def predict(test_set_x, params):
Fx = T.matrix(name="Fx_input") # the data is presented as rasterized images
Sx = T.matrix(name="Sx_input") # the data is presented as rasterized images
Fx_inp = T.matrix(name="Fx_inp") # the data is presented as rasterized images
Sx_inp = T.matrix(name="Sx_inp") # the data is presented as rasterized images
rng = numpy.random.RandomState(23455)
# dataset parameters
im_type = params["im_type"]
nc = params["nc"] # number of channcels
size = params["size"] # size = [480,640] orijinal size,[height,width]
# Conv an Pooling parameters
nkerns = params["nkerns"]
kern_mat = params["kern_mat"]
pool_mat = params["pool_mat"]
# learning parameters
batch_size = params["batch_size"]
print "... building the model"
cnnr = CNNRNet(rng, input, batch_size, nc, size, nkerns, kern_mat[0], kern_mat[1], pool_mat[0], pool_mat[1], Fx, Sx)
cnnr.load(params["model_name"])
# cnnr.set_params(model_saver.load_model(model_name))
print "Model parameters loaded"
# create a function to compute the mistakes that are made by the model
predict_model = theano.function(
[Fx_inp, Sx_inp], cnnr.y_pred, givens={Fx: Fx_inp, Sx: Sx_inp}, allow_input_downcast=True
)
n_test_batches = len(test_set_x)
n_test_batches /= batch_size
y_pred = []
print "Prediction on test images"
for i in xrange(n_test_batches):
Fx = test_set_x[i * batch_size : (i + 1) * batch_size]
data_Fx = dataset_loader.load_batch_images(size, nc, "F", Fx, im_type)
data_Sx = dataset_loader.load_batch_images(size, nc, "S", Fx, im_type)
if len(y_pred) == 0:
y_pred = predict_model(data_Fx, data_Sx)
else:
y_pred = numpy.concatenate((y_pred, predict_model(data_Fx, data_Sx)))
return y_pred
def train_model():
dataset = "/home/coskun/PycharmProjects/data/rgbd_dataset_freiburg3_large_cabinet/"
rng = numpy.random.RandomState(23455)
# size = [480,640] orijinal size
rn_id = 1
size = [120, 160]
nc = 1 # number of channcels
nkerns = [20, 50]
nkern1_size = [5, 5]
nkern2_size = [5, 5]
npool1_size = [2, 2]
npool2_size = [2, 2]
batch_size = 30
multi = 10
learning_rate = 0.0005
n_epochs = 400
initial_learning_rate = 0.0005
learning_rate_decay = 0.998
squared_filter_length_limit = 15.0
n_epochs = 3000
learning_rate = theano.shared(numpy.asarray(initial_learning_rate, dtype=theano.config.floatX))
#### the params for momentum
mom_start = 0.5
mom_end = 0.99
# for epoch in [0, mom_epoch_interval], the momentum increases linearly
# from mom_start to mom_end. After mom_epoch_interval, it stay at mom_end
mom_epoch_interval = 500
mom_params = {"start": mom_start, "end": mom_end, "interval": mom_epoch_interval}
lambda_1 = 0.01 # regulizer param
lambda_2 = 0.01
datasets = dataset_loader.load_tum_dataV2(dataset, rn_id, multi)
X_train, y_train = datasets[0]
X_val, y_val = datasets[1]
X_test, y_test = datasets[2]
# compute number of minibatches for training, validation and testing
n_train_batches = len(X_train)
n_valid_batches = len(X_val)
n_test_batches = len(X_test)
n_train_batches /= batch_size
n_valid_batches /= batch_size
n_test_batches /= batch_size
epoch = T.scalar()
Fx = T.matrix(name="Fx_input") # the data is presented as rasterized images
Sx = T.matrix(name="Sx_input") # the data is presented as rasterized images
y = T.matrix("y") # the output are presented as matrix 1*3.
Fx_inp = T.matrix(name="Fx_inp") # the data is presented as rasterized images
Sx_inp = T.matrix(name="Sx_inp") # the data is presented as rasterized images
y_inp = T.matrix("y_inp")
print "... building the model"
cnnr = CNNRNet(rng, input, batch_size, nc, size, nkerns, nkern1_size, nkern2_size, npool1_size, npool2_size, Fx, Sx)
# create a function to compute the mistakes that are made by the model
test_model = theano.function([Fx_inp, Sx_inp, y_inp], cnnr.errors(y), givens={Fx: Fx_inp, Sx: Sx_inp, y: y_inp})
validate_model = theano.function([Fx_inp, Sx_inp, y_inp], cnnr.errors(y), givens={Fx: Fx_inp, Sx: Sx_inp, y: y_inp})
cost = cnnr.mse(y) + lambda_1 * cnnr.L1 + lambda_2 * cnnr.L2_sqr
# Compute gradients of the model wrt parameters
gparams = []
for param in cnnr.params:
# Use the right cost function here to train with or without dropout.
gparam = T.grad(cost, param)
gparams.append(gparam)
# ... and allocate mmeory for momentum'd versions of the gradient
gparams_mom = []
for param in cnnr.params:
gparam_mom = theano.shared(numpy.zeros(param.get_value(borrow=True).shape, dtype=theano.config.floatX))
gparams_mom.append(gparam_mom)
# Compute momentum for the current epoch
mom = (
mom_start * (1.0 - epoch / mom_epoch_interval) + mom_end * (epoch / mom_epoch_interval)
if T.lt(epoch, mom_epoch_interval)
else mom_end
)
# Update the step direction using momentum
updates = OrderedDict()
for gparam_mom, gparam in zip(gparams_mom, gparams):
# Misha Denil's original version
# updates[gparam_mom] = mom * gparam_mom + (1. - mom) * gparam
# change the update rule to match Hinton's dropout paper
updates[gparam_mom] = mom * gparam_mom - (1.0 - mom) * learning_rate * gparam
# ... and take a step along that direction
for param, gparam_mom in zip(cnnr.params, gparams_mom):
# Misha Denil's original version
# stepped_param = param - learning_rate * updates[gparam_mom]
# since we have included learning_rate in gparam_mom, we don't need it
# here
stepped_param = param + updates[gparam_mom]
# This is a silly hack to constrain the norms of the rows of the weight
# matrices. This just checks if there are two dimensions to the
# parameter and constrains it if so... maybe this is a bit silly but it
# should work for now.
if param.get_value(borrow=True).ndim == 2:
# squared_norms = T.sum(stepped_param**2, axis=1).reshape((stepped_param.shape[0],1))
# scale = T.clip(T.sqrt(squared_filter_length_limit / squared_norms), 0., 1.)
# updates[param] = stepped_param * scale
# constrain the norms of the COLUMNs of the weight, according to
# https://github.com/BVLC/caffe/issues/109
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
# Compile theano function for training. This returns the training cost and
# updates the model parameters.
output = cost
train_model = theano.function(
[Fx_inp, Sx_inp, y_inp, epoch], outputs=output, updates=updates, givens={Fx: Fx_inp, Sx: Sx_inp, y: y_inp}
)
# create a function to compute the mistakes that are made by the model
predict_model = theano.function([Fx_inp, Sx_inp], cnnr.y_pred, givens={Fx: Fx_inp, Sx: Sx_inp})
decay_learning_rate = theano.function(
inputs=[], outputs=learning_rate, updates={learning_rate: learning_rate * learning_rate_decay}
)
# 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 = 0.0
start_time = timeit.default_timer()
epoch = 0
done_looping = False
epoch_counter = 0
while (epoch_counter < n_epochs) and (not done_looping):
epoch_counter = epoch_counter + 1
for minibatch_index in xrange(n_train_batches):
iter = (epoch_counter - 1) * n_train_batches + minibatch_index
if iter % 100 == 0:
print "training @ iter = ", iter
Fx = X_train[minibatch_index * batch_size : (minibatch_index + 1) * batch_size]
data_Fx = dataset_loader.load_batch_images(size, nc, "F", Fx)
data_Sx = dataset_loader.load_batch_images(size, nc, "S", Fx)
data_y = y_train[minibatch_index * batch_size : (minibatch_index + 1) * batch_size]
cost_ij = train_model(data_Fx, data_Sx, data_y, epoch)
# model_saver.save_model(epoch % 3, params)
print (
"epoch %i, minibatch %i/%i, training cost %f "
% (epoch_counter, minibatch_index + 1, n_train_batches, cost_ij)
)
if (iter + 1) % validation_frequency == 0:
# compute zero-one loss on validation set
validation_losses = 0
for i in xrange(n_valid_batches):
Fx = X_val[i * batch_size : (i + 1) * batch_size]
data_Fx = dataset_loader.load_batch_images(size, nc, "F", Fx)
data_Sx = dataset_loader.load_batch_images(size, nc, "S", Fx)
data_y = y_val[i * batch_size : (i + 1) * batch_size]
validation_losses = validation_losses + validate_model(data_Fx, data_Sx, data_y)
this_validation_loss = validation_losses / n_valid_batches
new_learning_rate = decay_learning_rate()
print (
"epoch %i, minibatch %i/%i, learning_rate %f validation error %f %%"
% (
epoch_counter,
minibatch_index + 1,
n_train_batches,
learning_rate.get_value(borrow=True),
this_validation_loss * 100.0,
)
)
# 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_losses = 0
for i in xrange(n_test_batches):
Fx = X_test[i * batch_size : (i + 1) * batch_size]
data_Fx = dataset_loader.load_batch_images(size, nc, "F", Fx)
data_Sx = dataset_loader.load_batch_images(size, nc, "S", Fx)
data_y = y_test[i * batch_size : (i + 1) * batch_size]
err = test_model(data_Fx, data_Sx, data_y)
test_losses = test_losses + err
if i % 10 == 0:
store = []
ypred = predict_model(data_Fx, data_Sx)
store.append(Fx)
store.append(ypred)
store.append(data_y)
model_saver.save_garb(store)
print ("Iteration saved %i, err %f" % (i, err))
test_score = test_losses / n_test_batches
ext = str(rn_id) + "_" + str(epoch_counter % 3)
model_saver.save_model(ext, cnnr.params)
print (
(" epoch %i, minibatch %i/%i, test error of " "best model %f %%")
% (epoch_counter, minibatch_index + 1, n_train_batches, test_score * 100.0)
)
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 * 100.0, best_iter + 1, test_score * 100.0)
)
print >> sys.stderr, (
"The code for file " + os.path.split(__file__)[1] + " ran for %.2fm" % ((end_time - start_time) / 60.0)
)
def train_model(params):
rn_id=params["rn_id"]
im_type=params["im_type"]
nc =params["nc"] # number of channcels
size =params["size"] # size = [480,640] orijinal size,[height,width]
# Conv an Pooling parameters
nkerns =params["nkerns"]
kern_mat =params["kern_mat"]
pool_mat =params["pool_mat"]
# learning parameters
batch_size =params["batch_size"]
n_epochs =params["n_epochs"]
initial_learning_rate =params["initial_learning_rate"]
learning_rate_decay =params["learning_rate_decay"]
squared_filter_length_limit =params["squared_filter_length_limit"]
learning_rate = theano.shared(numpy.asarray(initial_learning_rate, dtype=theano.config.floatX))
lambda_1 = params["lambda_1"] # regulizer param
lambda_2 = params["lambda_2"]
#### the params for momentum
mom_start =params["mom_start"]
mom_end = params["mom_end"]
# for epoch in [0, mom_epoch_interval], the momentum increases linearly
# from mom_start to mom_end. After mom_epoch_interval, it stay at mom_end
mom_epoch_interval =params["mom_epoch_interval"]
# early-stopping parameters
patience = params["patience"] # look as this many examples regardless
patience_increase = params["patience_increase"] # wait this much longer when a new best is
# found
improvement_threshold = params["improvement_threshold"] # a relative improvement of this much is
#Loading dataset
datasets = dataset_loader.load_tum_dataV2(params)
X_train, y_train,overlaps_train = datasets[0]
X_val, y_val,overlaps_val = datasets[1]
X_test, y_test,overlaps_test = datasets[2]
# compute number of minibatches for training, validation and testing
n_train_batches = len(X_train)
n_valid_batches = len(X_val)
n_test_batches = len(X_test)
n_train_batches /= batch_size
n_valid_batches /= batch_size
n_test_batches /= batch_size
#Parameters to be passed net
epoch = T.scalar()
Fx = T.matrix(name='Fx_input') # the data is presented as rasterized images
Sx = T.matrix(name='Sx_input') # the data is presented as rasterized images
y = T.matrix('y') # the output are presented as matrix 1*3.
Fx_inp = T.matrix(name='Fx_inp') # the data is presented as rasterized images
Sx_inp = T.matrix(name='Sx_inp') # the data is presented as rasterized images
y_inp = T.matrix('y_inp')
print '... building the model'
rng = numpy.random.RandomState(23455)
cnnr = CNNRNet(rng, input, batch_size, nc, size, nkerns,
kern_mat[0], kern_mat[1],
pool_mat[0],pool_mat[1],
Fx, Sx)
# create a function to compute the mistakes that are made by the model
test_model = theano.function(
[Fx_inp, Sx_inp, y_inp],
cnnr.errors(y),
givens={
Fx: Fx_inp,
Sx: Sx_inp,
y: y_inp,
},allow_input_downcast=True
)
validate_model = theano.function(
[Fx_inp, Sx_inp, y_inp],
cnnr.errors(y),
givens={
Fx: Fx_inp,
Sx: Sx_inp,
y: y_inp,
},allow_input_downcast=True
)
cost = cnnr.mse(y) + lambda_1 * cnnr.L1 + lambda_2 * cnnr.L2_sqr
# Compute gradients of the model wrt parameters
gparams = []
for param in cnnr.params:
# Use the right cost function here to train with or without dropout.
gparam = T.grad(cost, param)
gparams.append(gparam)
# ... and allocate mmeory for momentum'd versions of the gradient
gparams_mom = []
for param in cnnr.params:
gparam_mom = theano.shared(numpy.zeros(param.get_value(borrow=True).shape,
dtype=theano.config.floatX))
gparams_mom.append(gparam_mom)
# Compute momentum for the current epoch
mom = mom_start * (1.0 - epoch / mom_epoch_interval) + mom_end * (epoch / mom_epoch_interval) if T.lt(epoch,
mom_epoch_interval) else mom_end
# Update the step direction using momentum
updates = OrderedDict()
for gparam_mom, gparam in zip(gparams_mom, gparams):
# Misha Denil's original version
# updates[gparam_mom] = mom * gparam_mom + (1. - mom) * gparam
# change the update rule to match Hinton's dropout paper
updates[gparam_mom] = mom * gparam_mom - (1. - mom) * learning_rate * gparam
# ... and take a step along that direction
for param, gparam_mom in zip(cnnr.params, gparams_mom):
# Misha Denil's original version
# stepped_param = param - learning_rate * updates[gparam_mom]
# since we have included learning_rate in gparam_mom, we don't need it
# here
stepped_param = param + updates[gparam_mom]
# This is a silly hack to constrain the norms of the rows of the weight
# matrices. This just checks if there are two dimensions to the
# parameter and constrains it if so... maybe this is a bit silly but it
# should work for now.
if param.get_value(borrow=True).ndim == 2:
# squared_norms = T.sum(stepped_param**2, axis=1).reshape((stepped_param.shape[0],1))
# scale = T.clip(T.sqrt(squared_filter_length_limit / squared_norms), 0., 1.)
# updates[param] = stepped_param * scale
# constrain the norms of the COLUMNs of the weight, according to
# https://github.com/BVLC/caffe/issues/109
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
# Compile theano function for training. This returns the training cost and
# updates the model parameters.
output = cost
train_model = theano.function(
[Fx_inp, Sx_inp, y_inp, epoch],
outputs=output,
updates=updates,
givens={
Fx: Fx_inp,
Sx: Sx_inp,
y: y_inp,
},allow_input_downcast=True
)
# create a function to compute the mistakes that are made by the model
predict_model = theano.function(
[Fx_inp, Sx_inp],
cnnr.y_pred,
givens={
Fx: Fx_inp,
Sx: Sx_inp,
},allow_input_downcast=True
)
decay_learning_rate = theano.function(inputs=[], outputs=learning_rate,
updates={learning_rate: learning_rate * learning_rate_decay})
# end-snippet-1
###############
# TRAIN MODEL #
###############
print '... training'
# 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
epoch_counter = 0
while (epoch_counter < n_epochs) and (not done_looping):
epoch_counter = epoch_counter + 1
for minibatch_index in xrange(n_train_batches):
iter = (epoch_counter - 1) * n_train_batches + minibatch_index
if iter % 100 == 0:
print 'training @ iter = ', iter
Fx = X_train[minibatch_index * batch_size: (minibatch_index + 1) * batch_size]
data_Fx = dataset_loader.load_batch_images(size, nc, "F", Fx,im_type)
data_Sx = dataset_loader.load_batch_images(size, nc, "S", Fx,im_type)
data_y = y_train[minibatch_index * batch_size: (minibatch_index + 1) * batch_size]
cost_ij = train_model(data_Fx, data_Sx, data_y, epoch)
# model_saver.save_model(epoch % 3, params)
print('epoch %i, minibatch %i/%i, training cost %f ' %
(epoch_counter, minibatch_index + 1, n_train_batches,
cost_ij))
if (iter + 1) % validation_frequency == 0:
# compute zero-one loss on validation set
validation_losses = 0
for i in xrange(n_valid_batches):
Fx = X_val[i * batch_size: (i + 1) * batch_size]
data_Fx = dataset_loader.load_batch_images(size, nc, "F", Fx,im_type)
data_Sx = dataset_loader.load_batch_images(size, nc, "S", Fx,im_type)
data_y = y_val[i * batch_size: (i + 1) * batch_size]
validation_losses = validation_losses + validate_model(data_Fx, data_Sx, data_y)
this_validation_loss = validation_losses / n_valid_batches
new_learning_rate = decay_learning_rate()
print('epoch %i, minibatch %i/%i, learning_rate %f validation error %f %%' %
(epoch_counter, minibatch_index + 1, n_train_batches,
learning_rate.get_value(borrow=True),
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_losses = 0
for i in xrange(n_test_batches):
Fx = X_test[i * batch_size: (i + 1) * batch_size]
data_Fx = dataset_loader.load_batch_images(size, nc, "F", Fx,im_type)
data_Sx = dataset_loader.load_batch_images(size, nc, "S", Fx,im_type)
data_y = y_test[i * batch_size: (i + 1) * batch_size]
err= test_model(data_Fx, data_Sx, data_y)
test_losses = test_losses + err
if(i%100==-1):
store=[]
ypred= predict_model(data_Fx, data_Sx)
print(ypred)
print(Fx)
store.append(Fx)
store.append(ypred)
store.append(data_y)
model_saver.save_garb(store)
print("Iteration saved %i, err %f"%(i,err))
test_score = test_losses / n_test_batches
ext="models/"+str(rn_id)+"_"+str(epoch_counter % 3)+"_model_numpy"
cnnr.save(ext)
#model_saver.save_model(ext, cnnr.params)
print((' epoch %i, minibatch %i/%i, test error of '
'best model %f %%') %
(epoch_counter, 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 >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def test_dA(learning_rate=0.1, training_epochs=15,output_folder='dA_plots'):
size = [160, 120] #[width,height]
batch_size=20
dataset="/home/coskun/PycharmProjects/data/rgbd_dataset_freiburg3_large_cabinet/"
X_Pairs=dataset_loader.load_pairs(dataset,step_size=[])
n_train_batches = len(X_Pairs)
n_train_batches /= batch_size
Fx = T.matrix(name='Fx_input') # the data is presented as rasterized images
Sx = T.matrix(name='Sx_input') # the data is presented as rasterized images
Fx_inp = T.matrix(name='Fx_inp') # the data is presented as rasterized images
Sx_inp = T.matrix(name='Sx_inp') # the data is presented as rasterized images
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(
numpy_rng=rng,
theano_rng=theano_rng,
Fx=Fx,
Sx=Sx,
n_visible=size[0] * size[1],
n_hidden=2000
)
cost, updates = da.get_cost_updates(
learning_rate=learning_rate
)
train_da = theano.function(
[Fx_inp, Sx_inp],
cost,
updates=updates,
givens={
Fx: Fx_inp,
Sx: Sx_inp
}
,mode="DebugMode"
)
start_time = timeit.default_timer()
############
# TRAINING #
############
print "Training Started"
# go through training epochs
for epoch in xrange(training_epochs):
# go through trainng set
c = []
for i in xrange(n_train_batches):
Fx = X_Pairs[i * batch_size: (i + 1) * batch_size]
data_Fx = dataset_loader.load_batch_images(size,1, "F", Fx)
data_Sx = dataset_loader.load_batch_images(size,1, "S", Fx)
print("Trainin on images")
cst=train_da(data_Fx,data_Sx)
print("Cost:")
print(str(cst))
c.append(cst)
print 'Training epoch %d, cost ' % epoch, numpy.mean(c)
end_time = timeit.default_timer()
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.W.get_value(borrow=True).T,
img_shape=(size[0], size[1]), tile_shape=(10, 10),
tile_spacing=(1, 1)))
image.save('filters_corruption_0.png')
os.chdir('../')
