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chbnn_gen.py
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chbnn_gen.py
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#!/usr/bin/env python
from __future__ import print_function
import sys
import os
import time
import chb
import matplotlib.pyplot as plt
import numpy as np
import theano
import theano.tensor as T
import lasagne
from lasagne import layers
from lasagne.objectives import binary_crossentropy, binary_accuracy
from sklearn import metrics
import networks as nw
# ##################### Build the neural network model #######################
def compile_model(input_var, target_var, net):
prediction = layers.get_output(net['out'])
loss = binary_crossentropy(prediction, target_var)
loss = lasagne.objectives.aggregate(loss)
params = layers.get_all_params(net['out'], trainable=True)
updates = lasagne.updates.adam(loss, params, learning_rate=1e-5)
test_prediction = layers.get_output(net['out'], deterministic=True)
test_loss = binary_crossentropy(test_prediction, target_var)
test_loss = lasagne.objectives.aggregate(test_loss)
train_fn = theano.function([input_var, target_var], loss, updates=updates)
val_fn = theano.function([input_var, target_var], test_loss)
prob_fn = theano.function([input_var], test_prediction)
return train_fn, val_fn, prob_fn
# ##################### Testing function for ConvNet #######################
def nn_test(x_test, y_test, val_fn, prob_fn, batch_size=10, thresh=0.5):
print('Test Results:')
print('=' * 80)
batch_err = []
for batch in iterate_minibatches(x_test, y_test, batch_size):
inputs, targets = batch
err = val_fn(inputs, targets)
batch_err.append(err)
test_err = np.mean(batch_err)
print('Test loss: %.6f' % test_err)
print('-' * 80)
y_prob = prob_fn(x_test)
y_pred = y_prob > thresh
print('Confusion matrix:\n', metrics.confusion_matrix(y_test, y_pred))
print('Matthews Correlation Coefficient:', metrics.matthews_corrcoef(y_test, y_pred))
print('=' * 80)
return test_err, y_pred, y_prob
# ############################# Batch iterator ###############################
def iterate_minibatches(inputs, targets, batchsize):
assert len(inputs) == len(targets)
for start_idx in range(0, len(inputs) - batchsize + 1, batchsize):
excerpt = slice(start_idx, start_idx + batchsize)
yield inputs[excerpt], targets[excerpt]
# ############################## Main program ################################
def main(subject='chb05', num_epochs=10, thresh=0.5, osr=1, usp=0,
tiger=False, tag='test', plotter=False):
# Load the dataset
subj = chb.load_dataset(subject, tiger=tiger)
sys.stdout.flush()
batch_size = 10
num_szr = subj.get_num()
test_accs = [0] * num_szr
out_dict = {}
for szr in range(1, num_szr + 1):
print('\nLeave-One-Out: %d of %d' % (szr, num_szr))
input_var = T.tensor4('inputs')
target_var = T.ivector('targets')
net = nw.simple(input_var)
train_fn, val_fn, prob_fn = compile_model(input_var, target_var, net)
train_err_list = [0] * num_epochs
val_err_list = [0] * num_epochs
print('=' * 80)
print('| epoch\t\t| train loss\t\t| time\t')
print('=' * 80)
x_test, y_test = chb.loowinTest(subj, szr)
for epoch in range(num_epochs):
st = time.clock()
# make generator
data = chb.loowinTrain(subj, szr, osr, usp)\
batch_train_errs = []
for idx, batch in enumerate(data):
x_train, y_train = batch
err = train_fn(x_train, y_train)
batch_train_errs.append(err)
epoch_train_err = np.mean(batch_train_errs)
train_err_list[epoch] = epoch_train_err
en = time.clock()
print('| %d \t\t| %.6f\t\t| %.2f s' %
(epoch + 1, epoch_train_err, en - st))
sys.stdout.flush()
print('-' * 80)
print('Training Complete.\n')
if plotter:
fig = plt.figure()
plt.plot(range(num_epochs), train_err, label='Training error')
plt.plot(range(num_epochs), val_err, label='Validation error')
plt.title('ConvNet Training')
plt.xlabel('Epochs')
plt.ylabel('Error')
plt.legend()
plt.show()
test_err, y_pred, y_prob = nn_test(x_test, y_test, val_fn, prob_fn, batch_size, thresh)
out_dict['_'.join(['prob', str(szr)])] = y_prob
out_dict['_'.join(['true', str(szr)])] = y_test
np.savez(''.join(['./outputs/',subject,'model','LOO',str(szr),tag,'.npz']), *lasagne.layers.get_all_param_values(net['out']))
np.savez(''.join([subject, tag, '.npz']), **out_dict)
if __name__ == '__main__':
kwargs = {}
if len(sys.argv) > 1:
kwargs['subject'] = sys.argv[1]
if len(sys.argv) > 2:
kwargs['num_epochs'] = int(sys.argv[2])
if len(sys.argv) > 3:
kwargs['thresh'] = float(sys.argv[3])
if len(sys.argv) > 4:
kwargs['osr'] = int(sys.argv[4])
if len(sys.argv) > 5:
kwargs['usp'] = float(sys.argv[5])
if len(sys.argv) > 6:
kwargs['tiger'] = bool(sys.argv[6])
if len(sys.argv) > 7:
kwargs['tag'] = sys.argv[7]
if len(sys.argv) > 8:
kwargs['plotter'] = bool(sys.argv[8])
main(**kwargs)