def learnAndPredict(Ti, C, TOList):
rng = np.random.RandomState(SEED)
learning_rate = learning_rate0
print np.mean(Ti[1000,:])
aminW = np.amin(Ti[:1000,:])
amaxW = np.amax(Ti[:1000,:])
Ti[:1000,:] = (Ti[:1000,:] - aminW) / (amaxW - aminW)
astdW = np.std(Ti[:1000,:])
ameanW = np.mean(Ti[:1000,:])
Ti[:1000,:] = (Ti[:1000,:] - ameanW) / astdW
aminacW = np.amin(Ti[1000,:])
amaxacW = np.amax(Ti[1000,:])
print aminW, amaxW, aminacW, amaxacW
Ti[1000,:] = (Ti[1000,:] - aminacW) / (amaxacW - aminacW)
astdacW = np.std(Ti[1000,:])
ameanacW = np.mean(Ti[1000,:])
Ti[1000,:] = (Ti[1000,:] - ameanacW) / astdacW
ile__ = len(TOList)
ileList = np.zeros(ile__)
for titer in range(len(TOList)):
print np.mean(TOList[titer][1000,:])
TOList[titer][:1000,:] = (TOList[titer][:1000,:] - aminW)/(amaxW - aminW)
TOList[titer][:1000,:] = (TOList[titer][:1000,:] - ameanW)/astdW
TOList[titer][1000,:] = (TOList[titer][1000,:] - aminacW)/(amaxacW - aminacW)
TOList[titer][1000,:] = (TOList[titer][1000,:] - ameanacW)/astdacW
_, ileList[titer] = TOList[titer].shape
_, ile = Ti.shape
N = NN
data = []; yyy = []; need = 1; BYL = {}; j= 0; dwa = 0; ONES = []; ZEROS = []
for i in range(NN):
for j in range(NN):
if i!= j:
if C[i][j]==1:
ONES.append((i,j))
else:
ZEROS.append((i,j))
Nones = len(ONES)
rng.shuffle(ONES)
Nzeros = len(ZEROS)
print Nones
print Nzeros
Needed = NUM_TRAIN/2
onesPerPair = Needed / Nones + 1
onesIter = 0
jj = 0
while jj < NUM_TRAIN:
if jj%300000 == 0:
print jj/300000,
need = 1 - need
if need == 1:
pairNo = onesIter % Nones
ppp = onesIter / Nones
s,t = ONES[pairNo]
shift = rng.randint(0, ile - L)
onesIter += 1
if need == 0:
zer = rng.randint(Nzeros)
s,t = ZEROS[zer]
del ZEROS[zer]
Nzeros -= 1
shift = rng.randint(0, ile - L)
x = np.hstack(( Ti[s][shift:shift+L], Ti[t][shift:shift+L], Ti[1000][shift:shift+L]))
y = C[s][t]
data.append(x); yyy.append(y)
jj+=1
data = np.array(data, dtype=theano.config.floatX)
is_train = np.array( ([0]*96 + [1,1,2,2]) * (NUM_TRAIN / 100))
yyy = np.array(yyy)
train_set_x0, train_set_y0 = np.array(data[is_train==0]), yyy[is_train==0]
test_set_x, test_set_y = np.array(data[is_train==1]), yyy[is_train==1]
valid_set_x, valid_set_y = np.array(data[is_train==2]), yyy[is_train==2]
n_train_batches = len(train_set_y0) / batch_size
n_valid_batches = len(valid_set_y) / batch_size
n_test_batches = len(test_set_y) / batch_size
epoch = T.scalar()
index = T.lscalar()
x = T.matrix('x')
inone2 = T.matrix('inone2')
y = T.ivector('y')
print '... building the model'
#-------- my layers -------------------
#---------------------
layer0_input = x.reshape((batch_size, 1, 3, L))
Cx = 5
layer0 = ConvolutionalLayer(rng, input=layer0_input,
image_shape=(batch_size, 1, 3, L),
filter_shape=(nkerns[0], 1, 2, Cx), poolsize=(1, 1), fac = 0)
ONE = (3 - 2 + 1) / 1
L2 = (L - Cx + 1) / 1
#---------------------
Cx2 = 5
layer1 = ConvolutionalLayer(rng, input=layer0.output,
image_shape=(batch_size, nkerns[0], ONE, L2),
filter_shape=(nkerns[1], nkerns[0], 2, Cx2), poolsize=(1, 1), activation=ReLU, fac = 0)
ONE = (ONE - 2 + 1) /1
L3 = (L2 - Cx2 + 1) /1
#---------------------
Cx3 = 1
layer1b = ConvolutionalLayer(rng, input=layer1.output,
image_shape=(batch_size, nkerns[1], ONE, L3),
filter_shape=(nkerns[2], nkerns[1], 1, Cx3), poolsize=(1, POOL), activation=ReLU, fac = 0)
ONE = (ONE - 1 + 1) /1
L4 = (L3 - Cx3 + 1) /POOL
REGx = 100
#---------------------
layer2_input = layer1b.output.flatten(2)
print layer2_input.shape
use_b = False
layer2 = HiddenLayer(rng, input=layer2_input, n_in=nkerns[2]*L4 , n_out=REGx, activation=T.tanh,
use_bias = use_b)
layer3 = LogisticRegression(input=layer2.output, n_in=REGx, n_out=2)
cost = layer3.negative_log_likelihood(y)
out_x2 = theano.shared(np.asarray(np.zeros((N,L)), dtype=theano.config.floatX))
inone2 = theano.shared(np.asarray(np.zeros((1,L)), dtype=theano.config.floatX))
inone3 = theano.shared(np.asarray(np.zeros((1,L)), dtype=theano.config.floatX))
inone4 = theano.shared(np.asarray(np.zeros((1,L)), dtype=theano.config.floatX))
test_set_x = theano.shared(np.asarray(test_set_x, dtype=theano.config.floatX))
train_set_x = theano.shared(np.asarray(train_set_x0, dtype=theano.config.floatX))
train_set_y = T.cast(theano.shared(np.asarray(train_set_y0, dtype=theano.config.floatX)), 'int32')
test_set_y = T.cast(theano.shared(np.asarray(test_set_y, dtype=theano.config.floatX)), 'int32')
valid_set_y = T.cast(theano.shared(np.asarray(valid_set_y, dtype=theano.config.floatX)), 'int32')
valid_set_x = theano.shared(np.asarray(valid_set_x, dtype=theano.config.floatX))
test_model = theano.function([index], layer3.errors(y),
givens={
x: test_set_x[index * batch_size: (index + 1) * batch_size],
y: test_set_y[index * batch_size: (index + 1) * batch_size]})
validate_model = theano.function([index], layer3.errors(y),
givens={
x: valid_set_x[index * batch_size: (index + 1) * batch_size],
y: valid_set_y[index * batch_size: (index + 1) * batch_size]})
mom_start = 0.5; mom_end = 0.98; mom_epoch_interval = n_epochs * 1.0
#### @@@@@@@@@@@
class_params0 = [layer3, layer2, layer1, layer1b, layer0]
class_params = [ param for layer in class_params0 for param in layer.params ]
gparams = []
for param in class_params:
gparam = T.grad(cost, param)
gparams.append(gparam)
gparams_mom = []
for param in class_params:
gparam_mom = theano.shared(np.zeros(param.get_value(borrow=True).shape,
dtype=theano.config.floatX))
gparams_mom.append(gparam_mom)
mom = ifelse(epoch < mom_epoch_interval,
mom_start*(1.0 - epoch/mom_epoch_interval) + mom_end*(epoch/mom_epoch_interval),
mom_end)
updates = OrderedDict()
for gparam_mom, gparam in zip(gparams_mom, gparams):
updates[gparam_mom] = mom * gparam_mom - (1. - mom) * learning_rate * gparam
for param, gparam_mom in zip(class_params, gparams_mom):
stepped_param = param + updates[gparam_mom]
squared_filter_length_limit = 15.0
if param.get_value(borrow=True).ndim == 2:
col_norms = T.sqrt(T.sum(T.sqr(stepped_param), axis=0))
desired_norms = T.clip(col_norms, 0, T.sqrt(squared_filter_length_limit))
scale = desired_norms / (1e-7 + col_norms)
updates[param] = stepped_param * scale
else:
updates[param] = stepped_param
output = cost
train_model = theano.function(inputs=[epoch, index], outputs=output,
updates=updates,
givens={
x: train_set_x[index * batch_size:(index + 1) * batch_size],
y: train_set_y[index * batch_size:(index + 1) * batch_size]})
keep = theano.function([index], layer3.errorsFull(y),
givens={
x: train_set_x[index * batch_size:(index + 1) * batch_size],
y: train_set_y[index * batch_size:(index + 1) * batch_size]}, on_unused_input='warn')
timer = time.clock()
print "finished reading", (timer - start_time0) /60. , "minutes "
# TRAIN MODEL #
print '... training'
validation_frequency = n_train_batches; best_params = None; best_validation_loss = np.inf
best_iter = 0; test_score = 0.; epochc = 0;
while (epochc < n_epochs):
epochc = epochc + 1
learning_rate = learning_rate0 * (1.2 - ((1.0 * epochc)/n_epochs))
for minibatch_index in xrange(n_train_batches):
iter = (epochc - 1) * n_train_batches + minibatch_index
cost_ij = train_model(epochc, minibatch_index)
if (iter + 1) % validation_frequency == 0:
validation_losses = [validate_model(i) for i in xrange(n_valid_batches)]
this_validation_loss = np.mean(validation_losses)
print(' %i) err %.2f ' % (epochc, this_validation_loss/10)), L, nkerns, REGx, "|", Cx, Cx2, Cx3, batch_size
if this_validation_loss < best_validation_loss or epochc % 30 == 0:
best_validation_loss = this_validation_loss
best_iter = iter
test_losses = [test_model(i) for i in xrange(n_test_batches)]
test_score = np.mean(test_losses)
print((' epoch %i, minibatch %i/%i, test error of best '
'model %f %%') % (epochc, minibatch_index + 1, n_train_batches, test_score/10))
############
timel = time.clock()
print "finished learning", (timel - timer) /60. , "minutes "
ppm = theano.function([index], layer3.pred_proba_mine(),
givens={
x: T.horizontal_stack(T.tile(inone2, (batch_size ,1)),
out_x2[index * batch_size: (index + 1) * batch_size], T.tile(inone3, (batch_size ,1))),
y: train_set_y[0 * (batch_size): (0 + 1) * (batch_size)]
}, on_unused_input='warn')
NONZERO = (N*N-N)
gc.collect()
RESList = [np.zeros((N,N)) for it in range(ile__)]
for __net in range(ile__):
TO = TOList[__net]
ileO = ileList[__net]
RES = RESList[__net]
shift = 0.1
DELTAshift = (ileO-L) / (Q-1)
print "DELTAshift:", DELTAshift
for q in range (Q):
dataO = []; print (q+1),"/", Q , " ",
out_x2.set_value(np.asarray(np.array(TO[:,shift:shift+L]), dtype=theano.config.floatX))
PARTIAL = np.zeros((N,N))
inone3.set_value(np.asarray(np.array(TO[1000][shift:shift+L]).reshape(1,L), dtype=theano.config.floatX))
for i in range(N):
inone2.set_value(np.asarray(np.array(TO[i][shift:shift+L]).reshape(1,L), dtype=theano.config.floatX))
p = [ppm(ii) for ii in xrange( N / batch_size)]
for pos in range(N):
if pos != i:
PARTIAL[i][pos] += p[pos / batch_size][pos % batch_size][1]
for i in range(N):
for j in range(N):
RES[i][j] += PARTIAL[i][j]
shift += DELTAshift
print "Finished", __net
RESList[__net] = RES/np.max(RES)
gc.collect()
end_time = time.clock()
print "finished predicting", (end_time - timel) /60. , "minutes ", str(nkerns), "using SEED = ", SEED
print('The code for file ' + os.path.split(__file__)[1] + ' ran for %.2fm' % ((end_time - start_time0) / 60.))
return RESList
def learnAndPredict(Ti, C, TOList):
rng = np.random.RandomState(SEED)
learning_rate = learning_rate0
print np.mean(Ti[1000, :])
aminW = np.amin(Ti[:1000, :])
amaxW = np.amax(Ti[:1000, :])
Ti[:1000, :] = (Ti[:1000, :] - aminW) / (amaxW - aminW)
astdW = np.std(Ti[:1000, :])
ameanW = np.mean(Ti[:1000, :])
Ti[:1000, :] = (Ti[:1000, :] - ameanW) / astdW
aminacW = np.amin(Ti[1000, :])
amaxacW = np.amax(Ti[1000, :])
print aminW, amaxW, aminacW, amaxacW
Ti[1000, :] = (Ti[1000, :] - aminacW) / (amaxacW - aminacW)
astdacW = np.std(Ti[1000, :])
ameanacW = np.mean(Ti[1000, :])
Ti[1000, :] = (Ti[1000, :] - ameanacW) / astdacW
ile__ = len(TOList)
ileList = np.zeros(ile__)
for titer in range(len(TOList)):
print np.mean(TOList[titer][1000, :])
TOList[titer][:1000, :] = (TOList[titer][:1000, :] - aminW) / (amaxW -
aminW)
TOList[titer][:1000, :] = (TOList[titer][:1000, :] - ameanW) / astdW
TOList[titer][1000, :] = (TOList[titer][1000, :] -
aminacW) / (amaxacW - aminacW)
TOList[titer][1000, :] = (TOList[titer][1000, :] - ameanacW) / astdacW
_, ileList[titer] = TOList[titer].shape
_, ile = Ti.shape
N = NN
data = []
yyy = []
need = 1
BYL = {}
j = 0
dwa = 0
ONES = []
ZEROS = []
for i in range(NN):
for j in range(NN):
if i != j:
if C[i][j] == 1:
ONES.append((i, j))
else:
ZEROS.append((i, j))
Nones = len(ONES)
rng.shuffle(ONES)
Nzeros = len(ZEROS)
print Nones
print Nzeros
Needed = NUM_TRAIN / 2
onesPerPair = Needed / Nones + 1
onesIter = 0
jj = 0
while jj < NUM_TRAIN:
if jj % 300000 == 0:
print jj / 300000,
need = 1 - need
if need == 1:
pairNo = onesIter % Nones
ppp = onesIter / Nones
s, t = ONES[pairNo]
shift = rng.randint(0, ile - L)
onesIter += 1
if need == 0:
zer = rng.randint(Nzeros)
s, t = ZEROS[zer]
del ZEROS[zer]
Nzeros -= 1
shift = rng.randint(0, ile - L)
x = np.hstack((Ti[s][shift:shift + L], Ti[t][shift:shift + L],
Ti[1000][shift:shift + L]))
y = C[s][t]
data.append(x)
yyy.append(y)
jj += 1
data = np.array(data, dtype=theano.config.floatX)
is_train = np.array(([0] * 96 + [1, 1, 2, 2]) * (NUM_TRAIN / 100))
yyy = np.array(yyy)
train_set_x0, train_set_y0 = np.array(
data[is_train == 0]), yyy[is_train == 0]
test_set_x, test_set_y = np.array(data[is_train == 1]), yyy[is_train == 1]
valid_set_x, valid_set_y = np.array(
data[is_train == 2]), yyy[is_train == 2]
n_train_batches = len(train_set_y0) / batch_size
n_valid_batches = len(valid_set_y) / batch_size
n_test_batches = len(test_set_y) / batch_size
epoch = T.scalar()
index = T.lscalar()
x = T.matrix('x')
inone2 = T.matrix('inone2')
y = T.ivector('y')
print '... building the model'
#-------- my layers -------------------
#---------------------
layer0_input = x.reshape((batch_size, 1, 3, L))
Cx = 5
layer0 = ConvolutionalLayer(rng,
input=layer0_input,
image_shape=(batch_size, 1, 3, L),
filter_shape=(nkerns[0], 1, 2, Cx),
poolsize=(1, 1),
fac=0)
ONE = (3 - 2 + 1) / 1
L2 = (L - Cx + 1) / 1
#---------------------
Cx2 = 5
layer1 = ConvolutionalLayer(rng,
input=layer0.output,
image_shape=(batch_size, nkerns[0], ONE, L2),
filter_shape=(nkerns[1], nkerns[0], 2, Cx2),
poolsize=(1, 1),
activation=ReLU,
fac=0)
ONE = (ONE - 2 + 1) / 1
L3 = (L2 - Cx2 + 1) / 1
#---------------------
Cx3 = 1
layer1b = ConvolutionalLayer(rng,
input=layer1.output,
image_shape=(batch_size, nkerns[1], ONE, L3),
filter_shape=(nkerns[2], nkerns[1], 1, Cx3),
poolsize=(1, POOL),
activation=ReLU,
fac=0)
ONE = (ONE - 1 + 1) / 1
L4 = (L3 - Cx3 + 1) / POOL
REGx = 100
#---------------------
layer2_input = layer1b.output.flatten(2)
print layer2_input.shape
use_b = False
layer2 = HiddenLayer(rng,
input=layer2_input,
n_in=nkerns[2] * L4,
n_out=REGx,
activation=T.tanh,
use_bias=use_b)
layer3 = LogisticRegression(input=layer2.output, n_in=REGx, n_out=2)
cost = layer3.negative_log_likelihood(y)
out_x2 = theano.shared(
np.asarray(np.zeros((N, L)), dtype=theano.config.floatX))
inone2 = theano.shared(
np.asarray(np.zeros((1, L)), dtype=theano.config.floatX))
inone3 = theano.shared(
np.asarray(np.zeros((1, L)), dtype=theano.config.floatX))
inone4 = theano.shared(
np.asarray(np.zeros((1, L)), dtype=theano.config.floatX))
test_set_x = theano.shared(
np.asarray(test_set_x, dtype=theano.config.floatX))
train_set_x = theano.shared(
np.asarray(train_set_x0, dtype=theano.config.floatX))
train_set_y = T.cast(
theano.shared(np.asarray(train_set_y0, dtype=theano.config.floatX)),
'int32')
test_set_y = T.cast(
theano.shared(np.asarray(test_set_y, dtype=theano.config.floatX)),
'int32')
valid_set_y = T.cast(
theano.shared(np.asarray(valid_set_y, dtype=theano.config.floatX)),
'int32')
valid_set_x = theano.shared(
np.asarray(valid_set_x, dtype=theano.config.floatX))
test_model = theano.function(
[index],
layer3.errors(y),
givens={
x: test_set_x[index * batch_size:(index + 1) * batch_size],
y: test_set_y[index * batch_size:(index + 1) * batch_size]
})
validate_model = theano.function(
[index],
layer3.errors(y),
givens={
x: valid_set_x[index * batch_size:(index + 1) * batch_size],
y: valid_set_y[index * batch_size:(index + 1) * batch_size]
})
mom_start = 0.5
mom_end = 0.98
mom_epoch_interval = n_epochs * 1.0
#### @@@@@@@@@@@
class_params0 = [layer3, layer2, layer1, layer1b, layer0]
class_params = [param for layer in class_params0 for param in layer.params]
gparams = []
for param in class_params:
gparam = T.grad(cost, param)
gparams.append(gparam)
gparams_mom = []
for param in class_params:
gparam_mom = theano.shared(
np.zeros(param.get_value(borrow=True).shape,
dtype=theano.config.floatX))
gparams_mom.append(gparam_mom)
mom = ifelse(
epoch < mom_epoch_interval,
mom_start * (1.0 - epoch / mom_epoch_interval) + mom_end *
(epoch / mom_epoch_interval), mom_end)
updates = OrderedDict()
for gparam_mom, gparam in zip(gparams_mom, gparams):
updates[gparam_mom] = mom * gparam_mom - (1. -
mom) * learning_rate * gparam
for param, gparam_mom in zip(class_params, gparams_mom):
stepped_param = param + updates[gparam_mom]
squared_filter_length_limit = 15.0
if param.get_value(borrow=True).ndim == 2:
col_norms = T.sqrt(T.sum(T.sqr(stepped_param), axis=0))
desired_norms = T.clip(col_norms, 0,
T.sqrt(squared_filter_length_limit))
scale = desired_norms / (1e-7 + col_norms)
updates[param] = stepped_param * scale
else:
updates[param] = stepped_param
output = cost
train_model = theano.function(
inputs=[epoch, index],
outputs=output,
updates=updates,
givens={
x: train_set_x[index * batch_size:(index + 1) * batch_size],
y: train_set_y[index * batch_size:(index + 1) * batch_size]
})
keep = theano.function(
[index],
layer3.errorsFull(y),
givens={
x: train_set_x[index * batch_size:(index + 1) * batch_size],
y: train_set_y[index * batch_size:(index + 1) * batch_size]
},
on_unused_input='warn')
timer = time.clock()
print "finished reading", (timer - start_time0) / 60., "minutes "
# TRAIN MODEL #
print '... training'
validation_frequency = n_train_batches
best_params = None
best_validation_loss = np.inf
best_iter = 0
test_score = 0.
epochc = 0
while (epochc < n_epochs):
epochc = epochc + 1
learning_rate = learning_rate0 * (1.2 - ((1.0 * epochc) / n_epochs))
for minibatch_index in xrange(n_train_batches):
iter = (epochc - 1) * n_train_batches + minibatch_index
cost_ij = train_model(epochc, minibatch_index)
if (iter + 1) % validation_frequency == 0:
validation_losses = [
validate_model(i) for i in xrange(n_valid_batches)
]
this_validation_loss = np.mean(validation_losses)
print(' %i) err %.2f ' % (epochc, this_validation_loss / 10)
), L, nkerns, REGx, "|", Cx, Cx2, Cx3, batch_size
if this_validation_loss < best_validation_loss or epochc % 30 == 0:
best_validation_loss = this_validation_loss
best_iter = iter
test_losses = [
test_model(i) for i in xrange(n_test_batches)
]
test_score = np.mean(test_losses)
print(
(' epoch %i, minibatch %i/%i, test error of best '
'model %f %%') % (epochc, minibatch_index + 1,
n_train_batches, test_score / 10))
############
timel = time.clock()
print "finished learning", (timel - timer) / 60., "minutes "
ppm = theano.function(
[index],
layer3.pred_proba_mine(),
givens={
x:
T.horizontal_stack(
T.tile(inone2, (batch_size, 1)),
out_x2[index * batch_size:(index + 1) * batch_size],
T.tile(inone3, (batch_size, 1))),
y:
train_set_y[0 * (batch_size):(0 + 1) * (batch_size)]
},
on_unused_input='warn')
NONZERO = (N * N - N)
gc.collect()
RESList = [np.zeros((N, N)) for it in range(ile__)]
for __net in range(ile__):
TO = TOList[__net]
ileO = ileList[__net]
RES = RESList[__net]
shift = 0.1
DELTAshift = (ileO - L) / (Q - 1)
print "DELTAshift:", DELTAshift
for q in range(Q):
dataO = []
print(q + 1), "/", Q, " ",
out_x2.set_value(
np.asarray(np.array(TO[:, shift:shift + L]),
dtype=theano.config.floatX))
PARTIAL = np.zeros((N, N))
inone3.set_value(
np.asarray(np.array(TO[1000][shift:shift + L]).reshape(1, L),
dtype=theano.config.floatX))
for i in range(N):
inone2.set_value(
np.asarray(np.array(TO[i][shift:shift + L]).reshape(1, L),
dtype=theano.config.floatX))
p = [ppm(ii) for ii in xrange(N / batch_size)]
for pos in range(N):
if pos != i:
PARTIAL[i][pos] += p[pos / batch_size][pos %
batch_size][1]
for i in range(N):
for j in range(N):
RES[i][j] += PARTIAL[i][j]
shift += DELTAshift
print "Finished", __net
RESList[__net] = RES / np.max(RES)
gc.collect()
end_time = time.clock()
print "finished predicting", (end_time - timel) / 60., "minutes ", str(
nkerns), "using SEED = ", SEED
print('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time0) / 60.))
return RESList
def train_logisticRegression(learning_rate=0.13,
n_epochs=1000,
dataset="mnist.pkl.gz",
batch_size=600):
###############################################################
# Get Data
###############################################################
# Load datasets
datasets = shared_dataset(dataset)
train_set_x, train_set_y = datasets[0]
valid_set_x, valid_set_y = datasets[1]
test_set_x, test_set_y = datasets[2]
# Visualize some data samples
plot_image(train_set_x.get_value(borrow=True)[10], 28, 28)
plot_image(valid_set_x.get_value(borrow=True)[15], 28, 28)
plot_image(test_set_x.get_value(borrow=True)[5], 28, 28)
# Split sets into batches
n_train_batches = train_set_x.get_value(borrow=True).shape[0] // batch_size
n_valid_batches = valid_set_x.get_value(borrow=True).shape[0] // batch_size
n_test_batches = test_set_x.get_value(borrow=True).shape[0] // batch_size
###############################################################
# Build model
###############################################################
# Allocate symbolic variables
index = T.lscalar()
x = T.matrix('x')
y = T.ivector('y')
# Build classifier
classifier = LogisticRegression(input=x, n_in=28 * 28, n_out=10)
# Define gradient descent
cost = classifier.negative_log_likelihood(y)
g_W = T.grad(cost=cost, wrt=classifier.W)
g_b = T.grad(cost=cost, wrt=classifier.b)
updates = [(classifier.W, classifier.W - g_W * learning_rate),
(classifier.b, classifier.b - g_b * learning_rate)]
# Test function
test_model = theano.function(
inputs=[index],
outputs=classifier.errorRate(y),
givens={
x: test_set_x[index * batch_size:(index + 1) * batch_size],
y: test_set_y[index * batch_size:(index + 1) * batch_size]
})
# Validation function
validate_model = theano.function(
inputs=[index],
outputs=classifier.errorRate(y),
givens={
x: valid_set_x[index * batch_size:(index + 1) * batch_size],
y: valid_set_y[index * batch_size:(index + 1) * batch_size]
})
# Training function
train_model = theano.function(
inputs=[index],
outputs=cost,
updates=updates,
givens={
x: train_set_x[index * batch_size:(index + 1) * batch_size],
y: train_set_y[index * batch_size:(index + 1) * batch_size]
})
###############################################################
# Train Model
###############################################################
print("Training the model...")
patience = 5000 # look at this many batches 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
test_score = 0.
start_time = timeit.default_timer()
done_looping = False
epoch = 0
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
for batch_index in range(n_train_batches):
batch_avg_cost = train_model(batch_index)
iter = (epoch - 1) * n_train_batches + batch_index
if (iter + 1) % validation_frequency == 0:
validation_losses = [
validate_model(i) for i in range(n_valid_batches)
]
this_validation_loss = numpy.mean(validation_losses)
print('epoch %i, batch %i/%i, validation error rate %f %%' %
(epoch, batch_index + 1, n_train_batches,
this_validation_loss * 100))
if this_validation_loss < best_validation_loss:
if this_validation_loss < best_validation_loss * improvement_threshold:
patience = max(patience, iter * patience_increase)
best_validation_loss = this_validation_loss
test_losses = [
test_model(i) for i in range(n_test_batches)
]
test_score = numpy.mean(test_losses)
print(' epoch %i, batch %i/%i, test error rate %f %%' %
(epoch, batch_index + 1, n_train_batches,
test_score * 100))
with open('best_model.pkl', 'wb') as f:
_pickle.dump(classifier, f)
if (patience <= iter):
done_looping = True
break
end_time = timeit.default_timer()
print(('Optimization completed with best validation loss of %f %%,'
'with test score of %f %%.') %
(best_validation_loss * 100., test_score * 100.))
print('The code ran for %d epochs, withiin %f seconds.' %
(epoch, end_time - start_time))
class SdA(object):
def __init__(
self,
numpy_rng,
n_ins,
n_outs,
hidden_layers_sizes,
corruption_levels=[0.1, 0.1],
theano_rng=None
):
""" This class is made to support a variable number of layers.
:type numpy_rng: numpy.random.RandomState
:param numpy_rng: numpy random number generator used to draw initial
weights
:type theano_rng: theano.tensor.shared_randomstreams.RandomStreams
:param theano_rng: Theano random generator; if None is given one is
generated based on a seed drawn from `rng`
:type n_ins: int
:param n_ins: dimension of the input to the sdA
:type n_layers_sizes: list of ints
:param n_layers_sizes: intermediate layers size, must contain
at least one value
:type n_outs: int
:param n_outs: dimension of the output of the network
:type corruption_levels: list of float
:param corruption_levels: amount of corruption to use for each
layer
"""
self.sigmoid_layers = []
self.dA_layers = []
self.params = []
self.n_layers = len(hidden_layers_sizes)
self.n_ins=n_ins
self.n_outs=n_outs
# allocate symbolic variables for the data
self.x = T.matrix('x')
self.y = T.ivector('y')
assert self.n_layers > 0
if not theano_rng:
theano_rng = RandomStreams(numpy_rng.randint(2 ** 30))
for i in xrange(self.n_layers):
# construct the sigmoidal layer
# the size of the input is either the number of hidden units of
# the layer below or the input size if we are on the first layer
if i == 0:
input_size = n_ins
else:
input_size = hidden_layers_sizes[i - 1]
# the input to this layer is either the activation of the hidden
# layer below or the input of the SdA if you are on the first
# layer
if i == 0:
layer_input = self.x
else:
layer_input = self.sigmoid_layers[-1].output
sigmoid_layer = HiddenLayer(
rng=numpy_rng,
input=layer_input,
n_in=input_size,
n_out=hidden_layers_sizes[i],
activation=T.nnet.sigmoid
)
# add the layer to our list of layers
self.sigmoid_layers.append(sigmoid_layer)
self.params.append(sigmoid_layer.theta)
# Construct a denoising autoencoder that shared weights with this layer
dA_layer = dA(
numpy_rng=numpy_rng,
theano_rng=theano_rng,
input=layer_input,
n_visible=input_size,
n_hidden=hidden_layers_sizes[i],
theta=sigmoid_layer.theta
)
self.dA_layers.append(dA_layer)
sda_input = T.matrix('sda_input')
self.da_layers_output_size = hidden_layers_sizes[-1]
self.get_da_output = theano.function(
inputs=[sda_input],
outputs=self.sigmoid_layers[-1].output.reshape((-1, self.da_layers_output_size)),
givens={
self.x: sda_input
}
)
self.logLayer = LogisticRegression(
rng = numpy.random.RandomState(),
input=self.sigmoid_layers[-1].output,
n_in=hidden_layers_sizes[-1],
n_out=n_outs
)
#self.params.extend(self.logLayer.params)
self.finetune_cost = self.logLayer.negative_log_likelihood(self.y)
self.errors = self.logLayer.errors(self.y)
def evaluate_lenet5(datasets,
learning_rate=0.1,
n_epochs=10,
nkerns=[20, 50],
batch_size=2):
""" Demonstrates lenet on MNIST dataset
:param datasets:
:param batch_size:
:type learning_rate: float
:param learning_rate: learning rate used (factor for the stochastic
gradient)
:type n_epochs: int
:param n_epochs: maximal number of epochs to run the optimizer
:type nkerns: list of ints
:param nkerns: number of kernels on each layer
"""
train_set_x, train_set_y = datasets[0]
valid_set_x, valid_set_y = datasets[1]
test_set_x, test_set_y = datasets[2]
# compute number of minibatches for training, validation and testing
n_train_batches = train_set_x.get_value(borrow=True).shape[0] // batch_size
n_valid_batches = valid_set_x.get_value(borrow=True).shape[0] // batch_size
n_test_batches = test_set_x.get_value(borrow=True).shape[0] // batch_size
# allocate symbolic variables for the data
index = T.lscalar() # index to a [mini]batch
x = T.matrix('x') # the data is presented as rasterized images
y = T.ivector('y') # the labels are presented as 1D vector of
# [int] labels
rng = numpy.random.RandomState(1234)
######################
# BUILD ACTUAL MODEL #
######################
print('... building the model')
# Reshape matrix of rasterized images of shape (batch_size, 28 * 28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
# (28, 28) is the size of MNIST images.
layer0_input = x.reshape((batch_size, 1, 1, 47))
# Construct the first convolutional pooling layer:
# filtering reduces the image size to (28-5+1 , 28-5+1) = (24, 24)
# maxpooling reduces this further to (24/2, 24/2) = (12, 12)
# 4D output tensor is thus of shape (batch_size, nkerns[0], 12, 12)
layer0 = LeNetConvPoolLayer(rng,
input=layer0_input,
image_shape=(batch_size, 1, 1, 47),
filter_shape=(nkerns[0], 1, 1, 6),
poolsize=(1, 2))
# Construct the second convolutional pooling layer
# filtering reduces the image size to (12-5+1, 12-5+1) = (8, 8)
# maxpooling reduces this further to (8/2, 8/2) = (4, 4)
# 4D output tensor is thus of shape (batch_size, nkerns[1], 4, 4)
layer1 = LeNetConvPoolLayer(rng,
input=layer0.output,
image_shape=(batch_size, nkerns[0], 1, 21),
filter_shape=(nkerns[1], nkerns[0], 1, 6),
poolsize=(1, 2))
# the HiddenLayer being fully-connected, it operates on 2D matrices of
# shape (batch_size, num_pixels) (i.e matrix of rasterized images).
# This will generate a matrix of shape (batch_size, nkerns[1] * 4 * 4),
# or (500, 50 * 4 * 4) = (500, 800) with the default values.
layer2_input = layer1.output.flatten(2)
# construct a fully-connected sigmoidal layer
layer2 = HiddenLayer(rng,
input=layer2_input,
n_in=nkerns[1] * 8,
n_out=500,
activation=T.tanh)
# classify the values of the fully-connected sigmoidal layer
layer3 = LogisticRegression(input=layer2.output, n_in=500, n_out=10)
# the cost we minimize during training is the NLL of the model
cost = layer3.negative_log_likelihood(y)
# create a function to compute the mistakes that are made by the model
test_model = theano.function(
[index],
layer3.errors(y),
givens={
x: test_set_x[index * batch_size:(index + 1) * batch_size],
y: test_set_y[index * batch_size:(index + 1) * batch_size]
})
validate_model = theano.function(
[index],
layer3.errors(y),
givens={
x: valid_set_x[index * batch_size:(index + 1) * batch_size],
y: valid_set_y[index * batch_size:(index + 1) * batch_size]
})
# create a list of all model parameters to be fit by gradient descent
params = layer3.params + layer2.params + layer1.params + layer0.params
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
# train_model is a function that updates the model parameters by
# SGD Since this model has many parameters, it would be tedious to
# manually create an update rule for each model parameter. We thus
# create the updates list by automatically looping over all
# (params[i], grads[i]) pairs.
updates = [(param_i, param_i - learning_rate * grad_i)
for param_i, grad_i in zip(params, grads)]
train_model = theano.function(
[index],
cost,
updates=updates,
givens={
x: train_set_x[index * batch_size:(index + 1) * batch_size],
y: train_set_y[index * batch_size:(index + 1) * batch_size]
})
# end-snippet-1
###############
# TRAIN MODEL #
###############
print('... training')
# early-stopping parameters
patience = 10000 # 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)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = timeit.default_timer()
epoch = 0
done_looping = False
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
for minibatch_index in range(n_train_batches):
iter = (epoch - 1) * n_train_batches + minibatch_index
if iter % 100 == 0:
print('training @ iter = ', iter)
cost_ij = train_model(minibatch_index)
if (iter + 1) % validation_frequency == 0:
# compute zero-one loss on validation set
validation_losses = [
validate_model(i) for i in range(n_valid_batches)
]
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
# test it on the test set
test_losses = [
test_model(i) for i in range(n_test_batches)
]
test_score = numpy.mean(test_losses)
print((' epoch %i, minibatch %i/%i, test error of '
'best model %f %%') %
(epoch, minibatch_index + 1, n_train_batches,
test_score * 100.))
if patience <= iter:
done_looping = True
break
end_time = timeit.default_timer()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i, '
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print(
('The code for file ' + os.path.split(__file__)[1] + ' ran for %.2fm' %
((end_time - start_time) / 60.)),
file=sys.stderr)
def evaluate_lenet5(
learning_rate = 0.1,
n_epochs = 200,
dataset = 'mnist.pkl.gz',
nkerns = [20, 50],
batch_size = 500):
"""
learning_rate (type: float;
content: learning rate used (factor for the stochastic gradient)
n_epochs (type: int;
content: maximal number of epochs to run the optimizer)
dataset (type: string;
content: path to the dataset used for training /testing (MNIST here))
nkerns (type: list of ints;
content: number of kernels on each layer
"""
# Initialise random number (used to initialise weights)
rng = numpy.random.RandomState(23455)
## --------------------------------------------------------------------------------------
## Load MNIST data (using load_data() [defined above], and the dataset path)
## --------------------------------------------------------------------------------------
datasets = load_data(dataset)
train_set_x, train_set_y = datasets[0] # devided into training set...
valid_set_x, valid_set_y = datasets[1] # validation set
test_set_x, test_set_y = datasets[2] # and test set
# Compute number of minibatches for training, validation and testing
n_train_batches = train_set_x.get_value(borrow=True).shape[0] // batch_size
n_valid_batches = valid_set_x.get_value(borrow=True).shape[0] // batch_size
n_test_batches = test_set_x.get_value(borrow=True).shape[0] // batch_size
#########################################################################################
# BUILD THE MODEL #
#########################################################################################
print('... building the model')
# Allocate (initialise) symbolic variables and generate symbolic variables for input (x and y represent a minibatch)
index = T.lscalar() # index to a [mini]batch (lscalar() returns a zero-dimension value)
x = T.matrix('x') # data, presented as rasterized images
y = T.ivector('y') # labels, presented as 1D vector of [int] labels
## --------------------------------------------------------------------------------------
## Define the FIRST layer
## --------------------------------------------------------------------------------------
# Reshape matrix of rasterized images of shape (batch_size, 28 * 28) to a 4D tensor,
# compatible with our LeNetConvPoolLayer. (28, 28) is the size of MNIST images.
layer0_input = x.reshape((batch_size, 1, 28, 28))
# Construct the first convolutional pooling layer:
# filtering reduces the image size to (28-5+1 , 28-5+1) = (24, 24)
# maxpooling reduces this further to (24/2, 24/2) = (12, 12)
# 4D output tensor is thus of shape (batch_size, nkerns[0], 12, 12)
layer0 = LeNetConvPoolLayer(
rng,
input = layer0_input,
image_shape = (batch_size, 1, 28, 28),
filter_shape = (nkerns[0], 1, 5, 5),
poolsize = (2, 2)
)
## --------------------------------------------------------------------------------------
## Define the SECOND layer
## --------------------------------------------------------------------------------------
# Construct the second convolutional pooling layer
# filtering reduces the image size to (12-5+1, 12-5+1) = (8, 8)
# maxpooling reduces this further to (8/2, 8/2) = (4, 4)
# 4D output tensor is thus of shape (batch_size, nkerns[1], 4, 4)
layer1 = LeNetConvPoolLayer(
rng,
input=layer0.output,
image_shape=(batch_size, nkerns[0], 12, 12),
filter_shape=(nkerns[1], nkerns[0], 5, 5),
poolsize=(2, 2)
)
## --------------------------------------------------------------------------------------
## Define the THIRD layer
## --------------------------------------------------------------------------------------
# the HiddenLayer being fully-connected, it operates on 2D matrices of
# shape (batch_size, num_pixels) (i.e matrix of rasterized images).
# This will generate a matrix of shape (batch_size, nkerns[1] * 4 * 4),
# or (500, 50 * 4 * 4) = (500, 800) with the default values.
layer2_input = layer1.output.flatten(2)
# construct a fully-connected sigmoidal layer
layer2 = HiddenLayer(
rng,
input=layer2_input,
n_in=nkerns[1] * 4 * 4,
n_out=500,
activation=T.tanh
)
## --------------------------------------------------------------------------------------
## Define the FOURTH layer
## --------------------------------------------------------------------------------------
# Classify the values of the fully-connected sigmoidal layer
layer3 = LogisticRegression(input=layer2.output, n_in=500, n_out=10)
## --------------------------------------------------------------------------------------
## Define cost and test functions
## --------------------------------------------------------------------------------------
cost = layer3.negative_log_likelihood(y) # Calulate the cost (negative_log_likelihood)
# Compile a Theano function that computes the mistakes that are made by the model on a minibatch
# Both for the test model
test_model = theano.function(
[index],
layer3.errors(y),
givens={
x: test_set_x[index * batch_size: (index + 1) * batch_size],
y: test_set_y[index * batch_size: (index + 1) * batch_size]
}
)
# And for the validation model
validate_model = theano.function(
[index],
layer3.errors(y),
givens={
x: valid_set_x[index * batch_size: (index + 1) * batch_size],
y: valid_set_y[index * batch_size: (index + 1) * batch_size]
}
)
# Create a list of all model parameters to be fit by gradient descent
params = layer3.params + layer2.params + layer1.params + layer0.params
# Create a list of gradients for all model parameters
grads = T.grad(cost, params)
## Specify how to update the parameters of the model
""" train_model is a function that updates the model parameters by SGD.
Since this model has many parameters, it would be tedious to manually
create an update rule for each model parameter. We thus create the
updates list by automatically looping over all (params[i], grads[i]) pairs.
"""
updates = [
(param_i, param_i - learning_rate * grad_i)
for param_i, grad_i in zip(params, grads)
]
# Compile a Theano function `train_model` that returns the cost, but at the same time updates
# the parameter of the model based on the rules defined in `updates`.
train_model = theano.function(
[index],
cost,
updates=updates,
givens={
x: train_set_x[index * batch_size: (index + 1) * batch_size],
y: train_set_y[index * batch_size: (index + 1) * batch_size]
}
)
#########################################################################################
# TRAIN MODEL #
#########################################################################################
print('... training the model')
## --------------------------------------------------------------------------------------
## Define early-stopping parameters
## --------------------------------------------------------------------------------------
patience = 10000 # 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)
# go through this many minibatches before checking the network
# on the validation set; in this case we check every epoch
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = timeit.default_timer()
## --------------------------------------------------------------------------------------
## Start iterating loop (i.e. through multibatches for repeated SGD)
## --------------------------------------------------------------------------------------
epoch = 0
done_looping = False
# Loop through epochs
while (epoch < n_epochs) and (not done_looping): # n_epochs defined in definition of this large function
epoch = epoch + 1 # Increment epoch on each loop
# Loop through minibatches
for minibatch_index in range(n_train_batches):
iter = (epoch - 1) * n_train_batches + minibatch_index # iteration number
## On every 100 iterations...
if iter % 100 == 0:
print('training @ iter = ', iter)
cost_ij = train_model(minibatch_index)
# When the iteration is fully divisible by the validation frequency
if (iter + 1) % validation_frequency == 0:
# Check for performance (zero-one loss) on validation data set
validation_losses = [
validate_model(i)
for i in range(n_valid_batches)
]
this_validation_loss = numpy.mean(validation_losses)
# Print current validation test results
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:
# ...and if loss improvement is good enough
if this_validation_loss < best_validation_loss * \
improvement_threshold:
patience = max(patience, iter * patience_increase)
# Save the best validation score and iteration number
best_validation_loss = this_validation_loss
best_iter = iter
# Test it on the test set
test_losses = [
test_model(i)
for i in range(n_test_batches)
]
test_score = numpy.mean(test_losses)
# Print test results
print((' epoch %i, minibatch %i/%i, test error of '
'best model %f %%') %
(epoch, minibatch_index + 1, n_train_batches,
test_score * 100.))
## -----------------------------------------------------------------
## Save model parameters using cPickle
## -----------------------------------------------------------------
fname = 'bestCNNModel.pkl'
saveFile = open(fname, 'wb')
# model weights
cPickle.dump(layer0.W, saveFile)
cPickle.dump(layer0.b, saveFile)
cPickle.dump(layer1.W, saveFile)
cPickle.dump(layer1.b, saveFile)
cPickle.dump(layer2.W, saveFile)
cPickle.dump(layer2.b, saveFile)
"""
# hyperparameters and performance
cPickle.dump(learning_rate, saveFile)
cPickle.dump(best_validation_loss, saveFile)
cPickle.dump(test_score, saveFile)
cPickle.dump(test_losses, saveFile)
cPickle.dump(nkerns, saveFile)
cPickle.dump(n_epochs, saveFile)
cPickle.dump(batch_size, saveFile)
"""
saveFile.close()
# Else, if patience is expired
if patience <= iter:
done_looping = True # Break the loop
break
# Now that the loop has ended...
end_time = timeit.default_timer() # note the time of loop ending
# Print the ending results
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i, '
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print(('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.)), file=sys.stderr)
class SdA(object):
def __init__(self,
numpy_rng,
n_ins,
n_outs,
hidden_layers_sizes,
corruption_levels=[0.1, 0.1],
theano_rng=None):
""" This class is made to support a variable number of layers.
:type numpy_rng: numpy.random.RandomState
:param numpy_rng: numpy random number generator used to draw initial
weights
:type theano_rng: theano.tensor.shared_randomstreams.RandomStreams
:param theano_rng: Theano random generator; if None is given one is
generated based on a seed drawn from `rng`
:type n_ins: int
:param n_ins: dimension of the input to the sdA
:type n_layers_sizes: list of ints
:param n_layers_sizes: intermediate layers size, must contain
at least one value
:type n_outs: int
:param n_outs: dimension of the output of the network
:type corruption_levels: list of float
:param corruption_levels: amount of corruption to use for each
layer
"""
self.sigmoid_layers = []
self.dA_layers = []
self.params = []
self.n_layers = len(hidden_layers_sizes)
self.n_ins = n_ins
self.n_outs = n_outs
# allocate symbolic variables for the data
self.x = T.matrix('x')
self.y = T.ivector('y')
assert self.n_layers > 0
if not theano_rng:
theano_rng = RandomStreams(numpy_rng.randint(2**30))
for i in xrange(self.n_layers):
# construct the sigmoidal layer
# the size of the input is either the number of hidden units of
# the layer below or the input size if we are on the first layer
if i == 0:
input_size = n_ins
else:
input_size = hidden_layers_sizes[i - 1]
# the input to this layer is either the activation of the hidden
# layer below or the input of the SdA if you are on the first
# layer
if i == 0:
layer_input = self.x
else:
layer_input = self.sigmoid_layers[-1].output
sigmoid_layer = HiddenLayer(rng=numpy_rng,
input=layer_input,
n_in=input_size,
n_out=hidden_layers_sizes[i],
activation=T.nnet.sigmoid)
# add the layer to our list of layers
self.sigmoid_layers.append(sigmoid_layer)
self.params.append(sigmoid_layer.theta)
# Construct a denoising autoencoder that shared weights with this layer
dA_layer = dA(numpy_rng=numpy_rng,
theano_rng=theano_rng,
input=layer_input,
n_visible=input_size,
n_hidden=hidden_layers_sizes[i],
theta=sigmoid_layer.theta)
self.dA_layers.append(dA_layer)
sda_input = T.matrix('sda_input')
self.da_layers_output_size = hidden_layers_sizes[-1]
self.get_da_output = theano.function(
inputs=[sda_input],
outputs=self.sigmoid_layers[-1].output.reshape(
(-1, self.da_layers_output_size)),
givens={self.x: sda_input})
self.logLayer = LogisticRegression(
rng=numpy.random.RandomState(),
input=self.sigmoid_layers[-1].output,
n_in=hidden_layers_sizes[-1],
n_out=n_outs)
#self.params.extend(self.logLayer.params)
self.finetune_cost = self.logLayer.negative_log_likelihood(self.y)
self.errors = self.logLayer.errors(self.y)