def train_model():
rng = numpy.random.RandomState(23455)
#size = [480,640] orijinal size
size = [120,160]
nkerns = [20, 50]
nkern1_size = [5, 5]
nkern2_size = [5, 5]
npool1_size = [2, 2]
npool2_size = [2, 2]
batch_size = 30
fl_size = size[0] * size[1]
multi = 100
learning_rate = 0.001
n_epochs = 400
datasets = dataset_loader.load_tum_dataV2(size, 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
x = T.tensor3(name='input') # the data is presented as rasterized images
y = T.matrix('y') # the output are presented as matrix 1*3.
x_inp = T.tensor3(name='x_inp') # the data is presented as rasterized images
y_inp = T.matrix('y_inp')
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# number of channels
layer0_input = x.reshape((batch_size, 2, size[0], size[1]))
# Construct the first convolutional pooling layer:
# filtering reduces the image size to (640-5+1 , 480-5+1) = (636, 476)
# maxpooling reduces this further to (636/2, 476/2) = (318, 238)
# 4D output tensor is thus of shape (batch_size, nkerns[0], size[0], size[1])
layer0 = ConvPoolLayer(
rng,
input=layer0_input,
image_shape=(batch_size, 2, size[0], size[1]),
filter_shape=(nkerns[0], 2, nkern1_size[0], nkern1_size[1]),
poolsize=(npool1_size[0], npool1_size[1])
)
l0out = ((size[0] - nkern1_size[0] + 1) / npool1_size[0], (size[1] - nkern1_size[0] + 1) / npool1_size[1])
# Construct the second convolutional pooling layer
# filtering reduces the image size to (318-5+1, 238-5+1) = (314, 234)
# maxpooling reduces this further to (314/2, 234/2) = (157, 117)
# 4D output tensor is thus of shape (batch_size, nkerns[1], 157, 117)
layer1 = ConvPoolLayer(
rng,
input=layer0.output,
image_shape=(batch_size, nkerns[0]) + l0out,
filter_shape=(nkerns[1], nkerns[0], nkern2_size[0], nkern2_size[1]),
poolsize=(npool2_size[0], npool2_size[1])
)
l2out = ((l0out[0] - nkern2_size[0] + 1) / npool2_size[0], (l0out[1] - nkern2_size[0] + 1) / npool2_size[0])
# 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] * 5 * 3),
# or (500, 50 * 5 * 3) = (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] * l2out[0] * l2out[1],
n_out=500
)
# classify the values of the fully-connected sigmoidal layer
layer3 = OutputLayer(input=layer2.output, n_in=500, n_out=3)
# create a function to compute the mistakes that are made by the model
test_model = theano.function(
[x_inp, y_inp],
layer3.errors(y),
givens={
x: x_inp,
y: y_inp,
}
)
validate_model = theano.function(
[x_inp, y_inp],
layer3.errors(y),
givens={
x: x_inp,
y: y_inp,
}
)
#Creat cost
L1 = (abs(layer3.W).sum()) + (abs(layer2.W).sum()) + (abs(layer1.W).sum()) + (abs(layer0.W).sum())
L2_sqr = ((layer3.W ** 2).sum()) + ((layer1.W ** 2).sum()) + ((layer1.W ** 2).sum()) + ((layer0.W ** 2).sum())
lambda_1 = 0.1
lambda_2 = 0.1
cost = layer3.mse(y) + lambda_1 * L1 + lambda_2 * L2_sqr
# 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(
[x_inp, y_inp],
cost,
updates=updates,
givens={
x: x_inp,
y: y_inp,
}
)
# 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 xrange(n_train_batches):
iter = (epoch - 1) * n_train_batches + minibatch_index
if iter % 100 == 0:
print 'training @ iter = ', iter
x = X_train[minibatch_index * batch_size: (minibatch_index + 1) * batch_size]
data_x = dataset_loader.load_batch_imagesV2(size, x)
data_y = y_train[minibatch_index * batch_size: (minibatch_index + 1) * batch_size]
cost_ij = train_model(data_x, data_y)
#model_saver.save_model(epoch % 3, params)
print('epoch %i, minibatch %i/%i, training cost %f ' %
(epoch, 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):
x = X_val[i * batch_size: (i + 1) * batch_size]
data_x = dataset_loader.load_batch_imagesV2(size, x)
data_y = y_val[i * batch_size: (i + 1) * batch_size]
validation_losses = validation_losses + validate_model(data_x, data_y)
this_validation_loss = validation_losses / n_valid_batches
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_losses = 0
for i in xrange(n_test_batches):
x = X_test[i * batch_size: (i + 1) * batch_size]
data_x = dataset_loader.load_batch_imagesV2(size, x)
data_y = y_test[i * batch_size: (i + 1) * batch_size]
test_losses = test_losses + validate_model(data_x, data_y)
test_score = test_losses / n_valid_batches
model_saver.save_model(epoch % 3, params)
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 >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
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)
)