def __init__(self, numpy_rng, n=2, d=1, w=10, h=10, hidden_layers_sizes=[10]):
self.n = n
self.d = d
self.w = w
self.h = h
self.n_inputs = n*d*w*h
self.n_hidden_layers = len(hidden_layers_sizes)
self.n_outputs = d*w*h
print('SdAIndividual: building the model...'),
self.sda = SdA(
numpy_rng,
n_ins=self.n_inputs,
hidden_layers_sizes=hidden_layers_sizes,
n_outs=self.n_outputs
)
print('done')
print('SdAIndividual: building pretrain function...'),
self.pretrain_fns = self.sda.pretraining_functions()
print('done')
print('SdAIndividual: building finetune function...'),
self.finetune_fn, self.validate_fn = self.sda.build_finetune_function()
print('done')
print('SdAIndividual: building predict function...'),
self.predict_fn = self.sda.build_prediction_function()
print('done')
class SdAIndividual(Model):
def __init__(self, numpy_rng, n=2, d=1, w=10, h=10, hidden_layers_sizes=[10]):
self.n = n
self.d = d
self.w = w
self.h = h
self.n_hidden_layers = len(hidden_layers_sizes)
print('SdAIndividual: building the model...'),
self.dnns = \
[
[
SdA(
numpy_rng=numpy_rng,
n_ins=n*d,
hidden_layers_sizes=hidden_layers_sizes,
n_outs=d
) for i in xrange(w)
] for j in xrange(h)
]
self.sda = SdA(
numpy_rng=numpy_rng,
n_ins=n,
hidden_layers_sizes=hidden_layers_sizes,
n_outs=d
)
print('done')
print('SdAIndividual: building pretrain function...'),
self.pretrain_fns = self.sda.pretraining_functions()
print('done')
print('SdAIndividual: building finetune function...'),
self.finetune_fn, self.validate_fn = self.sda.build_finetune_function()
print('done')
print('SdAIndividual: building predict function...'),
self.predict_fn = self.sda.build_prediction_function()
print('done')
# print('SdAIndividual: getting pretrain functions...'),
# self.pretrain_fns = [ [dnn.pretraining_functions() for dnn in dnns] for dnns in self.dnns ]
# print('done')
# print('SdAIndividual: getting finetune functions...'),
# self.finetune_fns = [ [dnn.build_finetune_function() for dnn in dnns] for dnns in self.dnns ]
# print('done')
# print('SdAIndividual: getting predict functions...'),
# self.predict_fns = [ [dnn.build_prediction_function() for dnn in dnns] for dnns in self.dnns ]
# print('done')
def _make_input(self, dataset, idx, i, j):
'''
(i,j) の SdA に対する入力ベクトルを ndata から作る
:param ndata: an array of ndarray of (d-by-h-by-w) dimention, whose size is n
:param i:
:param j:
:return:
'''
# return numpy.append([], [chunk[:,j,i] for chunk in ndata]).reshape((1, self.n*self.d))
return dataset[[range(n,n+self.n) for n in idx], :, j, i].reshape((len(idx), self.n*self.d))
# x = []
# for n in idx:
# x.append(numpy.append([], [chunk[:,j,i] for chunk in dataset[n:n+self.n]]))
# return numpy.asarray(x, dtype=theano.config.floatX)
def _make_output(self, dataset, idx, i, j):
'''
(i,j) の SdA に対する出力ベクトルをつくる
:param data:
:param i:
:param j:
:return:
'''
# return data[:,j,i].reshape((1, self.d))
return dataset[[n+self.n for n in idx], :, j, i].reshape((len(idx), self.d))
# y = []
# for n in idx:
# y.append(dataset[n+self.n][:,j,i])
# return numpy.asarray(y, dtype=theano.config.floatX)
# def _build_pretrain_functions(self, dataset):
# index = T.lscalar('index')
# corruption_level = T.iscalar('corruption')
# learning_rate = T.scalar('lr')
#
# costs = [[] for i in xrange(self.n_hidden_layers)]
# updates = [[] for i in xrange(self.n_hidden_layers)]
# givens={}
# for j in xrange(self.h):
# for i in xrange(self.w):
# cost_updates = self.dnns[j][i].get_pretraining_cost_updates(corruption_level, learning_rate)
# for l, cost_update in enumerate(cost_updates):
# cost, update = cost_update
# costs[l].append(cost)
# updates[l].extend(update)
# givens[self.dnns[j][i].x] = self._make_input(dataset[(index-self.n):index-1], i, j)
#
# fns = []
# for l in xrange(self.n_hidden_layers):
# fn = theano.function(
# inputs=[
# index,
# theano.Param(corruption_level, default=0.2),
# theano.Param(learning_rate, default=0.1)
# ],
# outputs=costs[l],
# updates=updates[l],
# givens=givens,
# name='pretrain'
# )
# fns.append(fn)
#
# return fns
#
# def _build_finetune_function(self, dataset):
# index = T.iscalar('index')
# learning_rate = T.scalar('lr')
#
# costs = []
# updates = []
# givens = {}
# for j in xrange(self.h):
# for i in xrange(self.w):
# cost, update = self.dnns[j][i].get_finetune_cost_updates(learning_rate)
# costs.append(cost)
# updates.extend(update)
# givens[self.dnns[j][i].x] = self._make_input(dataset, [1], i, j)
# givens[self.dnns[j][i].y] = self._make_output(dataset, [1], i, j)
#
# fn = theano.function(
# inputs=[
# theano.Param(learning_rate, default=0.1)
# ],
# outputs=costs,
# updates=updates,
# givens=givens,
# name='train'
# )
#
# return fn
def prepare(self, i, j):
dnn = self.dnns[j][i]
for i in xrange(len(self.sda.params)):
self.sda.params[i].set_value(dnn.params[i].get_value(borrow=True), borrow=True)
def _pretrain_step(self, layer, dataset, index, corruption, learning_rate, batch_size):
avg_cost = 0.0
idx = range(index*batch_size, (index+1)*batch_size)
for j in xrange(self.h):
for i in xrange(self.w):
self.prepare(i, j)
xdata = self._make_input(dataset, idx, i, j)
cost = self.pretrain_fns[layer](xdata, corruption=corruption, lr=learning_rate)
avg_cost += cost
avg_cost /= self.n_hidden_layers * (self.w*self.h)
return avg_cost
def pretrain(self, dataset, epochs=100, learning_rate=0.1, batch_size=1):
'''
pretrain the model using the dataset
:param dataset:
:param epochs:
:param learning_rate:
:return:
'''
n_train_batches = (len(dataset) - self.n) / batch_size
avg_cost = numpy.inf
for layer in xrange(self.n_hidden_layers):
loop_done = False
epoch = 0
while (epoch < epochs) and not loop_done:
c = []
for minibatch_index in xrange(n_train_batches):
minibatch_avg_cost = self._pretrain_step(layer, dataset, minibatch_index, 0.0, learning_rate, batch_size)
c.append(minibatch_avg_cost)
avg_cost = numpy.mean(c)
# print(' pretrain({0}): cost={1}'.format(epoch, cost))
epoch = epoch + 1
return avg_cost
def _finetune_step(self, dataset, idx, learning_rate):
avg_cost = 0.0
for j in xrange(self.h):
for i in xrange(self.w):
self.prepare(i,j)
xdata = self._make_input(dataset, idx, i, j)
ydata = self._make_output(dataset, idx, i, j)
cost = self.finetune_fn(xdata, ydata, lr=learning_rate)
avg_cost += cost
avg_cost /= len(idx) * (self.w*self.h)
return avg_cost
def finetune(self, dataset, train_idx, valid_idx, epochs=100, learning_rate=0.1, batch_size=1):
'''
finetune the model using the dataset
:param dataset: an array of ndarray of (d-by-h-by-w) dimention, whose size is bigger than n
:return:
'''
n_train_batches = len(train_idx) / batch_size
# early-stopping parameters
patience = 10 * n_train_batches # look as this many examples regardless
patience_increase = 2. # wait this much longer when a new best is found
improvement_threshold = 0.995 # a relative improvement of this much is considered significant
validation_frequency = min(n_train_batches, patience / 2)
best_validation_loss = numpy.inf
done_looping = False
epoch = 0
avg_cost = numpy.inf
while (epoch < epochs) and not done_looping:
c = []
for minibatch_index in xrange(n_train_batches):
idx = train_idx[minibatch_index*batch_size:(minibatch_index+1)*batch_size]
minibatch_avg_cost = self._finetune_step(dataset, idx, learning_rate)
c.append(minibatch_avg_cost)
iter = (epoch - 1) * n_train_batches + minibatch_index
# early-stop
if (iter + 1) % validation_frequency == 0:
validation_losses = self.validate(dataset, valid_idx, batch_size)
this_validation_loss = numpy.mean(validation_losses)
print('epoch %i, minibatch %i/%i, validation error %f %%' %
(epoch, minibatch_index + 1, n_train_batches,
this_validation_loss * 100.))
# if we got the best validation score until now
if this_validation_loss < best_validation_loss:
#improve patience if loss improvement is good enough
if (
this_validation_loss < best_validation_loss *
improvement_threshold
):
patience = max(patience, iter * patience_increase)
# save best validation score and iteration number
best_validation_loss = this_validation_loss
best_iter = iter
if patience <= iter:
done_looping = True
break
# print(' finetune({0}): cost={1}'.format(epoch, cost))
avg_cost = numpy.mean(c)
epoch = epoch + 1
return avg_cost
def validate_step(self, dataset, idx):
avg_cost = 0.0
for j in xrange(self.h):
for i in xrange(self.w):
self.prepare(i,j)
xdata = self._make_input(dataset, idx, i, j)
ydata = self._make_output(dataset, idx, i, j)
cost = self.validate_fn(xdata, ydata)
avg_cost += cost
avg_cost /= len(idx) * (self.w*self.h)
return avg_cost
def validate(self, dataset, valid_idx, batch_size):
n_validate_batches = len(valid_idx) / batch_size
costs = []
for minibatch_index in xrange(n_validate_batches):
idx = valid_idx[minibatch_index*batch_size:(minibatch_index+1)*batch_size]
costs.append(self.validate_step(dataset, idx))
return costs
def train(self, dataset, train_idx, valid_idx, epochs=100, learning_rate=0.1, batch_size=1):
return self.finetune(dataset, train_idx, valid_idx, epochs=epochs, learning_rate=learning_rate, batch_size=batch_size)
def predict(self, dataset):
'''
predict the next value
:param n: an array of ndarray of (d-by-h-by-w) dimention, whose size is n
:return:
'''
y = numpy.zeros((self.d,self.h,self.w), dtype=theano.config.floatX)
for j in xrange(self.h):
for i in xrange(self.w):
self.prepare(i,j)
y[:,j,i] = self.predict_fn(self._make_input(dataset, [len(dataset)-self.n], i, j))[-1]
return y
class SdAFullyConnected(Model):
def __init__(self, numpy_rng, n=2, d=1, w=10, h=10, hidden_layers_sizes=[10]):
self.n = n
self.d = d
self.w = w
self.h = h
self.n_inputs = n*d*w*h
self.n_hidden_layers = len(hidden_layers_sizes)
self.n_outputs = d*w*h
print('SdAIndividual: building the model...'),
self.sda = SdA(
numpy_rng,
n_ins=self.n_inputs,
hidden_layers_sizes=hidden_layers_sizes,
n_outs=self.n_outputs
)
print('done')
print('SdAIndividual: building pretrain function...'),
self.pretrain_fns = self.sda.pretraining_functions()
print('done')
print('SdAIndividual: building finetune function...'),
self.finetune_fn, self.validate_fn = self.sda.build_finetune_function()
print('done')
print('SdAIndividual: building predict function...'),
self.predict_fn = self.sda.build_prediction_function()
print('done')
def _make_input(self, dataset, idx):
'''
(i,j) の SdA に対する入力ベクトルを ndata から作る
:param ndata: an array of ndarray of (d-by-h-by-w) dimention, whose size is n
:return:
'''
return dataset[[range(n,n+self.n) for n in idx], :].reshape((len(idx), self.n_inputs))
def _make_output(self, dataset, idx):
'''
(i,j) の SdA に対する出力ベクトルをつくる
:param data:
:return:
'''
return dataset[[n+self.n for n in idx], :].reshape((len(idx), self.n_outputs))
def _pretrain_step(self, layer, dataset, index, corruption, learning_rate, batch_size):
avg_cost = 0.0
idx = range(index*batch_size, (index+1)*batch_size)
xdata = self._make_input(dataset, idx)
cost = self.pretrain_fns[layer](xdata, corruption=corruption, lr=learning_rate)
avg_cost += cost
avg_cost /= self.n_hidden_layers * (self.w*self.h)
return avg_cost
def pretrain(self, dataset, epochs=100, learning_rate=0.1, batch_size=1):
'''
pretrain the model using the dataset
:param dataset:
:param epochs:
:param learning_rate:
:return:
'''
n_train_batches = (len(dataset) - self.n) / batch_size
avg_cost = numpy.inf
for layer in xrange(self.n_hidden_layers):
loop_done = False
epoch = 0
while (epoch < epochs) and not loop_done:
c = []
for minibatch_index in xrange(n_train_batches):
minibatch_avg_cost = self._pretrain_step(layer, dataset, minibatch_index, 0.0, learning_rate, batch_size)
c.append(minibatch_avg_cost)
avg_cost = numpy.mean(c)
# print(' pretrain({0}): cost={1}'.format(epoch, cost))
epoch = epoch + 1
return avg_cost
def _finetune_step(self, dataset, idx, learning_rate):
avg_cost = 0.0
xdata = self._make_input(dataset, idx)
ydata = self._make_output(dataset, idx)
cost = self.finetune_fn(xdata, ydata, lr=learning_rate)
avg_cost += cost
avg_cost /= len(idx) * (self.w*self.h)
return avg_cost
def finetune(self, dataset, train_idx, valid_idx, epochs=100, learning_rate=0.1, batch_size=1):
'''
finetune the model using the dataset
:param dataset: an array of ndarray of (d-by-h-by-w) dimention, whose size is bigger than n
:return:
'''
n_train_batches = len(train_idx) / batch_size
# early-stopping parameters
patience = 10 * n_train_batches # look as this many examples regardless
patience_increase = 2. # wait this much longer when a new best is found
improvement_threshold = 0.995 # a relative improvement of this much is considered significant
validation_frequency = min(n_train_batches, patience / 2)
best_validation_loss = numpy.inf
done_looping = False
epoch = 0
avg_cost = numpy.inf
while (epoch < epochs) and not done_looping:
c = []
for minibatch_index in xrange(n_train_batches):
idx = train_idx[minibatch_index*batch_size:(minibatch_index+1)*batch_size]
minibatch_avg_cost = self._finetune_step(dataset, idx, learning_rate)
c.append(minibatch_avg_cost)
iter = (epoch - 1) * n_train_batches + minibatch_index
# early-stop
if (iter + 1) % validation_frequency == 0:
validation_losses = self.validate(dataset, valid_idx, batch_size)
this_validation_loss = numpy.mean(validation_losses)
print('epoch %i, minibatch %i/%i, validation error %f %%' %
(epoch, minibatch_index + 1, n_train_batches,
this_validation_loss * 100.))
# if we got the best validation score until now
if this_validation_loss < best_validation_loss:
#improve patience if loss improvement is good enough
if (
this_validation_loss < best_validation_loss *
improvement_threshold
):
patience = max(patience, iter * patience_increase)
# save best validation score and iteration number
best_validation_loss = this_validation_loss
best_iter = iter
if patience <= iter:
done_looping = True
break
# print(' finetune({0}): cost={1}'.format(epoch, cost))
avg_cost = numpy.mean(c)
epoch = epoch + 1
return avg_cost
def validate(self, dataset, valid_idx, batch_size):
n_validate_batches = len(valid_idx) / batch_size
costs = []
for minibatch_index in xrange(n_validate_batches):
idx = valid_idx[minibatch_index*batch_size:(minibatch_index+1)*batch_size]
xdata = self._make_input(dataset, idx)
ydata = self._make_output(dataset, idx)
costs.append(self.validate_fn(xdata, ydata))
return costs
def train(self, dataset, train_idx, valid_idx, epochs=100, learning_rate=0.1, batch_size=1):
return self.finetune(dataset, train_idx, valid_idx, epochs=epochs, learning_rate=learning_rate, batch_size=batch_size)
def predict(self, dataset):
'''
predict the next value
:param n: an array of ndarray of (d-by-h-by-w) dimention, whose size is n
:return:
'''
xdata = self._make_input(dataset, [len(dataset)-self.n])
y = self.predict_fn(xdata)[-1].reshape((self.d, self.h, self.w))
return y