break
end_time = timeit.default_timer()
print(
(
'Optimization complete with 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 fine tuning code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time)
/ 60.))
if __name__ == '__main__':
sup_learn_data = SupLearningData()
num_entries, num_input_fields, num_output_fields, input_matrix, output_matrix = sup_learn_data.read_fann_data('datasets/fann/mushroom.train')
X_train = numpy.array(input_matrix)
y_train = numpy.array(output_matrix)
num_entries, num_input_fields, num_output_fields, input_matrix, output_matrix = sup_learn_data.read_fann_data('datasets/fann/mushroom.test')
X_val = numpy.array(input_matrix)
y_val = numpy.array(output_matrix)
datasets = [(X_train, y_train), (X_val, y_val)]
test_DBN(datasets=datasets,
n_ins=num_input_fields,
n_outs=num_output_fields,
hidden_layers_sizes=[32, 32, 32])
test_sup_learn_data = SupLearningData()
try:
exit_code = 1
if args.cmd == 'train':
# call the do_this class method
if (args.input_data_file_format == 'fann'):
"""
Test with FANN's building data set, 14 inputs and 3 outputs
"""
# fann_training_data = 'datasets/fann/building.train'
# fann_test_data = 'datasets/fann/building.test'
fann_training_data = 'datasets/fann/mushroom.train'
fann_test_data = 'datasets/fann/mushroom.test'
sup_learn_data = SupLearningData()
num_entries, num_input_fields, num_output_fields, input_matrix, output_matrix = sup_learn_data.read_fann_data(fann_training_data)
log.info('input_matrix has %d rows and %d cols', len(input_matrix), len(input_matrix[0]))
log.info('output_matrix has %d rows and %d cols', len(output_matrix), len(output_matrix[0]))
X_train = np.array(input_matrix)
y_train = np.array(output_matrix)
num_entries, num_input_fields, num_output_fields, input_matrix, output_matrix = sup_learn_data.read_fann_data(fann_test_data)
X_val = np.array(input_matrix)
y_val = np.array(output_matrix)
data = [(X_train, y_train), (X_val, y_val)]
do_mlp(dataset=data, n_hidden=[12, 12, 6], mean_loss_threshold=0.001, batch_size=1)
elif (args.input_data_file_format == 'jsonz'):
"""
Test with image data
"""
jsonz_training_data = '/home/hemkenhg/workspace/theano/examples/image_data/enlarge_center_2x-8-1k-train-a.jsonz'
jsonz_test_data = '/home/hemkenhg/workspace/theano/examples/image_data/enlarge_center_2x-8-1k-test-a.jsonz'
