 = T.repeat(self.h_m1, X.shape[1], axis=0)
c0 = T.repeat(self.c_m1, X.shape[1], axis=0)
[outputs, _], updates = theano.scan(
self._step,
sequences=Y,
outputs_info=[h0, c0],
non_sequences=[self.R], n_steps=max_time,
truncate_gradient=self.truncate_gradient, strict=True,
allow_gc=theano.config.scan.allow_gc)
res = T.concatenate([h0.dimshuffle('x', 0, 1), outputs], axis=0).dimshuffle((1, 0, 2))
if self.return_sequences:
return res
#return outputs[-1]
return res[T.arange(mask.shape[0], dtype='int32'), ind]
def _match(self, sample):
diff = (T.sqr(self.codebook)).sum(
axis=1, keepdims=True) + (T.sqr(sample)).sum(
axis=1, keepdims=True) - 2 * T.dot(self.codebook, sample.T)
bmu = T.argmin(diff)
err = T.min(diff)
return err, bmu
def get_output(self, train=False):
X = self.get_input(train)
# mask = self.get_padded_shuffled_mask(train, X, pad=0)
mask = self.get_input_mask(train=train)
ind = T.switch(T.eq(mask[:, -1], 1.), mask.shape[-1],
T.argmin(mask, axis=-1)).astype('int32').ravel()
max_time = T.max(ind)
X = X.dimshuffle((1, 0, 2))
Y = T.dot(X, self.W) + self.b
# h0 = T.unbroadcast(alloc_zeros_matrix(X.shape[1], self.output_dim), 1)
h0 = T.repeat(self.h_m1, X.shape[1], axis=0)
c0 = T.repeat(self.c_m1, X.shape[1], axis=0)
[outputs,
_], updates = theano.scan(self._step,
sequences=Y,
outputs_info=[h0, c0],
non_sequences=[self.R],
n_steps=max_time,
truncate_gradient=self.truncate_gradient,
strict=True,
allow_gc=theano.config.scan.allow_gc)
res = T.concatenate([h0.dimshuffle('x', 0, 1), outputs],
axis=0).dimshuffle((1, 0, 2))
if self.return_sequences:
return res
# return outputs[-1]
return res[T.arange(mask.shape[0], dtype='int32'), ind]
def __init__(self, input, n_in, n_out):
self.W = theano.shared(
value=np.zeros(
(n_in, n_out),
dtype=theano.config.floatX
),
name='W',
borrow=True
)
self.b = theano.shared(
value=np.zeros(
(n_out,),
dtype=theano.config.floatX
),
name='b',
borrow=True
)
self.cost_predicted_given_x = T.dot(input, self.W) + self.b
self.y_pred = T.argmin(self.cost_predicted_given_x, axis=1)
self.params = [self.W, self.b]
self.input = input
self.y = T.ivector('y')
self.cost_vector = T.matrix('cost_vector')
self.MSE = T.mean((self.cost_predicted_given_x - self.cost_vector) ** 2)
self.error = T.mean(T.neq(self.y_pred, self.y))
self.future_cost = T.sum(self.cost_vector[T.arange(self.y_pred.shape[0]), self.y_pred])
def __init__(self, cooccurrence, z_k):
eps = 1e-9
self.z_k = z_k
n = cooccurrence.shape[0]
self.n = n
h = cooccurrence.astype(np.float32)
p = h / np.sum(h, axis=None)
pc = T.constant(p, name="p")
z_init = np.random.random_integers(0, z_k - 1, (n,)).astype(np.int32)
z = theano.shared(z_init, name="z")
self.z = z
c = T.zeros((z_k, n), dtype='float32') # (z_k, n)
c = T.set_subtensor(c[z, T.arange(c.shape[1])], 1)
pyz = T.dot(c, pc) # (z_k, x_k)
marg = T.sum(pyz, axis=1, keepdims=True)
cond = pyz / (marg + eps)
nll = -T.sum(pyz * T.log(eps + cond), axis=None) # scalar
nllyzr = T.transpose(-T.log(eps + cond), (1, 0)) # (x_k, z_k)
losses = T.dot(pc, nllyzr) # (x_k, z_k)
nz = T.cast(T.argmin(losses, axis=1), 'int32') # (x_k,)
updates = [(z, nz)]
flag = T.gt(T.sum(T.neq(z, nz)), 0)
self.train_fun = theano.function([], [nll, flag], updates=updates)
self.val_fun = theano.function([], nll)
def _get_cluster_symbol(self):
output = self._get_output_symbol()
Y_hat = T.reshape(output, (self.batch, self.y_n, self.k))
y = self._get_y_symbol()
Y = T.tile(y[:, :, None], (1, 1, self.k))
diff = T.mean((Y - Y_hat)**2, axis=1)
cluster = T.argmin(diff, axis=1)
return cluster
def get_output_for(self, inputs):
A = inputs[0]
X = inputs[1]
max_degree_node = T.argmax(A.sum(0))
min_degree_node = T.argmin(A.sum(0))
return self.reduce(A, [max_degree_node, min_degree_node])
def batch_get_nearest_neighbours(samples, dataset):
sample = Te.matrix(name="sample")
data = Te.matrix(name="dataset")
find_nearest_neighbour = theano.function(name="find_nearest_neighbour",
inputs=[sample],
outputs=data[Te.argmin(Te.sum((data[:, None, :] - sample) ** 2, axis=2), axis=0)],
givens={data: dataset['train']['data']})
return find_nearest_neighbour(samples)
def _get_cluster_symbol(self):
output = self._get_output_symbol()
Y_hat = T.reshape(output, (self.batch, self.y_n, self.k))
y = self._get_y_symbol()
Y = T.tile(y[:, :, None], (1, 1, self.k))
diff = T.mean((Y - Y_hat)**2, axis=1)
cluster = T.argmin(diff, axis=1)
return cluster
def perform(self):
mask = self.mask
assert mask.ndim == 2, 'Only 2D mask are supported'
ind = T.switch(T.eq(mask[:, -1], 1.), mask.shape[-1], T.argmin(mask, axis=-1)).astype('int32')
for is_train in [True, False]:
y = self.get_input(is_train)
res = y[T.arange(mask.shape[0], dtype='int32'), ind]
self.output_vars[is_train] = [res]
def dtw(i, q_p, b_p, Q, D, inf): i0 = T.eq(i, 0) # inf = T.cast(1e10,'float32') * T.cast(T.switch(T.eq(self.n,0), T.switch(T.eq(i,0), 0, 1), 1), 'float32') penalty = T.switch(T.and_(T.neg(n0), i0), big, T.constant(0.0, 'float32')) loop = T.constant(0.0, 'float32') + q_p forward = T.constant(0.0, 'float32') + T.switch(T.or_(n0, i0), 0, Q[i - 1]) opt = T.stack([loop, forward]) k_out = T.cast(T.argmin(opt, axis=0), 'int32') return opt[k_out, T.arange(opt.shape[1])] + D[i] + penalty, k_out
def get_output_for(self, inputs):
A = inputs[0]
eigenvals_eigenvecs = T.nlinalg.eig(A)
smallest_eigenval_index = T.argmin(eigenvals_eigenvecs[0])
smallest_eigenvec = eigenvals_eigenvecs[1][smallest_eigenval_index]
return smallest_eigenvec
def dtw(i, q_p, b_p, Q, D, inf): i0 = T.eq(i, 0) # inf = T.cast(1e10,'float32') * T.cast(T.switch(T.eq(self.n,0), T.switch(T.eq(i,0), 0, 1), 1), 'float32') penalty = T.switch(T.and_(T.neg(n0), i0), big, T.constant(0.0, 'float32')) loop = T.constant(0.0, 'float32') + q_p forward = T.constant(0.0, 'float32') + T.switch(T.or_(n0, i0), 0, Q[i - 1]) opt = T.stack([loop, forward]) k_out = T.cast(T.argmin(opt, axis=0), 'int32') return opt[k_out, T.arange(opt.shape[1])] + D[i] + penalty, k_out
def batch_get_nearest_neighbours(samples, dataset):
sample = Te.matrix(name="sample")
data = Te.matrix(name="dataset")
find_nearest_neighbour = theano.function(
name="find_nearest_neighbour",
inputs=[sample],
outputs=data[Te.argmin(Te.sum((data[:, None, :] - sample)**2, axis=2),
axis=0)],
givens={data: dataset['train']['data']})
return find_nearest_neighbour(samples)
def get_min_index(self,origin):
""" This function computes the cost and the updates for one trainng
step of the RAE """
merge_input = self.get_input_values(origin)
encode = self.get_hidden_values(merge_input)
decode = self.get_reconstructed_input(encode)
L = T.sum((0.5*numpy.array(decode-merge_input)**2), axis=1)
min_index=T.argmin(L)
return (min_index,merge_input[min_index],encode[min_index])
def indexing_bilinear(self):
# Declare variables
a = tt.dvector()
b = tt.dscalar()
# Build symbolic expression
out = tt.argmin(tt.abs_(a - b))
# Compile function
self.tt_index_array = function([a, b], out)
def __init__(self, input, n_in, n_out):
# initialize with 0 the weights W as a matrix of shape (n_in, n_out)
self.W = theano.shared(
value=np.zeros(
(n_in, n_out),
dtype=theano.config.floatX
),
name='W',
borrow=True
)
# initialize the baises b as a vector of n_out 0s
self.b = theano.shared(
value=np.zeros(
(n_out,),
dtype=theano.config.floatX
),
name='b',
borrow=True
)
# keep track of model input
self.input = input
# symbolic variable of cost vector in terms of a single example;
# for a batch of examples, it's a matrix, so don't get confused
# with the variable name `cost_vector`
self.cost_vector = T.matrix('cost_vector')
# symbolic variable of Z_{n,k}
self.Z_nk = T.matrix('Z_nk')
self.cost_predicted_given_x = T.dot(self.input, self.W) + self.b
# elementwise comparison with 0
self.xi = T.maximum((self.Z_nk * (self.cost_predicted_given_x - self.cost_vector)), 0.)
# define the linear one-sisded regression loss
self.one_sided_regression_loss = T.sum(self.xi)
# symbolic description of how to compute prediction as class whose
# cost is minimum
self.y_pred = T.argmin(self.cost_predicted_given_x, axis=1)
# parameters of the model
self.params = [self.W, self.b]
# symbolic variable of labels, will only be used for computing 0/1 errors
self.y = T.ivector('y')
# compute the 0/1 loss
self.error = T.mean(T.neq(self.y_pred, self.y))
# when a new example comes in, the model first computes (predicts) its
# cost on classifying into each class (a vector) by
# `self.cost_predicted_given_x`; then the model will predict this new
# example as label with the smallest cost;
# self.future_cost = T.sum(self.cost_vector[T.arange(self.y_pred.shape[0]), self.y_pred])
self.future_cost = T.mean(self.cost_vector[T.arange(self.y_pred.shape[0]), self.y_pred])
def init_H(self):
if not hasattr(self, "_clusters"):
a = (self.W * tensor.dot(self.W, self._kernel_matrix)).sum(axis=1) \
- 2.0 * tensor.dot(self._kernel_matrix, self.W.T)
b = tensor.argmin(a, axis=1)
self._clusters = function([], b)
H = .2 * numpy.ones((self._data_size, self._num_latent_topics)).astype(self.W.dtype)
clusters = self._clusters()
for i, cluster in enumerate(clusters):
H[i, cluster] += 1.0
self.H.set_value(H)
def get_nearest_neighbours(samples, dataset):
sample = Te.vector(name="sample")
data = Te.matrix(name="dataset")
find_nearest_neighbour = theano.function(name="find_nearest_neighbour",
inputs=[sample],
outputs=data[Te.argmin(Te.sum((data - sample) ** 2, axis=1))],
givens={data: dataset['train']['data']})
neighbours = []
for s in samples:
neighbours += [find_nearest_neighbour(s)]
return neighbours
def get_nearest_neighbours(samples, dataset):
sample = Te.vector(name="sample")
data = Te.matrix(name="dataset")
find_nearest_neighbour = theano.function(
name="find_nearest_neighbour",
inputs=[sample],
outputs=data[Te.argmin(Te.sum((data - sample)**2, axis=1))],
givens={data: dataset['train']['data']})
neighbours = []
for s in samples:
neighbours += [find_nearest_neighbour(s)]
return neighbours
def greed_step(self, vec_len, node_index, seq_index, vectors, path):
hs, _ = theano.scan(fn=self.compose_step,
sequences=T.arange(vec_len - 1),
non_sequences=vectors,
name="compose_phrase")
comp_vec = hs[0]
comp_rec = hs[1]
min_index = T.argmin(comp_rec)
T.set_subtensor(vectors[min_index:-1], vectors[min_index + 1:])
T.set_subtensor(vectors[min_index], comp_vec[min_index])
T.set_subtensor(path[seq_index],
T.concatenate([min_index, min_index + 1, node_index]))
return vectors, min_index
def asymMSE(y_true, y_pred):
d = y_true-y_pred # Get prediction errors
mins = T.argmin(y_true,axis=1) # Get indices of optimal action
mins_onehot = T.extra_ops.to_one_hot(mins,5) # Convert min index to one hot array
others_onehot = mins_onehot-1 # Get the indices of the non-optimal actions, which will be -1's
d_opt = d*mins_onehot # Get the error of the optimal action
d_sub = d*others_onehot # Get the errors of the non-optimal actions
a = 160*d_opt**2 # 160 times error of optimal action squared
b = d_opt**2 # 1 times error of optimal action squared
c = 40*d_sub**2 # 40 times error of suboptimal actions squared
d = d_sub**2 # 1 times error of suboptimal actions squared
l = T.switch(d_sub<0,c,d) + T.switch(d_opt<0,a,b) #This chooses which errors to use depending on the sign of the errors
#If true, use the steeper penalty. If false, use the milder penalty
return l
def __init__(self, data, dims, cluster_penalty=2.1):
self.dims = dims
self.cluster_penalty = cluster_penalty
self.n_clusters = theano.shared(value=np.int(1), name='n_clusters')
self.data = theano.shared(value=data, name="data")
self.indicators = theano.shared(value=np.ones(self.dims[0],
dtype='uint32'),
name="indices")
mu_init = np.zeros((self.dims[0], 3))
mu_init[1, :] = data.mean(axis=0)
self.mu = theano.shared(value=mu_init, name="mu")
t_idx = T.iscalar('t_idx')
t_vec = T.vector('t_vec')
self.D_i_c = (self.euclidean_dist(self.data,
self.mu)[:, :self.n_clusters])**2
self.min_dic = T.min(self.D_i_c, axis=1) > self.cluster_penalty
self.getDics = theano.function(inputs=[], outputs=self.min_dic)
self.updateClusters = theano.function(
inputs=[t_idx],
updates=[ \
(self.indicators, T.set_subtensor(self.indicators[t_idx], self.n_clusters)), \
(self.mu, T.set_subtensor(self.mu[self.n_clusters], self.data[t_idx])), \
(self.n_clusters, self.n_clusters+1)
]
)
self.updateIndicators = theano.function(
inputs=[t_idx],
updates=[ \
(self.indicators, T.set_subtensor(self.indicators[t_idx], T.argmin(self.D_i_c[t_idx])))
]
)
self.getLkFromIdx = theano.function(inputs=[t_idx],
outputs=self.getLk(t_idx))
self.getMu = theano.function(inputs=[], outputs=self.mu)
self.getNClusters = theano.function(inputs=[], outputs=self.n_clusters)
def constructMinimalDistanceIndicesVariable(x, y, n, m):
sDistances = constructSquaredDistanceMatrixVariable(x, y, n, m)
lamblinsTrick = False
if lamblinsTrick:
# https://github.com/Theano/Theano/issues/1399
# https://gist.github.com/danielvarga/d0eeacea92e65b19188c
# https://groups.google.com/forum/#!topic/theano-users/E7ProqnGUMk
s = sDistances
bestIndices = T.cast( ( T.arange(n).dimshuffle(0, 'x') * T.cast(T.eq(s, s.min(axis=0, keepdims=True)), 'float32') ).sum(axis=0), 'int32')
# This is a heavy-handed workaround for the fact that in
# lamblin's hack, ties lead to completely screwed results.
bestIndices = T.clip(bestIndices, 0, n-1)
else:
bestIndices = T.argmin(sDistances, axis=0)
return bestIndices
def __init__(self, y, uv, params):
self.layer0_W_y, self.layer0_b_y = params[0]
self.layer0_W_uv, self.layer0_b_uv = params[1]
self.layer1_W, self.layer1_b = params[2]
self.layer2_W = params[3]
self.layer3_W, self.layer3_b = params[4]
self.layer4_W, self.layer4_b = params[5]
poolsize = (2, 2)
# layer0_y: conv-maxpooling-tanh
layer0_y_conv = conv.conv2d(input=y, filters=self.layer0_W_y,
border_mode='full')
layer0_y_pool = downsample.max_pool_2d(input=layer0_y_conv,
ds=poolsize, ignore_border=True)
layer0_y_out = T.tanh(layer0_y_pool + \
self.layer0_b_y.reshape(1, -1, 1, 1))
# layer0_uv: conv-maxpooling-tanh
layer0_uv_conv = conv.conv2d(input=uv, filters=self.layer0_W_uv,
border_mode='full')
layer0_uv_pool = downsample.max_pool_2d(input=layer0_uv_conv,
ds=poolsize, ignore_border=True)
layer0_uv_out = T.tanh(layer0_uv_pool + \
self.layer0_b_uv.reshape(1, -1, 1, 1))
layer1_input = T.concatenate((layer0_y_out, layer0_uv_out), axis=1)
# layer1: conv-maxpooling-tanh
layer1_conv = conv.conv2d(input=layer1_input, filters=self.layer1_W,
border_mode='full')
layer1_pool = downsample.max_pool_2d(input=layer1_conv,
ds=poolsize, ignore_border=True)
layer1_out = T.tanh(layer1_pool + self.layer1_b.reshape(1, -1, 1, 1))
# layer2: conv
layer2_out = conv.conv2d(input=layer1_out, filters=self.layer2_W,
border_mode='valid')
layer3_input = layer2_out.reshape((256, -1)).dimshuffle(1, 0)
# layer3: hidden-layer
layer3_lin = T.dot(layer3_input, self.layer3_W) + self.layer3_b
layer3_out = T.tanh(layer3_lin)
# layer4: logistic-regression
layer4_out = T.nnet.softmax(T.dot(layer3_out, self.layer4_W) + \
self.layer4_b)
self.pred = T.argmin(layer4_out, axis=1)
def straight_through(p, u):
sts = StraightThroughSampler()
cum = T.extra_ops.cumsum(p, axis = 1) - T.addbroadcast(T.reshape(u, (u.shape[0], 1)),1)
cum = T.switch(T.lt(cum, 0.0), 10.0, cum)
ideal_bucket = T.argmin(cum, axis = 1)
one_hot = T.extra_ops.to_one_hot(ideal_bucket, 4)
y = sts(p, one_hot)
return y
def __init__(self, cooccurrence, z_k):
eps = 1e-9
self.z_k = z_k
n = cooccurrence.shape[0]
self.n = n
h = cooccurrence.astype(np.float32)
p = h / np.sum(h, axis=None)
pc = T.constant(p, name="p")
z_init = np.random.random_integers(0, z_k - 1, (n, ))
zt = theano.shared(z_init)
self.z_shared = zt
idx = T.iscalar(name='idx')
c = T.zeros((z_k, n), dtype='float32') # (z_k, n)
c = T.set_subtensor(c[zt, T.arange(c.shape[1])], 1)
c = T.set_subtensor(c[zt[idx], idx], 0)
p_yz0 = T.dot(c, pc) # (z_k, n) x (n, n) = (z_k, n)
marg0 = T.sum(p_yz0, axis=1, keepdims=True)
cond0 = p_yz0 / (eps + marg0)
ent0 = -T.sum(p_yz0 * T.log(eps + cond0), axis=1) # (z_k,)
sum0 = T.sum(ent0, axis=0)
p_yz1 = p_yz0 + (pc[idx, :].dimshuffle(('x', 0)))
marg1 = T.sum(p_yz1, axis=1, keepdims=True)
cond1 = p_yz1 / (eps + marg1)
ent1 = -T.sum(p_yz1 * T.log(eps + cond1), axis=1) # (z_k,)
ed = ent1 - ent0 # (z_k)
sel = T.argmin(ed) # (scalar,)
ztn = T.set_subtensor(zt[idx], sel)
entn = sum0 + (ed[sel])
changed = T.neq(sel, zt[idx])
updates = [(zt, ztn)]
self.train_fun = theano.function([idx], [entn, changed],
updates=updates)
c = T.zeros((z_k, n), dtype='float32') # (z_k, n)
c = T.set_subtensor(c[zt, T.arange(c.shape[1])], 1)
p_yz = T.dot(c, pc) # (z_k, n) x (n, n) = (z_k, n)
marg = T.sum(p_yz, axis=1, keepdims=True)
cond = p_yz / (eps + marg)
ent = -T.sum(p_yz * T.log(eps + cond), axis=None)
self.val_fun = theano.function([], ent)
def apply(self, y_hat):
# reshape 1d vector to 2d matrix
y_hat_2d = y_hat.reshape((y_hat.shape[0] / self.examples_group_size,
self.examples_group_size))
#y_hat_2d = tt.printing.Print("Y hat 2d in correct rank: ")(y_hat_2d)
# sort each group by relevance
# we sort the responses in decreasing order, that is why we multiply y_hat by -1
sorting_indices = tt.argsort(-1 * y_hat_2d, axis=1)
#sorting_indices = tt.printing.Print("sorting indices in correct rank: ")(sorting_indices)
# check where is the ground truth whose index should be 0 in the original array
correct_rank = tt.argmin(sorting_indices, axis=1) + 1
#correct_rank = tt.printing.Print("correct rank: ")(correct_rank)
correct_rank.name = "correct_rank"
return correct_rank
def recurrence(x, cur_time, prev_hidden):
act_modules = modules - T.argmin((cur_time % p)[::-1])
update_indices = act_modules * sizeof_mod
w_subtensor = self.wh[:update_indices]
b_subtensor = self.bh[:update_indices]
inp_updates = T.dot(x, self.wi)[:update_indices]
pre_new_h = T.dot(w_subtensor,
prev_hidden) + inp_updates + b_subtensor
new_h = T.set_subtensor(prev_hidden[:update_indices],
h_nonlinearity(pre_new_h))
out = nonlinearity(T.dot(new_h, self.wo) + self.bo)
return (cur_time + 1.0), new_h, out
def find_perturb(perturbation):
logits_os = model(inputs + (1 + over_shoot) * perturbation)
y_pred = T.argmax(logits_os, axis=1)
is_mistake = T.neq(y_pred, labels)
current_ind = batch_indices[(1 - is_mistake).nonzero()]
should_stop = T.all(is_mistake)
# continue generating perturbation only for correctly classified
inputs_subset = inputs[current_ind]
perturbation_subset = perturbation[current_ind]
labels_subset = labels[current_ind]
batch_subset = T.arange(inputs_subset.shape[0])
x_adv = inputs_subset + perturbation_subset
logits = model(x_adv)
corrects = logits[batch_subset, labels_subset]
jac = jacobian(logits, x_adv, num_classes)
# deepfool
f = logits - T.shape_padright(corrects)
w = jac - T.shape_padaxis(jac[batch_subset, labels_subset], axis=1)
reduce_ind = range(2, inputs.ndim + 1)
if norm == 'l2':
dist = T.abs_(f) / w.norm(2, axis=reduce_ind)
else:
dist = T.abs_(f) / T.sum(T.abs_(w), axis=reduce_ind)
# remove correct targets
dist = T.set_subtensor(dist[batch_subset, labels_subset],
T.constant(np.inf))
l = T.argmin(dist, axis=1)
dist_l = dist[batch_subset, l].dimshuffle(0, 'x', 'x', 'x')
# avoid numerical instability and clip max value
if clip_dist is not None:
dist_l = T.clip(dist_l, 0, clip_dist)
w_l = w[batch_subset, l]
if norm == 'l2':
reduce_ind = range(1, inputs.ndim)
perturbation_upd = dist_l * w_l / w_l.norm(
2, reduce_ind, keepdims=True)
else:
perturbation_upd = dist_l * T.sgn(w_l)
perturbation = ifelse(
should_stop, perturbation,
T.inc_subtensor(perturbation[current_ind], perturbation_upd))
return perturbation, scan_module.until(should_stop)
def __init__(self,
weights,
neurons_topology,
learning_rate=0.1,
learning_rate_decay=0.985,
collaboration_sigma=1.0,
collaboration_sigma_decay=0.95,
verbosity=2):
self._verbosity = verbosity
self._history = []
self.neurons_number = weights.shape[0]
self.W_shar_mat = theano.shared(weights)
self.D_shar_mat = theano.shared(neurons_topology)
self.collaboration_sigma = theano.shared(collaboration_sigma)
self.collaboration_sigma_decay = collaboration_sigma_decay
self.x_row = T.vector("exemplar")
self.x_mat = T.matrix("batch")
self.learning_rate = theano.shared(learning_rate)
self.learning_rate_decay = learning_rate_decay
self.distance_from_y_row = ((T.sub(self.W_shar_mat,
self.x_row)**2).sum(axis=1))
self.closest_neuron_idx = T.argmin(self.distance_from_y_row)
self.distances_from_closest_neuron = self.D_shar_mat[
self.closest_neuron_idx]
self.affinities_to_closest_neuron = T.exp(
-self.distances_from_closest_neuron /
(self.collaboration_sigma)**2)
self.smoothed_distances_from_closest_neuron = T.mul(
self.distance_from_y_row,
G.disconnected_grad(self.affinities_to_closest_neuron))
self.cost_scal = self.smoothed_distances_from_closest_neuron.sum()
self.updates = sgd(self.cost_scal, [self.W_shar_mat],
learning_rate=self.learning_rate)
self.update_neurons = theano.function([self.x_row],
self.cost_scal,
updates=self.updates)
def min_risk_choice(Posterior):
#The Loss function is a function of the predictiveness profiles
Preds = predictiveness_profiles(Models, K, num_M)
Loss = ifelse(T.eq(Choice_type, 1), T.pow(1.0 - Preds,2), ifelse(T.eq(Choice_type, 2), T.abs_(1.0 - Preds), - Preds))
#Kroneckering Loss up num_Obs times (tile Loss, making it num_M by num_M*num_Obs)
Loss = kron(T.ones((1,num_Obs)), Loss)
#Kroneckering up the Posterior, making it num_M by num_Obs*numM
Posterior = kron(Posterior, T.ones((1,num_M)))
#Dotting and reshaping down to give num_M by num_Obs expected loss matrix
Expected_Loss = T.dot(T.ones((1,num_M)),Posterior*Loss)
Expected_Loss = T.reshape(Expected_Loss, (num_Obs,num_M)).T
#Choice minimizes risk
Choice = T.argmin(Expected_Loss, axis = 0)
return Choice
def min_risk_choice(Posterior):
#The Loss function is a function of the predictiveness profiles
Preds = predictiveness_profiles(Models, K, num_M)
Loss = ifelse(
T.eq(Choice_type, 1), T.pow(1.0 - Preds, 2),
ifelse(T.eq(Choice_type, 2), T.abs_(1.0 - Preds), -Preds))
#Kroneckering Loss up num_Obs times (tile Loss, making it num_M by num_M*num_Obs)
Loss = kron(T.ones((1, num_Obs)), Loss)
#Kroneckering up the Posterior, making it num_M by num_Obs*numM
Posterior = kron(Posterior, T.ones((1, num_M)))
#Dotting and reshaping down to give num_M by num_Obs expected loss matrix
Expected_Loss = T.dot(T.ones((1, num_M)), Posterior * Loss)
Expected_Loss = T.reshape(Expected_Loss, (num_Obs, num_M)).T
#Choice minimizes risk
Choice = T.argmin(Expected_Loss, axis=0)
return Choice
def getDeployFunction(self, cr):
from algorithms.algorithm import beam_search, greed
print "Compiling computing graph."
get_question_hidden = theano.function([self.question, self.question_mask],
self.last_hidden_state,
name='get_question_hidden')
_, pred_word_probability = self.softmax_layer.getOutput(self.last_decoder_hidden)
self.last_decoder_hidden
self.tparams['Wemb']
recons_v = self.tparams['recons_v']
recons_b = self.tparams['recons_b']
recons_b = recons_b.dimshuffle(['x', 0])
media_h = T.dot(self.tparams['Wemb'], recons_v) + recons_b
recons_h_error_L = T.tanh(media_h) - T.addbroadcast(self.last_decoder_hidden, 0)
recons_h_error_L = T.sqr(recons_h_error_L).sum(axis=1)
recons_h_error_L = recons_h_error_L / self.options['hidden_dim']
error = -T.log(pred_word_probability) + recons_h_error_L
score = T.exp(-error)
pred_word = T.argmin(error)
deploy_model = theano.function(inputs=[self.answer, self.answer_mask, self.last_hidden_state],
outputs=[pred_word, score],
allow_input_downcast=True)
print "Compiled."
def dm(sentence):
print "feed %s: " % sentence
(x, x_mask) = cr.transformInputData(sentence)
x = x[:-1]
x_mask = x_mask[:-1]
last_s = get_question_hidden(x, x_mask)
def f(y, y_mask):
return deploy_model(y, y_mask, last_s)
return beam_search('', cr, f)
return dm
def __SOM(self, X, W, n):
learning_rate_op = T.exp(-1. * self.som_lr * n)
_alpha_op = self.alpha * learning_rate_op
_sigma_op = self.sigma * learning_rate_op
locations = self.locs
maps = T.sub(X, W)
measure = T.sum(T.pow(T.sub(X, W), 2), axis=1)
err = measure.min()
self.bmu_index = T.argmin(measure)
bmu_loc = locations[self.bmu_index]
dist_square = T.sum(T.square(T.sub(locations, bmu_loc)), axis=1)
H = T.cast(T.exp(-dist_square / (2 * T.square(_sigma_op))),
dtype=theano.config.floatX)
w_update = W + _alpha_op * \
T.tile(H, [self.latent_size, 1]).T * maps
Qs = self.__soft_probs(X, W)
P = Qs**2 / Qs.sum()
P = (P.T / P.sum()).T
cost = self.__kld(P, Qs)
return [err, cost, bmu_loc], {W: w_update}
def generate_optimize_basis():
# original solution
tx0 = partial.x
# optimized solution
tx1 = T.dot(tl.matrix_inverse(T.dot(partial.A.T, partial.A)),
T.dot(partial.A.T, y) - gamma/2*partial.theta)
# investigate zero crossings between tx0 and tx1
tbetas = tx0 / (tx0 - tx1)
# investigate tx1
tbetas = T.concatenate([tbetas, [1.0]])
# only between tx0 and inclusively tx1
tbetas = tbetas[(T.lt(0, tbetas) * T.le(tbetas, 1)).nonzero()]
txbs, _ = theano.map(lambda b: (1-b)*tx0 + b*tx1, [tbetas])
tlosses, _ = theano.map(loss, [txbs])
# select the optimum
txb = txbs[T.argmin(tlosses)]
return theano.function([tpart, full.x, full.theta],
[T.set_subtensor(partial.x, txb),
T.set_subtensor(partial.theta, T.sgn(txb))])
def __build_measure(self):
#print(self.archetypes.shape.eval())
#print(self.layers_bw[-1].shape.eval())
### FW
E_fw = T.mean(self.__energy(self.layers_fw, self.weights_fw, self.weights_bw, self.biases_fw))
C_fw = T.mean(self.__cost(self.layers_fw[-1], direction="fw"))
y_prediction = T.argmax(self.layers_fw[-1], axis=1)
# Error count for y-error
error_fw = T.mean(T.neq(y_prediction, self.y_data))
### BW
#print([layer.shape.eval() for layer in self.layers_fw])
E_bw = T.mean(self.__energy(self.layers_bw, self.weights_bw, self.weights_fw, self.biases_bw))
# IDENTIFY CLOSEST ARCHETYPE W/ MINIMAL SUM-SQUARED DISTANCE
# CLOSEST ARCHETYPE IS THE SAME AS THE Y-LABEL
arches_reshaped = T.reshape(self.archetypes, [1, D*D, D*D])
output_reshaped = T.reshape(self.layers_bw[-1], [self.batch_size, 1, D*D])
sum_squared = ((arches_reshaped - output_reshaped) ** 2).sum(axis=2)
closest_arch = T.argmin(sum_squared, axis=1)
#closest_arch_one_hot = T.extra_ops.to_one_hot(closest_arch, D*D)
#C_bw = T.mean(self.__cost(closest_arch_one_hot))
C_bw = T.mean(self.__cost(self.layers_bw[-1], direction="bw"))
# Index of closest archetype for x-error
error_bw = T.mean(T.neq(closest_arch, self.y_data))
measure = theano.function(
inputs=[],
outputs=[E_fw, C_fw, error_fw, E_bw, C_bw, error_bw]#, closest_arch, self.y_data]
)
return measure
def generate_functions(A, y, gamma):
tA = T.matrix('A')
ty = T.vector('y')
tx = T.vector('x')
ttheta = T.vector('theta')
tx0 = T.vector('x0')
tx1 = T.vector('x1')
tbetas = T.vector('betas')
error = lambda x: T.sum((T.dot(tA, x) - ty)**2)
derror = lambda x: T.grad(error(x), x)
penalty = lambda x: x.norm(1)
loss = lambda x: error(x) + penalty(x)
entering_index = T.argmax(abs(derror(tx)))
txs, _ = theano.map(lambda b, x0, x1: (1-b)*x0 + b*x1,
[tbetas], [tx0, tx1])
return {
"select_entering": theano.function([tx],
[entering_index, derror(tx)[entering_index]],
givens = {tA: A, ty: y}),
"qp_optimum": theano.function([tA, ttheta],
T.dot(T.inv(T.dot(tA.T, tA)), T.dot(tA.T, ty) - gamma/2*ttheta),
givens = {ty: y}),
"txs": theano.function([tbetas, tx0, tx1], txs),
"select_candidate": theano.function([tA, tbetas, tx0, tx1],
txs[T.argmin(theano.map(loss, [txs])[0])],
givens = {ty: y}),
"optimal_nz": theano.function([tA, tx],
derror(tx) + gamma*T.sgn(tx),
givens = {ty: y}),
"optimal_z": theano.function([tA, tx],
abs(derror(tx)),
givens = {ty: y}),
}
def argmin(x):
return T.argmin(x)
def argmin(x, dim = None, keep = False): return T.argmin(x, axis = dim, keepdims = keep)
def __call__(self, X, termination_criterion, initial_H=None):
"""
Compute for each sample its representation.
Parameters
----------
X : Sample matrix. numpy.ndarray
termination_criterion: pylearn TerminationCriterion object
initial_H: Numpy matrix.
Returns
-------
H: H matrix with the representation.
"""
dataset_size = X.shape[0]
H = None
if initial_H is not None:
if H.shape[0] == dataset_size and H.shape[1] == self._num_latent_topics:
H = initial_H
if H is None:
if not hasattr(self, "predict_clusters"):
h = tensor.matrix(name="h")
x = tensor.matrix(name="x")
kxb = self._kernel(x, self._budget)
a = (self.W * tensor.dot(self.W, self._kernel_matrix)).sum(axis=1) \
- 2.0 * tensor.dot(kxb, self.W.T)
b = tensor.argmin(a, axis=1)
self.predict_clusters = function([x], b)
H = .2 * numpy.ones((self._data_size, self._num_latent_topics)).astype(self.W.dtype)
clusters = self.predict_clusters(X)
for i, cluster in enumerate(clusters):
H[i, cluster] += 1.0
if not hasattr(self, "predict_representation"):
h = tensor.matrix(name="h")
x = tensor.matrix(name="x")
kxb = self._kernel(x, self._budget)
kxbp = 0.5 * (numpy.abs(kxb) + kxb)
kxbn = 0.5 * (numpy.abs(kxb) - kxb)
a = tensor.dot(h, tensor.dot(self.W, self.kbn))
b = tensor.dot(kxbp + a, self.W.T)
c = tensor.dot(h, tensor.dot(self.W, self.kbp))
d = tensor.dot(kxbn + c, self.W.T)
e = h * tensor.sqrt(b / (d + self.lambda_vals))
f = tensor.maximum(e, eps)
self.predict_representation = function([x, h], f)
keep_training = True
if not isfinite(H):
raise Exception("NaN or Inf in H")
while keep_training:
H = self.predict_representation(X, H)
if not isfinite(H):
raise Exception("NaN or Inf in H")
keep_training = termination_criterion.continue_learning(self)
return H
def argmin(x, axis=None, keepdims=False):
return T.argmin(x, axis=axis, keepdims=keepdims)
def main(model='mlp', num_epochs=500):
# Load the dataset
print("Loading data...")
num_per_class = 100
print("Using %d per class" % num_per_class)
X_train, y_train, X_test, y_test = load_data("/X_train.npy", "/Y_train.npy", "/X_test.npy", "/Y_test.npy")
X_train_final = []
y_train_final = []
for i in range(10):
X_train_class = X_train[y_train == i]
# permutated_index = np.random.permutation(X_train_class.shape[0])
permutated_index = np.arange(X_train_class.shape[0])
X_train_final.append(X_train_class[permutated_index[:100]])
y_train_final += [i] * num_per_class
X_train = np.vstack(X_train_final)
y_train = np.array(y_train_final, dtype = np.int32)
X_train = extend_image(X_train, 40)
X_test = extend_image(X_test, 40)
#X_train, y_train, X_test, y_test = load_data("/cluttered_train_x.npy", "/cluttered_train_y.npy", "/cluttered_test_x.npy", "/cluttered_test_y.npy", dataset = "MNIST_CLUTTER")
# Prepare Theano variables for inputs and targets
nRotation = 8
# The dimension would be (nRotation * n, w, h)
input_var = T.tensor4('inputs')
# The dimension would be (n, )
vanilla_target_var = T.ivector('vanilla_targets')
# The dimension would be (nRotation * n , )
target_var = T.ivector('targets')
# Create neural network model (depending on first command line parameter)
network = build_cnn(input_var)
saved_weights = np.load("../data/mnist_Chi_dec_100.npy")
lasagne.layers.set_all_param_values(network, saved_weights)
# Create a loss expression for training, i.e., a scalar objective we want
# to minimize (for our multi-class problem, it is the cross-entropy loss):
# The dimension would be (nRotation * n, 10)
predictions = lasagne.layers.get_output(network)
# The dimension would be (nRotation * n, 10)
one_hot_targets = T.extra_ops.to_one_hot(target_var, 10)
# rests = [T.reshape(predictions[i][(1 - one_hot_targets).nonzero()], (-1, 9)) for i in range(nRotation)]
rests = T.reshape(predictions[(1 - one_hot_targets).nonzero()], (nRotation, -1, 9))
# a list of $nRotation tensor, each tensor is of shape (n, 1)
rests = [T.max(rests[i], axis = 1) for i in range(nRotation)]
latent_vector = T.argmin(T.as_tensor_variable(rests), axis = 0)
# selected_index is a (n, nRotation) matrix of binary values.
selected_index = T.extra_ops.to_one_hot(latent_vector, nRotation)
predictions = T.reshape(predictions, (nRotation, -1, 10))
predictions = T.as_tensor_variable(predictions).swapaxes(0, 1)
predictions = predictions[selected_index.nonzero()]
# loss = lasagne.objectives.categorical_crossentropy(prediction, target_var)
loss = lasagne.objectives.multiclass_hinge_loss(predictions, vanilla_target_var)
loss = loss.mean()
# We could add some weight decay as well here, see lasagne.regularization.
# Create update expressions for training, i.e., how to modify the
# parameters at each training step. Here, we'll use Stochastic Gradient
# Descent (SGD) with Nesterov momentum, but Lasagne offers plenty more.
params = lasagne.layers.get_all_params(network, trainable=True)
# updates = lasagne.updates.nesterov_momentum(
# loss, params, learning_rate=0.01, momentum=0.9)
updates = lasagne.updates.adagrad(loss, params, learning_rate = 0.01)
# Create a loss expression for validation/testing. The crucial difference
# here is that we do a deterministic forward pass through the network,
# disabling dropout layers.
test_prediction = lasagne.layers.get_output(network, deterministic=True)
test_prediction = T.reshape(test_prediction,(nRotation, -1, 10))
test_prediction_max = test_prediction.max(axis = 2)
rotation_index = T.extra_ops.to_one_hot(T.argmax(test_prediction_max, axis = 0), nRotation)
test_prediction = test_prediction.swapaxes(0, 1)[rotation_index.nonzero()]
test_loss = lasagne.objectives.multiclass_hinge_loss(test_prediction, vanilla_target_var)
test_loss = test_loss.mean()
# As a bonus, also create an expression for the classification accuracy:
test_acc = T.mean(T.eq(T.argmax(test_prediction, axis=1), vanilla_target_var),
dtype=theano.config.floatX)
# Compile a function performing a training step on a mini-batch (by giving
# the updates dictionary) and returning the corresponding training loss:
train_fn = theano.function([input_var, target_var, vanilla_target_var], loss, updates=updates)
# Compile a second function computing the validation loss and accuracy:
val_fn = theano.function([input_var, vanilla_target_var], [test_loss, test_acc])
# Finally, launch the training loop.
print("Starting training...")
# We iterate over epochs:
for epoch in range(num_epochs):
# In each epoch, we do a full pass over the training data:
train_err = 0
train_batches = 0
start_time = time.time()
for batch in iterate_minibatches(X_train, y_train, 100, shuffle=True):
inputs, targets = batch
inputs = inputs.reshape(100, 40, 40)
inputs = rotateImage_batch(inputs, nRotation).reshape(100 * nRotation, 1, 40, 40)
duplicated_targets = np.array([targets for i in range(nRotation)]).reshape(100 * nRotation,)
train_err += train_fn(inputs, duplicated_targets, targets)
train_batches += 1
# Then we print the results for this epoch:
print("Epoch {} of {} took {:.3f}s".format(
epoch + 1, num_epochs, time.time() - start_time))
print(" training loss:\t\t{:.6f}".format(train_err / train_batches))
if epoch % 5 == 0:
# After training, we compute and print the test error:
test_err = 0
test_acc = 0
test_batches = 0
for batch in iterate_minibatches(X_test, y_test, 500, shuffle=False):
inputs, targets = batch
inputs = inputs.reshape(500, 40, 40)
inputs = rotateImage_batch(inputs, nRotation).reshape(500 * nRotation, 1, 40, 40)
err, acc = val_fn(inputs, targets)
test_err += err
test_acc += acc
test_batches += 1
print("Final results:")
print(" test loss:\t\t\t{:.6f}".format(test_err / test_batches))
print(" test accuracy:\t\t{:.2f} %".format(
test_acc / test_batches * 100))
# Optionally, you could now dump the network weights to a file like this:
# np.savez('model.npz', *lasagne.layers.get_all_param_values(network))
#
# And load them again later on like this:
# with np.load('model.npz') as f:
# param_values = [f['arr_%d' % i] for i in range(len(f.files))]
# lasagne.layers.set_all_param_values(network, param_values)
weightsOfParams = lasagne.layers.get_all_param_values(network)
#np.save("../data/mnist_clutter_CNN_params_sigmoid.npy", weightsOfParams)
#np.save("../data/mnist_CNN_params_sigmoid.npy", weightsOfParams)
#np.save("../data/mnist_CNN_params.npy", weightsOfParams)
#np.save("../data/mnist_CNN_params_drop_out_semi_Chi_Dec7.npy", weightsOfParams)
np.save("../data/mnist_CNN_params_drop_out_Chi_2017.npy", weightsOfParams)
n = 5000 # number of candidates
m = 1000 # number of targets
f = 500 # number of features
x = T.matrix('x') # candidates
y = T.matrix('y') # targets
xL2S = T.sum(x*x, axis=-1) # [n]
yL2S = T.sum(y*y, axis=-1) # [m]
xL2SM = T.zeros((m, n)) + xL2S # broadcasting, [m, n]
yL2SM = T.zeros((n, m)) + yL2S # # broadcasting, [n, m]
squaredPairwiseDistances = xL2SM.T + yL2SM - 2.0*T.dot(x, y.T) # [n, m]
np.random.seed(1)
N = randomMatrix(n, f)
M = randomMatrix(m, f)
lamblinsTrick = True
if lamblinsTrick:
# from https://github.com/Theano/Theano/issues/1399
s = squaredPairwiseDistances
bestIndices = T.cast( ( T.arange(n).dimshuffle(0, 'x') * T.cast(T.eq(s, s.min(axis=0, keepdims=True)), 'float32') ).sum(axis=0), 'int32')
else:
bestIndices = T.argmin(squaredPairwiseDistances, axis=0)
nearests_fn = theano.function([x, y], bestIndices, profile=True)
print nearests_fn(N, M).sum()
def min_dist(self, data):
return T.argmin(T.sqrt(T.sum((data - self.W)**2, 1)))
def buildAttenBased(d_in, d_out, LR, alpha):
# fw_lstm_doc: forward LSTM of document encoding
# bw_lstm_doc: backward LSTM of document encoding
# fw_lstm_que: forward LSTM of question encoding
# bw_lstm_que: backward LSTM of question encoding
# y: words encoding, total t words, so t vectors
# u: question encoding, only one vector
x_seq = T.matrix('x_seq')
q_seq = T.matrix('q_seq')
fw_lstm_doc = LSTM(d_in, d_out, LR, alpha, 0, x_seq)
bw_lstm_doc = LSTM(d_in, d_out, LR, alpha, 0, x_seq[::-1])
fw_lstm_que = LSTM(d_in, d_out, LR, alpha, 0, q_seq)
bw_lstm_que = LSTM(d_in, d_out, LR, alpha, 0, q_seq[::-1])
y = T.concatenate([
fw_lstm_doc.output_encoding(),
bw_lstm_doc.output_encoding()[::-1]
],
axis=1)
u = T.concatenate([
[fw_lstm_que.output_encoding()[-1]],
[bw_lstm_que.output_encoding()[-1]]
],
axis=1)
Wym = shared_uniform("Wym", 2*d_out, 2*d_out)
Wum = shared_uniform("Wum", 2*d_out, 2*d_out)
wms = shared_uniform("wms", 2*d_out)
Wrg = shared_uniform("Wrg", 2*d_out, 2*d_out)
Wug = shared_uniform("Wug", 2*d_out, 2*d_out)
yT = T.transpose(y)
uT = T.transpose(u)
um = T.dot(Wum,uT)
m = T.tanh( T.dot(Wym, yT) + um )
# s is a vector (t,)
mT = T.transpose(m)
s = T.exp( T.sum(wms*mT, axis = 1) )
s = s/T.sqrt(T.sum(s**2))
# r is a vector (2*d_out,)
# ug is (1, 2*d_out)
# g is a vector (2*d_out,)
r = T.dot(T.transpose(y), s)
ug = T.transpose(T.dot(Wug,uT))
g = T.sum( T.tanh( T.dot(Wrg,r) + ug ), axis = 0)
g_hat = T.vector('g_hat')
### Cost Function ###
cost = T.sum((g-g_hat)**2)
params = [Wym, Wum, wms, Wrg, Wug]
params.extend(fw_lstm_doc.params)
params.extend(bw_lstm_doc.params)
params.extend(fw_lstm_que.params)
params.extend(bw_lstm_que.params)
### Calclate Gradients ###
gradients = T.grad(cost, params)
### Model Functions ###
train_model = theano.function(
inputs = [x_seq, q_seq, g_hat],
updates = fw_lstm_doc.rmsprop(params, gradients, LR),
outputs = [cost],
allow_input_downcast = True
)
test_model = theano.function(
inputs = [x_seq, q_seq],
outputs = g,
allow_input_downcast = True
)
A = T.vector('A_Opt')
B = T.vector('B_Opt')
C = T.vector('C_Opt')
D = T.vector('D_Opt')
ser = [ T.sum((g-A)**2),T.sum((g-B)**2),
T.sum((g-C)**2),T.sum((g-D)**2) ]
opt = T.argmin(ser)
testAns_model = theano.function(
inputs = [x_seq, q_seq, A, B, C, D],
outputs = opt,
allow_input_downcast = True
)
return train_model, test_model, testAns_model
w_2 = init_weights((h_size, y_size))
# Forward propagation
yhat = forwardprop(X, w_1, w_2)
# Backward propagation
cost = T.mean(T.nnet.categorical_crossentropy(yhat, Y))
params = [w_1, w_2]
updates = backprop(cost, params)
# Train and predict
train = theano.function(inputs=[X, Y], outputs=cost, updates=updates, allow_input_downcast=True)
pred_y = T.argmin(yhat, axis=1)
predict = theano.function(inputs=[X], outputs=pred_y, allow_input_downcast=True)
# Run SGD
"""for iter in range(500):
train(train_X, train_y)
train_accuracy = np.mean(np.argmax(train_y, axis=1) == predict(train_X))
test_accuracy = np.mean(np.argmax(test_y, axis=1) == predict(test_X))
print predict(test_X)
print("Iteration = %d, train accuracy = %.2f%%, test accuracy = %.2f%%"
% (iter + 1, 100 * train_accuracy, 100 * test_accuracy))
break"""
train(train_X, train_y)
def argmin(x, axis=-1):
return T.argmin(x, axis=axis, keepdims=False)
def _match(self, sample): diff = (T.sqr(self.codebook)).sum(axis = 1, keepdims = True) + (T.sqr(sample)).sum(axis = 1, keepdims = True) - 2 * T.dot(self.codebook, sample.T) bmu = T.argmin(diff) err = T.min(diff) return err, bmu
def constructMinimalDistancesVariable(x, y, initials, n, m):
sDistances = constructSquaredDistanceMatrixVariable(x, y, n, m)
bestIndices = T.argmin(sDistances, axis=0)
bestXes = x[bestIndices]
bestInitials = initials[bestIndices]
return bestXes, bestInitials
def get_time_range(self, train):
mask = self.get_input_mask(train=train)
ind = T.switch(T.eq(mask[:, -1], 1.), mask.shape[-1], T.argmin(mask, axis=-1)).astype('int32')
self.time_range = ind
return ind
def argmin(x, axis=-1):
return T.argmin(x, axis=axis, keepdims=False)
def pred(self, output):
W1 = 0.5
W2 = 0.5
pred = T.argmin(W1 * T.arccos(T.dot(output, mappings_vec.T)/(absolute(output)*absolute(mappings_vec, axis=1))) +
W2 * (absolute(mappings_vec, axis=1) - absolute(output)))
return pred
