def pretrain_sda_cg(sda, train_names, read_window, read_algo, read_rank,
window_size, pretraining_epochs, corruption_levels):
## Pre-train layer-wise
print '... getting the pretraining functions'
import scipy.optimize
for i in xrange(sda.n_layers):
train_reader = ICHISeqDataReader(train_names)
n_train_patients = len(train_names)
for patients in xrange(n_train_patients):
train_set_x, train_set_y = train_reader.read_next_doc(
algo=read_algo, window=read_window, rank=read_rank)
pretraining_fn, pretraining_update = pretraining_functions_sda_cg(
sda=sda,
train_set_x=train_set_x,
window_size=window_size,
corruption_levels=corruption_levels)
print '... pre-training the model'
# using scipy conjugate gradient optimizer
print("Optimizing using scipy.optimize.fmin_cg...")
best_w_b = scipy.optimize.fmin_cg(
f=partial(pretraining_fn, da_index=i),
x0=numpy.zeros((sda.dA_layers[i].n_visible + 1) *
sda.dA_layers[i].n_hidden,
dtype=sda.dA_layers[i].input.dtype),
fprime=partial(pretraining_update, da_index=i),
maxiter=pretraining_epochs)
return sda
def train_logistic_cg(read_algo, read_window, read_rank, train_names,
valid_names, window_size, n_epochs, classifier):
# read the datasets
train_reader = ICHISeqDataReader(train_names)
valid_reader = ICHISeqDataReader(valid_names)
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# allocate symbolic variables for the data
index = T.lscalar()
# generate symbolic variables for input
x = classifier.x # data, presented as window with x, y, x for each sample
y = T.iscalar('y') # labels, presented as int label
# the cost we minimize during training is the negative log likelihood of
# the model in symbolic format
cost = classifier.negative_log_likelihood(y)
for pat_num in xrange(len(train_names)):
# go through the training set
train_set_x, train_set_y = train_reader.read_next_doc(
algo=read_algo, window=read_window, rank=read_rank)
valid_set_x, valid_set_y = valid_reader.read_next_doc(
algo=read_algo, window=read_window, rank=read_rank)
n_train_samples = train_set_x.get_value(
borrow=True).shape[0] - window_size + 1
n_valid_samples = valid_set_x.get_value(
borrow=True).shape[0] - window_size + 1
validate_model = theano.function(
[index],
classifier.errors(y),
givens={
x: valid_set_x[index:index + window_size],
y: valid_set_y[index + window_size - 1]
},
name="validate")
# compile a theano function that returns the cost
conj_cost = theano.function(
inputs=[index],
outputs=[cost, classifier.errors(y),
classifier.predict(), y],
givens={
x: train_set_x[index:index + window_size],
y: train_set_y[index + window_size - 1]
},
name="conj_cost")
# compile a theano function that returns the gradient with respect to theta
conj_grad = theano.function(
[index],
T.grad(cost, classifier.theta),
givens={
x: train_set_x[index:index + window_size],
y: train_set_y[index + window_size - 1]
},
name="conj_grad")
train_confusion_matrix = numpy.zeros((7, 7))
# creates a function that computes the average cost on the training set
def train_fn(theta_value):
classifier.theta.set_value(theta_value, borrow=True)
cur_train_cost = []
cur_train_error = []
for i in xrange(n_train_samples):
sample_cost, sample_error, cur_pred, cur_actual = conj_cost(i)
cur_train_cost.append(sample_cost)
cur_train_error.append(sample_error)
train_confusion_matrix[cur_actual][cur_pred] += 1
this_train_loss = float(numpy.mean(cur_train_cost))
classifier.train_cost_array.append([])
classifier.train_cost_array[-1].append(classifier.epoch)
classifier.train_cost_array[-1].append(this_train_loss)
classifier.train_error_array.append([])
classifier.train_error_array[-1].append(classifier.epoch)
classifier.train_error_array[-1].append(
float(numpy.mean(cur_train_error) * 100))
classifier.epoch += 1
return this_train_loss
# creates a function that computes the average gradient of cost with
# respect to theta
def train_fn_grad(theta_value):
classifier.theta.set_value(theta_value, borrow=True)
grad = conj_grad(0)
for i in xrange(1, n_train_samples):
grad += conj_grad(i)
return grad / n_train_samples
# creates the validation function
def callback(theta_value):
classifier.theta.set_value(theta_value, borrow=True)
#compute the validation loss
validation_losses = [
validate_model(i) for i in xrange(n_valid_samples)
]
this_validation_loss = float(
numpy.mean(validation_losses) * 100., )
print('validation error %f %%' % (this_validation_loss))
classifier.valid_error_array.append([])
classifier.valid_error_array[-1].append(classifier.epoch)
classifier.valid_error_array[-1].append(this_validation_loss)
###############
# TRAIN MODEL #
###############
# using scipy conjugate gradient optimizer
print("Optimizing using scipy.optimize.fmin_cg...")
best_theta = scipy.optimize.fmin_cg(
f=train_fn,
x0=numpy.zeros((classifier.n_in + 1) * classifier.n_out,
dtype=x.dtype),
fprime=train_fn_grad,
callback=callback,
disp=0,
maxiter=n_epochs)
return classifier
