def DSSIM(p, y, eps=1e-7):
# Taken/Modified from https://github.com/fchollet/keras/issues/4292
# Nan issue : T.maximum(x, eps)
y_patch = neigh.images2neibs(y, [4, 4], mode='ignore_borders')
p_patch = neigh.images2neibs(p, [4, 4], mode='ignore_borders')
y_mean = T.mean(y_patch, axis=-1)
p_mean = T.mean(p_patch, axis=-1)
y_var = T.var(y_patch, axis=-1, corrected=True)
p_var = T.var(p_patch, axis=-1, corrected=True)
y_std = T.sqrt(T.maximum(y_var, eps))
p_std = T.sqrt(T.maximum(p_var, eps))
c1 = 0.01**2
c2 = 0.02**2
num = (2 * y_mean * p_mean + c1) * (2 * y_std * p_std + c2)
denom = (T.pow(y_mean, 2) + T.pow(p_mean, 2) + c1) * (y_var + p_var +
c2)
ssim = num / T.maximum(denom, eps)
return T.mean(1.0 - ssim)
def computeA(self, symmetric_double_encoder, params):
regularization = 0
if self._layer == -1:
for layer in symmetric_double_encoder:
hidden_x = layer.output_forward_x
hidden_y = layer.output_forward_y
cov_x = Tensor.dot(hidden_x, hidden_x.T)
cov_y = Tensor.dot(hidden_y, hidden_y.T)
regularization += Tensor.mean(Tensor.sum(abs(cov_x), axis=1, dtype=Tensor.config.floatX)) + Tensor.mean(
Tensor.sum(abs(cov_y), axis=1, dtype=Tensor.config.floatX))
elif self._layer < len(symmetric_double_encoder):
hidden_x = symmetric_double_encoder[self._layer].output_forward_x
hidden_y = symmetric_double_encoder[self._layer].output_forward_y
var_x = Tensor.var(hidden_x, axis=1)
var_y = Tensor.var(hidden_y, axis=1)
norm_x = Tensor.mean(Tensor.sum(hidden_x ** 2, axis=1, dtype=Tensor.config.floatX))
norm_y = Tensor.mean(Tensor.sum(hidden_y ** 2, axis=1, dtype=Tensor.config.floatX))
regularization -= norm_x
regularization -= norm_y
#
# cov_x = Tensor.dot(hidden_x.T, hidden_x)
# cov_y = Tensor.dot(hidden_y.T, hidden_y)
#
# regularization -= ((Tensor.sum(abs(cov_x))) + (Tensor.sum(abs(cov_y))))
return self.weight * regularization
def build(self, output, tparams=None, BNparams=None):
if self.BN_mode:
self.BN_eps = npt(self.BN_eps)
if not hasattr(self, 'BN_mean'):
self.BN_mean = T.mean(output)
if not hasattr(self, 'BN_std'):
m2 = (1 + 1 / (T.prod(output.shape) - 1)).astype(floatX)
self.BN_std = T.sqrt(m2 * T.var(output) + self.BN_eps)
if self.BN_mode == 2:
t_mean = T.mean(output, axis=[0, 2, 3], keepdims=True)
t_var = T.var(output, axis=[0, 2, 3], keepdims=True)
BN_mean = BNparams[p_(self.prefix, 'mean')].dimshuffle(
'x', 0, 'x', 'x')
BN_std = BNparams[p_(self.prefix, 'std')].dimshuffle(
'x', 0, 'x', 'x')
output = ifelse(
self.training,
(output - t_mean) / T.sqrt(t_var + self.BN_eps),
(output - BN_mean) / BN_std)
output *= tparams[p_(self.prefix, 'BN_scale')].dimshuffle(
'x', 0, 'x', 'x')
output += tparams[p_(self.prefix, 'BN_shift')].dimshuffle(
'x', 0, 'x', 'x')
elif self.BN_mode == 1:
t_mean = T.mean(output)
t_var = T.var(output)
output = ifelse(
self.training,
(output - t_mean) / T.sqrt(t_var + self.BN_eps),
((output - BNparams[p_(self.prefix, 'mean')])
/ BNparams[p_(self.prefix, 'std')]))
output *= tparams[p_(self.prefix, 'BN_scale')]
output += tparams[p_(self.prefix, 'BN_shift')]
self.output = self.activation(output)
def __init__(self, network):
self.network = network
self.parameters = network.parameters
num_trails = self.parameters.num_trials
n_layers = network.n_layers
self.channels = {}
for channel in self.training_values:
self.channels[channel] = np.zeros((n_layers, num_trails))
for channel in self.training_mean_std:
self.channels[channel] = np.zeros((n_layers, num_trails, 2))
outputs = []
for layer in range(n_layers):
if layer == 0:
X = self.network.X
else:
X = self.network.Y[layer - 1]
Y = self.network.Y[layer]
Q = self.network.Q[layer]
W = self.network.W[layer]
theta = self.network.theta[layer]
y_bar = Y.mean()
Cyy_bar = (Y.T.dot(Y) / network.parameters.batch_size).mean()
outputs.extend([y_bar, Cyy_bar])
X_rec = Y.dot(Q.T)
X_rec_norm = T.sqrt(T.sum(T.sqr(X_rec), axis=1, keepdims=True))
X_norm = T.sqrt(T.sum(T.sqr(X), axis=1, keepdims=True))
X_rec_bar = X_rec_norm.mean()
X_rec_std = X_rec_norm.std()
outputs.extend([X_rec_bar, X_rec_std])
X_bar = X_norm.mean()
X_std = X_norm.std()
outputs.extend([X_bar, X_std])
SNR_Norm = T.mean(T.var(X, axis=0)) / T.mean(
T.var(X - X_rec * X_norm / X_rec_norm, axis=0))
SNR = T.mean(T.var(X, axis=0)) / T.mean(
T.var(X - X_rec_norm, axis=0))
outputs.extend([SNR, SNR_Norm])
Q_norm = T.sqrt(T.sum(T.sqr(Q), axis=0))
Q_bar = Q_norm.mean()
Q_std = Q_norm.std()
outputs.extend([Q_bar, Q_std])
W_bar = W.mean()
W_std = W.std()
outputs.extend([W_bar, W_std])
theta_bar = theta.mean()
theta_std = theta.std()
outputs.extend([theta_bar, theta_std])
self.f = theano.function([], outputs)
def decorate(self, layer):
if self.onTrain:
std = tt.sqrt(tt.var(layer.outputs) + self.espilon)
layer.output = (layer.output - tt.mean(layer.output) / std)
if self.onTest:
std = tt.sqrt(tt.var(layer.testOutputs) + self.espilon)
layer.testOutput = (layer.testOutput -
tt.mean(layer.testOutput)) / std
def __init__(self, network):
self.network = network
self.parameters = network.parameters
num_trails = self.parameters.num_trials
n_layers = network.n_layers
self.channels = {}
for channel in self.training_values:
self.channels[channel] = np.zeros((n_layers, num_trails))
for channel in self.training_mean_std:
self.channels[channel] = np.zeros((n_layers, num_trails, 2))
outputs = []
for layer in range(n_layers):
if layer == 0:
X = self.network.X
else:
X = self.network.Y[layer-1]
Y = self.network.Y[layer]
Q = self.network.Q[layer]
W = self.network.W[layer]
theta = self.network.theta[layer]
y_bar = Y.mean()
Cyy_bar = (Y.T.dot(Y)/network.parameters.batch_size).mean()
outputs.extend([y_bar, Cyy_bar])
X_rec = Y.dot(Q.T)
X_rec_norm = T.sqrt(T.sum(T.sqr(X_rec),axis =1,keepdims=True))
X_norm = T.sqrt(T.sum(T.sqr(X),axis =1,keepdims=True))
X_rec_bar = X_rec_norm.mean()
X_rec_std = X_rec_norm.std()
outputs.extend([X_rec_bar, X_rec_std])
X_bar = X_norm.mean()
X_std = X_norm.std()
outputs.extend([X_bar, X_std])
SNR_Norm = T.mean(T.var(X,axis=0))/T.mean(T.var(X-X_rec*X_norm/X_rec_norm,axis=0))
SNR = T.mean(T.var(X,axis=0))/T.mean(T.var(X-X_rec_norm,axis=0))
outputs.extend([SNR, SNR_Norm])
Q_norm = T.sqrt(T.sum(T.sqr(Q), axis=0))
Q_bar = Q_norm.mean()
Q_std = Q_norm.std()
outputs.extend([Q_bar, Q_std])
W_bar = W.mean()
W_std = W.std()
outputs.extend([W_bar, W_std])
theta_bar = theta.mean()
theta_std = theta.std()
outputs.extend([theta_bar, theta_std])
self.f = theano.function([], outputs)
def instance(self, train_x, infer_x, dropout=None, epsilon=1e-8, **kwargs):
"""Returns (train_output, inference_output, statistics_updates, train_reconstruction, infer_reconstruction)"""
# dropout
dropout = dropout or 0.
mask = self.srng.binomial(n=1, p=1 - dropout, size=train_x.shape)
# cast because int * float32 = float64 which does not run on GPU
train_x = train_x * T.cast(mask, theano.config.floatX)
# outputs with batch-specific normalization
train_lin_output = T.dot(train_x, self.t_W) + self.t_b
train_lin_output.name = self.subname("trainLinOutput")
batch_mean = T.mean(train_lin_output, axis=0)
offset_output = train_lin_output - batch_mean
batch_var = T.var(offset_output, axis=0)
batch_sd = T.sqrt(batch_var + epsilon)
normalized_lin_output = offset_output / batch_sd
train_output = self.activation_fn(self.gamma * normalized_lin_output + self.beta)
train_output.name = self.subname("trainOutput")
# reconstruct batch-specific output
W_T = self.t_W.T
W_T.name = self.subname("W_T")
recon_lin_output = T.dot(train_output, W_T) + self.t_decode_b
recon_lin_output.name = self.subname("reconLinOutput")
decode_batch_mean = T.mean(recon_lin_output, axis=0)
recon_offset_output = recon_lin_output - decode_batch_mean
decode_batch_var = T.var(recon_offset_output, axis=0)
decode_batch_sd = T.sqrt(decode_batch_var + epsilon)
normalized_recon_lin_output = recon_offset_output / decode_batch_sd
reconstructed_output = self.activation_fn(self.decode_gamma * normalized_recon_lin_output + self.decode_beta)
# outputs with rolling-average normalization
infer_lin_output = T.dot(infer_x, self.t_W) + self.t_b
infer_lin_output.name = self.subname("inferLinOutput")
sd = T.sqrt(self.variance + epsilon)
normalized_infer_lin_output = infer_lin_output - self.mean
inference_output = self.activation_fn(self.gamma / sd * normalized_infer_lin_output + self.beta)
infer_lin_output.name = self.subname("inferenceOutput")
# reconstruct batch-specific output
recon_infer_lin_output = T.dot(inference_output, W_T) + self.t_decode_b
recon_infer_lin_output.name = self.subname("reconInferLinOutput")
decode_sd = T.sqrt(self.decode_variance + epsilon)
normalized_recon_infer_lin_output = recon_infer_lin_output - self.decode_mean
recon_infer_output = self.activation_fn(self.decode_gamma / decode_sd * normalized_recon_infer_lin_output + self.decode_beta)
# save exponential moving average for batch mean/variance
statistics_updates = [
(self.mean, self.alpha * self.mean + (1.0 - self.alpha) * batch_mean),
(self.variance, self.alpha * self.variance + (1.0 - self.alpha) * batch_var),
(self.decode_mean, self.alpha * self.decode_mean + (1.0 - self.alpha) * decode_batch_mean),
(self.decode_variance, self.alpha * self.decode_variance + (1.0 - self.alpha) * decode_batch_var),
]
return train_output, inference_output, statistics_updates, reconstructed_output, recon_infer_output
def f_prop(self, x):
if x.ndim == 2:
mean = T.mean(x, axis=0, keepdims=True)
std = T.sqrt(T.var(x, axis=0, keepdims=True)+self.epsilon)
elif x.ndim == 4:
mean = T.mean(x, axis=(0,2,3), keepdims=True)
std = T.sqrt(T.var(x, axis=(0,2,3), keepdims=True)+self.epsilon)
normalized_x = (x-mean)/std
self.z = self.gamma*normalized_x+self.beta
return self.z
def LayerNormalization(x, gamma, mask, estimated_mean=0.0, estimated_var=1.0):
assert x.ndim == 3 or x.ndim == 2
if x.ndim == 3:
x_mean = T.mean(x, axis=2).dimshuffle(0, 1, 'x')
x_var = T.var(x, axis=2).dimshuffle(0, 1, 'x')
return gamma * (
(x - x_mean) / T.sqrt(x_var + 1e-7)), x_mean[0, 0], x_var[0, 0]
elif x.ndim == 2:
x_mean = T.mean(x, axis=1).dimshuffle(0, 'x')
x_var = T.var(x, axis=1).dimshuffle(0, 'x')
return gamma * (
(x - x_mean) / T.sqrt(x_var + 1e-7)), x_mean[0], x_var[0]
def get_result(self, input):
# returns BN result for given input.
epsilon = 1e-06
if self.mode == 0:
if self.run_mode == 0:
now_mean = T.mean(input, axis=0)
now_var = T.var(input, axis=0)
now_normalize = (input - now_mean) / T.sqrt(
now_var + epsilon) # should be broadcastable..
output = self.gamma * now_normalize + self.beta
#print ('norm.shape =')
#print (now_normalize.shape.eval({x: np.random.rand(2,2).astype(dtype=theano.config.floatX)}))
# mean, var update
self.mean = self.momentum * self.mean + (
1.0 - self.momentum) * now_mean
self.var = self.momentum * self.var + (1.0 - self.momentum) * (
self.input_shape[0] / (self.input_shape[0] - 1) * now_var)
else:
output = self.gamma * (
input - self.mean) / T.sqrt(self.var + epsilon) + self.beta
else:
# in CNN mode, gamma and beta exists for every single channel separately.
# for each channel, calculate mean and std for (mini_batch_size * row * column) elements.
# then, each channel has own scalar gamma/beta parameters.
if self.run_mode == 0:
now_mean = T.mean(input, axis=(0, 2, 3))
now_var = T.var(input, axis=(0, 2, 3))
# mean, var update
self.mean = self.momentum * self.mean + (
1.0 - self.momentum) * now_mean
self.var = self.momentum * self.var + (1.0 - self.momentum) * (
self.input_shape[0] / (self.input_shape[0] - 1) * now_var)
else:
now_mean = self.mean
now_var = self.var
# change shape to fit input shape
now_mean = self.change_shape(now_mean)
now_var = self.change_shape(now_var)
now_gamma = self.change_shape(self.gamma)
now_beta = self.change_shape(self.beta)
output = now_gamma * (
input - now_mean) / T.sqrt(now_var + epsilon) + now_beta
return output
def build_trainer(input_data,
input_mask,
target_data,
target_mask,
network_params,
output_layer,
cond_layer_list,
feat_reg,
updater,
learning_rate,
load_updater_params=None):
output_score = get_output(output_layer, deterministic=False)
frame_prd_idx = T.argmax(output_score, axis=-1)
one_hot_target = T.extra_ops.to_one_hot(y=T.flatten(target_data, 1),
nb_class=output_dim,
dtype=floatX)
output_score = T.reshape(x=output_score,
newshape=(-1, output_dim),
ndim=2)
output_score = output_score - T.max(output_score, axis=-1, keepdims=True)
output_score = output_score - T.log(T.sum(T.exp(output_score), axis=-1, keepdims=True))
train_ce = -T.sum(T.mul(one_hot_target, output_score), axis=-1)*T.flatten(target_mask, 1)
train_loss = T.sum(train_ce)/target_mask.shape[0]
frame_loss = T.sum(train_ce)/T.sum(target_mask)
frame_accr = T.sum(T.eq(frame_prd_idx, target_data)*target_mask)/T.sum(target_mask)
train_feat_loss = 0
for cond_layer in cond_layer_list:
sample_feat = cond_layer.get_sample_feat()
sample_feat_cost = T.var(sample_feat, axis=0)
sample_feat_cost = -T.mean(sample_feat_cost)
train_feat_loss += sample_feat_cost
train_feat_loss /= len(cond_layer_list)
train_total_loss = train_loss + train_feat_loss*feat_reg
network_grads = theano.grad(cost=train_total_loss, wrt=network_params)
network_grads_norm = T.sqrt(sum(T.sum(grad**2) for grad in network_grads))
train_lr = theano.shared(lasagne.utils.floatX(learning_rate))
train_updates, updater_params = updater(loss_or_grads=network_grads,
params=network_params,
learning_rate=train_lr,
load_params_dict=load_updater_params)
training_fn = theano.function(inputs=[input_data,
input_mask,
target_data,
target_mask],
outputs=[frame_loss,
frame_accr,
train_feat_loss,
network_grads_norm],
updates=train_updates)
return training_fn, train_lr, updater_params
def get_stats(input, stat=None):
"""
Returns a dictionary mapping the name of the statistic to the result on the input.
Currently gets mean, var, std, min, max, l1, l2.
Parameters
----------
input : tensor
Theano tensor to grab stats for.
Returns
-------
dict
Dictionary of all the statistics expressions {string_name: theano expression}
"""
stats = {
'mean': T.mean(input),
'var': T.var(input),
'std': T.std(input),
'min': T.min(input),
'max': T.max(input),
'l1': input.norm(L=1),
'l2': input.norm(L=2),
#'num_nonzero': T.sum(T.nonzero(input)),
}
stat_list = raise_to_list(stat)
compiled_stats = {}
if stat_list is None:
return stats
for stat in stat_list:
if isinstance(stat, string_types) and stat in stats:
compiled_stats.update({stat: stats[stat]})
return compiled_stats
def ZCA(data, n_component=2):
'''
m is the number of data points
n is the dimension of the data
:param data: <numpy matrix, (m,n)> imput data
:param n_component: <int> number of dimension to be extracted
:return:
'''
# data standardization
x = T.matrix('x')
eps = T.scalar('eps')
y = (x - T.mean(x, axis=0)) / T.sqrt(T.var(x) + eps)
standardize = th.function([x, eps], y)
# zca whitening
x_n = T.matrix('x_n') # normalized input
eps2 = T.scalar('eps2') # small esp to prevent div by zero
x_cov = T.dot(x_n.T, x_n) / x_n.shape[0] # variance of input
u, s, v = T.nlinalg.svd(x_cov)
z = T.dot(T.dot(u, T.nlinalg.diag(1. / T.sqrt(s + eps2))), u.T)
x_zca = T.dot(x_n, z.T[:, :n_component])
zca_whiten = th.function([x_n, eps2], x_zca)
return zca_whiten(standardize(data, 0.1), 0.01)
def decorate(self, layer) : if not hasattr(layer, "batchnorm_W") or not hasattr(layer, "batchnorm_b") : self.paramShape = layer.getOutputShape()#(layer.nbOutputs, ) self.WInitialization.initialize(self) self.bInitialization.initialize(self) layer.batchnorm_W = self.W layer.batchnorm_b = self.b mu = tt.mean(layer.outputs) sigma = tt.sqrt( tt.var(layer.outputs) + self.epsilon ) layer.outputs = layer.batchnorm_W * ( (layer.outputs - mu) / sigma ) + layer.batchnorm_b mu = tt.mean(layer.testOutputs) sigma = tt.sqrt( tt.var(layer.testOutputs) + self.epsilon ) layer.testOutputs = layer.batchnorm_W * ( (layer.testOutputs - mu) / sigma ) + layer.batchnorm_b
def get_output_for(self,
input,
moving_avg_hooks=None,
deterministic=False,
*args,
**kwargs):
if deterministic is False:
m = T.mean(input, axis=self.axis, keepdims=True)
v = T.sqrt(
T.var(input, axis=self.axis, keepdims=True) + self.epsilon)
m.name = "tensor:mean"
v.name = "tensor:variance"
key = "BatchNormLayer:movingavg"
if key not in moving_avg_hooks:
moving_avg_hooks[key] = []
moving_avg_hooks[key].append(
[[m, v], [self.mean_inference, self.variance_inference]])
else:
m = self.mean_inference
v = self.variance_inference
input_norm = (input - m) / v # normalize
y = self.gamma * input_norm + self.beta # scale and shift
return self.nonlinearity(y)
def _compute_training_statistics(self, input_):
if self.n_iter:
axes = (0, ) + tuple(
(i + 1)
for i, b in enumerate(self.population_mean[0].broadcastable)
if b)
else:
axes = (0, ) + tuple(
(i + 1)
for i, b in enumerate(self.population_mean.broadcastable) if b)
mean = input_.mean(axis=axes, keepdims=True)
if self.n_iter:
assert mean.broadcastable[1:] == self.population_mean[
0].broadcastable
else:
assert mean.broadcastable[1:] == self.population_mean.broadcastable
stdev = tensor.sqrt(
tensor.var(input_, axis=axes, keepdims=True) +
numpy.cast[theano.config.floatX](self.epsilon))
if self.n_iter:
assert stdev.broadcastable[1:] == self.population_stdev[
0].broadcastable
else:
assert stdev.broadcastable[
1:] == self.population_stdev.broadcastable
add_role(mean, BATCH_NORM_MINIBATCH_ESTIMATE)
add_role(stdev, BATCH_NORM_MINIBATCH_ESTIMATE)
return mean, stdev
def process(self, input, tparams, BNparams):
mode = 'full' if self.border_mode == 'same' else self.border_mode
output = conv.conv2d(
input=input,
filters=tparams[p_(self.prefix, 'W')],
image_shape=[self.batch_size, self.n_in[0]] + self.image_shape,
filter_shape=[self.n_out] + self.n_in,
border_mode=mode,
subsample=self.stride)
if self.border_mode == 'same':
a1 = (self.filter_size[0] - 1) // 2
b1 = (self.filter_size[1] - 1) // 2
a2 = self.filter_size[0] - a1
b2 = self.filter_size[1] - b1
if a2 == 1:
if b2 == 1:
output = output[:, :, a1:, b1:]
else:
output = output[:, :, a1:, b1:-b2+1]
else:
if b2 == 1:
output = output[:, :, a1:-a2+1, b1:]
else:
output = output[:, :, a1:-a2+1, b1:-b2+1]
if self.with_bias:
output += tparams[p_(self.prefix, 'b')].dimshuffle('x', 0, 'x', 'x')
self.BN_mean = T.mean(output, axis=[0, 2, 3])
m2 = (1 + 1 / (T.prod(output.shape) / self.n_out - 1)).astype(floatX)
self.BN_std = T.sqrt(m2 * T.var(output, axis=[0, 2, 3])
+ npt(self.BN_eps))
return output
def __init__(self, rng, layers, mc_samples=None):
self.layers = layers
self.params = [param for layer in self.layers
for param in layer.params]
self.cost = self.layers[-1].cost # function pointer
if mc_samples is None:
# Standard dropout network.
try:
self.preds = self.layers[-1].preds
self.error = self.layers[-1].error # function pointer
except:
print('Could not access network outputs'
' - did you pass a (non-dropout) input?'
)
else:
# mc_dropout network.
self.mc_samples = mc_samples
mc_outputs, _ = theano.scan(lambda: self.layers[-1].output_dropout,
outputs_info=None,
n_steps = self.mc_samples)
self.predictive_distribution_mean = T.mean(mc_outputs, axis=0)
self.predictive_distribution_var = T.var(mc_outputs, axis=0)
self.preds = T.argmax(self.predictive_distribution_mean, axis=1)
self.error = self.__error_mc
self.L1 = (
T.sum([abs(layer.W).sum() for layer in self.layers])
)
self.L2_sqr = (
T.sum([(layer.W ** 2).sum() for layer in self.layers])
)
def batch_norm(X, gamma, beta, m_shared, v_shared, test, add_updates):
if X.ndim > 2:
output_shape = X.shape
X = X.flatten(2)
if test is False:
m = T.mean(X, axis=0, keepdims=True)
v = T.sqrt(T.var(X, axis=0, keepdims=True) + self.epsilon)
mulfac = 1.0 / 1000
if m_shared in add_updates:
add_updates[m_shared] = (
1.0 - mulfac) * add_updates[m_shared] + mulfac * m
add_updates[v_shared] = (
1.0 - mulfac) * add_updates[v_shared] + mulfac * v
else:
add_updates[m_shared] = (1.0 -
mulfac) * m_shared + mulfac * m
add_updates[v_shared] = (1.0 -
mulfac) * v_shared + mulfac * v
else:
m = m_shared
v = v_shared
X_hat = (X - m) / v
y = gamma * X_hat + beta
if X.ndim > 2:
y = T.reshape(y, output_shape)
return y
def batch_norm(X, gamma, beta, m_shared, v_shared, test, add_updates):
if X.ndim > 2:
output_shape = X.shape
X = X.flatten(2)
if test is False:
m = T.mean(X, axis=0, keepdims=True)
v = T.sqrt(T.var(X, axis=0, keepdims=True) + self.epsilon)
mulfac = 1.0/1000
if m_shared in add_updates:
add_updates[m_shared] = (1.0-mulfac)*add_updates[m_shared] + mulfac*m
add_updates[v_shared] = (1.0-mulfac)*add_updates[v_shared] + mulfac*v
else:
add_updates[m_shared] = (1.0-mulfac)*m_shared + mulfac*m
add_updates[v_shared] = (1.0-mulfac)*v_shared + mulfac*v
else:
m = m_shared
v = v_shared
X_hat = (X - m) / v
y = gamma*X_hat + beta
if X.ndim > 2:
y = T.reshape(y, output_shape)
return y
def layer_var(self):
# square of L2 norm ; one regularization option is to enforce
# square of L2 norm to be small
var = []
for layer in self.layers:
var.append(T.var(layer.W))
return var
def layer_var(self):
# square of L2 norm ; one regularization option is to enforce
# square of L2 norm to be small
var = []
for layer in self.layers:
var.append(T.var(layer.W))
return var
def layer_normalization(x, bias=None, scale=None, eps=1e-5):
"""
Layer Normalization, https://arxiv.org/abs/1607.06450
x is mean and variance normalized along its feature dimension.
After that, we allow a bias and a rescale. This is supposed to be trainable.
:param x: 3d tensor (time,batch,dim) (or any ndim, last dim is expected to be dim)
:param bias: 1d tensor (dim) or None
:param scale: 1d tensor (dim) or None
"""
mean = T.mean(x, axis=x.ndim - 1, keepdims=True)
std = T.sqrt(T.var(x, axis=x.ndim - 1, keepdims=True) + numpy.float32(eps))
assert mean.ndim == std.ndim == x.ndim
output = (x - mean) / std
assert output.ndim == x.ndim
if scale is not None:
assert scale.ndim == 1
scale = scale.dimshuffle(*(('x',) * (x.ndim - 1) + (0,)))
assert scale.ndim == x.ndim
output = output * scale
if bias is not None:
assert bias.ndim == 1
bias = bias.dimshuffle(*(('x',) * (x.ndim - 1) + (0,)))
assert bias.ndim == x.ndim
output = output + bias
return output
def normalize_samples(self, x, gamma, beta):
OutputLog().write('Normalizing Samples')
mean = Tensor.mean(x, axis=1, keepdims=True)
var = Tensor.var(x, axis=1, keepdims=True)
normalized_output = (x - mean) / Tensor.sqrt(var + self.epsilon)
return normalized_output / gamma + beta
def ln(input, alpha, beta=None):
output = (input - T.mean(input, axis=1, keepdims=True)
) / T.sqrt(T.var(input, axis=1, keepdims=True) + eps)
output *= alpha[None, :]
if beta:
output += beta[None, :]
return output
def _normalize_input(self):
X = T.matrix('X')
results, updates = theano.scan(
lambda x_i: (x_i - T.mean(x_i)) / T.sqrt(T.var(x_i) + 10),
sequences=[X]
)
return theano.function(inputs=[X], outputs=results)
def instance(self, train_x, infer_x, dropout=None, epsilon=1e-8, **kwargs):
"""Returns (train_output, inference_output, statistics_updates)"""
# dropout
dropout = dropout or 0.
mask = self.srng.binomial(n=1, p=1 - dropout, size=train_x.shape)
# cast because int * float32 = float64 which does not run on GPU
train_x = train_x * T.cast(mask, theano.config.floatX)
# outputs with batch-specific normalization
train_lin_output = T.dot(train_x, self.t_W) + self.t_b
batch_mean = T.mean(train_lin_output, axis=0)
offset_output = train_lin_output - batch_mean
batch_var = T.var(offset_output, axis=0)
normalized_lin_output = offset_output / T.sqrt(batch_var + epsilon)
train_output = self.activation_fn(self.gamma * normalized_lin_output + self.beta)
# outputs with rolling-average normalization
infer_lin_output = T.dot(infer_x, self.t_W) + self.t_b
sd = T.sqrt(self.variance + epsilon)
inference_output = self.activation_fn(self.gamma / sd * infer_lin_output + (self.beta - (self.gamma * self.mean) / sd))
# save exponential moving average for batch mean/variance
statistics_updates = [
(self.mean, self.alpha * self.mean + (1.0 - self.alpha) * batch_mean),
(self.variance, self.alpha * self.variance + (1.0 - self.alpha) * batch_var)
]
return train_output, inference_output, statistics_updates
def get_output_for(self, input, deterministic=False, **kwargs):
beta = self.beta
gamma = self.gamma
means = self.means
stdevs = self.stdevs
output_shape = input.shape
if input.ndim > 2:
# if the input has more than two dimensions, flatten it into a
# batch of feature vectors.
input = input.flatten(2)
if deterministic == False:
m = T.mean(input, axis=0, keepdims=False)
s = T.sqrt(T.var(input, axis=0, keepdims=False) + self.eta)
means.default_update = self.alpha * means + (1-self.alpha) * m
Es = self.alpha * stdevs + (1-self.alpha) * s
u = self.batch_size / (self.batch_size - 1)
stdevs.default_update = u * Es
else:
m = means
s = stdevs
output = input - m
output /= s
# transform normalized outputs based on learned shift and scale
if self.learn_transform is True:
output = gamma * output + beta
output = output.reshape(output_shape)
return self.nonlinearity(output)
def add_param(self, param, name="", constraints=True,
custom_update=None, custom_update_normalized=False, custom_update_exp_average=0,
custom_update_condition=None, custom_update_accumulate_batches=None):
"""
:type param: theano.SharedVariable
:type name: str
:rtype: theano.SharedVariable
"""
param = super(Layer, self).add_param(param, name)
if custom_update:
# Handled in Device and Updater.
param.custom_update = custom_update
param.custom_update_normalized = custom_update_normalized
param.custom_update_exp_average = custom_update_exp_average
param.custom_update_condition = custom_update_condition
param.custom_update_accumulate_batches = custom_update_accumulate_batches
if constraints:
if 'L1' in self.attrs and self.attrs['L1'] > 0:
self.constraints += T.constant(self.attrs['L1'], name="L1", dtype='floatX') * abs(param).sum()
if 'L2' in self.attrs and self.attrs['L2'] > 0:
self.constraints += T.constant(self.attrs['L2'], name="L2", dtype='floatX') * (param**2).sum()
if self.attrs.get('L2_eye', 0) > 0:
L2_eye = T.constant(self.attrs['L2_eye'], name="L2_eye", dtype='floatX')
if param.ndim == 2:
eye = tiled_eye(param.shape[0], param.shape[1], dtype=param.dtype)
self.constraints += L2_eye * ((param - eye)**2).sum()
else: # standard L2
self.constraints += L2_eye * (param**2).sum()
if 'varreg' in self.attrs and self.attrs['varreg'] > 0:
self.constraints += self.attrs['varreg'] * (1.0 * T.sqrt(T.var(param)) - 1.0 / numpy.sum(param.get_value().shape))**2
return param
def layer_normalization(x, bias=None, scale=None, eps=1e-5):
"""
Layer Normalization, https://arxiv.org/abs/1607.06450
x is mean and variance normalized along its feature dimension.
After that, we allow a bias and a rescale. This is supposed to be trainable.
:param x: 3d tensor (time,batch,dim) (or any ndim, last dim is expected to be dim)
:param bias: 1d tensor (dim) or None
:param scale: 1d tensor (dim) or None
"""
mean = T.mean(x, axis=x.ndim - 1, keepdims=True)
std = T.sqrt(T.var(x, axis=x.ndim - 1, keepdims=True) + numpy.float32(eps))
assert mean.ndim == std.ndim == x.ndim
output = (x - mean) / std
assert output.ndim == x.ndim
if scale is not None:
assert scale.ndim == 1
scale = scale.dimshuffle(*(('x', ) * (x.ndim - 1) + (0, )))
assert scale.ndim == x.ndim
output = output * scale
if bias is not None:
assert bias.ndim == 1
bias = bias.dimshuffle(*(('x', ) * (x.ndim - 1) + (0, )))
assert bias.ndim == x.ndim
output = output + bias
return output
def get_output_for(self, input, moving_avg_hooks=None,
deterministic=False, *args, **kwargs):
if deterministic is False:
m = T.mean(input, axis=0, keepdims=True)
m.name = "tensor:mean"
v = T.sqrt(T.var(input, axis=0, keepdims=True) + self.epsilon)
v.name = "tensor:variance"
R = T.dot(((input - m)).T, ((input - m)))
key = "WhiteningLayer:movingavg"
if key not in moving_avg_hooks:
moving_avg_hooks[key] = []
moving_avg_hooks[key].append(
[[self.R_inference], [self.W]])
key = "BatchNormalizationLayer:movingavg"
if key not in moving_avg_hooks:
moving_avg_hooks[key] = []
moving_avg_hooks[key].append(
[[m, v, R], [self.mean_inference, self.variance_inference, self.R_inference]])
else:
m = self.mean_inference
v = self.variance_inference
input_hat = T.dot((input - m), self.W.T) # normalize
y = input_hat / self.gamma + self.beta # scale and shift
return y
def get_stats(input, stat=None):
"""
Returns a dictionary mapping the name of the statistic to the result on the input.
Currently gets mean, var, std, min, max, l1, l2.
Parameters
----------
input : tensor
Theano tensor to grab stats for.
Returns
-------
dict
Dictionary of all the statistics expressions {string_name: theano expression}
"""
stats = {
'mean': T.mean(input),
'var': T.var(input),
'std': T.std(input),
'min': T.min(input),
'max': T.max(input),
'l1': input.norm(L=1),
'l2': input.norm(L=2),
#'num_nonzero': T.sum(T.nonzero(input)),
}
stat_list = raise_to_list(stat)
compiled_stats = {}
if stat_list is None:
return stats
for stat in stat_list:
if isinstance(stat, six.string_types) and stat in stats:
compiled_stats.update({stat: stats[stat]})
return compiled_stats
def __init__(self, inputData, image_shape):
self.input = inputData
num_out = image_shape[-3]
epsilon = 0.01
self.image_shape = image_shape
gamma_values = numpy.ones((num_out, ), dtype=theano.config.floatX)
self.gamma_vals = theano.shared(value=gamma_values, borrow=True)
beta_values = numpy.zeros((num_out, ), dtype=theano.config.floatX)
self.beta_vals = theano.shared(value=beta_values, borrow=True)
batch_mean = T.mean(self.input, keepdims=True, axis=(0, -2, -1))
batch_var = T.var(self.input, keepdims=True,
axis=(0, -2, -1)) + epsilon
self.batch_mean = self.adjustVals(batch_mean)
batch_var = self.adjustVals(batch_var)
self.batch_var = T.pow(batch_var, 0.5)
batch_normalize = (inputData - self.batch_mean) / (T.pow(
self.batch_var, 0.5))
if self.input.ndim == 5:
self.beta = self.beta_vals.dimshuffle('x', 'x', 0, 'x', 'x')
self.gamma = self.gamma_vals.dimshuffle('x', 'x', 0, 'x', 'x')
else:
self.beta = self.beta_vals.dimshuffle('x', 0, 'x', 'x')
self.gamma = self.gamma_vals.dimshuffle('x', 0, 'x', 'x')
self.output = batch_normalize * self.gamma + self.beta
#self.output=inputData-self.batch_mean
self.params = [self.gamma_vals, self.beta_vals]
def make_consensus(self, networks, axis=2):
cns = self.attrs['consensus']
if cns == 'max':
return T.max(networks, axis=axis)
elif cns == 'min':
return T.min(networks, axis=axis)
elif cns == 'mean':
return T.mean(networks, axis=axis)
elif cns == 'flat':
if self.depth == 1:
return networks
if axis == 2:
return networks.flatten(ndim=3)
#return T.reshape(networks, (networks.shape[0], networks.shape[1], T.prod(networks.shape[2:]) ))
else:
return networks.flatten(ndim=2) # T.reshape(networks, (networks.shape[0], T.prod(networks.shape[1:]) ))
elif cns == 'sum':
return T.sum(networks, axis=axis, acc_dtype=theano.config.floatX)
elif cns == 'prod':
return T.prod(networks, axis=axis)
elif cns == 'var':
return T.var(networks, axis=axis)
elif cns == 'project':
p = self.add_param(self.create_random_uniform_weights(self.attrs['n_out'], 1, self.attrs['n_out'] + self.depth + 1))
return T.tensordot(p, networks, [[1], [axis]])
elif cns == 'random':
idx = self.rng.random_integers(size=(1,), low=0, high=self.depth)
if axis == 0: return networks[idx]
if axis == 1: return networks[:,idx]
if axis == 2: return networks[:,:,idx]
if axis == 3: return networks[:,:,:,idx]
assert False, "axis too large"
else:
assert False, "consensus method unknown: " + cns
def get_output_for(self, input, moving_avg_hooks=None,
deterministic=False, *args, **kwargs):
reshape = False
if input.ndim > 2:
output_shape = input.shape
reshape = True
input = input.flatten(2)
if deterministic is False:
m = T.mean(input, axis=0, keepdims=True)
v = T.sqrt(T.var(input, axis=0, keepdims=True)+self.epsilon)
m.name = "tensor:mean-" + self.name
v.name = "tensor:variance-" + self.name
key = "BatchNormalizationLayer:movingavg"
if key not in moving_avg_hooks:
# moving_avg_hooks[key] = {}
moving_avg_hooks[key] = []
# moving_avg_hooks[key][self.name] = [[m,v], [self.mean_inference, self.variance_inference]]
moving_avg_hooks[key].append([[m,v], [self.mean_inference, self.variance_inference]])
else:
m = self.mean_inference
v = self.variance_inference
input_hat = (input - m) / v # normalize
y = self.gamma*input_hat + self.beta # scale and shift
if reshape:#input.ndim > 2:
y = T.reshape(y, output_shape)
return self.nonlinearity(y)
def decorate(self, layer) : if not hasattr(layer, "batchnorm_W") or not hasattr(layer, "batchnorm_b") : self.paramShape = layer.getOutputShape()#(layer.nbOutputs, ) self.WInitialization.initialize(self) self.bInitialization.initialize(self) layer.batchnorm_W = self.W layer.batchnorm_b = self.b mu = tt.mean(layer.outputs) sigma = tt.sqrt( tt.var(layer.outputs) + self.epsilon ) layer.outputs = layer.batchnorm_W * ( (layer.outputs - mu) / sigma ) + layer.batchnorm_b mu = tt.mean(layer.testOutputs) sigma = tt.sqrt( tt.var(layer.testOutputs) + self.epsilon ) layer.testOutputs = layer.batchnorm_W * ( (layer.testOutputs - mu) / sigma ) + layer.batchnorm_b
def add_param(self, param, name="", constraints=True,
custom_update=None, custom_update_normalized=False, custom_update_exp_average=0,
custom_update_condition=None, custom_update_accumulate_batches=None, live_update=None):
"""
:type param: theano.SharedVariable
:type name: str
:rtype: theano.SharedVariable
"""
param = super(Layer, self).add_param(param, name)
param.live_update = live_update
if custom_update:
# Handled in Device and Updater.
param.custom_update = custom_update
param.custom_update_normalized = custom_update_normalized
param.custom_update_exp_average = custom_update_exp_average
param.custom_update_condition = custom_update_condition
param.custom_update_accumulate_batches = custom_update_accumulate_batches
if constraints:
if 'L1' in self.attrs and self.attrs['L1'] > 0:
self.constraints += T.constant(self.attrs['L1'], name="L1", dtype='floatX') * abs(param).sum()
if 'L2' in self.attrs and self.attrs['L2'] > 0:
self.constraints += T.constant(self.attrs['L2'], name="L2", dtype='floatX') * (param**2).sum()
if self.attrs.get('L2_eye', 0) > 0:
L2_eye = T.constant(self.attrs['L2_eye'], name="L2_eye", dtype='floatX')
if param.ndim == 2:
eye = tiled_eye(param.shape[0], param.shape[1], dtype=param.dtype)
self.constraints += L2_eye * ((param - eye)**2).sum()
else: # standard L2
self.constraints += L2_eye * (param**2).sum()
if 'varreg' in self.attrs and self.attrs['varreg'] > 0:
self.constraints += self.attrs['varreg'] * (1.0 * T.sqrt(T.var(param)) - 1.0 / numpy.sum(param.get_value().shape))**2
return param
def normalise(X):
eps = 1e-4
X_m = T.mean(X, keepdims=True, axis=0)
X_var = T.var(X, keepdims=True, axis=0)
X = (X - X_m) / (T.sqrt(X_var + eps))
return X
def __init__(self,inputData,image_shape):
self.input=inputData
num_out=image_shape[1]
epsilon=0.01
self.image_shape=image_shape
gamma_values = numpy.ones((num_out,), dtype=theano.config.floatX)
self.gamma_vals = theano.shared(value=gamma_values, borrow=True)
beta_values = numpy.zeros((num_out,), dtype=theano.config.floatX)
self.beta_vals = theano.shared(value=beta_values, borrow=True)
batch_mean=T.mean(self.input,keepdims=True,axis=(0,2,3))
batch_var=T.var(self.input,keepdims=True,axis=(0,2,3))+epsilon
self.batch_mean=self.adjustVals(batch_mean)
batch_var=self.adjustVals(batch_var)
self.batch_var=T.pow(batch_var,0.5)
batch_normalize=(inputData-self.batch_mean)/(T.pow(self.batch_var,0.5))
self.beta = self.beta_vals.dimshuffle('x', 0, 'x', 'x')
self.gamma = self.gamma_vals.dimshuffle('x', 0, 'x', 'x')
self.output=batch_normalize*self.gamma+self.beta
#self.output=inputData-self.batch_mean
self.params=[self.gamma_vals,self.beta_vals]
def get_output_for(self, input, deterministic=False, **kwargs):
beta = self.beta
gamma = self.gamma
means = self.means
stdevs = self.stdevs
output_shape = input.shape
if input.ndim > 2:
# if the input has more than two dimensions, flatten it into a
# batch of feature vectors.
input = input.flatten(2)
if deterministic == False:
m = T.mean(input, axis=0, keepdims=False)
s = T.sqrt(T.var(input, axis=0, keepdims=False) + self.eta)
means.default_update = self.alpha * means + (1 - self.alpha) * m
Es = self.alpha * stdevs + (1 - self.alpha) * s
u = self.batch_size / (self.batch_size - 1)
stdevs.default_update = u * Es
else:
m = means
s = stdevs
output = input - m
output /= s
# transform normalized outputs based on learned shift and scale
if self.learn_transform is True:
output = gamma * output + beta
output = output.reshape(output_shape)
return self.nonlinearity(output)
def activations(self, dataset):
prev_activations = self._prev_layer.activations(dataset)
if prev_activations.ndim == 2:
# flat dataset: (example, vector)
mean = T.mean(prev_activations, axis=0)
variance = T.var(prev_activations, axis=0)
elif prev_activations.ndim == 3:
# sequence dataset: (seq num, example, vector)
mean = T.mean(prev_activations, axis=1).dimshuffle(0,'x',1)
variance = T.var(prev_activations, axis=1).dimshuffle(0,'x',1)
normalized = (prev_activations - mean) / T.sqrt(variance + self.EPSILON)
scaled_and_shifted = (normalized * self._scale) + self._shift
return scaled_and_shifted
def make_consensus(self, networks, axis=2):
cns = self.attrs['consensus']
if cns == 'max':
return T.max(networks, axis=axis)
elif cns == 'min':
return T.min(networks, axis=axis)
elif cns == 'mean':
return T.mean(networks, axis=axis)
elif cns == 'flat':
if self.depth == 1:
return networks
if axis == 2:
return networks.flatten(ndim=3)
#return T.reshape(networks, (networks.shape[0], networks.shape[1], T.prod(networks.shape[2:]) ))
else:
return networks.flatten(ndim=2) # T.reshape(networks, (networks.shape[0], T.prod(networks.shape[1:]) ))
elif cns == 'sum':
return T.sum(networks, axis=axis, acc_dtype=theano.config.floatX)
elif cns == 'prod':
return T.prod(networks, axis=axis)
elif cns == 'var':
return T.var(networks, axis=axis)
elif cns == 'project':
p = self.add_param(self.create_random_uniform_weights(self.attrs['n_out'], 1, self.attrs['n_out'] + self.depth + 1))
return T.tensordot(p, networks, [[1], [axis]])
elif cns == 'random':
idx = self.rng.random_integers(size=(1,), low=0, high=self.depth)
if axis == 0: return networks[idx]
if axis == 1: return networks[:,idx]
if axis == 2: return networks[:,:,idx]
if axis == 3: return networks[:,:,:,idx]
assert False, "axis too large"
else:
assert False, "consensus method unknown: " + cns
def kmeans(train_set_x):
if train_set_x is None:
train_set_x = T.matrix('train_set_x')
########################
# Normalize the inputs #
########################
epsilon_norm = 10
epsilon_zca = 0.015
K = 500
train_set_x = train_set_x - T.mean(train_set_x, axis=0) / T.sqrt(T.var(train_set_x, axis=0) + epsilon_norm)
#####################
# Whiten the inputs #
#####################
# a simple choice of whitening transform is the ZCA whitening transform
# epsilon_zca is small constant
# for contrast-normalizaed data, setting epsilon_zca to 0.01 for 16-by-16 pixel patches,
# or to 0.1 for 8-by-8 pixel patches
# is good starting point
cov = T.dot(train_set_x, T.transpose(train_set_x)) / train_set_x.shape[1]
U, S, V = linalg.svd(cov)
tmp = T.dot(U, T.diag(1/T.sqrt(S + epsilon_zca)))
tmp = T.dot(tmp, T.transpose(U))
whitened_x = T.dot(tmp, train_set_x)
######################
# Training the Model #
######################
# Initialization
dimension_size = whitened_x.shape[0]
num_samples = whitened_x.shape[1]
srng = RandomStreams(seed=234)
D = srng.normal(size=(dimension_size, K))
D = D / T.sqrt(T.sum(T.sqr(D), axis=0))
# typically 10 iterations is enough
num_iteration = 15
# compute new centroids, D_new
for i in xrange(num_iteration):
dx = T.dot(D.T, whitened_x)
arg_max_dx = T.argmax(dx, axis=0)
s = dx[arg_max_dx, T.arange(num_samples)]
S = T.zeros((K, num_samples))
S = T.set_subtensor(S[arg_max_dx, T.arange(num_samples)], s)
D = T.dot(whitened_x, T.transpose(S)) + D
D = D / T.sqrt(T.sum(T.sqr(D), axis=0))
return D
def activations(self, dataset):
prev_activations = self._prev_layer.activations(dataset)
if prev_activations.ndim == 2:
# flat dataset: (example, vector)
mean = T.mean(prev_activations, axis=0)
variance = T.var(prev_activations, axis=0)
elif prev_activations.ndim == 3:
# sequence dataset: (seq num, example, vector)
mean = T.mean(prev_activations, axis=1).dimshuffle(0, 'x', 1)
variance = T.var(prev_activations, axis=1).dimshuffle(0, 'x', 1)
normalized = (prev_activations - mean) / T.sqrt(variance +
self.EPSILON)
scaled_and_shifted = (normalized * self._scale) + self._shift
return scaled_and_shifted
def fprop(self, input):
"""Propogate the input through the layer."""
output = input - T.mean(input, axis=1, keepdims=True)
output = output / T.sqrt(T.var(input, axis=1, keepdims=True) + 1e-5)
output = self.alpha.dimshuffle('x', 0) * output + \
self.beta.dimshuffle('x', 0) # scale and shift
return output
def Kmeans(X_train=None, K=300, epsilon_whitening=0.015):
if X_train is None:
X_train = T.matrix('X_train')
########################
# Normalize the inputs #
########################
# A constant added to the variance to avoid division by zero
epsilon_norm = 10
# We subtract from each training sample (each column in X_train) its mean
X_train = X_train - T.mean(
X_train, axis=0) / T.sqrt(T.var(X_train, axis=0) + epsilon_norm)
#####################
# Whiten the inputs #
#####################
sigma = T.dot(X_train, T.transpose(X_train)) / X_train.shape[1]
U, s, V = linalg.svd(sigma, full_matrices=False)
tmp = T.dot(U, T.diag(1 / T.sqrt(s + epsilon_whitening)))
tmp = T.dot(tmp, T.transpose(U))
X_Whitened = T.dot(tmp, X_train)
######################
# Training the Model #
######################
# Initialization
dimensions = X_Whitened.shape[0]
samples = X_Whitened.shape[1]
srng = RandomStreams(seed=234)
# We initialize the centroids by sampling them from a normal
# distribution, and then normalizing them to unit length
# D \in R^{n \times k}
D = srng.normal(size=(dimensions, K))
D = D / T.sqrt(T.sum(T.sqr(D), axis=0))
iterations = 30
for i in xrange(iterations):
# Initialize new point representations
# for every pass of the algorithm
S = T.zeros((K, samples))
tmp = T.dot(D.T, X_Whitened)
res = T.argmax(tmp, axis=0)
max_values = tmp[res, T.arange(samples)]
S = T.set_subtensor(S[res, T.arange(samples)], max_values)
D = T.dot(X_Whitened, T.transpose(S))
D = D / T.sqrt(T.sum(T.sqr(D), axis=0))
return D
def sample_elbo(model, population=None, samples=1, pi=1, vp=None):
""" pi*KL[q(w|mu,rho)||p(w)] + E_q[log p(D|w)]
approximated by Monte Carlo sampling
Parameters
----------
model : pymc3.Model
population : dict - maps observed_RV to its population size
if not provided defaults to full population
samples : number of Monte Carlo samples used for approximation,
defaults to 1
pi : additional coefficient for KL[q(w|mu,rho)||p(w)] as proposed in [1]_
vp : gelato.variational.utils.VariatioanalParams
tuple, holding nodes mappings with shared params, if None - new
will be created
Returns
-------
(E_q[elbo], V_q[elbo], updates, VariationalParams)
mean, variance of elbo, updates for random streams, shared dicts
Notes
-----
You can pass tensors for `pi` and `samples` to control them while
training
References
----------
.. [1] Charles Blundell et al: "Weight Uncertainty in Neural Networks"
arXiv preprint arXiv:1505.05424
"""
if population is None:
population = dict()
if vp is None:
vp = variational_replacements(model.root)
x = flatten(vp.mapping.values())
mu = flatten(vp.shared.means.values())
rho = flatten(vp.shared.rhos.values())
def likelihood(var):
tot = population.get(var, population.get(var.name))
logpt = tt.sum(var.logpt)
if tot is not None:
tot = tt.as_tensor(tot)
logpt *= tot / var.size
return logpt
log_p_D = tt.add(*map(likelihood, model.root.observed_RVs))
log_p_W = model.root.varlogpt + tt.sum(model.root.potentials)
log_q_W = tt.sum(log_normal3(x, mu, rho))
_elbo_ = log_p_D + pi * (log_p_W - log_q_W)
_elbo_ = apply_replacements(_elbo_, vp)
samples = tt.as_tensor(samples)
elbos, updates = theano.scan(fn=lambda: _elbo_,
outputs_info=None,
n_steps=samples)
return tt.mean(elbos), tt.var(elbos), updates, vp
def fprop(self, x, can_fit, eval):
"""
x : input to the layer
can_fit :
eval :
"""
# shape the input as a matrix (batch_size, n_inputs)
self.x = x.flatten(2)
# apply dropout mask
if self.dropout < 1.:
if eval == False:
# The cast is important because
# int * float32 = float64 which pulls things off the gpu
# very slow ??
# srng = T.shared_randomstreams.RandomStreams(self.rng.randint(999999))
srng = theano.sandbox.rng_mrg.MRG_RandomStreams(self.rng.randint(999999))
mask = T.cast(srng.binomial(n=1, p=self.dropout, size=T.shape(self.x)), theano.config.floatX)
# apply the mask
self.x = self.x * mask
else:
self.x = self.x * self.dropout
# binarize the weights
self.Wb = self.binarize_weights(self.W, eval)
z = T.dot(self.x, self.Wb)
# for BN updates
self.z = z
# batch normalization
if self.BN == True:
self.batch_mean = T.mean(z,axis=0)
self.batch_var = T.var(z,axis=0)
if can_fit == True:
mean = self.batch_mean
var = self.batch_var
else:
mean = self.mean
var = self.var
z = (z - mean)/(T.sqrt(var+self.BN_epsilon))
z = self.a * z
self.z = z + self.b
# activation function
y = self.activation(self.z)
return y
def mom(cost, params, learning_rate, runningGradientStats, activations,
runningActGrad):
updates = []
resNumber = 0
for res in params:
insideNumber = 0
for current_params in res:
p_no = 0
for p in current_params: #weight bias gamma beta
p_no += 1
if (p_no == 4):
break
g = T.grad(cost, p)
updates.append((p, T.clip(p - learning_rate * g, -1.0, 1.0)))
#now update weight gradient stats
if (p_no == 1):
mu = T.mean(g)
sigma2 = T.var(g)
updates.append(
(runningGradientStats[resNumber][insideNumber][0],
0.9 * runningGradientStats[resNumber][insideNumber][0]
+ 0.1 * mu))
updates.append(
(runningGradientStats[resNumber][insideNumber][1],
0.9 * runningGradientStats[resNumber][insideNumber][1]
+ 0.1 * sigma2))
insideNumber += 1
resNumber += 1
resNumber = 0
for res in activations:
insideNumber = 0
for a in res:
g = T.grad(cost, a)
mu = T.mean(g)
sigma2 = T.var(g)
updates.append(
(runningActGrad[resNumber][insideNumber][0],
0.9 * runningActGrad[resNumber][insideNumber][0] + 0.1 * mu))
updates.append((runningActGrad[resNumber][insideNumber][1],
0.9 * runningActGrad[resNumber][insideNumber][1] +
0.1 * sigma2))
insideNumber += 1
resNumber += 1
return updates
def _ln(self, x, lnb, lns):
_eps = np.float32(1e-5)
out = (x - T.mean(x, axis=-1, keepdims=True)
) / T.sqrt(T.var(x, axis=-1, keepdims=True) + _eps)
out = lns * out + lnb
return out
def compute_output(self, network, in_vw):
super(MonitorVarianceNode, self).compute_output(network, in_vw)
if network.find_hyperparameter(["monitor"]):
network.create_vw(
"var",
variable=T.var(in_vw.variable),
shape=(),
tags={"monitor"},
)
def fprop(self, x, can_fit, eval):
# shape the input as a matrix (batch_size, n_inputs)
self.x = x.flatten(2)
# apply dropout mask
if self.dropout < 1.:
if eval == False:
# The cast is important because
# int * float32 = float64 which pulls things off the gpu
# very slow ??
# srng = T.shared_randomstreams.RandomStreams(self.rng.randint(999999))
srng = theano.sandbox.rng_mrg.MRG_RandomStreams(
self.rng.randint(999999))
mask = T.cast(
srng.binomial(n=1, p=self.dropout, size=T.shape(self.x)),
theano.config.floatX)
# apply the mask
self.x = self.x * mask
else:
self.x = self.x * self.dropout
# binarize the weights
self.Wb = self.binarize_weights(self.W, eval)
z = T.dot(self.x, self.Wb)
# for BN updates
self.z = z
# batch normalization
if self.BN == True:
self.batch_mean = T.mean(z, axis=0)
self.batch_var = T.var(z, axis=0)
if can_fit == True:
mean = self.batch_mean
var = self.batch_var
else:
mean = self.mean
var = self.var
z = (z - mean) / (T.sqrt(var + self.BN_epsilon))
z = self.a * z
self.z = z + self.b
# activation function
y = self.activation(self.z)
return y
def Kmeans(X_train=None, K=300, epsilon_whitening=0.015):
if X_train is None:
X_train = T.matrix("X_train")
########################
# Normalize the inputs #
########################
# A constant added to the variance to avoid division by zero
epsilon_norm = 10
# We subtract from each training sample (each column in X_train) its mean
X_train = X_train - T.mean(X_train, axis=0) / T.sqrt(T.var(X_train, axis=0) + epsilon_norm)
#####################
# Whiten the inputs #
#####################
sigma = T.dot(X_train, T.transpose(X_train)) / X_train.shape[1]
U, s, V = linalg.svd(sigma, full_matrices=False)
tmp = T.dot(U, T.diag(1 / T.sqrt(s + epsilon_whitening)))
tmp = T.dot(tmp, T.transpose(U))
X_Whitened = T.dot(tmp, X_train)
######################
# Training the Model #
######################
# Initialization
dimensions = X_Whitened.shape[0]
samples = X_Whitened.shape[1]
srng = RandomStreams(seed=234)
# We initialize the centroids by sampling them from a normal
# distribution, and then normalizing them to unit length
# D \in R^{n \times k}
D = srng.normal(size=(dimensions, K))
D = D / T.sqrt(T.sum(T.sqr(D), axis=0))
iterations = 30
for i in xrange(iterations):
# Initialize new point representations
# for every pass of the algorithm
S = T.zeros((K, samples))
tmp = T.dot(D.T, X_Whitened)
res = T.argmax(tmp, axis=0)
max_values = tmp[res, T.arange(samples)]
S = T.set_subtensor(S[res, T.arange(samples)], max_values)
D = T.dot(X_Whitened, T.transpose(S))
D = D / T.sqrt(T.sum(T.sqr(D), axis=0))
return D
def fprop(self, input):
""""Propogate input through the layer."""
if self.layer == 'fc':
# Training time
if self.run_mode == 0:
mean_t = T.mean(input, axis=0) # Compute mean
var_t = T.var(input, axis=0) # Compute variance
# Subtract mean and divide by std
norm_t = (input - mean_t) / T.sqrt(var_t + self.epsilon)
# Add parameters
output = self.gamma * norm_t + self.beta
# Update mean and variance
self.mean = self.momentum * self.mean + \
(1.0 - self.momentum) * mean_t
self.var = self.momentum * self.var + (1.0 - self.momentum) \
* (self.input_shape[0] / (self.input_shape[0] - 1) * var_t)
# Test time - use statistics from the training data
else:
output = self.gamma * (input - self.mean) / \
T.sqrt(self.var + self.epsilon) + self.beta
elif self.layer == 'conv':
if self.run_mode == 0:
# Mean across every channel
mean_t = T.mean(input, axis=(0, 2, 3))
var_t = T.var(input, axis=(0, 2, 3))
# mean, var update
self.mean = self.momentum * self.mean + \
(1.0 - self.momentum) * mean_t
self.var = self.momentum * self.var + (1.0 - self.momentum) * \
(self.input_shape[0] / (self.input_shape[0] - 1) * var_t)
else:
mean_t = self.mean
var_t = self.var
# change shape to fit input shape
mean_t = self.change_shape(mean_t)
var_t = self.change_shape(var_t)
gamma_t = self.change_shape(self.gamma)
beta_t = self.change_shape(self.beta)
output = gamma_t * (input - mean_t) / \
T.sqrt(var_t + self.epsilon) + beta_t
return output
def forward(self, x, train=True):
if train or (not self.moving):
if x.ndim == 2:
mean = T.mean(x, axis=0)
var = T.var(x, axis=0)
elif x.ndim == 4:
mean = T.mean(x, axis=(0, 2, 3))
var = T.var(x, axis=(0, 2, 3))
else:
raise ValueError('input.shape must be (batch_size, dim) '
'or (batch_size, filter_num, h, w).')
if self.moving:
bs = x.shape[0].astype(theano.config.floatX)
mean_inf_next = (self.momentum*self.mean_inf +
(1-self.momentum)*mean)
var_inf_next = (self.momentum*self.var_inf
+ (1-self.momentum)*var*bs/(bs-1.))
self.updates = [(self.mean_inf, mean_inf_next),
(self.var_inf, var_inf_next)]
else:
self.updates = []
else:
mean = self.mean_inf
var = self.var_inf
if x.ndim == 4:
mean = mean.dimshuffle('x', 0, 'x', 'x')
var = var.dimshuffle('x', 0, 'x', 'x')
output = (x-mean) / T.sqrt(var+self.eps)
if self.gamma is not None:
if x.ndim == 4:
output *= self.gamma.dimshuffle('x', 0, 'x', 'x')
else:
output *= self.gamma
if self.beta is not None:
if x.ndim == 4:
output += self.beta.dimshuffle('x', 0, 'x', 'x')
else:
output += self.beta
return output
def perform(self, x):
EPSI = 1e-5
S = self.params[0]
b = self.params[1]
x_ln = (x - T.mean(x_ln, axis=-1, keepdims=True))/T.sqrt(T.var(x, axis=-1, keepdims=True)+EPSI)
if x.ndim==3:
return x_ln * S.dimshuffle('x', 'x', 0) + b.dimshuffle('x', 'x', 0)
else:
return x_ln * S.dimshuffle('x', 0) + b.dimshuffle('x', 0)
def _compute_training_statistics(self, input_):
axes = (0,) + tuple((i + 1) for i, b in
enumerate(self.population_mean.broadcastable)
if b)
mean = input_.mean(axis=axes, keepdims=True)
assert mean.broadcastable[1:] == self.population_mean.broadcastable
stdev = tensor.sqrt(tensor.var(input_, axis=axes, keepdims=True) +
numpy.cast[theano.config.floatX](self.epsilon))
assert stdev.broadcastable[1:] == self.population_stdev.broadcastable
add_role(mean, BATCH_NORM_MINIBATCH_ESTIMATE)
add_role(stdev, BATCH_NORM_MINIBATCH_ESTIMATE)
return mean, stdev
def get_output(self, train):
X = self.get_input(train)
if self.mode == 0:
X_normed = (X - self.running_mean) / self.running_std
elif self.mode == 1:
m = T.mean(X, self.axis, keepdims=True)
std = T.sqrt(T.var(X, self.axis, keepdims=True) + self.epsilon)
X_normed = (X - m) / std
out = self.gamma * X_normed + self.beta
return out
