def test_dA(learning_rate=0.01,
training_epochs=15000,
dataset='mnist.pkl.gz',
batch_size=5,
output_folder='dA_plots'):
"""
This demo is tested on MNIST
:type learning_rate: float
:param learning_rate: learning rate used for training the DeNosing
AutoEncoder
:type training_epochs: int
:param training_epochs: number of epochs used for training
:type dataset: string
:param dataset: path to the picked dataset
"""
##datasets = load_data(dataset)
#from SdA_mapping import load_data_half
#datasets = load_data_half(dataset)
print 'loading data'
datasets, x_mean, y_mean, x_std, y_std = load_vc()
train_set_x, train_set_y = datasets[0]
valid_set_x, valid_set_y = datasets[1]
test_set_x, test_set_y = datasets[2]
print 'loaded data'
# compute number of minibatches for training, validation and testing
n_train_batches = train_set_x.get_value(borrow=True).shape[0] / batch_size
# allocate symbolic variables for the data
index = T.lscalar() # index to a [mini]batch
x1 = T.matrix('x1') # the data is presented as rasterized images
x2 = T.matrix('x2') # the data is presented as rasterized images
cor_reg = T.scalar('cor_reg')
if not os.path.isdir(output_folder):
os.makedirs(output_folder)
os.chdir(output_folder)
####################################
# BUILDING THE MODEL NO CORRUPTION #
####################################
rng = numpy.random.RandomState(123)
theano_rng = RandomStreams(rng.randint(2**30))
#da = dA_joint(
#numpy_rng=rng,
#theano_rng=theano_rng,
#input1=x1,
#input2=x2,
#n_visible1=28 * 28/2,
#n_visible2=28 * 28/2,
#n_hidden=500
#)
print 'initialize functions'
da = dA_joint(
numpy_rng=rng,
theano_rng=theano_rng,
input1=x1,
input2=x2,
cor_reg=cor_reg,
#n_visible1=28 * 28/2,
#n_visible2=28 * 28/2,
n_visible1=24,
n_visible2=24,
n_hidden=50)
cost, updates = da.get_cost_updates(corruption_level=0.3,
learning_rate=learning_rate)
cor_reg_val = numpy.float32(5.0)
train_da = theano.function(
[index],
cost,
updates=updates,
givens={
x1: train_set_x[index * batch_size:(index + 1) * batch_size],
x2: train_set_y[index * batch_size:(index + 1) * batch_size]
})
fprop_x1 = theano.function([],
outputs=da.output1,
givens={x1: test_set_x},
name='fprop_x1')
fprop_x2 = theano.function([],
outputs=da.output2,
givens={x2: test_set_y},
name='fprop_x2')
fprop_x1t = theano.function([],
outputs=da.output1,
givens={x1: train_set_x},
name='fprop_x1')
fprop_x2t = theano.function([],
outputs=da.output2,
givens={x2: train_set_y},
name='fprop_x2')
rec_x1 = theano.function([],
outputs=da.rec1,
givens={x1: test_set_x},
name='rec_x1')
rec_x2 = theano.function([],
outputs=da.rec2,
givens={x2: test_set_y},
name='rec_x2')
fprop_x1_to_x2 = theano.function([],
outputs=da.reg,
givens={x1: test_set_x},
name='fprop_x12x2')
updates_reg = [(da.cor_reg, da.cor_reg + theano.shared(numpy.float32(0.1)))
]
update_reg = theano.function([], updates=updates_reg)
print 'initialize functions ended'
start_time = time.clock()
############
# TRAINING #
############
print 'training started'
X1 = test_set_x.eval()
X1 *= x_std
X1 += x_mean
X2 = test_set_y.eval()
X2 *= y_std
X2 += y_mean
from dcca_numpy import cor_cost
# go through training epochs
for epoch in xrange(training_epochs):
# go through trainng set
c = []
for batch_index in xrange(n_train_batches):
c.append(train_da(batch_index))
#cor_reg_val += 1
#da.cor_reg = theano.shared(cor_reg_val)
update_reg()
X1H = rec_x1()
X2H = rec_x2()
X1H *= x_std
X1H += x_mean
X2H *= y_std
X2H += y_mean
H1 = fprop_x1()
H2 = fprop_x2()
print 'Training epoch'
print 'Reconstruction ', numpy.mean(numpy.mean((X1H-X1)**2,1)),\
numpy.mean(numpy.mean((X2H-X2)**2,1))
if epoch % 5 == 2: # pretrain middle layer
print '... pre-training MIDDLE layer'
H1t = fprop_x1t()
H2t = fprop_x2t()
h1 = T.matrix('x') # the data is presented as rasterized images
h2 = T.matrix('y') # the labels are presented as 1D vector of
from mlp import HiddenLayer
numpy_rng = numpy.random.RandomState(89677)
log_reg = HiddenLayer(numpy_rng, h1, 50, 50, activation=T.tanh)
if 1: # for middle layer
learning_rate = 0.1
#H1=theano.shared(H1)
#H2=theano.shared(H2)
# compute the gradients with respect to the model parameters
logreg_cost = log_reg.mse(h2)
gparams = T.grad(logreg_cost, log_reg.params)
# compute list of fine-tuning updates
updates = [(param, param - gparam * learning_rate)
for param, gparam in zip(log_reg.params, gparams)]
train_fn_middle = theano.function(inputs=[],
outputs=logreg_cost,
updates=updates,
givens={
h1: theano.shared(H1t),
h2: theano.shared(H2t)
},
name='train_middle')
epoch = 0
while epoch < 100:
print epoch, train_fn_middle()
epoch += 1
##X2H=fprop_x1_to_x2()
X2H = numpy.tanh(H1.dot(log_reg.W.eval()) + log_reg.b.eval())
X2H = numpy.tanh(X2H.dot(da.W2_prime.eval()) + da.b2_prime.eval())
X2H *= y_std
X2H += y_mean
print 'Regression ', numpy.mean(numpy.mean((X2H - X2)**2, 1))
print 'Correlation ', cor_cost(H1, H2)
end_time = time.clock()
training_time = (end_time - start_time)
print >> sys.stderr, ('The no corruption code for file ' +
os.path.split(__file__)[1] + ' ran for %.2fm' %
((training_time) / 60.))
image = Image.fromarray(
tile_raster_images(X=da.W1.get_value(borrow=True).T,
img_shape=(28, 14),
tile_shape=(10, 10),
tile_spacing=(1, 1)))
image.save('filters_corruption_0.png')
from matplotlib import pyplot as pp
pp.plot(H1[:10, :2], 'b')
pp.plot(H2[:10, :2], 'r')
pp.show()
print cor
def test_dA(learning_rate=0.01, training_epochs=15000,
dataset='mnist.pkl.gz',
batch_size=5, output_folder='dA_plots'):
"""
This demo is tested on MNIST
:type learning_rate: float
:param learning_rate: learning rate used for training the DeNosing
AutoEncoder
:type training_epochs: int
:param training_epochs: number of epochs used for training
:type dataset: string
:param dataset: path to the picked dataset
"""
##datasets = load_data(dataset)
#from SdA_mapping import load_data_half
#datasets = load_data_half(dataset)
print 'loading data'
datasets, x_mean, y_mean, x_std, y_std = load_vc()
train_set_x, train_set_y = datasets[0]
valid_set_x, valid_set_y = datasets[1]
test_set_x, test_set_y = datasets[2]
print 'loaded data'
# compute number of minibatches for training, validation and testing
n_train_batches = train_set_x.get_value(borrow=True).shape[0] / batch_size
# allocate symbolic variables for the data
index = T.lscalar() # index to a [mini]batch
x1 = T.matrix('x1') # the data is presented as rasterized images
x2 = T.matrix('x2') # the data is presented as rasterized images
cor_reg = T.scalar('cor_reg')
if not os.path.isdir(output_folder):
os.makedirs(output_folder)
os.chdir(output_folder)
####################################
# BUILDING THE MODEL NO CORRUPTION #
####################################
rng = numpy.random.RandomState(123)
theano_rng = RandomStreams(rng.randint(2 ** 30))
#da = dA_joint(
#numpy_rng=rng,
#theano_rng=theano_rng,
#input1=x1,
#input2=x2,
#n_visible1=28 * 28/2,
#n_visible2=28 * 28/2,
#n_hidden=500
#)
print 'initialize functions'
da = dA_joint(
numpy_rng=rng,
theano_rng=theano_rng,
input1=x1,
input2=x2,
cor_reg=cor_reg,
#n_visible1=28 * 28/2,
#n_visible2=28 * 28/2,
n_visible1=24,
n_visible2=24,
n_hidden=50
)
cost, updates = da.get_cost_updates(
corruption_level=0.3,
learning_rate=learning_rate
)
cor_reg_val = numpy.float32(5.0)
train_da = theano.function(
[index],
cost,
updates=updates,
givens={
x1: train_set_x[index * batch_size: (index + 1) * batch_size],
x2: train_set_y[index * batch_size: (index + 1) * batch_size]
}
)
fprop_x1 = theano.function(
[],
outputs=da.output1,
givens={
x1: test_set_x
},
name='fprop_x1'
)
fprop_x2 = theano.function(
[],
outputs=da.output2,
givens={
x2: test_set_y
},
name='fprop_x2'
)
fprop_x1t = theano.function(
[],
outputs=da.output1,
givens={
x1: train_set_x
},
name='fprop_x1'
)
fprop_x2t = theano.function(
[],
outputs=da.output2,
givens={
x2: train_set_y
},
name='fprop_x2'
)
rec_x1 = theano.function(
[],
outputs=da.rec1,
givens={
x1: test_set_x
},
name='rec_x1'
)
rec_x2 = theano.function(
[],
outputs=da.rec2,
givens={
x2: test_set_y
},
name='rec_x2'
)
fprop_x1_to_x2 = theano.function(
[],
outputs=da.reg,
givens={
x1: test_set_x
},
name='fprop_x12x2'
)
updates_reg = [
(da.cor_reg, da.cor_reg+theano.shared(numpy.float32(0.1)))
]
update_reg = theano.function(
[],
updates=updates_reg
)
print 'initialize functions ended'
start_time = time.clock()
############
# TRAINING #
############
print 'training started'
X1=test_set_x.eval()
X1 *= x_std
X1 += x_mean
X2=test_set_y.eval()
X2 *= y_std
X2 += y_mean
from dcca_numpy import cor_cost
# go through training epochs
for epoch in xrange(training_epochs):
# go through trainng set
c = []
for batch_index in xrange(n_train_batches):
c.append(train_da(batch_index))
#cor_reg_val += 1
#da.cor_reg = theano.shared(cor_reg_val)
update_reg()
X1H=rec_x1()
X2H=rec_x2()
X1H *= x_std
X1H += x_mean
X2H *= y_std
X2H += y_mean
H1=fprop_x1()
H2=fprop_x2()
print 'Training epoch'
print 'Reconstruction ', numpy.mean(numpy.mean((X1H-X1)**2,1)),\
numpy.mean(numpy.mean((X2H-X2)**2,1))
if epoch%5 == 2 : # pretrain middle layer
print '... pre-training MIDDLE layer'
H1t=fprop_x1t()
H2t=fprop_x2t()
h1 = T.matrix('x') # the data is presented as rasterized images
h2 = T.matrix('y') # the labels are presented as 1D vector of
from mlp import HiddenLayer
numpy_rng = numpy.random.RandomState(89677)
log_reg = HiddenLayer(numpy_rng, h1, 50, 50, activation=T.tanh)
if 1: # for middle layer
learning_rate = 0.1
#H1=theano.shared(H1)
#H2=theano.shared(H2)
# compute the gradients with respect to the model parameters
logreg_cost = log_reg.mse(h2)
gparams = T.grad(logreg_cost, log_reg.params)
# compute list of fine-tuning updates
updates = [
(param, param - gparam * learning_rate)
for param, gparam in zip(log_reg.params, gparams)
]
train_fn_middle = theano.function(
inputs=[],
outputs=logreg_cost,
updates=updates,
givens={
h1: theano.shared(H1t),
h2: theano.shared(H2t)
},
name='train_middle'
)
epoch = 0
while epoch < 100:
print epoch, train_fn_middle()
epoch += 1
##X2H=fprop_x1_to_x2()
X2H=numpy.tanh(H1.dot(log_reg.W.eval())+log_reg.b.eval())
X2H=numpy.tanh(X2H.dot(da.W2_prime.eval())+da.b2_prime.eval())
X2H *= y_std
X2H += y_mean
print 'Regression ', numpy.mean(numpy.mean((X2H-X2)**2,1))
print 'Correlation ', cor_cost(H1, H2)
end_time = time.clock()
training_time = (end_time - start_time)
print >> sys.stderr, ('The no corruption code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((training_time) / 60.))
image = Image.fromarray(
tile_raster_images(X=da.W1.get_value(borrow=True).T,
img_shape=(28, 14), tile_shape=(10, 10),
tile_spacing=(1, 1)))
image.save('filters_corruption_0.png')
from matplotlib import pyplot as pp
pp.plot(H1[:10,:2],'b');pp.plot(H2[:10,:2],'r');pp.show()
print cor
def test_SdA_regress(finetune_lr=0.05, pretraining_epochs=10,
pretrain_lr=0.1, training_epochs=10000,
dataset='mnist.pkl.gz', batch_size=20):
datasets = load_data_half(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]##
train_set_x=train_set_x.eval()
train_set_y=train_set_y.eval()
import theano
train_set_x_lab=train_set_x[:,:]
train_set_x_unlab=train_set_x[:,:]
train_set_y_lab=train_set_y[:,:]
train_set_y_unlab=train_set_y[:,:]
train_set_x_lab=theano.shared(numpy.asarray(train_set_x_lab,
dtype=theano.config.floatX),
borrow=True)
train_set_y_lab=theano.shared(numpy.asarray(train_set_y_lab,
dtype=theano.config.floatX),
borrow=True)
train_set_x_unlab=theano.shared(numpy.asarray(train_set_x_unlab,
dtype=theano.config.floatX),
borrow=True)
train_set_y_unlab=theano.shared(numpy.asarray(train_set_y_unlab,
dtype=theano.config.floatX),
borrow=True)
# compute number of minibatches for training, validation and testing
n_train_batches_l = train_set_y_lab.eval().shape[0]
n_train_batches_l /= batch_size
n_train_batches_u = train_set_y_unlab.eval().shape[0]
n_train_batches_u /= batch_size
# compute number of minibatches for training, validation and testing
#n_train_batches = train_set_x.get_value(borrow=True).shape[0]
#n_train_batches /= batch_size
# numpy random generator
# start-snippet-3
numpy_rng = numpy.random.RandomState(89677)
print '... building the model'
# construct the stacked denoising autoencoder class
#from SdA_orig import SdA as SdA_old
hidden_layer_size = 100
SdA_inp = SdA(numpy_rng,
n_ins=392,
hidden_layers_sizes=[hidden_layer_size]
)
SdA_out = SdA(numpy_rng,
n_ins=392,
hidden_layers_sizes=[hidden_layer_size]
)
# PRETRAINING THE MODEL #
if 0 : # pretrain inp ae
print '... getting the pretraining functions for INPUT AE'
pretraining_fns = SdA_inp.pretraining_functions(train_set_x=train_set_x_unlab,
batch_size=batch_size)
print '... pre-training the model'
start_time = time.clock()
## Pre-train layer-wise
corruption_levels = [.1, .2, .3]
for i in xrange(SdA_inp.n_layers):
# go through pretraining epochs
for epoch in xrange(pretraining_epochs):
# go through the training set
c = []
for batch_index in xrange(n_train_batches_u):
c.append(pretraining_fns[i](index=batch_index,
corruption=corruption_levels[i],
lr=pretrain_lr))
print 'Pre-training layer %i, epoch %d, cost ' % (i, epoch),
print numpy.mean(c)
end_time = time.clock()
print >> sys.stderr, ('The pretraining code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
if 0 : # pretrain out ae
print '... getting the pretraining functions for OUTPUT AE'
pretraining_fns = SdA_out.pretraining_functions(train_set_x=train_set_y_unlab,
batch_size=batch_size)
print '... pre-training the model'
start_time = time.clock()
## Pre-train layer-wise
corruption_levels = [.5, .2, .3]
for i in xrange(SdA_out.n_layers):
# go through pretraining epochs
for epoch in xrange(pretraining_epochs):
# go through the training set
c = []
for batch_index in xrange(n_train_batches_u):
c.append(pretraining_fns[i](index=batch_index,
corruption=corruption_levels[i],
lr=pretrain_lr))
print 'Pre-training layer %i, epoch %d, cost ' % (i, epoch),
print numpy.mean(c)
end_time = time.clock()
print >> sys.stderr, ('The pretraining code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
if 0: # save aes
f=open('aes_shallow_sig_nobias.pkl', 'w+')
import pickle
pickle.dump(SdA_inp, f)
pickle.dump(SdA_out, f)
f.flush()
f.close()
if 0: # load aes
f=open('aes_shallow_sig_nobias.pkl', 'r')
import pickle
SdA_inp=pickle.load(f)
SdA_out=pickle.load(f)
f.close()
if 1: # cca
from dcca_numpy import netCCA_nobias, netCCA, dCCA
from mlp_numpy import expit, logistic_prime, linear, linear_prime, relu, relu_prime, tanh, tanh_prime
train_y1 = train_set_x_lab.eval()
train_y2 = train_set_y_lab.eval()
test_y1 = test_set_x.eval()
test_y2 = test_set_y.eval()
##param1=((train_y1.shape[1],0,0),(2038, relu, relu_prime),(50, relu, relu_prime))
##param2=((train_y2.shape[1],0,0),(1608, relu, relu_prime),(50, relu, relu_prime))
param1=((train_y1.shape[1],0,0),(hidden_layer_size, expit, logistic_prime))
param2=((train_y2.shape[1],0,0),(hidden_layer_size, expit, logistic_prime))
W1s = []
b1s = []
for i in range(len(SdA_inp.dA_layers)):
W1s.append( SdA_inp.dA_layers[i].W.T.eval() )
##b1s.append( SdA_inp.dA_layers[i].b.eval() )
##b1s[-1] = b1s[-1].reshape((b1s[-1].shape[0], 1))
W2s = []
b2s = []
for i in range(len(SdA_out.dA_layers)):
W2s.append( SdA_out.dA_layers[i].W.T.eval() )
##b2s.append( SdA_out.dA_layers[i].b.eval() )
##b2s[-1] = b2s[-1].reshape((b2s[-1].shape[0], 1))
numpy.random.seed(0)
N1=netCCA_nobias(train_y1,param1, W1s)
N2=netCCA_nobias(train_y2,param2, W2s)
N = dCCA(train_y1, train_y2, N1, N2)
N1.reconstruct(test_set_x.eval()[0,:])
cnt = 0
from dcca_numpy import cca_cost, cca, order_cost, cor_cost
while True:
X=N1.predict(test_set_x.eval())
Y=N2.predict(test_set_y.eval())
_H1 = numpy.dot(X, N.A1)
_H2 = numpy.dot(Y, N.A2)
print '****', cnt, cor_cost(_H1, _H2)
X1_rec = numpy.tanh(X.dot(N1.weights[0]))
X2_rec = numpy.tanh(Y.dot(N2.weights[0]))
param=((hidden_layer_size,0,0),(hidden_layer_size, relu, relu_prime))
from mlp_numpy import NeuralNetwork as NN
lr=NN(X,Y,param)
lr.train(X[:,:],Y[:,:],10, 0.005)
Yh=lr.predict(X[:,:])
X2_reg = N2.fs[-1](numpy.dot(Yh,N2.weights[0]))
#X2_reg = N2.fs[-1](numpy.dot(_H1.dot(numpy.linalg.inv(N.A1)),N2.weights[0]))
print '****', 'mse1:', numpy.mean((X1_rec-test_set_x.eval())**2.0)
print '****', 'mse2:', numpy.mean((X2_rec-test_set_y.eval())**2.0)
print '****', 'mse_map:', numpy.mean((X2_reg-test_set_y.eval())**2.0)
if cnt % 2:
N.train(5, True, 10000.0)
else:
N.train(5, False, 10000.0)
cnt += 1
f=open('netcca.pkl', 'w+')
import pickle
pickle.dump(N, f)
pickle.dump(N, f)
f.flush()
f.close()
if cnt == 200:
break
for i in range(len(SdA_inp.dA_layers)):
SdA_inp.dA_layers[i].W = theano.shared( N1.weights[i].T )
SdA_inp.dA_layers[i].b = theano.shared( N1.biases[i][:,0] )
for i in range(len(SdA_out.dA_layers)):
SdA_out.dA_layers[i].W = theano.shared( N2.weights[i].T )
SdA_out.dA_layers[i].b = theano.shared( N2.weights[i][:,0] )
if 1 : # pretrain middle layer
print '... pre-training MIDDLE layer'
h1 = T.matrix('x') # the data is presented as rasterized images
h2 = T.matrix('y') # the labels are presented as 1D vector of
log_reg = HiddenLayer(numpy_rng, h1, hidden_layer_size, hidden_layer_size)
if 1: # for middle layer
learning_rate = 0.01
fprop_inp = theano.function(
[],
SdA_inp.sigmoid_layers[-1].output,
givens={
SdA_inp.sigmoid_layers[0].input: train_set_x_lab
},
name='fprop_inp'
)
fprop_out = theano.function(
[],
SdA_out.sigmoid_layers[-1].output,
givens={
SdA_out.sigmoid_layers[0].input: train_set_y_lab
},
name='fprop_out'
)
#H11=fprop_inp()
#H21=fprop_out()
##H1=N1.predict(train_set_x.eval())
##H2=N2.predict(train_set_y.eval())
H1=fprop_inp()
H2=fprop_out()
H1=theano.shared(H1)
H2=theano.shared(H2)
# compute the gradients with respect to the model parameters
logreg_cost = log_reg.mse(h2)
gparams = T.grad(logreg_cost, log_reg.params)
# compute list of fine-tuning updates
updates = [
(param, param - gparam * learning_rate)
for param, gparam in zip(log_reg.params, gparams)
]
train_fn_middle = theano.function(
inputs=[],
outputs=logreg_cost,
updates=updates,
givens={
h1: H1,
h2: H2
},
name='train_middle'
)
epoch = 0
while epoch < 10:
print epoch, train_fn_middle()
epoch += 1
sda = SdA_regress(
SdA_inp,
SdA_out,
log_reg,
numpy_rng=numpy_rng,
n_inp=28*28//2,
hidden_layers_sizes_inp=[hidden_layer_size],
hidden_layers_sizes_out=[hidden_layer_size],
n_out=28*28//2
)
# end-snippet-3 start-snippet-4
# end-snippet-4
# FINETUNING THE MODEL #
# get the training, validation and testing function for the model
print '... getting the finetuning functions'
train_fn, validate_model, test_model = sda.build_finetune_functions(
datasets=datasets,
batch_size=batch_size,
learning_rate=finetune_lr
)
print '... finetunning the model'
# early-stopping parameters
patience = 10 * n_train_batches_l # 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_l, 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
test_score = 0.
start_time = time.clock()
done_looping = False
epoch = 0
fprop = theano.function(
[],
sda.sigmoid_layers[-1].output,
givens={
sda.x: test_set_x
},
name='fprop'
)
while True:
epoch = epoch + 1
for minibatch_index in xrange(n_train_batches_l):
minibatch_avg_cost = train_fn(minibatch_index)
iter = (epoch - 1) * n_train_batches_l + minibatch_index
if (iter + 1) % validation_frequency == 0:
validation_losses = validate_model()
this_validation_loss = numpy.mean(validation_losses)
print('epoch %i, minibatch %i/%i, validation error %f %%' %
(epoch, minibatch_index + 1, n_train_batches_l,
this_validation_loss ))
# 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()
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_l,
test_score ))
if patience <= iter:
done_looping = True
#break
if 0: # vis weights
fprop = theano.function(
[],
sda.sigmoid_layers[-1].output,
givens={
sda.x: test_set_x
},
name='fprop'
)
yh=fprop()
yh=yh
end_time = time.clock()
print(
(
'Optimization complete with best validation score of %f %%, '
'on iteration %i, '
'with test performance %f %%'
)
% (best_validation_loss , best_iter + 1, test_score)
)
print >> sys.stderr, ('The training code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
