def get_dnn_cfg(dnn_fname):
""" Construct a minimum required NetworkConfig given a model file """
model_data = io.json_load(dnn_fname)
cfg = NetworkConfig()
cfg.hidden_layers_sizes = []
i = 0
while 'W{}'.format(i) in model_data:
W_shape = string_2_array(model_data['W{}'.format(i)]).shape
# Currently factored layer can only be the first hidden layer
# TODO: change this!
if i == 0:
if 'side_W{}_0'.format(i) in model_data:
factored_cfg = FactoredConfig()
j = 0
while 'side_b{}_{}'.format(i, j) in model_data:
assert 'side_W{}_{}'.format(i, j) in model_data
side_W_shape = string_2_array(model_data['side_W{}_{}'.format(i, j)]).shape
if j == 0:
factored_cfg.n_in_main = W_shape[0]
factored_cfg.n_in_side = side_W_shape[0]
# NOTE: this assumes that main and secondary features
# are disjoint, but this is not required by the model.
# TODO: find a way to relax this assumption.
cfg.n_ins = W_shape[0] + side_W_shape[0]
factored_cfg.side_layers.append(side_W_shape[1])
j += 1
cfg.factored_cfg = factored_cfg
else:
cfg.n_ins = W_shape[0]
if 'W{}'.format(i + 1) in model_data:
cfg.hidden_layers_sizes.append(W_shape[1])
else:
cfg.n_outs = W_shape[1]
i += 1
return cfg
def init_srbm(rbm_cfg, numpy_rng=None):
""" Initialize a SRBM given a RBM config """
if numpy_rng is None:
numpy_rng = numpy.random.RandomState()
theano_rng = RandomStreams(numpy_rng.randint(2 ** 30))
# Following pdnn, initialize a parallel DNN and use it to initialize SRBM
dnn_cfg = NetworkConfig()
dnn_cfg.n_ins = rbm_cfg.n_ins
dnn_cfg.hidden_layers_sizes = rbm_cfg.hidden_layers_sizes
dnn_cfg.n_outs = rbm_cfg.n_outs
dnn = DNN(numpy_rng, theano_rng=theano_rng, cfg=dnn_cfg)
return SRBM(numpy_rng, theano_rng=theano_rng, cfg=rbm_cfg, dnn=dnn)
# numpy random generator
numpy_rng = numpy.random.RandomState(89677)
theano_rng = RandomStreams(numpy_rng.randint(2 ** 30))
log('> ... initializing the model')
# parse network configuration from arguments, and initialize data reading
cfg = RBMConfig()
cfg.parse_config_common(arguments)
cfg.init_data_reading(train_data_spec)
# we also need to set up a DNN model, whose parameters are shared with RBM, for 2 reasons:
# first, we can use DNN's model reading and writing functions, instead of designing these functions for RBM specifically
# second, DNN generates
cfg_dnn = NetworkConfig()
cfg_dnn.n_ins = cfg.n_ins; cfg_dnn.hidden_layers_sizes = cfg.hidden_layers_sizes; cfg_dnn.n_outs = cfg.n_outs
dnn = DNN(numpy_rng=numpy_rng, theano_rng = theano_rng, cfg = cfg_dnn)
# now set up the RBM model with dnn as an argument
srbm = SRBM(numpy_rng=numpy_rng, theano_rng = theano_rng, cfg = cfg, dnn = dnn)
# get the pre-training function
log('> ... getting the pre-training functions')
pretraining_fns = srbm.pretraining_functions(train_set_x=cfg.train_x, batch_size=cfg.batch_size,
k = 1, weight_cost = 0.0002)
start_layer_index = 0
start_epoch_index = 0
if os.path.exists(wdir + '/nnet.tmp') and os.path.exists(wdir + '/training_state.tmp'):
start_layer_index, start_epoch_index = read_two_integers(wdir + '/training_state.tmp')
log('> ... found nnet.tmp and training_state.tmp, now resume training from layer #' + str(start_layer_index) + ' epoch #' + str(start_epoch_index))
_file2nnet(dnn.layers, filename = wdir + '/nnet.tmp')
numpy_rng = numpy.random.RandomState(89677)
theano_rng = RandomStreams(numpy_rng.randint(2**30))
log('> ... initializing the model')
# parse network configuration from arguments, and initialize data reading
cfg = RBMConfig()
cfg.parse_config_common(arguments)
cfg.init_data_reading(train_data_spec)
# we also need to set up a DNN model, whose parameters are shared with RBM, for 2 reasons:
# first, we can use DNN's model reading and writing functions, instead of designing these functions for RBM specifically
# second, DNN generates
cfg_dnn = NetworkConfig()
cfg_dnn.n_ins = cfg.n_ins
cfg_dnn.hidden_layers_sizes = cfg.hidden_layers_sizes
cfg_dnn.n_outs = cfg.n_outs
dnn = DNN(numpy_rng=numpy_rng, theano_rng=theano_rng, cfg=cfg_dnn)
# now set up the RBM model with dnn as an argument
srbm = SRBM(numpy_rng=numpy_rng, theano_rng=theano_rng, cfg=cfg, dnn=dnn)
# get the pre-training function
log('> ... getting the pre-training functions')
pretraining_fns = srbm.pretraining_functions(train_set_x=cfg.train_x,
batch_size=cfg.batch_size,
k=1,
weight_cost=0.0002)
start_layer_index = 0
start_epoch_index = 0
if os.path.exists(wdir +
'/nnet.tmp') and os.path.exists(wdir +
# numpy random generator
numpy_rng = numpy.random.RandomState(89677)
theano_rng = RandomStreams(numpy_rng.randint(2 ** 30))
log('> ... initializing the model')
# parse network configuration from arguments, and initialize data reading
cfg = RBMConfig()
cfg.parse_config_common(arguments)
cfg.init_data_reading(train_data_spec)
# we also need to set up a DNN model, whose parameters are shared with RBM, for 2 reasons:
# first, we can use DNN's model reading and writing functions, instead of designing these functions for RBM specifically
# second, DNN generates
cfg_dnn = NetworkConfig()
cfg_dnn.n_ins = cfg.n_ins; cfg_dnn.hidden_layers_sizes = cfg.hidden_layers_sizes; cfg_dnn.n_outs = cfg.n_outs
dnn = DNN(numpy_rng=numpy_rng, theano_rng = theano_rng, cfg = cfg_dnn)
# now set up the RBM model with dnn as an argument
srbm = SRBM(numpy_rng=numpy_rng, theano_rng = theano_rng, cfg = cfg, dnn = dnn)
# get the pre-training function
log('> ... getting the pre-training functions')
pretraining_fns = srbm.pretraining_functions(train_set_x=cfg.train_x, batch_size=cfg.batch_size,
k = 1, weight_cost = 0.0002)
start_layer_index = 0
start_epoch_index = 0
if os.path.exists(wdir + '/nnet.tmp') and os.path.exists(wdir + '/training_state.tmp'):
start_layer_index, start_epoch_index = read_two_integers(wdir + '/training_state.tmp')
log('> ... found nnet.tmp and training_state.tmp, now resume training from layer #' + str(start_layer_index) + ' epoch #' + str(start_epoch_index))
_file2nnet(dnn.layers, filename = wdir + '/nnet.tmp')
