# set up the model
dnn_shared = None
shared_layers = []
hidden_layers = shared_layers_sizes + task_specific_sizes[n]
# use the first networks shared layers dawg
if n > 0:
dnn_shared = dnn_array[0]
shared_layers = [m for m in xrange(shared_layers_num)]
# create the network for the task
# you can change the input dropout factor and the general dropout factor
# look at the DNNDropout class
if shareLayers:
dnn = DNNDropout(np_rng=np_rng, theano_rng=theano_rng, hidden_layers_sizes=hidden_layers,
n_ins=input_size, n_outs=output_size,
input_dropout_factor=0.0, dropout_factor=0.0,
dnn_shared=dnn_shared, shared_layers=shared_layers)
else:
dnn = DNNDropout(np_rng=np_rng, theano_rng=theano_rng, hidden_layers_sizes=hidden_layers,
n_ins=input_size, n_outs=output_size,
input_dropout_factor=0.1, dropout_factor=0.5)
# add dnn and the functions to the list
dnn_array.append(dnn)
# # consider the tasks which have nonzero learning rate
# active_tasks = [n for n in xrange(num_tasks)]
log('> ... bootstrapping all tasks datasets and building the functions')
# keep track of the training error in order to create the train/validation
# curve
# set up the model
dnn_shared = None
shared_layers = []
hidden_layers = shared_layers_sizes + task_specific_sizes[n]
# use the first networks shared layers dawg
if n > 0:
dnn_shared = dnn_array[0]
shared_layers = [m for m in xrange(shared_layers_num)]
# create the network for the task
# you can change the input dropout factor and the general dropout factor
# look at the DNNDropout class
if shareLayers:
dnn = DNNDropout(np_rng=np_rng, theano_rng=theano_rng, hidden_layers_sizes=hidden_layers,
n_ins=input_size, n_outs=output_size,
input_dropout_factor=0.0, dropout_factor=0.0,
dnn_shared=dnn_shared, shared_layers=shared_layers)
else:
dnn = DNNDropout(np_rng=np_rng, theano_rng=theano_rng, hidden_layers_sizes=hidden_layers,
n_ins=input_size, n_outs=output_size,
input_dropout_factor=0.1, dropout_factor=0.5)
# add dnn and the functions to the list
dnn_array.append(dnn)
# # consider the tasks which have nonzero learning rate
# active_tasks = [n for n in xrange(num_tasks)]
test_in, test_out, test_tasks = get_test_data()
complete = np.hstack((test_tasks.reshape((-1,1)),test_in,test_out.reshape((-1,1)) ))
total = 0.0
testin = []
