def get_updates(nnet, train_obj, trainable_params):
implemented_solvers = ("nesterov", "adagrad", "adadelta", "adam")
if not hasattr(nnet, "solver") or nnet.solver not in implemented_solvers:
nnet.sgd_solver = "nesterov"
else:
nnet.sgd_solver = nnet.solver
if nnet.sgd_solver == "nesterov":
updates = l_updates.nesterov_momentum(train_obj,
trainable_params,
learning_rate=Cfg.learning_rate,
momentum=0.9)
elif nnet.sgd_solver == "adagrad":
updates = l_updates.adagrad(train_obj,
trainable_params,
learning_rate=Cfg.learning_rate)
elif nnet.sgd_solver == "adadelta":
updates = l_updates.adadelta(train_obj,
trainable_params,
learning_rate=Cfg.learning_rate)
elif nnet.sgd_solver == "adam":
updates = l_updates.adam(train_obj,
trainable_params,
learning_rate=Cfg.learning_rate)
return updates
def train(self, X_train, X_valid, early_stop_count = 20 , X_test = None):
l2_norm_squared = 0.001*sum([layer.L2 for layer in self.layers])
mae = T.mean(T.sqrt(T.sum(T.sqr(self.layers[-1].output.flatten(2) - self.X), axis=1)), axis=0)
cost = mae + l2_norm_squared
updates = adadelta(cost,self.params)
# updates = adam(cost, self.params)
self.train_model = theano.function(inputs=[self.X], outputs=[cost, mae], updates=updates)
self.valid_model = theano.function(inputs=[self.X], outputs=[cost, mae])
num_training_batches = int(X_train.shape[0] / self.mini_batch_size)
num_validation_batches = int(X_valid.shape[0] / self.mini_batch_size)
counter = 0
best_valid_err = 100
early_stop = early_stop_count
epoch_i = 0
train_rand_idxs = list(range(0, X_train.shape[0]))
valid_rand_idxs = list(range(0, X_valid.shape[0]))
while counter < early_stop:
epoch_i +=1
train_costs = []
train_errs = []
valid_costs = []
valid_errs = []
np.random.shuffle(train_rand_idxs)
for batch_i in range(num_training_batches):
mnb_X = X_train[train_rand_idxs[batch_i*self.mini_batch_size: batch_i*self.mini_batch_size + self.mini_batch_size]]
train_cost, train_err = self.train_model(mnb_X)
train_costs.append(train_cost)
train_errs.append(train_err)
np.random.shuffle(valid_rand_idxs)
for batch_i in range(num_validation_batches):
mnb_X = X_train[train_rand_idxs[batch_i*self.mini_batch_size: batch_i*self.mini_batch_size + self.mini_batch_size]]
valid_cost, valid_err = self.valid_model(mnb_X)
valid_costs.append(valid_cost)
valid_errs.append(valid_err)
train_err = np.mean(np.array(train_errs))
train_cost = np.mean(np.array(train_costs))
val_err = np.mean(np.array(valid_errs))
val_cost = np.mean(np.array(valid_costs))
if val_err < best_valid_err:
best_valid_err = val_err
sys.stdout.write("Epoch "+str(epoch_i)+" Train cost: "+ str(train_cost)+ "Train mae: "+ str(train_err) + " Validation cost: "+ str(val_cost)+" Validation mae "+ str(val_err) + ",counter "+str(counter)+ " __best__ \n")
sys.stdout.flush()
counter = 0
with open("model/" + self.name +".model", mode="wb") as f:
cPickle.dump(self.params,f)
else:
counter +=1
sys.stdout.write("Epoch " + str(epoch_i)+" Train cost: "+ str(train_cost)+ "Train mae: "+ str(train_err) + " Validation cost: "+ str(val_cost)+" Validation mae "+ str(val_err) + ",counter "+str(counter) + "\n")
sys.stdout.flush()
def generate_theano_func(args, network, penalty, input_dict, target_var):
prediction = get_output(network, input_dict)
# loss = T.mean( target_var * ( T.log(target_var) - prediction ))
loss = T.mean(categorical_crossentropy(prediction, target_var))
# loss += 0.0001 * sum (T.sum(layer_params ** 2) for layer_params in get_all_params(network) )
# penalty = sum ( T.sum(lstm_param**2) for lstm_param in lstm_params )
# penalty = regularize_layer_params(l_forward_1_lstm, l2)
# penalty = T.sum(lstm_param**2 for lstm_param in lstm_params)
# penalty = 0.0001 * sum (T.sum(layer_params ** 2) for layer_params in get_all_params(l_forward_1) )
loss = loss + penalty
params = get_all_params(network, trainable=True)
if args.optimizer == "sgd":
updates = sgd(loss, params, learning_rate=args.step)
elif args.optimizer == "adagrad":
updates = adagrad(loss, params, learning_rate=args.step)
elif args.optimizer == "adadelta":
updates = adadelta(loss, params, learning_rate=args.step)
elif args.optimizer == "nesterov":
updates = nesterov_momentum(loss, params, learning_rate=args.step)
elif args.optimizer == "rms":
updates = rmsprop(loss, params, learning_rate=args.step)
elif args.optimizer == "adam":
updates = adam(loss, params, learning_rate=args.step)
else:
raise "Need set optimizer correctly"
test_prediction = get_output(network, input_dict, deterministic=True)
# test_prediction = get_output(network, deterministic=True)
# test_loss = T.mean( target_var * ( T.log(target_var) - test_prediction))
test_loss = T.mean(categorical_crossentropy(test_prediction, target_var))
train_fn = theano.function(
[input1_var, input1_mask_var, input2_var, input2_mask_var, target_var],
loss,
updates=updates,
allow_input_downcast=True,
)
if args.task == "sts":
val_fn = theano.function(
[input1_var, input1_mask_var, input2_var, input2_mask_var, target_var],
[test_loss, test_prediction],
allow_input_downcast=True,
)
elif args.task == "ent":
# test_acc = T.mean(T.eq(T.argmax(test_prediction, axis=1), target_var), dtype=theano.config.floatX)
test_acc = T.mean(categorical_accuracy(test_prediction, target_var))
val_fn = theano.function(
[input1_var, input1_mask_var, input2_var, input2_mask_var, target_var],
[test_loss, test_acc],
allow_input_downcast=True,
)
return train_fn, val_fn
def __init__(self, output_size, meta_size, depth=2):
encoder_sizes = [64, 64, 64]
input_var = TT.matrix()
meta_var = TT.matrix()
target_var = TT.matrix()
mask_var = TT.matrix()
input_layer = layers.InputLayer((None, output_size), input_var=input_var)
meta_layer = layers.InputLayer((None, meta_size), input_var=meta_var)
concat_input_layer = layers.ConcatLayer([input_layer, meta_layer])
dense = concat_input_layer
for idx in xrange(depth):
dense = layers.DenseLayer(dense, encoder_sizes[idx])
dense = layers.batch_norm(dense)
mu_and_logvar = layers.DenseLayer(dense, 2 * output_size, nonlinearity=nonlinearities.linear)
mu = layers.SliceLayer(mu_and_logvar, slice(0, output_size), axis=1)
log_var = layers.SliceLayer(mu_and_logvar, slice(output_size, None), axis=1)
loss = neg_log_likelihood2(
target_var,
layers.get_output(mu),
layers.get_output(log_var),
mask_var
).mean()
test_loss = neg_log_likelihood2(
target_var,
layers.get_output(mu, deterministic=True),
layers.get_output(log_var, deterministic=True),
mask_var
).mean()
params = layers.get_all_params(mu_and_logvar, trainable=True)
param_updates = updates.adadelta(loss, params)
self._train_fn = theano.function(
[input_var, meta_var, target_var],
updates=param_updates,
outputs=loss
)
self._loss_fn = theano.function(
[input_var, meta_var, target_var],
outputs=test_loss
)
self._predict_fn = theano.function(
[input_var, meta_var],
outputs=[
layers.get_output(mu, deterministic=True),
layers.get_output(log_var, deterministic=True)
]
)
def build_treatment_model(self, n_vars, **kwargs):
input_vars = TT.matrix()
instrument_vars = TT.matrix()
targets = TT.vector()
inputs = layers.InputLayer((None, n_vars), input_vars)
inputs = layers.DropoutLayer(inputs, p=0.2)
dense_layer = layers.DenseLayer(inputs, 2 * kwargs['dense_size'], nonlinearity=nonlinearities.rectify)
dense_layer = layers.batch_norm(dense_layer)
dense_layer= layers.DropoutLayer(dense_layer, p=0.2)
for _ in xrange(kwargs['n_dense_layers'] - 1):
dense_layer = layers.DenseLayer(dense_layer, kwargs['dense_size'], nonlinearity=nonlinearities.rectify)
dense_layer = layers.batch_norm(dense_layer)
self.treatment_output = layers.DenseLayer(dense_layer, 1, nonlinearity=nonlinearities.linear)
init_params = layers.get_all_param_values(self.treatment_output)
prediction = layers.get_output(self.treatment_output, deterministic=False)
test_prediction = layers.get_output(self.treatment_output, deterministic=True)
l2_cost = regularization.regularize_network_params(self.treatment_output, regularization.l2)
loss = gmm_loss(prediction, targets, instrument_vars) + 1e-4 * l2_cost
params = layers.get_all_params(self.treatment_output, trainable=True)
param_updates = updates.adadelta(loss, params)
self._train_fn = theano.function(
[
input_vars,
targets,
instrument_vars,
],
loss,
updates=param_updates
)
self._loss_fn = theano.function(
[
input_vars,
targets,
instrument_vars,
],
loss,
)
self._output_fn = theano.function(
[
input_vars,
],
test_prediction,
)
return init_params
def adadelta_momentum(grads,
params,
learning_rate=1.0,
momentum=0.9,
rho=0.95,
epsilon=1e-06):
return apply_nesterov_momentum(adadelta(grads, params, learning_rate, rho,
epsilon),
params=params,
momentum=momentum)
def build_instrument_model(self, n_vars, **kwargs):
targets = TT.vector()
instrument_vars = TT.matrix()
instruments = layers.InputLayer((None, n_vars), instrument_vars)
instruments = layers.DropoutLayer(instruments, p=0.2)
dense_layer = layers.DenseLayer(instruments,
kwargs['dense_size'],
nonlinearity=nonlinearities.tanh)
dense_layer = layers.DropoutLayer(dense_layer, p=0.2)
for _ in xrange(kwargs['n_dense_layers'] - 1):
dense_layer = layers.DenseLayer(dense_layer,
kwargs['dense_size'],
nonlinearity=nonlinearities.tanh)
dense_layer = layers.DropoutLayer(dense_layer, p=0.5)
self.instrument_output = layers.DenseLayer(
dense_layer, 1, nonlinearity=nonlinearities.linear)
init_params = layers.get_all_param_values(self.instrument_output)
prediction = layers.get_output(self.instrument_output,
deterministic=False)
test_prediction = layers.get_output(self.instrument_output,
deterministic=True)
# flexible here, endog variable can be categorical, continuous, etc.
l2_cost = regularization.regularize_network_params(
self.instrument_output, regularization.l2)
loss = objectives.squared_error(
prediction.flatten(), targets.flatten()).mean() + 1e-4 * l2_cost
loss_total = objectives.squared_error(prediction.flatten(),
targets.flatten()).mean()
params = layers.get_all_params(self.instrument_output, trainable=True)
param_updates = updates.adadelta(loss, params)
self._instrument_train_fn = theano.function([
targets,
instrument_vars,
],
loss,
updates=param_updates)
self._instrument_loss_fn = theano.function([
targets,
instrument_vars,
], loss_total)
self._instrument_output_fn = theano.function([instrument_vars],
test_prediction)
return init_params
def run(self, parameter, parameterName, loss, **kwargs) :
pVar = parameter.getVar()
gparam = tt.grad(loss, pVar)
updates = LUP.adadelta( [ gparam ], [pVar], learning_rate=self.getHP("lr"), rho=self.getHP("rho"), epsilon=self.getHP("epsilon"))
ret = OptimizerResult(pVar, parameterName, gparam, updates[pVar])
i = 0
for param, update in updates.items() :
if param is not pVar :
name = "%s_adadelta_%s" % (parameterName, i)
ret.addCoParameter(param, name, None, update)
i += 1
return ret
def get_updates(nnet, train_obj, trainable_params, solver=None):
implemented_solvers = ("sgd", "momentum", "nesterov", "adagrad", "rmsprop",
"adadelta", "adam", "adamax")
if solver not in implemented_solvers:
nnet.sgd_solver = "adam"
else:
nnet.sgd_solver = solver
if nnet.sgd_solver == "sgd":
updates = l_updates.sgd(train_obj,
trainable_params,
learning_rate=Cfg.learning_rate)
elif nnet.sgd_solver == "momentum":
updates = l_updates.momentum(train_obj,
trainable_params,
learning_rate=Cfg.learning_rate,
momentum=Cfg.momentum)
elif nnet.sgd_solver == "nesterov":
updates = l_updates.nesterov_momentum(train_obj,
trainable_params,
learning_rate=Cfg.learning_rate,
momentum=Cfg.momentum)
elif nnet.sgd_solver == "adagrad":
updates = l_updates.adagrad(train_obj,
trainable_params,
learning_rate=Cfg.learning_rate)
elif nnet.sgd_solver == "rmsprop":
updates = l_updates.rmsprop(train_obj,
trainable_params,
learning_rate=Cfg.learning_rate,
rho=Cfg.rho)
elif nnet.sgd_solver == "adadelta":
updates = l_updates.adadelta(train_obj,
trainable_params,
learning_rate=Cfg.learning_rate,
rho=Cfg.rho)
elif nnet.sgd_solver == "adam":
updates = l_updates.adam(train_obj,
trainable_params,
learning_rate=Cfg.learning_rate)
elif nnet.sgd_solver == "adamax":
updates = l_updates.adamax(train_obj,
trainable_params,
learning_rate=Cfg.learning_rate)
return updates
def build_instrument_model(self, n_vars, **kwargs):
targets = TT.vector()
instrument_vars = TT.matrix()
instruments = layers.InputLayer((None, n_vars), instrument_vars)
instruments = layers.DropoutLayer(instruments, p=0.2)
dense_layer = layers.DenseLayer(instruments, kwargs['dense_size'], nonlinearity=nonlinearities.tanh)
dense_layer = layers.DropoutLayer(dense_layer, p=0.2)
for _ in xrange(kwargs['n_dense_layers'] - 1):
dense_layer = layers.DenseLayer(dense_layer, kwargs['dense_size'], nonlinearity=nonlinearities.tanh)
dense_layer = layers.DropoutLayer(dense_layer, p=0.5)
self.instrument_output = layers.DenseLayer(dense_layer, 1, nonlinearity=nonlinearities.linear)
init_params = layers.get_all_param_values(self.instrument_output)
prediction = layers.get_output(self.instrument_output, deterministic=False)
test_prediction = layers.get_output(self.instrument_output, deterministic=True)
# flexible here, endog variable can be categorical, continuous, etc.
l2_cost = regularization.regularize_network_params(self.instrument_output, regularization.l2)
loss = objectives.squared_error(prediction.flatten(), targets.flatten()).mean() + 1e-4 * l2_cost
loss_total = objectives.squared_error(prediction.flatten(), targets.flatten()).mean()
params = layers.get_all_params(self.instrument_output, trainable=True)
param_updates = updates.adadelta(loss, params)
self._instrument_train_fn = theano.function(
[
targets,
instrument_vars,
],
loss,
updates=param_updates
)
self._instrument_loss_fn = theano.function(
[
targets,
instrument_vars,
],
loss_total
)
self._instrument_output_fn = theano.function([instrument_vars], test_prediction)
return init_params
def train_setup():
x = T.tensor3('input')
y = T.lvector('output')
network = cnn(x, config.input_length, config.output_length)
print 'Number of Parameters {0}'.format(count_params(network))
if config.init_model is not None:
with np.load(config.init_model) as f:
param_values = [f['arr_%d' % i] for i in range(len(f.files))]
set_all_param_values(decoding, param_values)
# training tasks in sequence
prediction = get_output(network)
ent = categorical_crossentropy(prediction, y)
ent = ent.mean()
l1_norm = config.l1_weight * regularize_network_params(network, l1)
l2_norm = config.l2_weight * regularize_network_params(network, l2)
total_error = ent + l1_norm + l2_norm
params = get_all_params(network, trainable=True)
updates = adadelta( total_error, params, config.learning_rate, \
config.rho, \
config.eps )
train_fn = function( [x, y], [ent, l1_norm, l2_norm, prediction], \
updates = updates, \
allow_input_downcast = True )
val_prediction = get_output(network, deterministic=True)
val_ent = categorical_crossentropy(val_prediction, y)
val_ent = val_ent.mean()
val_fn = function([x, y], [val_ent, val_prediction],
allow_input_downcast=True)
return network, train_fn, val_fn
def train_setup():
x = T.tensor3('input')
y = T.matrix('output')
encoding, decoding = cnn( x, config.input_length, config.output_length, \
config.encoding_length )
print 'Number of Parameters {0}'.format(count_params(decoding))
if config.init_model is not None:
with np.load(config.init_model) as f:
param_values = [f['arr_%d' % i] for i in range(len(f.files))]
set_all_param_values(decoding, param_values)
# training tasks in sequence
prediction = get_output(decoding)
error = squared_error(y, prediction)
error = error.mean()
l1_norm = config.l1_weight * regularize_network_params(decoding, l1)
l2_norm = config.l2_weight * regularize_network_params(decoding, l2)
total_error = error + l1_norm + l2_norm
params = get_all_params(decoding, trainable=True)
updates = adadelta( total_error, params, config.learning_rate, \
config.rho, \
config.eps )
train_fn = function( [x, y], [error, l1_norm, l2_norm], \
updates = updates, \
allow_input_downcast = True )
val_prediction = get_output(decoding, deterministic=True)
val_error = squared_error(y, val_prediction)
val_error = val_error.mean()
val_fn = function([x, y], val_error, allow_input_downcast=True)
return encoding, decoding, train_fn, val_fn
def main():
def signal_handler(signal, frame):
global terminate
terminate = True
print('terminating...'.format(terminate))
signal.signal(signal.SIGINT, signal_handler)
configure_theano()
options = parse_options()
X, X_val = generate_data()
# X = np.reshape(X, (-1, 1, 30, 40))[:-5]
print('X type and shape:', X.dtype, X.shape)
print('X.min():', X.min())
print('X.max():', X.max())
# X_val = np.reshape(X_val, (-1, 1, 30, 40))[:-1]
print('X_val type and shape:', X_val.dtype, X_val.shape)
print('X_val.min():', X_val.min())
print('X_val.max():', X_val.max())
# we need our target to be 1 dimensional
X_out = X.reshape((X.shape[0], -1))
X_val_out = X_val.reshape((X_val.shape[0], -1))
print('X_out:', X_out.dtype, X_out.shape)
print('X_val_out', X_val_out.dtype, X_val_out.shape)
# X_noisy = apply_gaussian_noise(X_out)
# visualize_reconstruction(X_noisy[0:25], X_out[0:25], shape=(28, 28))
# X = np.reshape(X_noisy, (-1, 1, 28, 28))
print('constructing and compiling model...')
# input_var = T.tensor4('input', dtype='float32')
input_var = T.tensor3('input', dtype='float32')
target_var = T.matrix('output', dtype='float32')
lr = theano.shared(np.array(0.8, dtype=theano.config.floatX), name='learning_rate')
lr_decay = np.array(0.9, dtype=theano.config.floatX)
# try building a reshaping layer
# network = create_model(input_var, (None, 1, 30, 40), options)
l_input = InputLayer((None, None, 1200), input_var, name='input')
l_input = ReshapeLayer(l_input, (-1, 1, 30, 40), name='reshape_input')
# l_input = InputLayer((None, 1, 30, 40), input_var, name='input')
if options['MODEL'] == 'normal':
network, encoder = avletters_convae.create_model(l_input, options)
if options['MODEL'] == 'batchnorm':
network, encoder = avletters_convae_bn.create_model(l_input, options)
if options['MODEL'] == 'dropout':
network, encoder = avletters_convae_drop.create_model(l_input, options)
if options['MODEL'] == 'bn+dropout':
network, encoder = avletters_convae_bndrop.create_model(l_input, options)
print('AE Network architecture: {}'.format(options['MODEL']))
print_network(network)
recon = las.layers.get_output(network, deterministic=False)
all_params = las.layers.get_all_params(network, trainable=True)
cost = T.mean(squared_error(recon, target_var))
updates = adadelta(cost, all_params, lr)
# updates = las.updates.apply_nesterov_momentum(updates, all_params, momentum=0.90)
use_max_constraint = False
print('apply max norm constraint: {}'.format(use_max_constraint))
if use_max_constraint:
MAX_NORM = 4
for param in las.layers.get_all_params(network, regularizable=True):
if param.ndim > 1: # only apply to dimensions larger than 1, exclude biases
# updates[param] = norm_constraint(param, MAX_NORM * las.utils.compute_norms(param.get_value()).mean())
updates[param] = norm_constraint(param, MAX_NORM)
train = theano.function([input_var, target_var], recon, updates=updates, allow_input_downcast=True)
train_cost_fn = theano.function([input_var, target_var], cost, allow_input_downcast=True)
eval_recon = las.layers.get_output(network, deterministic=True)
eval_cost = T.mean(las.objectives.squared_error(eval_recon, target_var))
eval_cost_fn = theano.function([input_var, target_var], eval_cost, allow_input_downcast=True)
recon_fn = theano.function([input_var], eval_recon, allow_input_downcast=True)
if terminate:
exit()
NUM_EPOCHS = options['NUM_EPOCHS']
EPOCH_SIZE = options['EPOCH_SIZE']
NO_STRIDES = options['NO_STRIDES']
VAL_NO_STRIDES = options['VAL_NO_STRIDES']
print('begin training for {} epochs...'.format(NUM_EPOCHS))
datagen = batch_iterator(X, X_out, 128)
costs = []
val_costs = []
for epoch in range(NUM_EPOCHS):
time_start = time.time()
for i in range(EPOCH_SIZE):
batch_X, batch_y = next(datagen)
print_str = 'Epoch {} batch {}/{}: {} examples at learning rate = {:.4f}'.format(
epoch + 1, i + 1, EPOCH_SIZE, len(batch_X), lr.get_value())
print(print_str, end='')
sys.stdout.flush()
batch_X = batch_X.reshape((-1, 1, 1200))
train(batch_X, batch_y)
print('\r', end='')
if terminate:
break
if terminate:
break
cost = batch_compute_cost(X, X_out, NO_STRIDES, train_cost_fn)
val_cost = batch_compute_cost(X_val, X_val_out, VAL_NO_STRIDES, eval_cost_fn)
costs.append(cost)
val_costs.append(val_cost)
print("Epoch {} train cost = {}, validation cost = {} ({:.1f}sec) "
.format(epoch + 1, cost, val_cost, time.time() - time_start))
if epoch > 10:
lr.set_value(lr.get_value() * lr_decay)
X_val_recon = recon_fn(X_val)
visualize_reconstruction(X_val_out[450:550], X_val_recon[450:550], shape=(30, 40), savefilename='avletters')
plot_validation_cost(costs, val_costs, None, savefilename='valid_cost')
conv2d1 = las.layers.get_all_layers(network)[2]
visualize.plot_conv_weights(conv2d1, (15, 14)).savefig('conv2d1.png')
print('saving encoder...')
save_model(encoder, 'models/conv_encoder.dat')
save_model(network, 'models/conv_ae.dat')
def main():
configure_theano()
config_file = 'config/separate_train.ini'
print('loading config file: {}'.format(config_file))
config = ConfigParser.ConfigParser()
config.read(config_file)
print('preprocessing dataset...')
data = load_mat_file(config.get('data', 'images'))
ae_pretrained = config.get('models', 'pretrained')
ae_finetuned = config.get('models', 'finetuned')
learning_rate = float(config.get('training', 'learning_rate'))
decay_rate = float(config.get('training', 'decay_rate'))
decay_start = int(config.get('training', 'decay_start'))
lstm_units = int(config.get('training', 'lstm_units'))
output_units = int(config.get('training', 'output_units'))
do_finetune = config.getboolean('training', 'do_finetune')
save_finetune = config.getboolean('training', 'save_finetune')
load_finetune = config.getboolean('training', 'load_finetune')
# 53 subjects, 70 utterances, 5 view angles
# s[x]_v[y]_u[z].mp4
# resized, height, width = (26, 44)
# ['dataMatrix', 'targetH', 'targetsPerVideoVec', 'videoLengthVec', '__header__', 'targetsVec',
# '__globals__', 'iterVec', 'filenamesVec', 'dataMatrixCells', 'subjectsVec', 'targetW', '__version__']
print(data.keys())
X = data['dataMatrix'].astype('float32') # .reshape((-1, 26, 44), order='f').reshape((-1, 26 * 44))
y = data['targetsVec'].astype('int32')
y = y.reshape((len(y),))
uniques = np.unique(y)
print('number of classifications: {}'.format(len(uniques)))
subjects = data['subjectsVec'].astype('int')
subjects = subjects.reshape((len(subjects),))
video_lens = data['videoLengthVec'].astype('int')
video_lens = video_lens.reshape((len(video_lens,)))
train_subject_ids = read_data_split_file('data/train.txt')
val_subject_ids = read_data_split_file('data/val.txt')
test_subject_ids = read_data_split_file('data/test.txt')
print('Train: {}'.format(train_subject_ids))
print('Validation: {}'.format(val_subject_ids))
print('Test: {}'.format(test_subject_ids))
train_X, train_y, train_vidlens, train_subjects, \
val_X, val_y, val_vidlens, val_subjects, \
test_X, test_y, test_vidlens, test_subjects = \
split_data(X, y, subjects, video_lens, train_subject_ids, val_subject_ids, test_subject_ids)
assert train_X.shape[0] + val_X.shape[0] + test_X.shape[0] == len(X)
assert train_y.shape[0] + val_y.shape[0] + test_y.shape[0] == len(y)
assert train_vidlens.shape[0] + val_vidlens.shape[0] + test_vidlens.shape[0] == len(video_lens)
assert train_subjects.shape[0] + val_subjects.shape[0] + test_subjects.shape[0] == len(subjects)
train_X = normalize_input(train_X, centralize=True)
test_X = normalize_input(test_X, centralize=True)
if do_finetune:
dbn = load_dbn(ae_pretrained)
dbn.initialize()
dbn.fit(train_X, train_X)
recon = dbn.predict(test_X)
visualize_reconstruction(reorder_data(test_X[800:864], (26, 44)),
reorder_data(recon[800:864], (26, 44)),
shape=(26, 44))
if save_finetune:
pickle.dump(dbn, open(ae_finetuned, 'wb'))
if load_finetune:
print('loading pre-trained encoding layers...')
dbn = pickle.load(open(ae_finetuned, 'rb'))
dbn.initialize()
# recon = dbn.predict(test_X)
# visualize_reconstruction(reorder_data(test_X[800:864], (26, 44)),
# reorder_data(recon[800:864], (26, 44)),
# shape=(26, 44))
encoder = extract_encoder(dbn)
train_X = encoder.predict(train_X)
val_X = encoder.predict(val_X)
test_X = encoder.predict(test_X)
# train_X = concat_first_second_deltas(train_X, train_vidlens)
# val_X = concat_first_second_deltas(val_X, val_vidlens)
# test_X = concat_first_second_deltas(test_X, test_vidlens)
# featurewise normalize
train_X, mean, std = featurewise_normalize_sequence(train_X)
val_X = (val_X - mean) / std
test_X = (test_X - mean) / std
# recon = dbn.predict(test_X)
# visualize_reconstruction(test_X[550:650], recon[550:650], (26, 44))
# exit()
# IMPT: the encoder was trained with fortan ordered images, so to visualize
# convert all the images to C order using reshape_images_order()
# output = dbn.predict(test_X)
# test_X = reshape_images_order(test_X, (26, 44))
# output = reshape_images_order(output, (26, 44))
# visualize_reconstruction(test_X[:36, :], output[:36, :], shape=(26, 44))
inputs = T.tensor3('inputs', dtype='float32')
mask = T.matrix('mask', dtype='uint8')
targets = T.ivector('targets')
lr = theano.shared(np.array(learning_rate, dtype=theano.config.floatX), name='learning_rate')
lr_decay = np.array(decay_rate, dtype=theano.config.floatX)
print('constructing lstm classifier...')
network = lstm_classifier_baseline.create_model((None, None, 50), inputs,
(None, None), mask,
lstm_units, output_units)
print_network(network)
print('compiling model...')
predictions = las.layers.get_output(network, deterministic=False)
all_params = las.layers.get_all_params(network, trainable=True)
cost = T.mean(las.objectives.categorical_crossentropy(predictions, targets))
updates = adadelta(cost, all_params, learning_rate=lr)
# updates = las.updates.apply_momentum(sgd(cost, all_params, learning_rate=lr), all_params, 0.1)
use_max_constraint = False
if use_max_constraint:
MAX_NORM = 4
for param in las.layers.get_all_params(network, regularizable=True):
if param.ndim > 1: # only apply to dimensions larger than 1, exclude biases
updates[param] = norm_constraint(param, MAX_NORM * las.utils.compute_norms(param.get_value()).mean())
train = theano.function(
[inputs, targets, mask],
cost, updates=updates, allow_input_downcast=True)
compute_train_cost = theano.function([inputs, targets, mask], cost, allow_input_downcast=True)
test_predictions = las.layers.get_output(network, deterministic=True)
test_cost = T.mean(las.objectives.categorical_crossentropy(test_predictions, targets))
compute_test_cost = theano.function(
[inputs, targets, mask], test_cost, allow_input_downcast=True)
val_fn = theano.function([inputs, mask], test_predictions, allow_input_downcast=True)
# We'll train the network with 10 epochs of 30 minibatches each
print('begin training...')
cost_train = []
cost_val = []
class_rate = []
NUM_EPOCHS = 30
EPOCH_SIZE = 120
BATCH_SIZE = 10
STRIP_SIZE = 3
MAX_LOSS = 0.2
VALIDATION_WINDOW = 10
val_window = circular_list(VALIDATION_WINDOW)
train_strip = np.zeros((STRIP_SIZE,))
best_val = float('inf')
best_conf = None
best_cr = 0.0
datagen = gen_lstm_batch_random(train_X, train_y, train_vidlens, batchsize=BATCH_SIZE)
val_datagen = gen_lstm_batch_random(val_X, val_y, val_vidlens, batchsize=len(val_vidlens))
test_datagen = gen_lstm_batch_random(test_X, test_y, test_vidlens, batchsize=len(test_vidlens))
# We'll use this "validation set" to periodically check progress
X_val, y_val, mask_val, _ = next(val_datagen)
X_test, y_test, mask_test, _ = next(test_datagen)
def early_stop(cost_window):
if len(cost_window) < 2:
return False
else:
curr = cost_window[0]
for idx, cost in enumerate(cost_window):
if curr < cost or idx == 0:
curr = cost
else:
return False
return True
for epoch in range(NUM_EPOCHS):
time_start = time.time()
for i in range(EPOCH_SIZE):
X, y, m, _ = next(datagen)
print_str = 'Epoch {} batch {}/{}: {} examples at learning rate = {:.4f}'.format(
epoch + 1, i + 1, EPOCH_SIZE, len(X), float(lr.get_value()))
print(print_str, end='')
sys.stdout.flush()
train(X, y, m)
print('\r', end='')
cost = compute_train_cost(X, y, m)
val_cost = compute_test_cost(X_val, y_val, mask_val)
cost_train.append(cost)
cost_val.append(val_cost)
train_strip[epoch % STRIP_SIZE] = cost
val_window.push(val_cost)
gl = 100 * (cost_val[-1] / np.min(cost_val) - 1)
pk = 1000 * (np.sum(train_strip) / (STRIP_SIZE * np.min(train_strip)) - 1)
pq = gl / pk
cr, val_conf = evaluate_model(X_val, y_val, mask_val, val_fn)
class_rate.append(cr)
if val_cost < best_val:
best_val = val_cost
best_conf = val_conf
best_cr = cr
test_cr, test_conf = evaluate_model(X_test, y_test, mask_test, val_fn)
print("Epoch {} train cost = {}, val cost = {}, "
"GL loss = {:.3f}, GQ = {:.3f}, CR = {:.3f}, Test CR= {:.3f} ({:.1f}sec)"
.format(epoch + 1, cost_train[-1], cost_val[-1], gl, pq, cr, test_cr, time.time() - time_start))
else:
print("Epoch {} train cost = {}, val cost = {}, "
"GL loss = {:.3f}, GQ = {:.3f}, CR = {:.3f} ({:.1f}sec)"
.format(epoch + 1, cost_train[-1], cost_val[-1], gl, pq, cr, time.time() - time_start))
if epoch >= VALIDATION_WINDOW and early_stop(val_window):
break
# learning rate decay
if epoch > decay_start:
lr.set_value(lr.get_value() * lr_decay)
phrases = ['p1', 'p2', 'p3', 'p4', 'p5', 'p6', 'p7', 'p8', 'p9', 'p10']
print('Final Model')
print('CR: {}, val loss: {}, Test CR: {}'.format(best_cr, best_val, test_cr))
print('confusion matrix: ')
plot_confusion_matrix(test_conf, phrases, fmt='grid')
plot_validation_cost(cost_train, cost_val, savefilename='valid_cost')
def build_network_2dconv(args, input_var, target_var, wordEmbeddings, maxlen=60):
print("Building model with 2D Convolution")
vocab_size = wordEmbeddings.shape[1]
wordDim = wordEmbeddings.shape[0]
num_filters = 100
stride = 1
# CNN_sentence config
filter_size = (3, wordDim)
pool_size = (maxlen - 3 + 1, 1)
input = InputLayer((None, maxlen), input_var=input_var)
batchsize, seqlen = input.input_var.shape
emb = EmbeddingLayer(input, input_size=vocab_size, output_size=wordDim, W=wordEmbeddings.T)
emb.params[emb.W].remove("trainable") # (batchsize, maxlen, wordDim)
reshape = ReshapeLayer(emb, (batchsize, 1, maxlen, wordDim))
conv2d = Conv2DLayer(
reshape,
num_filters=num_filters,
filter_size=(filter_size),
stride=stride,
nonlinearity=rectify,
W=GlorotUniform(),
) # (None, 100, 34, 1)
maxpool = MaxPool2DLayer(conv2d, pool_size=pool_size) # (None, 100, 1, 1)
forward = FlattenLayer(maxpool) # (None, 100) #(None, 50400)
hid = DenseLayer(forward, num_units=args.hiddenDim, nonlinearity=sigmoid)
network = DenseLayer(hid, num_units=2, nonlinearity=softmax)
prediction = get_output(network)
loss = T.mean(binary_crossentropy(prediction, target_var))
lambda_val = 0.5 * 1e-4
layers = {conv2d: lambda_val, hid: lambda_val, network: lambda_val}
penalty = regularize_layer_params_weighted(layers, l2)
loss = loss + penalty
params = get_all_params(network, trainable=True)
if args.optimizer == "sgd":
updates = sgd(loss, params, learning_rate=args.step)
elif args.optimizer == "adagrad":
updates = adagrad(loss, params, learning_rate=args.step)
elif args.optimizer == "adadelta":
updates = adadelta(loss, params, learning_rate=args.step)
elif args.optimizer == "nesterov":
updates = nesterov_momentum(loss, params, learning_rate=args.step)
elif args.optimizer == "rms":
updates = rmsprop(loss, params, learning_rate=args.step)
elif args.optimizer == "adam":
updates = adam(loss, params, learning_rate=args.step)
else:
raise "Need set optimizer correctly"
test_prediction = get_output(network, deterministic=True)
test_loss = T.mean(binary_crossentropy(test_prediction, target_var))
train_fn = theano.function([input_var, target_var], loss, updates=updates, allow_input_downcast=True)
test_acc = T.mean(binary_accuracy(test_prediction, target_var))
val_fn = theano.function([input_var, target_var], [test_loss, test_acc], allow_input_downcast=True)
return train_fn, val_fn
def __init__(self,
hidden_size=100,
nclasses=73,
num_embeddings=11359,
embedding_dim=100,
window_size=1,
memory_size=40,
n_memory_slots=8,
go_code=1,
depth=2,
load_dir=None):
articles, titles = T.imatrices('articles', 'titles')
n_article_slots = int(n_memory_slots /
2) # TODO derive this from an arg
n_title_slots = n_memory_slots - n_article_slots
n_instances = articles.shape[0]
self.window_size = window_size
randoms = {
# attr: shape
# 'emb': (num_embeddings + 1, embedding_dim),
'M_a': (memory_size, n_article_slots),
'M_t': (memory_size, n_title_slots),
'w_a': (n_article_slots, ),
'w_t': (n_title_slots, ),
'Wg_a': (window_size * embedding_dim, n_article_slots),
'Wg_t': (window_size * embedding_dim, n_title_slots),
'Wk': (hidden_size, memory_size),
'Wb': (hidden_size, 1),
'Wv': (hidden_size, memory_size),
'We_a': (hidden_size, n_article_slots),
'We_t': (hidden_size, n_title_slots),
'Wx': (window_size * embedding_dim, hidden_size),
'Wh': (memory_size, hidden_size),
'W': (hidden_size, nclasses),
'h0': hidden_size
}
zeros = {
# attr: shape
'bg_a': n_article_slots,
'bg_t': n_title_slots,
'bk': memory_size,
'bb': 1,
'bv': memory_size,
'be_a': n_article_slots,
'be_t': n_title_slots,
'bh': hidden_size,
'b': nclasses,
}
for l in range(depth):
randoms['gru' + str(l)] = (1, embedding_dim)
def random_shared(name):
shape = randoms[name]
return theano.shared(
0.2 *
np.random.normal(size=shape).astype(theano.config.floatX),
name=name)
def zeros_shared(name):
shape = zeros[name]
return theano.shared(np.zeros(shape, dtype=theano.config.floatX),
name=name)
for key in randoms:
# create an attribute with associated shape and random values
setattr(self, key, random_shared(key))
for key in zeros:
# create an attribute with associated shape and values equal to 0
setattr(self, key, zeros_shared(key))
self.names = randoms.keys() + zeros.keys()
# self.names.remove('emb') # no need to save or update embeddings
scan_vars = 'h0 w_a M_a w_t M_t'.split()
def repeat_for_each_instance(param):
""" repeat param along new axis once for each instance """
return T.repeat(T.shape_padleft(param),
repeats=n_instances,
axis=0)
for key in scan_vars:
setattr(self, key,
repeat_for_each_instance(self.__getattribute__(key)))
self.names.remove(key)
if load_dir is not None:
with open(os.path.join(load_dir, 'params.pkl')) as handle:
params = pickle.load(handle)
self.__dict__.update(params)
def recurrence(i, h_tm1, w_a, M_a, *args, **kwargs):
"""
notes
Headers from paper in all caps
mem = n_article slots if is_article else n_title_slots
:param i: center index of sliding window
:param h_tm1: h_{t-1} (hidden state)
:param w_a: attention weights for article memory
:param M_a: article memory
:param args: gru_weights, maybe w_t, maybe M_t
gru_weights: weights with which to initialize GRULayer on each time step
w_t: attention weights for titles memory
M_t: titles memory
:param kwargs: is_training, is_article
is_training:
is_article: we use different parts of memory when working with a article
:return: [y = model outputs,
i + 1 = increment index,
h w, M (see above)]
"""
is_training = kwargs['is_training']
is_article = kwargs['is_article']
gru_weights = args[:depth]
if len(args) > depth:
w_t = args[depth]
M_t = args[depth + 1]
i_type = T.iscalar if is_article or is_training else T.ivector
assert i.type == i_type
if not is_article:
assert w_t is not None and M_t is not None
word_idxs = i
if is_article or is_training:
# get representation of word window
document = articles if is_article else titles # [instances, bucket_width]
word_idxs = document[:, i:i + 1] # [instances, 1]
# x_i = self.emb[word_idxs].flatten(ndim=2) # [instances, embedding_dim]
input = InputLayer(shape=(None, 1), input_var=word_idxs)
embed = EmbeddingLayer(input, num_embeddings, embedding_dim)
gru = GRULayer(incoming=embed,
num_units=embedding_dim,
hid_init=self.gru0)
for weight in gru_weights:
gru = GRULayer(incoming=gru,
num_units=embedding_dim,
hid_init=weight)
x_i = get_output(gru).flatten(ndim=2)
x_i = Print('x_i')(x_i) # [instances, embedding_dim]
gru_weights = []
if is_article:
M_read = M_a # [instances, memory_size, n_article_slots]
w_read = w_a # [instances, n_article_slots]
else:
M_read = T.concatenate(
[M_a, M_t],
axis=2) # [instances, memory_size, n_title_slots]
w_read = T.concatenate([w_a, w_t],
axis=1) # [instances, n_title_slots]
# eqn 15
c = T.batched_dot(M_read, w_read) # [instances, memory_size]
# EXTERNAL MEMORY READ
def get_attention(Wg, bg, M, w):
g = T.nnet.sigmoid(T.dot(x_i, Wg) + bg) # [instances, mem]
# eqn 11
k = T.dot(h_tm1, self.Wk) + self.bk # [instances, memory_size]
# eqn 13
beta = T.dot(h_tm1, self.Wb) + self.bb
beta = T.nnet.softplus(beta)
beta = T.addbroadcast(beta, 1) # [instances, 1]
# eqn 12
w_hat = T.nnet.softmax(beta * cosine_dist(M, k))
# eqn 14
return (1 - g) * w + g * w_hat # [instances, mem]
w_a = get_attention(self.Wg_a, self.bg_a, M_a,
w_a) # [instances, n_article_slots]
if not is_article:
w_t = get_attention(self.Wg_t, self.bg_t, M_t,
w_t) # [instances, n_title_slots]
# MODEL INPUT AND OUTPUT
# eqn 9
h = T.dot(c, self.Wh) + T.dot(
x_i, self.Wx) + self.bh # [instances, hidden_size]
# eqn 10
y = T.nnet.softmax(T.dot(h, self.W) +
self.b) # [instances, nclasses]
# EXTERNAL MEMORY UPDATE
def update_memory(We, be, w_update, M_update):
# eqn 17
e = T.nnet.sigmoid(T.dot(h_tm1, We) + be) # [instances, mem]
f = 1. - w_update * e # [instances, mem]
# eqn 16
v = T.tanh(T.dot(h, self.Wv) +
self.bv) # [instances, memory_size]
# need to add broadcast layers for memory update
f = f.dimshuffle(0, 'x', 1) # [instances, 1, mem]
u = w_update.dimshuffle(0, 'x', 1) # [instances, 1, mem]
v = v.dimshuffle(0, 1, 'x') # [instances, memory_size, 1]
# eqn 19
return M_update * f + T.batched_dot(v, u) * (
1 - f) # [instances, memory_size, mem]
M_a = update_memory(self.We_a, self.be_a, w_a, M_a)
attention_and_memory = [w_a, M_a]
if not is_article:
M_t = update_memory(self.We_t, self.be_t, w_t, M_t)
attention_and_memory += [w_t, M_t]
y_max = y.argmax(axis=1).astype(int32)
next_idxs = i + 1 if is_training or is_article else y_max
return [y, y_max, next_idxs, h] + attention_and_memory
read_article = partial(recurrence, is_training=True, is_article=True)
# for read_article, it actually doesn't matter whether is_training is true
i0 = T.constant(0, dtype=int32, name='first_value_of_i')
gru_weights = [eval('self.gru' + str(l)) for l in range(depth)]
outputs_info = [None, None, i0, self.h0, self.w_a, self.M_a
] + gru_weights
[_, _, _, h, w, M], _ = theano.scan(fn=read_article,
outputs_info=outputs_info,
n_steps=articles.shape[1],
name='read_scan')
produce_title = partial(recurrence, is_training=True, is_article=False)
outputs_info[3:6] = [param[-1, :, :] for param in (h, w, M)]
outputs_info.extend([self.w_t, self.M_t])
bucket_width = titles.shape[
1] - 1 # subtract 1 because <go> is omitted in y_true
[y, y_max, _, _, _, _, _,
_], _ = theano.scan(fn=produce_title,
outputs_info=outputs_info,
n_steps=bucket_width,
name='train_scan')
# loss and updates
y_clip = T.clip(y, .01, .99)
y_flatten = y_clip.dimshuffle(2, 1, 0).flatten(ndim=2).T
y_true = titles[:, 1:].ravel() # [:, 1:] in order to omit <go>
counts = T.extra_ops.bincount(y_true, assert_nonneg=True)
weights = 1.0 / (counts[y_true] + 1) * T.neq(y_true, 0)
losses = T.nnet.categorical_crossentropy(y_flatten, y_true)
loss = objectives.aggregate(losses, weights, mode='sum')
updates = adadelta(loss, self.params())
self.learn = theano.function(inputs=[articles, titles],
outputs=[y_max.T, loss],
updates=updates,
allow_input_downcast=True,
name='learn')
produce_title_test = partial(recurrence,
is_training=False,
is_article=False)
self.test = theano.function(inputs=[articles, titles],
outputs=[y_max.T],
on_unused_input='ignore')
outputs_info[2] = T.zeros([n_instances], dtype=int32) + go_code
[_, y_max, _, _, _, _, _,
_], _ = theano.scan(fn=produce_title_test,
outputs_info=outputs_info,
n_steps=bucket_width,
name='test_scan')
self.predict = theano.function(inputs=[articles, titles],
outputs=y_max.T,
name='infer')
def event_span_classifier(args, input_var, target_var, wordEmbeddings, seqlen, num_feats):
print("Building model with 1D Convolution")
vocab_size = wordEmbeddings.shape[1]
wordDim = wordEmbeddings.shape[0]
kw = 2
num_filters = seqlen-kw+1
stride = 1
#important context words as channels
#CNN_sentence config
filter_size=wordDim
pool_size=seqlen-filter_size+1
input = InputLayer((None, seqlen, num_feats),input_var=input_var)
batchsize, _, _ = input.input_var.shape
emb = EmbeddingLayer(input, input_size=vocab_size, output_size=wordDim, W=wordEmbeddings.T)
#emb.params[emb.W].remove('trainable') #(batchsize, seqlen, wordDim)
#print get_output_shape(emb)
reshape = ReshapeLayer(emb, (batchsize, seqlen, num_feats*wordDim))
#print get_output_shape(reshape)
conv1d = Conv1DLayer(reshape, num_filters=num_filters, filter_size=wordDim, stride=1,
nonlinearity=tanh,W=GlorotUniform()) #nOutputFrame = num_flters,
#nOutputFrameSize = (num_feats*wordDim-filter_size)/stride +1
#print get_output_shape(conv1d)
conv1d = DimshuffleLayer(conv1d, (0,2,1))
#print get_output_shape(conv1d)
pool_size=num_filters
maxpool = MaxPool1DLayer(conv1d, pool_size=pool_size)
#print get_output_shape(maxpool)
#forward = FlattenLayer(maxpool)
#print get_output_shape(forward)
hid = DenseLayer(maxpool, num_units=args.hiddenDim, nonlinearity=sigmoid)
network = DenseLayer(hid, num_units=2, nonlinearity=softmax)
prediction = get_output(network)
loss = T.mean(binary_crossentropy(prediction,target_var))
lambda_val = 0.5 * 1e-4
layers = {emb:lambda_val, conv1d:lambda_val, hid:lambda_val, network:lambda_val}
penalty = regularize_layer_params_weighted(layers, l2)
loss = loss + penalty
params = get_all_params(network, trainable=True)
if args.optimizer == "sgd":
updates = sgd(loss, params, learning_rate=args.step)
elif args.optimizer == "adagrad":
updates = adagrad(loss, params, learning_rate=args.step)
elif args.optimizer == "adadelta":
updates = adadelta(loss, params, learning_rate=args.step)
elif args.optimizer == "nesterov":
updates = nesterov_momentum(loss, params, learning_rate=args.step)
elif args.optimizer == "rms":
updates = rmsprop(loss, params, learning_rate=args.step)
elif args.optimizer == "adam":
updates = adam(loss, params, learning_rate=args.step)
else:
raise "Need set optimizer correctly"
test_prediction = get_output(network, deterministic=True)
test_loss = T.mean(binary_crossentropy(test_prediction,target_var))
train_fn = theano.function([input_var, target_var],
loss, updates=updates, allow_input_downcast=True)
test_acc = T.mean(binary_accuracy(test_prediction, target_var))
val_fn = theano.function([input_var, target_var], [test_loss, test_acc], allow_input_downcast=True)
return train_fn, val_fn, network
def build_chain_trainer(self):
bs = self.bs
td = self.td
wi = T.ivector('wi') # bs (disamb. word indices)
nwi = T.ivector('nwi') # negative samples
lr = T.dscalar('lr').astype(theano.config.floatX) # learning rate
lam = T.dscalar('lam').astype(theano.config.floatX)
L = self.params['L']
L1 = self.params['L1'] # hd x td
#Wt = self.params['Wt']
if not self.hinge_cost:
L2 = self.params['L2']
B = self.params['B'] # td
B2 = self.params['B2']
dwe = self.params['dwe']
df = self.dat[wi, :] #T.itensor3('df')# bs x mw x ms
pr = self.sense_priors[wi, :] # bs x mw x ms
mk = self.dmask[wi, :] #T.itensor3('mk')# bs x mw x ms
pd = self.pd[
wi, :] #T.imatrix('pd') # bs x mdw (plain definition sentence)
pe = self.ex[wi, :] # plain example sentences bs x mew
dw = dwe[wi, :] # bs x td
msk = self.wmask[wi, :].dimshuffle(0, 1, 'x') # bs x mw x 1
ndw = dwe[nwi, :] # negative words
def to_vect(d, m, p):
hid_inp = dwe[d, :] # mw x ms x hd
logit = T.exp(T.dot(hid_inp, L0)[:, :, p]) # (mw x ms) x mw
mk = T.switch(T.lt(p, 0), 0,
1) # mw: word-level mask (different mask from m)
mask = mk.dimshuffle(0, 'x', 'x')
l2 = logit * mask # mw x ms x mw
l2 = T.sum(l2 * mk.dimshuffle('x', 'x', 0), axis=2) * m # mw x ms
w0 = l2 / T.sum(l2, axis=1).dimshuffle(0, 'x')
w1 = T.switch(T.isnan(w0), 0, w0)
w = w1.dimshuffle(0, 1, 'x') # mw x ms x 1
res = T.sum(w * hid_inp, axis=1) # mw x hd
return res #, logit, weights
def to_weight(d, m, p, prior):
logit = T.tensordot(dwe[d, :], dwe.T,
axes=1)[:, :, d] # mw x ms x mw x ms
cnt = T.sum(m, axis=1).dimshuffle('x', 'x', 0) # 1 x 1 x mw
logit = T.sum(logit * m.dimshuffle('x', 'x', 0, 1),
axis=3) / cnt # mw x ms x mw
logit = T.exp(10 *
T.switch(T.isnan(logit), 0, logit)) # mw x ms x mw
logit = T.prod(logit, axis=2) * prior # mw x ms
sm = T.sum(logit * m, axis=1, keepdims=True) # mw x 1
#mask = T.switch(T.lt(p, 0), 0, 1).dimshuffle(0, 'x') #
logit = (logit * m) / sm # mw x ms
return T.switch(T.or_(T.isnan(logit), T.isinf(logit)), 0, logit)
'''def to_weight(d, m, p, prior):
A = dwe[d, :] # mw x ms x td
#tmp = T.tensordot(T.dot(A, Wt), A.T, axes=1) # mw x ms x ms x mw
#B = A * Wt.dimshuffle('x', 'x', 0) # 'diag' setting
#tmp = T.tensordot(B, B.T, axes = 1)
tmp = T.tensordot(A, A.T, axes = 1) # 'iden' setting
tmp = T.exp(1000 * tmp.dimshuffle(0, 1, 3, 2)) # mw x ms x mw x ms
tmp = tmp * m.dimshuffle('x', 'x', 0, 1)
nrm = T.sum(tmp, axis=3)
tmp = tmp / nrm.dimshuffle(0, 1, 2, 'x')
tmp = T.switch(T.isnan(tmp), 0, tmp)
mk = T.switch(T.lt(p, 0), 0, 1) # mw: word-level mask (different mask from m)
tmp = T.max(tmp, axis=3) * mk.dimshuffle('x', 'x', 0) # mw x ms x mw
tmp = T.exp(T.sum(T.log(T.switch(T.eq(tmp, 0), 1, tmp)), axis=2)) * m # mw x ms
tmp = tmp * prior
tmp = tmp / T.sum(tmp, axis=1).dimshuffle(0, 'x')
return T.switch(T.isnan(tmp), 0, tmp)'''
def cosim(x, y):
return T.mean(
T.sum(x * y, axis=1) / (x.norm(2, axis=1) * y.norm(2, axis=1)))
#dat, _ = theano.scan(fn=to_vect, sequences=[df, mk, pd]) # bs x mw x td
#ndat, _ = theano.scan(fn=to_vect_tmp, sequences=[ndf, nmk, npd]) # bs x mw x td
weights, _ = theano.scan(fn=to_weight, sequences=[df, mk, pd,
pr]) # bs x mw x ms
hid_inp = dwe[df, :] # bs x mw x ms x td
dat = T.sum(weights.dimshuffle(0, 1, 2, 'x') * hid_inp,
axis=2) # bs x mw x td '''
inp = dat.astype(theano.config.floatX)
def_emb = T.sum(T.dot(inp, L) * msk, axis=1) # bs x hd
#neg_inp = ndat.astype(theano.config.floatX)
#def_emb = get_sentence(inp, msk) # bs x hd
#neg_def_emb = get_sentence(neg_inp, neg_msk)
#w_cost = T.sum((def_emb - dw) ** 2)
#w_neg_cost = T.sum((def_emb - ndw) ** 2)
if self.hinge_cost:
def_emb = T.dot(def_emb, L1)
w_cost = -cosim(def_emb, dw)
rep = nwi.shape[0] / wi.shape[
0] # b/c there are more negative samples than pos.
de = T.extra_ops.repeat(def_emb, rep, axis=0)
w_neg_cost = -cosim(de, ndw)
cost = T.mean(T.maximum(0,
0.01 + w_cost - w_neg_cost)) # hingeloss
else:
regress = T.dot(T.nnet.sigmoid(T.dot(def_emb, L1) + B),
L2) + B2 # bs x td
cost = T.mean(
(regress - dw)**
2) + 0.01 * T.sum(abs(L2)) # only regularize the last
if self.reg_alpha:
cost += 0.1 * T.sum(abs(weights))
#w_cost = get_word_probs(def_emb, wi, L1) #dwe.T) # dwe instead of L1
#w_neg_cost = get_word_probs(def_emb, nwi, L1) #dwe.T) # dwe instead of L1
#c_cost = -get_context_probs(def_emb, pe, L0) # negative of the likelihood
#c_neg_cost = -get_context_probs(def_emb, npe, L0)
#all_params = [self.params[k] for k in self.params if k != 'dwe' and not k.startswith('L')]
all_params = [self.params[k] for k in self.params if k != 'dwe']
#L_params = [L0]
'''Copy of the same function in Lasagne (with minor changes)'''
def apply_nesterov_momentum(ups, mom, shape=None):
params = ups.keys()
ups = OrderedDict(ups)
if shape is None:
shape = [p.get_value(borrow=True).shape for p in params]
for (param, shp) in zip(params, shape):
velocity = theano.shared(np.zeros(shp,
dtype=theano.config.floatX),
broadcastable=param.broadcastable)
x = mom * velocity + ups[param] - param
ups[velocity] = x
ups[param] = mom * x + ups[param]
return ups
dwe_params = [dw, ndw]
if self.do_sgd:
grads = T.grad(cost, all_params)
updates = OrderedDict()
for (p, g) in zip(all_params, grads):
updates[p] = p - lr * g
apply_nesterov_momentum(updates, mom=0.9)
if self.no_alt or not self.do_fixedpoint:
dgrads = T.grad(cost, dwe_params)
dwe_update = OrderedDict()
for (p, g) in zip(dwe_params, dgrads):
dwe_update[p] = p - lr * g
foo = lr * g
apply_nesterov_momentum(dwe_update,
mom=0.9,
shape=[(bs, td), (bs, td)])
else:
updates = adadelta(cost, all_params, learning_rate=lr)
#L_update = adadelta(cost, L_params, learning_rate = lr)
if self.no_alt or not self.do_fixedpoint:
dwe_update = adadelta(cost, dwe_params, learning_rate=lr)
if not self.no_alt and self.do_fixedpoint: # because no alternating training means optimization
if self.do_rw:
#posword = self.base[wi] + 0.3 * def_emb #0.3 * ((1 - self.lam) * def_emb + self.lam * dw)
idf = self.idf[wi].dimshuffle(
0, 1, 'x') # bs x mw x 1 (dat is bs x mw x hd)
rw_term = T.sum(dat * idf, axis=1) # bs x hd
disc_fact = 0.9
if self.init_dwe:
#posword = disc_fact * rw_term # + self.base[wi] # truerw
posword = (
1 - lam
) * dw + lam * disc_fact * rw_term # + self.base[wi] # truerw
else:
base = self.lam * def_emb + (1 - self.lam) * dw
posword = base + disc_fact * rw_term
word_update = T.set_subtensor(
dw, posword.astype(theano.config.floatX))
dwe_update = {dwe: word_update}
dwe_ret = T.max(T.abs_(posword -
dw)) # max-norm of the increment
else:
posword = (1 - self.lam) * def_emb + self.lam * dw
word_update = T.set_subtensor(dw, posword - self.lam * ndw)
dwe_update = {dwe: word_update}
dwe_ret = word_update
else: #elif not self.do_fixedpoint or self.no_alt:
word_update = dwe_update[dw]
word_update = T.set_subtensor(dw, word_update)
nword_update = dwe_update[ndw]
word_update = T.set_subtensor(word_update[nwi, :], nword_update)
dwe_update = {dwe: word_update} #T.set_subtensor(dw, word_update)
if self.no_alt:
updates.update({dwe: word_update})
dwe_ret = word_update
#updates.update({dwe: dwe_update[dwe]}) #word_update})
#updates.update({dwe: word_update})
self.train_step = theano.function([wi, nwi, lr], [cost, weights],
updates=updates)
if not self.no_alt:
self.dwe_train_step = theano.function([wi, nwi, lam],
[cost, dwe_ret, weights],
updates=dwe_update)
def main():
configure_theano()
options = parse_options()
config_file = options['config']
config = ConfigParser.ConfigParser()
config.read(config_file)
print('CLI options: {}'.format(options.items()))
print('Reading Config File: {}...'.format(config_file))
print(config.items('data'))
print(config.items('models'))
print(config.items('training'))
print('preprocessing dataset...')
data = load_mat_file(config.get('data', 'images'))
dct_data = load_mat_file(config.get('data', 'dct'))
ae_finetuned = config.get('models', 'finetuned')
ae_finetuned_diff = config.get('models', 'finetuned_diff')
fusiontype = config.get('models', 'fusiontype')
learning_rate = float(config.get('training', 'learning_rate'))
decay_rate = float(config.get('training', 'decay_rate'))
decay_start = int(config.get('training', 'decay_start'))
load_finetune = config.getboolean('training', 'load_finetune')
load_finetune_diff = config.getboolean('training', 'load_finetune_diff')
train_vidlens = data['trVideoLengthVec'].astype('int').reshape((-1, ))
val_vidlens = data['valVideoLengthVec'].astype('int').reshape((-1, ))
test_vidlens = data['testVideoLengthVec'].astype('int').reshape((-1, ))
train_X = data['trData'].astype('float32')
val_X = data['valData'].astype('float32')
test_X = data['testData'].astype('float32')
train_dct = dct_data['trDctFeatures'].astype('float32')
val_dct = dct_data['valDctFeatures'].astype('float32')
test_dct = dct_data['testDctFeatures'].astype('float32')
train_X_diff = compute_diff_images(train_X, train_vidlens)
val_X_diff = compute_diff_images(val_X, val_vidlens)
test_X_diff = compute_diff_images(test_X, test_vidlens)
train_y = data['trTargetsVec'].astype('int').reshape(
(-1, )) + 1 # +1 to handle the -1 introduced in lstm_gendata
val_y = data['valTargetsVec'].astype('int').reshape((-1, )) + 1
test_y = data['testTargetsVec'].astype('int').reshape((-1, )) + 1
# featurewise normalize dct features
train_dct, dct_mean, dct_std = featurewise_normalize_sequence(train_dct)
val_dct = (val_dct - dct_mean) / dct_std
test_dct = (test_dct - dct_mean) / dct_std
if load_finetune:
print('loading finetuned encoder: {}...'.format(ae_finetuned))
ae = pickle.load(open(ae_finetuned, 'rb'))
ae.initialize()
if load_finetune_diff:
print('loading finetuned encoder: {}...'.format(ae_finetuned_diff))
ae_diff = pickle.load(open(ae_finetuned_diff, 'rb'))
ae_diff.initialize()
# IMPT: the encoder was trained with fortan ordered images, so to visualize
# convert all the images to C order using reshape_images_order()
# output = dbn.predict(test_X)
# test_X = reshape_images_order(test_X, (26, 44))
# output = reshape_images_order(output, (26, 44))
# visualize_reconstruction(test_X[:36, :], output[:36, :], shape=(26, 44))
window = T.iscalar('theta')
dct = T.tensor3('dct', dtype='float32')
inputs = T.tensor3('inputs', dtype='float32')
inputs_diff = T.tensor3('inputs_diff', dtype='float32')
mask = T.matrix('mask', dtype='uint8')
targets = T.ivector('targets')
lr = theano.shared(np.array(learning_rate, dtype=theano.config.floatX),
name='learning_rate')
lr_decay = np.array(decay_rate, dtype=theano.config.floatX)
print('constructing end to end model...')
network, l_fuse = adenet_v3.create_model(ae, ae_diff, (None, None, 1500),
inputs, (None, None), mask,
(None, None, 90), dct,
(None, None, 1500), inputs_diff,
250, window, 10, fusiontype)
print_network(network)
# draw_to_file(las.layers.get_all_layers(network), 'network.png')
print('compiling model...')
predictions = las.layers.get_output(network, deterministic=False)
all_params = las.layers.get_all_params(network, trainable=True)
cost = T.mean(las.objectives.categorical_crossentropy(
predictions, targets))
updates = adadelta(cost, all_params, learning_rate=lr)
# updates = adagrad(cost, all_params, learning_rate=lr)
train = theano.function([inputs, targets, mask, dct, inputs_diff, window],
cost,
updates=updates,
allow_input_downcast=True)
compute_train_cost = theano.function(
[inputs, targets, mask, dct, inputs_diff, window],
cost,
allow_input_downcast=True)
test_predictions = las.layers.get_output(network, deterministic=True)
test_cost = T.mean(
las.objectives.categorical_crossentropy(test_predictions, targets))
compute_test_cost = theano.function(
[inputs, targets, mask, dct, inputs_diff, window],
test_cost,
allow_input_downcast=True)
val_fn = theano.function([inputs, mask, dct, inputs_diff, window],
test_predictions,
allow_input_downcast=True)
# We'll train the network with 10 epochs of 30 minibatches each
print('begin training...')
cost_train = []
cost_val = []
class_rate = []
NUM_EPOCHS = 30
EPOCH_SIZE = 45
BATCH_SIZE = 20
WINDOW_SIZE = 9
STRIP_SIZE = 3
MAX_LOSS = 0.2
VALIDATION_WINDOW = 4
val_window = circular_list(VALIDATION_WINDOW)
train_strip = np.zeros((STRIP_SIZE, ))
best_val = float('inf')
best_conf = None
best_cr = 0.0
datagen = gen_lstm_batch_random(train_X,
train_y,
train_vidlens,
batchsize=BATCH_SIZE)
integral_lens = compute_integral_len(train_vidlens)
val_datagen = gen_lstm_batch_random(val_X,
val_y,
val_vidlens,
batchsize=len(val_vidlens))
test_datagen = gen_lstm_batch_random(test_X,
test_y,
test_vidlens,
batchsize=len(test_vidlens))
# We'll use this "validation set" to periodically check progress
X_val, y_val, mask_val, idxs_val = next(val_datagen)
integral_lens_val = compute_integral_len(val_vidlens)
dct_val = gen_seq_batch_from_idx(val_dct, idxs_val, val_vidlens,
integral_lens_val, np.max(val_vidlens))
X_diff_val = gen_seq_batch_from_idx(val_X_diff, idxs_val, val_vidlens,
integral_lens_val, np.max(val_vidlens))
# we use the test set to check final classification rate
X_test, y_test, mask_test, idxs_test = next(test_datagen)
integral_lens_test = compute_integral_len(test_vidlens)
dct_test = gen_seq_batch_from_idx(test_dct, idxs_test, test_vidlens,
integral_lens_test, np.max(test_vidlens))
X_diff_test = gen_seq_batch_from_idx(test_X_diff, idxs_test, test_vidlens,
integral_lens_test,
np.max(test_vidlens))
def early_stop(cost_window):
if len(cost_window) < 2:
return False
else:
curr = cost_window[0]
for idx, cost in enumerate(cost_window):
if curr < cost or idx == 0:
curr = cost
else:
return False
return True
for epoch in range(NUM_EPOCHS):
time_start = time.time()
for i in range(EPOCH_SIZE):
X, y, m, batch_idxs = next(datagen)
d = gen_seq_batch_from_idx(train_dct, batch_idxs, train_vidlens,
integral_lens, np.max(train_vidlens))
X_diff = gen_seq_batch_from_idx(train_X_diff, batch_idxs,
train_vidlens, integral_lens,
np.max(train_vidlens))
print_str = 'Epoch {} batch {}/{}: {} examples at learning rate = {:.4f}'.format(
epoch + 1, i + 1, EPOCH_SIZE, len(X), float(lr.get_value()))
print(print_str, end='')
sys.stdout.flush()
train(X, y, m, d, X_diff, WINDOW_SIZE)
print('\r', end='')
cost = compute_train_cost(X, y, m, d, X_diff, WINDOW_SIZE)
val_cost = compute_test_cost(X_val, y_val, mask_val, dct_val,
X_diff_val, WINDOW_SIZE)
cost_train.append(cost)
cost_val.append(val_cost)
train_strip[epoch % STRIP_SIZE] = cost
val_window.push(val_cost)
gl = 100 * (cost_val[-1] / np.min(cost_val) - 1)
pk = 1000 * (np.sum(train_strip) /
(STRIP_SIZE * np.min(train_strip)) - 1)
pq = gl / pk
cr, val_conf = evaluate_model(X_val, y_val, mask_val, dct_val,
X_diff_val, WINDOW_SIZE, val_fn)
class_rate.append(cr)
if val_cost < best_val:
best_val = val_cost
best_cr = cr
if fusiontype == 'adasum':
adascale_param = las.layers.get_all_param_values(
l_fuse, scaling_param=True)
test_cr, test_conf = evaluate_model(X_test, y_test, mask_test,
dct_test, X_diff_test,
WINDOW_SIZE, val_fn)
print(
"Epoch {} train cost = {}, val cost = {}, "
"GL loss = {:.3f}, GQ = {:.3f}, CR = {:.3f}, Test CR= {:.3f} ({:.1f}sec)"
.format(epoch + 1, cost_train[-1], cost_val[-1], gl, pq, cr,
test_cr,
time.time() - time_start))
else:
print("Epoch {} train cost = {}, val cost = {}, "
"GL loss = {:.3f}, GQ = {:.3f}, CR = {:.3f} ({:.1f}sec)".
format(epoch + 1, cost_train[-1], cost_val[-1], gl, pq, cr,
time.time() - time_start))
if epoch >= VALIDATION_WINDOW and early_stop(val_window):
break
# learning rate decay
if epoch + 1 >= decay_start:
lr.set_value(lr.get_value() * lr_decay)
numbers = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
print('Final Model')
print('CR: {}, val loss: {}, Test CR: {}'.format(best_cr, best_val,
test_cr))
if fusiontype == 'adasum':
print("final scaling params: {}".format(adascale_param))
print('confusion matrix: ')
plot_confusion_matrix(test_conf, numbers, fmt='latex')
plot_validation_cost(cost_train, cost_val, savefilename='valid_cost')
if options['write_results']:
results_file = options['write_results']
with open(results_file, mode='a') as f:
f.write('{},{},{}\n'.format(fusiontype, test_cr, best_val))
def construct_lstm(input_size, lstm_size, output_size, train_data_gen, val_data_gen):
# All gates have initializers for the input-to-gate and hidden state-to-gate
# weight matrices, the cell-to-gate weight vector, the bias vector, and the nonlinearity.
# The convention is that gates use the standard sigmoid nonlinearity,
# which is the default for the Gate class.
gate_parameters = Gate(
W_in=las.init.Orthogonal(), W_hid=las.init.Orthogonal(),
b=las.init.Constant(0.))
cell_parameters = Gate(
W_in=las.init.Orthogonal(), W_hid=las.init.Orthogonal(),
# Setting W_cell to None denotes that no cell connection will be used.
W_cell=None, b=las.init.Constant(0.),
# By convention, the cell nonlinearity is tanh in an LSTM.
nonlinearity=tanh)
# prepare the input layers
# By setting the first and second dimensions to None, we allow
# arbitrary minibatch sizes with arbitrary sequence lengths.
# The number of feature dimensions is 150, as described above.
l_in = InputLayer(shape=(None, None, input_size))
# This input will be used to provide the network with masks.
# Masks are expected to be matrices of shape (n_batch, n_time_steps);
# both of these dimensions are variable for us so we will use
# an input shape of (None, None)
l_mask = InputLayer(shape=(None, None))
# Our LSTM will have 180 hidden/cell units as published in paper
N_HIDDEN = lstm_size
l_lstm = LSTMLayer(
l_in, N_HIDDEN,
# We need to specify a separate input for masks
mask_input=l_mask,
# Here, we supply the gate parameters for each gate
ingate=gate_parameters, forgetgate=gate_parameters,
cell=cell_parameters, outgate=gate_parameters,
# We'll learn the initialization and use gradient clipping
learn_init=True, grad_clipping=5.)
# The "backwards" layer is the same as the first,
# except that the backwards argument is set to True.
l_lstm_back = LSTMLayer(
l_in, N_HIDDEN, ingate=gate_parameters,
mask_input=l_mask, forgetgate=gate_parameters,
cell=cell_parameters, outgate=gate_parameters,
learn_init=True, grad_clipping=5., backwards=True)
# We'll combine the forward and backward layer output by summing.
# Merge layers take in lists of layers to merge as input.
l_sum = ElemwiseSumLayer([l_lstm, l_lstm_back])
# implement drop-out regularization
l_dropout = DropoutLayer(l_sum)
l_lstm2 = LSTMLayer(
l_dropout, N_HIDDEN,
# We need to specify a separate input for masks
mask_input=l_mask,
# Here, we supply the gate parameters for each gate
ingate=gate_parameters, forgetgate=gate_parameters,
cell=cell_parameters, outgate=gate_parameters,
# We'll learn the initialization and use gradient clipping
learn_init=True, grad_clipping=5.)
# The "backwards" layer is the same as the first,
# except that the backwards argument is set to True.
l_lstm_back2 = LSTMLayer(
l_dropout, N_HIDDEN, ingate=gate_parameters,
mask_input=l_mask, forgetgate=gate_parameters,
cell=cell_parameters, outgate=gate_parameters,
learn_init=True, grad_clipping=5., backwards=True)
# We'll combine the forward and backward layer output by summing.
# Merge layers take in lists of layers to merge as input.
l_sum2 = ElemwiseSumLayer([l_lstm2, l_lstm_back2])
'''
l_dropout2 = DropoutLayer(l_sum2)
l_lstm3 = LSTMLayer(
l_dropout2, N_HIDDEN,
# We need to specify a separate input for masks
mask_input=l_mask,
# Here, we supply the gate parameters for each gate
ingate=gate_parameters, forgetgate=gate_parameters,
cell=cell_parameters, outgate=gate_parameters,
# We'll learn the initialization and use gradient clipping
learn_init=True, grad_clipping=5.)
# The "backwards" layer is the same as the first,
# except that the backwards argument is set to True.
l_lstm_back3 = LSTMLayer(
l_dropout2, N_HIDDEN, ingate=gate_parameters,
mask_input=l_mask, forgetgate=gate_parameters,
cell=cell_parameters, outgate=gate_parameters,
learn_init=True, grad_clipping=5., backwards=True)
# We'll combine the forward and backward layer output by summing.
# Merge layers take in lists of layers to merge as input.
l_sum3 = ElemwiseSumLayer([l_lstm3, l_lstm_back3])
'''
# The l_forward layer creates an output of dimension (batch_size, SEQ_LENGTH, N_HIDDEN)
# Since we are only interested in the final prediction, we isolate that quantity and feed it to the next layer.
# The output of the sliced layer will then be of size (batch_size, N_HIDDEN)
l_forward_slice = SliceLayer(l_sum2, -1, 1)
# Now, we can apply feed-forward layers as usual.
# We want the network to predict a classification for the sequence,
# so we'll use a the number of classes.
l_out = DenseLayer(
l_forward_slice, num_units=output_size, nonlinearity=las.nonlinearities.softmax)
# Now, the shape will be n_batch*n_timesteps, output_size. We can then reshape to
# n_batch, n_timesteps to get a single value for each timstep from each sequence
# l_out = las.layers.ReshapeLayer(l_dense, (n_batch, n_time_steps))
# Symbolic variable for the target network output.
# It will be of shape n_batch, because there's only 1 target value per sequence.
target_values = T.ivector('target_output')
# This matrix will tell the network the length of each sequences.
# The actual values will be supplied by the gen_data function.
mask = T.matrix('mask')
# lasagne.layers.get_output produces an expression for the output of the net
network_output = las.layers.get_output(l_out)
# The value we care about is the final value produced for each sequence
# so we simply slice it out.
# predicted_values = network_output[:, -1]
# Our cost will be categorical cross entropy error
cost = T.mean(las.objectives.categorical_crossentropy(network_output, target_values))
# cost = T.mean((predicted_values - target_values) ** 2)
# Retrieve all parameters from the network
all_params = las.layers.get_all_params(l_out)
# Compute adam updates for training
# updates = las.updates.adam(cost, all_params)
updates = adadelta(cost, all_params)
# Theano functions for training and computing cost
train = theano.function(
[l_in.input_var, target_values, l_mask.input_var],
cost, updates=updates, allow_input_downcast=True)
compute_cost = theano.function(
[l_in.input_var, target_values, l_mask.input_var], cost, allow_input_downcast=True)
probs = theano.function([l_in.input_var, l_mask.input_var], network_output, allow_input_downcast=True)
# We'll use this "validation set" to periodically check progress
X_val, y_val, mask_val = next(val_data_gen)
# We'll train the network with 10 epochs of 100 minibatches each
cost_train = []
cost_val = []
class_rate = []
NUM_EPOCHS = 20
EPOCH_SIZE = 26
for epoch in range(NUM_EPOCHS):
for _ in range(EPOCH_SIZE):
X, y, m = next(train_data_gen)
train(X, y, m)
cost_train.append(compute_cost(X, y, m))
cost_val.append(compute_cost(X_val, y_val, mask_val))
cr, _ = evaluate_model(X_val, y_val, mask_val, probs)
class_rate.append(cr)
# one good value to early stop using GL technique, alpha = 0.10 (10% worst)
gl = cost_val[-1] / np.min(cost_val) - 1
# PQ, GL / Pk(t) where Pk(t) = 1000 * (sum(training strip error) / k * min(training strip error) - 1
print("Epoch {} train cost = {}, validation cost = {}, generalization loss = {}, classification rate = {}"
.format(epoch + 1, cost_train[-1], cost_val[-1], gl, cr))
cr, conf = evaluate_model(X_val, y_val, mask_val, probs)
print('Final Model')
print('classification rate: {}'.format(cr))
print('confusion matrix: ')
plot_confusion_matrix(conf, fmt='grid')
plot_validation_cost(cost_train, cost_val, class_rate)
def construct_lstm(input_size, lstm_size, output_size, train_data_gen,
val_data_gen):
# All gates have initializers for the input-to-gate and hidden state-to-gate
# weight matrices, the cell-to-gate weight vector, the bias vector, and the nonlinearity.
# The convention is that gates use the standard sigmoid nonlinearity,
# which is the default for the Gate class.
gate_parameters = Gate(W_in=las.init.Orthogonal(),
W_hid=las.init.Orthogonal(),
b=las.init.Constant(0.))
cell_parameters = Gate(
W_in=las.init.Orthogonal(),
W_hid=las.init.Orthogonal(),
# Setting W_cell to None denotes that no cell connection will be used.
W_cell=None,
b=las.init.Constant(0.),
# By convention, the cell nonlinearity is tanh in an LSTM.
nonlinearity=tanh)
# prepare the input layers
# By setting the first and second dimensions to None, we allow
# arbitrary minibatch sizes with arbitrary sequence lengths.
# The number of feature dimensions is 150, as described above.
l_in = InputLayer(shape=(None, None, input_size), name='input')
# This input will be used to provide the network with masks.
# Masks are expected to be matrices of shape (n_batch, n_time_steps);
# both of these dimensions are variable for us so we will use
# an input shape of (None, None)
l_mask = InputLayer(shape=(None, None), name='mask')
# Our LSTM will have 250 hidden/cell units
N_HIDDEN = lstm_size
l_lstm = LSTMLayer(
l_in,
N_HIDDEN,
# We need to specify a separate input for masks
mask_input=l_mask,
# Here, we supply the gate parameters for each gate
ingate=gate_parameters,
forgetgate=gate_parameters,
cell=cell_parameters,
outgate=gate_parameters,
# We'll learn the initialization and use gradient clipping
learn_init=True,
grad_clipping=5.,
name='lstm1')
'''
# The "backwards" layer is the same as the first,
# except that the backwards argument is set to True.
l_lstm_back = LSTMLayer(
l_in, N_HIDDEN, ingate=gate_parameters,
mask_input=l_mask, forgetgate=gate_parameters,
cell=cell_parameters, outgate=gate_parameters,
learn_init=True, grad_clipping=5., backwards=True)
# We'll combine the forward and backward layer output by summing.
# Merge layers take in lists of layers to merge as input.
l_sum = ElemwiseSumLayer([l_lstm, l_lstm_back])
# implement drop-out regularization
l_dropout = DropoutLayer(l_sum)
l_lstm2 = LSTMLayer(
l_dropout, N_HIDDEN,
# We need to specify a separate input for masks
mask_input=l_mask,
# Here, we supply the gate parameters for each gate
ingate=gate_parameters, forgetgate=gate_parameters,
cell=cell_parameters, outgate=gate_parameters,
# We'll learn the initialization and use gradient clipping
learn_init=True, grad_clipping=5.)
# The "backwards" layer is the same as the first,
# except that the backwards argument is set to True.
l_lstm_back2 = LSTMLayer(
l_dropout, N_HIDDEN, ingate=gate_parameters,
mask_input=l_mask, forgetgate=gate_parameters,
cell=cell_parameters, outgate=gate_parameters,
learn_init=True, grad_clipping=5., backwards=True)
# We'll combine the forward and backward layer output by summing.
# Merge layers take in lists of layers to merge as input.
l_sum2 = ElemwiseSumLayer([l_lstm2, l_lstm_back2])
'''
# The l_forward layer creates an output of dimension (batch_size, SEQ_LENGTH, N_HIDDEN)
# Since we are only interested in the final prediction, we isolate that quantity and feed it to the next layer.
# The output of the sliced layer will then be of size (batch_size, N_HIDDEN)
l_forward_slice = SliceLayer(l_lstm, -1, 1, name='slice')
# Now, we can apply feed-forward layers as usual.
# We want the network to predict a classification for the sequence,
# so we'll use a the number of classes.
l_out = DenseLayer(l_forward_slice,
num_units=output_size,
nonlinearity=las.nonlinearities.softmax,
name='output')
print_network(l_out)
# draw_to_file(las.layers.get_all_layers(l_out), 'network.png')
# Symbolic variable for the target network output.
# It will be of shape n_batch, because there's only 1 target value per sequence.
target_values = T.ivector('target_output')
# This matrix will tell the network the length of each sequences.
# The actual values will be supplied by the gen_data function.
mask = T.matrix('mask')
# lasagne.layers.get_output produces an expression for the output of the net
prediction = las.layers.get_output(l_out)
# The value we care about is the final value produced for each sequence
# so we simply slice it out.
# predicted_values = network_output[:, -1]
# Our cost will be categorical cross entropy error
cost = T.mean(
las.objectives.categorical_crossentropy(prediction, target_values))
# cost = T.mean((predicted_values - target_values) ** 2)
# Retrieve all parameters from the network
all_params = las.layers.get_all_params(l_out, trainable=True)
# Compute adam updates for training
# updates = las.updates.adam(cost, all_params)
updates = adadelta(cost, all_params)
# Theano functions for training and computing cost
train = theano.function([l_in.input_var, target_values, l_mask.input_var],
cost,
updates=updates,
allow_input_downcast=True)
compute_train_cost = theano.function(
[l_in.input_var, target_values, l_mask.input_var],
cost,
allow_input_downcast=True)
test_prediction = las.layers.get_output(l_out, deterministic=True)
test_cost = T.mean(
las.objectives.categorical_crossentropy(test_prediction,
target_values))
compute_val_cost = theano.function(
[l_in.input_var, target_values, l_mask.input_var],
test_cost,
allow_input_downcast=True)
val_fn = theano.function([l_in.input_var, l_mask.input_var],
test_prediction,
allow_input_downcast=True)
# We'll use this "validation set" to periodically check progress
X_val, y_val, mask_val = next(val_data_gen)
# We'll train the network with 10 epochs of 100 minibatches each
cost_train = []
cost_val = []
class_rate = []
best_val = float('inf')
best_conf = None
best_cr = 0.0
NUM_EPOCHS = 30
EPOCH_SIZE = 26
STRIP_SIZE = 3
MAX_LOSS = 0.05
VALIDATION_WINDOW = 4
val_window = circular_list(VALIDATION_WINDOW)
train_strip = np.zeros((STRIP_SIZE, ))
def early_stop(cost_window):
if len(cost_window) < 2:
return False
else:
curr = cost_window[0]
for idx, cost in enumerate(cost_window):
if curr < cost or idx == 0:
curr = cost
else:
return False
return True
for epoch in range(NUM_EPOCHS):
time_start = time.time()
for _ in range(EPOCH_SIZE):
X, y, m, _ = next(train_data_gen)
train(X, y, m)
train_cost = compute_train_cost(X, y, m)
val_cost = compute_val_cost(X_val, y_val, mask_val)
cr, conf = evaluate_model(X_val, y_val, mask_val, val_fn)
cost_train.append(train_cost)
cost_val.append(val_cost)
class_rate.append(cr)
train_strip[epoch % STRIP_SIZE] = train_cost
val_window.push(val_cost)
gl = 100 * (cost_val[-1] / np.min(cost_val) - 1)
pk = 1000 * (np.sum(train_strip) /
(STRIP_SIZE * np.min(train_strip)) - 1)
pq = gl / pk
print(
"Epoch {} train cost = {}, validation cost = {}, "
"generalization loss = {:.3f}, GQ = {:.3f}, classification rate = {:.3f} ({:.1f}sec)"
.format(epoch + 1, cost_train[-1], cost_val[-1], gl, pq, cr,
time.time() - time_start))
if val_cost < best_val:
best_val = val_cost
best_cr = cr
best_conf = conf
if epoch >= VALIDATION_WINDOW and early_stop(val_window):
break
letters = [
'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n',
'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z'
]
print('Final Model')
print('classification rate: {}'.format(best_cr))
print('validation loss: {}'.format(best_val))
print('confusion matrix: ')
plot_confusion_matrix(best_conf, letters, fmt='grid')
plot_validation_cost(cost_train, cost_val, class_rate)
def _prepare(self, X, y, X_valid=None, y_valid=None, sample_weight=None,
whole_dataset_in_device=True):
self._stats = []
self._class_label_encoder = LabelEncoder()
if self.is_classification is True:
self._class_label_encoder.fit(y)
self.classes_ = self._class_label_encoder.classes_
y = self._class_label_encoder.transform(y).astype(y.dtype)
self.y_train_transformed = y
if y_valid is not None:
y_valid_transformed = self._class_label_encoder.transform(
y_valid).astype(y_valid.dtype)
self._l_x_in = layers.InputLayer(shape=(None, X.shape[1]))
batch_index, X_batch, y_batch, batch_slice = get_theano_batch_variables(
self.batch_size, y_softmax=self.is_classification)
if sample_weight is not None:
t_sample_weight = T.vector('sample_weight')
sample_weight = sample_weight.astype(theano.config.floatX)
else:
t_sample_weight = T.scalar('sample_weight')
if self.is_classification is True:
y_dim = len(set(y.flatten().tolist()))
else:
y_dim = y.shape[1]
self._prediction_layer = self._build_model(y_dim)
self._layers = layers.get_all_layers(self._prediction_layer)
self._build_prediction_functions(X_batch, self._prediction_layer)
if self.input_noise_function is None:
output = layers.get_output(self._prediction_layer, X_batch)
else:
X_batch_noisy = self.input_noise_function(X_batch)
output = layers.get_output(self._prediction_layer, X_batch_noisy)
if self.is_classification:
loss = -T.mean(t_sample_weight * T.log(output)
[T.arange(y_batch.shape[0]), y_batch])
else:
loss = T.mean(
t_sample_weight * T.sum((output - y_batch) ** 2, axis=1))
loss_unreg = loss
all_params = layers.get_all_params(self._prediction_layer)
if self._output_softener_coefs is not None:
all_params.append(self._output_softener_coefs)
W_params = layers.get_all_param_values(
self._prediction_layer, regularizable=True)
# regularization
if self.L1_factor is not None:
for L1_factor_layer, W in zip(self.L1_factor, W_params):
loss = loss + L1_factor_layer * T.sum(abs(W))
if self.L2_factor is not None:
for L2_factor_layer, W in zip(self.L2_factor, W_params):
loss = loss + L2_factor_layer * T.sum(W**2)
if self.optimization_method == 'nesterov_momentum':
gradient_updates = updates.nesterov_momentum(loss, all_params, learning_rate=self.learning_rate,
momentum=self.momentum)
elif self.optimization_method == 'adadelta':
# don't need momentum there
gradient_updates = updates.adadelta(
loss, all_params, learning_rate=self.learning_rate)
elif self.optimization_method == 'adam':
gradient_updates = updates.Adam(
loss, all_params, learning_rate=self.learning_rate)
elif self.optimization_method == 'momentum':
gradient_updates = updates.momentum(
loss, all_params, learning_rate=self.learning_rate,
momentum=self.momentum
)
elif self.optimization_method == 'adagrad':
gradient_updates = updates.adadelta(
loss, all_params, learning_rate=self.learning_rate)
elif self.optimization_method == 'rmsprop':
gradient_updates = updates.adadelta(
loss, all_params, learning_rate=self.learning_rate)
elif self.optimization_method == 'sgd':
gradient_updates = updates.sgd(
loss, all_params, learning_rate=self.learning_rate,
)
else:
raise Exception("wrong optimization method")
nb_batches = X.shape[0] // self.batch_size
if (X.shape[0] % self.batch_size) != 0:
nb_batches += 1
X = X.astype(theano.config.floatX)
if self.is_classification == True:
y = y.astype(np.int32)
else:
y = y.astype(theano.config.floatX)
if whole_dataset_in_device == True:
X_shared = theano.shared(X, borrow=True)
y_shared = theano.shared(y, borrow=True)
givens = {
X_batch: X_shared[batch_slice],
y_batch: y_shared[batch_slice]
}
if sample_weight is not None:
sample_weight_shared = theano.shared(
sample_weight, borrow=True)
givens[t_sample_weight] = sample_weight_shared[batch_slice]
else:
givens[t_sample_weight] = T.as_tensor_variable(
np.array(1., dtype=theano.config.floatX))
iter_update_batch = theano.function(
[batch_index], loss,
updates=gradient_updates,
givens=givens,
)
else:
if sample_weight is None:
iter_update_gradients = theano.function(
[X_batch, y_batch],
loss,
updates=gradient_updates,
givens={t_sample_weight: T.as_tensor_variable(
np.array(1., dtype=theano.config.floatX))},
)
def iter_update_batch(batch_index):
sl = slice(batch_index * self.batch_size,
(batch_index + 1) * self.batch_size)
return iter_update_gradients(X[sl], y[sl])
else:
iter_update_gradients = theano.function(
[X_batch, y_batch, t_sample_weight],
loss,
updates=gradient_updates
)
def iter_update_batch(batch_index):
sl = slice(batch_index * self.batch_size,
(batch_index + 1) * self.batch_size)
return iter_update_gradients(X[sl], y[sl], sample_weight[sl])
self._iter_update_batch = iter_update_batch
self._get_loss = theano.function(
[X_batch, y_batch, t_sample_weight], loss_unreg, allow_input_downcast=True)
def iter_update(epoch):
losses = []
#self.learning_rate.set_value(self.learning_rate.get_value() * np.array(0.99, dtype=theano.config.floatX))
for i in xrange(nb_batches):
losses.append(self._iter_update_batch(i))
# max norm
if self.max_norm is not None:
for max_norm_layer, layer in zip(self.max_norm, self._layers):
layer.W = updates.norm_constraint(
layer.W, self.max_norm)
losses = np.array(losses)
d = OrderedDict()
d["epoch"] = epoch
#d["loss_train_std"] = losses.std()
#d["loss_train"] = losses.mean()
d["loss_train"] = self._get_loss(
self.X_train, self.y_train_transformed, 1.)
d["accuracy_train"] = (
self.predict(self.X_train) == self.y_train).mean()
if X_valid is not None and y_valid is not None:
d["loss_valid"] = self._get_loss(
X_valid, y_valid_transformed, 1.)
if self.is_classification == True:
d["accuracy_valid"] = (
self.predict(X_valid) == y_valid).mean()
if self.verbose > 0:
if (epoch % self.report_each) == 0:
print(tabulate([d], headers="keys"))
self._stats.append(d)
return d
def quitter(update_status):
cur_epoch = len(self._stats) - 1
if self.patience_nb_epochs > 0:
# patience heuristic (for early stopping)
cur_patience_stat = update_status[self.patience_stat]
if self.cur_best_patience_stat is None:
self.cur_best_patience_stat = cur_patience_stat
first_time = True
else:
first_time = False
thresh = self.patience_progression_rate_threshold
if cur_patience_stat < self.cur_best_patience_stat * thresh or first_time:
if self.verbose >= 2:
fmt = "--Early stopping-- good we have a new best value : {0}={1}, last best : epoch {2}, value={3}"
print(fmt.format(self.patience_stat, cur_patience_stat,
self.cur_best_epoch, self.cur_best_patience_stat))
self.cur_best_epoch = cur_epoch
self.cur_best_patience_stat = cur_patience_stat
if hasattr(self, "set_state") and hasattr(self, "get_state"):
self.cur_best_model = self.get_state()
else:
self.cur_best_model = pickle.dumps(
self.__dict__, protocol=pickle.HIGHEST_PROTOCOL)
if (cur_epoch - self.cur_best_epoch) >= self.patience_nb_epochs:
finish = True
if hasattr(self, "set_state") and hasattr(self, "get_state"):
self.set_state(self.cur_best_model)
else:
self.__dict__.update(pickle.loads(self.cur_best_model))
self._stats = self._stats[0:self.cur_best_epoch + 1]
if self.verbose >= 2:
print("out of patience...take the model at epoch {0} and quit".format(
self.cur_best_epoch + 1))
else:
finish = False
return finish
else:
return False
def monitor(update_status):
pass
def observer(monitor_output):
pass
return (iter_update, quitter, monitor, observer)
def event_span_classifier(args, input_var, input_mask_var, target_var, wordEmbeddings, seqlen):
print("Building model with LSTM")
vocab_size = wordEmbeddings.shape[1]
wordDim = wordEmbeddings.shape[0]
GRAD_CLIP = wordDim
args.lstmDim = 150
input = InputLayer((None, seqlen),input_var=input_var)
batchsize, seqlen = input.input_var.shape
input_mask = InputLayer((None, seqlen),input_var=input_mask_var)
emb = EmbeddingLayer(input, input_size=vocab_size, output_size=wordDim, W=wordEmbeddings.T)
#emb.params[emb_1.W].remove('trainable')
lstm = LSTMLayer(emb, num_units=args.lstmDim, mask_input=input_mask, grad_clipping=GRAD_CLIP,
nonlinearity=tanh)
lstm_back = LSTMLayer(
emb, num_units=args.lstmDim, mask_input=input_mask, grad_clipping=GRAD_CLIP,
nonlinearity=tanh, backwards=True)
slice_forward = SliceLayer(lstm, indices=-1, axis=1) # out_shape (None, args.lstmDim)
slice_backward = SliceLayer(lstm_back, indices=0, axis=1) # out_shape (None, args.lstmDim)
concat = ConcatLayer([slice_forward, slice_backward])
hid = DenseLayer(concat, num_units=args.hiddenDim, nonlinearity=sigmoid)
network = DenseLayer(hid, num_units=2, nonlinearity=softmax)
prediction = get_output(network)
loss = T.mean(binary_crossentropy(prediction,target_var))
lambda_val = 0.5 * 1e-4
layers = {emb:lambda_val, lstm:lambda_val, hid:lambda_val, network:lambda_val}
penalty = regularize_layer_params_weighted(layers, l2)
loss = loss + penalty
params = get_all_params(network, trainable=True)
if args.optimizer == "sgd":
updates = sgd(loss, params, learning_rate=args.step)
elif args.optimizer == "adagrad":
updates = adagrad(loss, params, learning_rate=args.step)
elif args.optimizer == "adadelta":
updates = adadelta(loss, params, learning_rate=args.step)
elif args.optimizer == "nesterov":
updates = nesterov_momentum(loss, params, learning_rate=args.step)
elif args.optimizer == "rms":
updates = rmsprop(loss, params, learning_rate=args.step)
elif args.optimizer == "adam":
updates = adam(loss, params, learning_rate=args.step)
else:
raise "Need set optimizer correctly"
test_prediction = get_output(network, deterministic=True)
test_loss = T.mean(binary_crossentropy(test_prediction,target_var))
train_fn = theano.function([input_var, input_mask_var,target_var],
loss, updates=updates, allow_input_downcast=True)
test_acc = T.mean(binary_accuracy(test_prediction, target_var))
val_fn = theano.function([input_var, input_mask_var, target_var], [test_loss, test_acc], allow_input_downcast=True)
return train_fn, val_fn, network
layer1_input = T.concatenate(layer1_inputs,1)
input_dims = feature_maps*len(filter_hs)
regess = RegressionNeuralNetwork(rng, input=layer1_input,n_in=input_dims,n_hidden=100,n_out=1,activation=[Sigmoid,Sigmoid])
mse = regess.entropy(Y)
L2 = sum([conv_layer.L2 for conv_layer in conv_layers]) + regess.L2
cost = mse + L2
params = regess.params
for conv_layer in conv_layers:
params+=conv_layer.params
updates = adadelta(cost,params)
train_model = theano.function([X,Y],[mse, cost],updates=updates)
valid_model = theano.function([X,Y],[mse, cost])
showfunction = theano.function(inputs=[X],outputs=regess.regressionlayer.y_pred)
patience = 0
best_valid_mse_global = 100
early_stop = 20
epoch_i = 0
train_rand_idxs = list(range(0,X_train.shape[0]))
valid_rand_idxs = list(range(0,X_valid.shape[0]))
while patience < early_stop:
def __init__(self,
rng,
n_in,
n_per_base,
n_out,
n_layer=1,
basefuncs1=None,
basefuncs2=None,
gradient=None,
with_shortcuts=False):
"""Initialize the parameters for the multilayer function graph
:type rng: numpy.random.RandomState
:param rng: a random number generator used to initialize weights
:type n_in: int
:param n_in: number of input units, the dimension of the space in
which the datapoints lie
:type n_layer: int
:param n_layer: number of hidden layers
:type n_per_base: int
:param n_per_base: number of nodes per basis function see FGLayer
:type n_out: int
:param n_out: number of output units, the dimension of the space in
which the labels lie
:type basefuncs1: [int]
:param basefuncs1: see FGLayer
:type basefuncs2: [int]
:param basefuncs2: see FGLayer
:type gradient: string
:param gradient: type of gradient descent algo (None=="sgd+","adagrad","adadelta","nag")
:type with_shortcuts: bool
:param with_shortcuts: whether to use shortcut connections (output is connected to all units)
"""
self.input = T.matrix('input') # the data is presented as vector input
self.labels = T.matrix(
'labels') # the labels are presented as vector of continous values
self.rng = rng
self.n_layers = n_layer
self.hidden_layers = []
self.params = []
self.n_in = n_in
self.n_out = n_out
self.with_shortcuts = with_shortcuts
self.fixL0 = False
for l in xrange(n_layer):
if l == 0:
layer_input = self.input
n_input = n_in
else:
layer_input = self.hidden_layers[l - 1].output
n_input = self.hidden_layers[l - 1].n_out
hiddenLayer = FGLayer(
rng=rng,
inp=layer_input,
n_in=n_input,
n_per_base=n_per_base,
basefuncs1=basefuncs1,
basefuncs2=basefuncs2,
layer_idx=l,
)
self.hidden_layers.append(hiddenLayer)
self.params.extend(hiddenLayer.params)
div_thresh = T.scalar("div_thresh")
# The linear output layer, either it gets as input the output of ALL previous layers
if self.with_shortcuts:
output_layer_inp = T.concatenate(
[l.output for l in reversed(self.hidden_layers)], axis=1)
output_layer_n_in = sum([l.n_out for l in self.hidden_layers])
else: # or just of the last hidden layer
output_layer_inp = self.hidden_layers[-1].output
output_layer_n_in = self.hidden_layers[-1].n_out
self.output_layer = DivisionRegression(rng=rng,
inp=output_layer_inp,
n_in=output_layer_n_in,
n_out=n_out,
div_thresh=div_thresh)
self.params.extend(self.output_layer.params)
self.evalfun = theano.function(
inputs=[self.input, In(div_thresh, value=0.0001)],
outputs=self.output_layer.output)
L1_reg = T.scalar('L1_reg')
L2_reg = T.scalar('L2_reg')
fixL0 = T.bscalar('fixL0')
self.L1 = self.output_layer.L1 + sum(
[l.L1 for l in self.hidden_layers])
self.L2_sqr = self.output_layer.L2_sqr + sum(
[l.L2_sqr for l in self.hidden_layers])
self.penalty = self.output_layer.penalty
self.loss = self.output_layer.loss
self.errors = self.loss
self.cost = (self.loss(self.labels) + L1_reg * self.L1 +
L2_reg * self.L2_sqr + self.penalty)
#Extrapol penalty
self.extrapol_cost = self.output_layer.extrapol_loss
learning_rate = T.scalar('learning_rate')
def process_updates(par, newp):
# print par.name
if par.name == "W":
# if fixL0 is True, then keep small weights at 0
return par, ifelse(
fixL0, T.switch(T.abs_(par) < 0.001, par * 0, newp), newp)
return par, newp
print "Gradient:", gradient
update = None
if gradient == 'sgd+' or gradient == 'sgd' or gradient == None:
gparams = [T.grad(self.cost, param) for param in self.params]
update = OrderedDict([
(param, param - (learning_rate * gparam).clip(-1.0, 1.0))
for param, gparam in zip(self.params, gparams)
])
elif gradient == 'adam':
update = Lupdates.adam(self.cost,
self.params,
learning_rate,
epsilon=1e-04)
elif gradient == 'adadelta':
update = Lupdates.adadelta(self.cost, self.params, learning_rate)
elif gradient == 'rmsprop':
update = Lupdates.rmsprop(self.cost, self.params, learning_rate)
elif gradient == 'nag':
update = Lupdates.nesterov_momentum(self.cost, self.params,
learning_rate)
else:
assert ("unknown gradient " + gradient)
#Extrapol sanity gradient computation:
extrapol_updates = Lupdates.adam(self.extrapol_cost,
self.params,
learning_rate,
epsilon=1e-04)
updates = [process_updates(*up) for up in update.items()]
self.train_model = theano.function(
inputs=[
self.input, self.labels, L1_reg, L2_reg, fixL0, learning_rate,
div_thresh
],
outputs=self.cost,
updates=updates,
)
# avoid too large outputs in extrapolation domain
self.remove_extrapol_error = theano.function(
inputs=[self.input, learning_rate, div_thresh],
outputs=self.extrapol_cost,
updates=extrapol_updates,
)
self.test_model = theano.function(
inputs=[self.input, self.labels,
In(div_thresh, value=0.0001)],
outputs=self.errors(self.labels),
)
self.validate_model = theano.function(
inputs=[self.input, self.labels,
In(div_thresh, value=0.0001)],
outputs=self.errors(self.labels),
)
self.L1_loss = theano.function(
inputs=[],
outputs=self.L1,
)
self.MSE = theano.function(
inputs=[self.input, self.labels,
In(div_thresh, value=0.0001)],
outputs=self.errors(self.labels),
)
def __init__(self, hidden_size=100, nclasses=73, num_embeddings=11359, embedding_dim=100, window_size=1,
memory_size=40, n_memory_slots=8, go_code=1, depth=2, load_dir=None):
articles, titles = T.imatrices('articles', 'titles')
n_article_slots = int(n_memory_slots / 2) # TODO derive this from an arg
n_title_slots = n_memory_slots - n_article_slots
n_instances = articles.shape[0]
self.window_size = window_size
randoms = {
# attr: shape
# 'emb': (num_embeddings + 1, embedding_dim),
'M_a': (memory_size, n_article_slots),
'M_t': (memory_size, n_title_slots),
'w_a': (n_article_slots,),
'w_t': (n_title_slots,),
'Wg_a': (window_size * embedding_dim, n_article_slots),
'Wg_t': (window_size * embedding_dim, n_title_slots),
'Wk': (hidden_size, memory_size),
'Wb': (hidden_size, 1),
'Wv': (hidden_size, memory_size),
'We_a': (hidden_size, n_article_slots),
'We_t': (hidden_size, n_title_slots),
'Wx': (window_size * embedding_dim, hidden_size),
'Wh': (memory_size, hidden_size),
'W': (hidden_size, nclasses),
'h0': hidden_size
}
zeros = {
# attr: shape
'bg_a': n_article_slots,
'bg_t': n_title_slots,
'bk': memory_size,
'bb': 1,
'bv': memory_size,
'be_a': n_article_slots,
'be_t': n_title_slots,
'bh': hidden_size,
'b': nclasses,
}
for l in range(depth):
randoms['gru' + str(l)] = (1, embedding_dim)
def random_shared(name):
shape = randoms[name]
return theano.shared(
0.2 * np.random.normal(size=shape).astype(theano.config.floatX),
name=name)
def zeros_shared(name):
shape = zeros[name]
return theano.shared(np.zeros(shape, dtype=theano.config.floatX), name=name)
for key in randoms:
# create an attribute with associated shape and random values
setattr(self, key, random_shared(key))
for key in zeros:
# create an attribute with associated shape and values equal to 0
setattr(self, key, zeros_shared(key))
self.names = randoms.keys() + zeros.keys()
# self.names.remove('emb') # no need to save or update embeddings
scan_vars = 'h0 w_a M_a w_t M_t'.split()
def repeat_for_each_instance(param):
""" repeat param along new axis once for each instance """
return T.repeat(T.shape_padleft(param), repeats=n_instances, axis=0)
for key in scan_vars:
setattr(self, key, repeat_for_each_instance(self.__getattribute__(key)))
self.names.remove(key)
if load_dir is not None:
with open(os.path.join(load_dir, 'params.pkl')) as handle:
params = pickle.load(handle)
self.__dict__.update(params)
def recurrence(i,
h_tm1,
w_a,
M_a,
*args,
**kwargs):
"""
notes
Headers from paper in all caps
mem = n_article slots if is_article else n_title_slots
:param i: center index of sliding window
:param h_tm1: h_{t-1} (hidden state)
:param w_a: attention weights for article memory
:param M_a: article memory
:param args: gru_weights, maybe w_t, maybe M_t
gru_weights: weights with which to initialize GRULayer on each time step
w_t: attention weights for titles memory
M_t: titles memory
:param kwargs: is_training, is_article
is_training:
is_article: we use different parts of memory when working with a article
:return: [y = model outputs,
i + 1 = increment index,
h w, M (see above)]
"""
is_training = kwargs['is_training']
is_article = kwargs['is_article']
gru_weights = args[:depth]
if len(args) > depth:
w_t = args[depth]
M_t = args[depth + 1]
i_type = T.iscalar if is_article or is_training else T.ivector
assert i.type == i_type
if not is_article:
assert w_t is not None and M_t is not None
word_idxs = i
if is_article or is_training:
# get representation of word window
document = articles if is_article else titles # [instances, bucket_width]
word_idxs = document[:, i:i+1] # [instances, 1]
# x_i = self.emb[word_idxs].flatten(ndim=2) # [instances, embedding_dim]
input = InputLayer(shape=(None, 1),
input_var=word_idxs)
embed = EmbeddingLayer(input, num_embeddings, embedding_dim)
gru = GRULayer(incoming=embed, num_units=embedding_dim, hid_init=self.gru0)
for weight in gru_weights:
gru = GRULayer(incoming=gru, num_units=embedding_dim,
hid_init=weight)
x_i = get_output(gru).flatten(ndim=2)
x_i = Print('x_i')(x_i) # [instances, embedding_dim]
gru_weights = []
if is_article:
M_read = M_a # [instances, memory_size, n_article_slots]
w_read = w_a # [instances, n_article_slots]
else:
M_read = T.concatenate([M_a, M_t], axis=2) # [instances, memory_size, n_title_slots]
w_read = T.concatenate([w_a, w_t], axis=1) # [instances, n_title_slots]
# eqn 15
c = T.batched_dot(M_read, w_read) # [instances, memory_size]
# EXTERNAL MEMORY READ
def get_attention(Wg, bg, M, w):
g = T.nnet.sigmoid(T.dot(x_i, Wg) + bg) # [instances, mem]
# eqn 11
k = T.dot(h_tm1, self.Wk) + self.bk # [instances, memory_size]
# eqn 13
beta = T.dot(h_tm1, self.Wb) + self.bb
beta = T.nnet.softplus(beta)
beta = T.addbroadcast(beta, 1) # [instances, 1]
# eqn 12
w_hat = T.nnet.softmax(beta * cosine_dist(M, k))
# eqn 14
return (1 - g) * w + g * w_hat # [instances, mem]
w_a = get_attention(self.Wg_a, self.bg_a, M_a, w_a) # [instances, n_article_slots]
if not is_article:
w_t = get_attention(self.Wg_t, self.bg_t, M_t, w_t) # [instances, n_title_slots]
# MODEL INPUT AND OUTPUT
# eqn 9
h = T.dot(c, self.Wh) + T.dot(x_i, self.Wx) + self.bh # [instances, hidden_size]
# eqn 10
y = T.nnet.softmax(T.dot(h, self.W) + self.b) # [instances, nclasses]
# EXTERNAL MEMORY UPDATE
def update_memory(We, be, w_update, M_update):
# eqn 17
e = T.nnet.sigmoid(T.dot(h_tm1, We) + be) # [instances, mem]
f = 1. - w_update * e # [instances, mem]
# eqn 16
v = T.tanh(T.dot(h, self.Wv) + self.bv) # [instances, memory_size]
# need to add broadcast layers for memory update
f = f.dimshuffle(0, 'x', 1) # [instances, 1, mem]
u = w_update.dimshuffle(0, 'x', 1) # [instances, 1, mem]
v = v.dimshuffle(0, 1, 'x') # [instances, memory_size, 1]
# eqn 19
return M_update * f + T.batched_dot(v, u) * (1 - f) # [instances, memory_size, mem]
M_a = update_memory(self.We_a, self.be_a, w_a, M_a)
attention_and_memory = [w_a, M_a]
if not is_article:
M_t = update_memory(self.We_t, self.be_t, w_t, M_t)
attention_and_memory += [w_t, M_t]
y_max = y.argmax(axis=1).astype(int32)
next_idxs = i + 1 if is_training or is_article else y_max
return [y, y_max, next_idxs, h] + attention_and_memory
read_article = partial(recurrence, is_training=True, is_article=True)
# for read_article, it actually doesn't matter whether is_training is true
i0 = T.constant(0, dtype=int32, name='first_value_of_i')
gru_weights = [eval('self.gru' + str(l)) for l in range(depth)]
outputs_info = [None, None, i0, self.h0, self.w_a, self.M_a] + gru_weights
[_, _, _, h, w, M], _ = theano.scan(fn=read_article,
outputs_info=outputs_info,
n_steps=articles.shape[1],
name='read_scan')
produce_title = partial(recurrence, is_training=True, is_article=False)
outputs_info[3:6] = [param[-1, :, :] for param in (h, w, M)]
outputs_info.extend([self.w_t, self.M_t])
bucket_width = titles.shape[1] - 1 # subtract 1 because <go> is omitted in y_true
[y, y_max, _, _, _, _, _, _], _ = theano.scan(fn=produce_title,
outputs_info=outputs_info,
n_steps=bucket_width,
name='train_scan')
# loss and updates
y_clip = T.clip(y, .01, .99)
y_flatten = y_clip.dimshuffle(2, 1, 0).flatten(ndim=2).T
y_true = titles[:, 1:].ravel() # [:, 1:] in order to omit <go>
counts = T.extra_ops.bincount(y_true, assert_nonneg=True)
weights = 1.0 / (counts[y_true] + 1) * T.neq(y_true, 0)
losses = T.nnet.categorical_crossentropy(y_flatten, y_true)
loss = objectives.aggregate(losses, weights, mode='sum')
updates = adadelta(loss, self.params())
self.learn = theano.function(inputs=[articles, titles],
outputs=[y_max.T, loss],
updates=updates,
allow_input_downcast=True,
name='learn')
produce_title_test = partial(recurrence, is_training=False, is_article=False)
self.test = theano.function(inputs=[articles, titles],
outputs=[y_max.T],
on_unused_input='ignore')
outputs_info[2] = T.zeros([n_instances], dtype=int32) + go_code
[_, y_max, _, _, _, _, _, _], _ = theano.scan(fn=produce_title_test,
outputs_info=outputs_info,
n_steps=bucket_width,
name='test_scan')
self.predict = theano.function(inputs=[articles, titles],
outputs=y_max.T,
name='infer')
def event_span_classifier(args, input_var, target_var, wordEmbeddings, seqlen, num_feats):
print("Building model with 1D Convolution")
vocab_size = wordEmbeddings.shape[1]
wordDim = wordEmbeddings.shape[0]
kw = 2
num_filters = seqlen-kw+1
stride = 1
#important context words as channels
#CNN_sentence config
filter_size=wordDim
pool_size=seqlen-filter_size+1
input = InputLayer((None, seqlen, num_feats),input_var=input_var)
batchsize, _, _ = input.input_var.shape
emb = EmbeddingLayer(input, input_size=vocab_size, output_size=wordDim, W=wordEmbeddings.T)
#emb.params[emb.W].remove('trainable') #(batchsize, seqlen, wordDim)
#print get_output_shape(emb)
reshape = ReshapeLayer(emb, (batchsize, seqlen, num_feats*wordDim))
#print get_output_shape(reshape)
conv1d = Conv1DLayer(reshape, num_filters=num_filters, filter_size=wordDim, stride=1,
nonlinearity=tanh,W=GlorotUniform()) #nOutputFrame = num_flters,
#nOutputFrameSize = (num_feats*wordDim-filter_size)/stride +1
#print get_output_shape(conv1d)
conv1d = DimshuffleLayer(conv1d, (0,2,1))
#print get_output_shape(conv1d)
pool_size=num_filters
maxpool = MaxPool1DLayer(conv1d, pool_size=pool_size)
#print get_output_shape(maxpool)
#forward = FlattenLayer(maxpool)
#print get_output_shape(forward)
hid = DenseLayer(maxpool, num_units=args.hiddenDim, nonlinearity=sigmoid)
network = DenseLayer(hid, num_units=2, nonlinearity=softmax)
prediction = get_output(network)
loss = T.mean(binary_crossentropy(prediction,target_var))
lambda_val = 0.5 * 1e-4
layers = {emb:lambda_val, conv1d:lambda_val, hid:lambda_val, network:lambda_val}
penalty = regularize_layer_params_weighted(layers, l2)
loss = loss + penalty
params = get_all_params(network, trainable=True)
if args.optimizer == "sgd":
updates = sgd(loss, params, learning_rate=args.step)
elif args.optimizer == "adagrad":
updates = adagrad(loss, params, learning_rate=args.step)
elif args.optimizer == "adadelta":
updates = adadelta(loss, params, learning_rate=args.step)
elif args.optimizer == "nesterov":
updates = nesterov_momentum(loss, params, learning_rate=args.step)
elif args.optimizer == "rms":
updates = rmsprop(loss, params, learning_rate=args.step)
elif args.optimizer == "adam":
updates = adam(loss, params, learning_rate=args.step)
else:
raise "Need set optimizer correctly"
test_prediction = get_output(network, deterministic=True)
test_loss = T.mean(binary_crossentropy(test_prediction,target_var))
train_fn = theano.function([input_var, target_var],
loss, updates=updates, allow_input_downcast=True)
test_acc = T.mean(binary_accuracy(test_prediction, target_var))
val_fn = theano.function([input_var, target_var], [test_loss, test_acc], allow_input_downcast=True)
return train_fn, val_fn, network
regess.set_params(save_params[:4])
for i in xrange(len(filter_hs)):
print(4 + i * 2)
print(4 + i * 2 + 2)
conv_layers[i].set_params(save_params[4 + i * 2:4 + i * 2 + 2])
mse = regess.mse(Y)
L2 = sum([conv_layer.L2 for conv_layer in conv_layers]) + regess.L2
cost = mse + L2
params = regess.params
for conv_layer in conv_layers:
params += conv_layer.params
updates = adadelta(cost, params)
train_model = theano.function([X, Y], [mse, cost], updates=updates)
valid_model = theano.function([X, Y], [mse, cost])
showfunction = theano.function(inputs=[X],
outputs=regess.hiddenlayer.output)
X_mnb = X_valid[:batch_size]
Y_mnb = Y_valid_rouge2[:batch_size]
print(X_mnb.shape, X_mnb.dtype, Y_mnb, Y_mnb.dtype)
pred = showfunction(X_mnb)
print pred
print Y_valid_rouge2[:batch_size]
def main():
configure_theano()
options = parse_options()
config_file = 'config/leave_one_out.ini'
print('loading config file: {}'.format(config_file))
config = ConfigParser.ConfigParser()
config.read(config_file)
print('preprocessing dataset...')
data = load_mat_file(config.get('data', 'images'))
dct_data = load_mat_file(config.get('data', 'dct'))
ae_pretrained = config.get('models', 'pretrained')
ae_finetuned = config.get('models', 'finetuned')
ae_finetuned_diff = config.get('models', 'finetuned_diff')
learning_rate = float(config.get('training', 'learning_rate'))
decay_rate = float(config.get('training', 'decay_rate'))
decay_start = int(config.get('training', 'decay_start'))
do_finetune = config.getboolean('training', 'do_finetune')
save_finetune = config.getboolean('training', 'save_finetune')
load_finetune = config.getboolean('training', 'load_finetune')
load_finetune_diff = config.getboolean('training', 'load_finetune_diff')
# 53 subjects, 70 utterances, 5 view angles
# s[x]_v[y]_u[z].mp4
# resized, height, width = (26, 44)
# ['dataMatrix', 'targetH', 'targetsPerVideoVec', 'videoLengthVec', '__header__', 'targetsVec',
# '__globals__', 'iterVec', 'filenamesVec', 'dataMatrixCells', 'subjectsVec', 'targetW', '__version__']
print(data.keys())
X = data['dataMatrix'].astype('float32')
y = data['targetsVec'].astype('int32')
y = y.reshape((len(y), ))
dct_feats = dct_data['dctFeatures'].astype('float32')
uniques = np.unique(y)
print('number of classifications: {}'.format(len(uniques)))
subjects = data['subjectsVec'].astype('int')
subjects = subjects.reshape((len(subjects), ))
video_lens = data['videoLengthVec'].astype('int')
video_lens = video_lens.reshape((len(video_lens, )))
# X = reorder_data(X, (26, 44), 'f', 'c')
# print('performing sequencewise mean image removal...')
# X = sequencewise_mean_image_subtraction(X, video_lens)
# visualize_images(X[550:650], (26, 44))
X_diff = compute_diff_images(X, video_lens)
# mean remove dct features
dct_feats = sequencewise_mean_image_subtraction(dct_feats, video_lens)
test_subject_ids = [options['test_subj']]
train_subject_ids = range(1, 54)
for subj in test_subject_ids:
train_subject_ids.remove(subj)
if 'results' in options:
results_file = options['results']
f = open(results_file, mode='a')
print(train_subject_ids)
print(test_subject_ids)
train_X, train_y, train_dct, train_X_diff, train_vidlens, train_subjects, \
test_X, test_y, test_dct, test_X_diff, test_vidlens, test_subjects = \
split_data(X, y, dct_feats, X_diff, subjects, video_lens, train_subject_ids, test_subject_ids)
assert train_X.shape[0] + test_X.shape[0] == len(X)
assert train_y.shape[0] + test_y.shape[0] == len(y)
assert train_vidlens.shape[0] + test_vidlens.shape[0] == len(video_lens)
assert train_subjects.shape[0] + test_subjects.shape[0] == len(subjects)
train_X = normalize_input(train_X, centralize=True)
test_X = normalize_input(test_X, centralize=True)
# featurewise normalize dct features
train_dct, dct_mean, dct_std = featurewise_normalize_sequence(train_dct)
test_dct = (test_dct - dct_mean) / dct_std
if do_finetune:
print('performing finetuning on pretrained encoder: {}'.format(
ae_pretrained))
ae = load_dbn(ae_pretrained)
ae.initialize()
ae.fit(train_X, train_X)
if save_finetune:
print('saving finetuned encoder: {}...'.format(ae_finetuned))
pickle.dump(ae, open(ae_finetuned, 'wb'))
if load_finetune:
print('loading finetuned encoder: {}...'.format(ae_finetuned))
ae = pickle.load(open(ae_finetuned, 'rb'))
ae.initialize()
if load_finetune_diff:
print('loading finetuned encoder: {}...'.format(ae_finetuned_diff))
ae_diff = pickle.load(open(ae_finetuned_diff, 'rb'))
ae_diff.initialize()
# IMPT: the encoder was trained with fortan ordered images, so to visualize
# convert all the images to C order using reshape_images_order()
# output = dbn.predict(test_X)
# test_X = reshape_images_order(test_X, (26, 44))
# output = reshape_images_order(output, (26, 44))
# visualize_reconstruction(test_X[:36, :], output[:36, :], shape=(26, 44))
window = T.iscalar('theta')
dct = T.tensor3('dct', dtype='float32')
inputs = T.tensor3('inputs', dtype='float32')
inputs_diff = T.tensor3('inputs_diff', dtype='float32')
mask = T.matrix('mask', dtype='uint8')
targets = T.ivector('targets')
lr = theano.shared(np.array(learning_rate, dtype=theano.config.floatX),
name='learning_rate')
lr_decay = np.array(decay_rate, dtype=theano.config.floatX)
print('constructing end to end model...')
'''
network = create_end_to_end_model(dbn, (None, None, 1144), inputs,
(None, None), mask, 250, window)
'''
network = adenet_v5.create_model(ae, ae_diff, (None, None, 1144), inputs,
(None, None), mask, (None, None, 90), dct,
(None, None, 1144), inputs_diff, 250,
window, 10)
print_network(network)
print('compiling model...')
predictions = las.layers.get_output(network, deterministic=False)
all_params = las.layers.get_all_params(network, trainable=True)
cost = T.mean(las.objectives.categorical_crossentropy(
predictions, targets))
updates = adadelta(cost, all_params, learning_rate=lr)
# updates = adagrad(cost, all_params, learning_rate=lr)
use_max_constraint = False
if use_max_constraint:
MAX_NORM = 4
for param in las.layers.get_all_params(network, regularizable=True):
if param.ndim > 1: # only apply to dimensions larger than 1, exclude biases
updates[param] = norm_constraint(
param,
MAX_NORM *
las.utils.compute_norms(param.get_value()).mean())
train = theano.function([inputs, targets, mask, dct, inputs_diff, window],
cost,
updates=updates,
allow_input_downcast=True)
compute_train_cost = theano.function(
[inputs, targets, mask, dct, inputs_diff, window],
cost,
allow_input_downcast=True)
test_predictions = las.layers.get_output(network, deterministic=True)
test_cost = T.mean(
las.objectives.categorical_crossentropy(test_predictions, targets))
compute_test_cost = theano.function(
[inputs, targets, mask, dct, inputs_diff, window],
test_cost,
allow_input_downcast=True)
val_fn = theano.function([inputs, mask, dct, inputs_diff, window],
test_predictions,
allow_input_downcast=True)
# We'll train the network with 10 epochs of 30 minibatches each
print('begin training...')
cost_train = []
cost_val = []
class_rate = []
NUM_EPOCHS = 10
EPOCH_SIZE = 120
BATCH_SIZE = 10
WINDOW_SIZE = 9
STRIP_SIZE = 3
MAX_LOSS = 0.2
VALIDATION_WINDOW = 4
val_window = circular_list(VALIDATION_WINDOW)
train_strip = np.zeros((STRIP_SIZE, ))
best_val = float('inf')
best_conf = None
best_cr = 0.0
datagen = gen_lstm_batch_random(train_X,
train_y,
train_vidlens,
batchsize=BATCH_SIZE)
val_datagen = gen_lstm_batch_random(test_X,
test_y,
test_vidlens,
batchsize=len(test_vidlens))
integral_lens = compute_integral_len(train_vidlens)
# We'll use this "validation set" to periodically check progress
X_val, y_val, mask_val, idxs_val = next(val_datagen)
integral_lens_val = compute_integral_len(test_vidlens)
dct_val = gen_seq_batch_from_idx(test_dct, idxs_val, test_vidlens,
integral_lens_val, np.max(test_vidlens))
X_diff_val = gen_seq_batch_from_idx(test_X_diff, idxs_val,
test_vidlens, integral_lens_val,
np.max(test_vidlens))
def early_stop(cost_window):
if len(cost_window) < 2:
return False
else:
curr = cost_window[0]
for idx, cost in enumerate(cost_window):
if curr < cost or idx == 0:
curr = cost
else:
return False
return True
for epoch in range(NUM_EPOCHS):
time_start = time.time()
for i in range(EPOCH_SIZE):
X, y, m, batch_idxs = next(datagen)
d = gen_seq_batch_from_idx(train_dct, batch_idxs, train_vidlens,
integral_lens, np.max(train_vidlens))
X_diff = gen_seq_batch_from_idx(train_X_diff, batch_idxs,
train_vidlens, integral_lens,
np.max(train_vidlens))
print_str = 'Epoch {} batch {}/{}: {} examples at learning rate = {:.4f}'.format(
epoch + 1, i + 1, EPOCH_SIZE, len(X), float(lr.get_value()))
print(print_str, end='')
sys.stdout.flush()
train(X, y, m, d, X_diff, WINDOW_SIZE)
print('\r', end='')
cost = compute_train_cost(X, y, m, d, X_diff, WINDOW_SIZE)
val_cost = compute_test_cost(X_val, y_val, mask_val, dct_val,
X_diff_val, WINDOW_SIZE)
cost_train.append(cost)
cost_val.append(val_cost)
train_strip[epoch % STRIP_SIZE] = cost
val_window.push(val_cost)
gl = 100 * (cost_val[-1] / np.min(cost_val) - 1)
pk = 1000 * (np.sum(train_strip) /
(STRIP_SIZE * np.min(train_strip)) - 1)
pq = gl / pk
cr, val_conf = evaluate_model(X_val, y_val, mask_val, dct_val,
X_diff_val, WINDOW_SIZE, val_fn)
class_rate.append(cr)
print(
"Epoch {} train cost = {}, validation cost = {}, "
"generalization loss = {:.3f}, GQ = {:.3f}, classification rate = {:.3f} ({:.1f}sec)"
.format(epoch + 1, cost_train[-1], cost_val[-1], gl, pq, cr,
time.time() - time_start))
if val_cost < best_val:
best_val = val_cost
best_conf = val_conf
best_cr = cr
if epoch >= VALIDATION_WINDOW and early_stop(val_window):
break
# learning rate decay
if epoch >= decay_start - 1:
lr.set_value(lr.get_value() * lr_decay)
phrases = ['p1', 'p2', 'p3', 'p4', 'p5', 'p6', 'p7', 'p8', 'p9', 'p10']
print('Final Model')
print('classification rate: {}, validation loss: {}'.format(
best_cr, best_val))
print('confusion matrix: ')
plot_confusion_matrix(best_conf, phrases, fmt='grid')
plot_validation_cost(cost_train,
cost_val,
class_rate,
savefilename='valid_cost')
if 'results' in options:
print('writing to results file: {}...'.format(options['results']))
f.write('{}, {}, {}\n'.format(test_subject_ids[0], best_cr, best_val))
f.close()
def main():
configure_theano()
options = parse_options()
config_file = options['config']
config = ConfigParser.ConfigParser()
config.read(config_file)
print('CLI options: {}'.format(options.items()))
print('Reading Config File: {}...'.format(config_file))
print(config.items('data'))
print(config.items('models'))
print(config.items('training'))
print('preprocessing dataset...')
data = load_mat_file(config.get('data', 'images'))
dct_data = load_mat_file(config.get('data', 'dct'))
ae_finetuned = config.get('models', 'finetuned')
ae_finetuned_diff = config.get('models', 'finetuned_diff')
fusiontype = config.get('models', 'fusiontype')
learning_rate = float(config.get('training', 'learning_rate'))
decay_rate = float(config.get('training', 'decay_rate'))
decay_start = int(config.get('training', 'decay_start'))
load_finetune = config.getboolean('training', 'load_finetune')
load_finetune_diff = config.getboolean('training', 'load_finetune_diff')
train_vidlens = data['trVideoLengthVec'].astype('int').reshape((-1,))
val_vidlens = data['valVideoLengthVec'].astype('int').reshape((-1,))
test_vidlens = data['testVideoLengthVec'].astype('int').reshape((-1,))
train_X = data['trData'].astype('float32')
val_X = data['valData'].astype('float32')
test_X = data['testData'].astype('float32')
train_dct = dct_data['trDctFeatures'].astype('float32')
val_dct = dct_data['valDctFeatures'].astype('float32')
test_dct = dct_data['testDctFeatures'].astype('float32')
train_X_diff = compute_diff_images(train_X, train_vidlens)
val_X_diff = compute_diff_images(val_X, val_vidlens)
test_X_diff = compute_diff_images(test_X, test_vidlens)
train_y = data['trTargetsVec'].astype('int').reshape((-1,)) + 1 # +1 to handle the -1 introduced in lstm_gendata
val_y = data['valTargetsVec'].astype('int').reshape((-1,)) + 1
test_y = data['testTargetsVec'].astype('int').reshape((-1,)) + 1
# featurewise normalize dct features
train_dct, dct_mean, dct_std = featurewise_normalize_sequence(train_dct)
val_dct = (val_dct - dct_mean) / dct_std
test_dct = (test_dct - dct_mean) / dct_std
if load_finetune:
print('loading finetuned encoder: {}...'.format(ae_finetuned))
ae = pickle.load(open(ae_finetuned, 'rb'))
ae.initialize()
if load_finetune_diff:
print('loading finetuned encoder: {}...'.format(ae_finetuned_diff))
ae_diff = pickle.load(open(ae_finetuned_diff, 'rb'))
ae_diff.initialize()
# IMPT: the encoder was trained with fortan ordered images, so to visualize
# convert all the images to C order using reshape_images_order()
# output = dbn.predict(test_X)
# test_X = reshape_images_order(test_X, (26, 44))
# output = reshape_images_order(output, (26, 44))
# visualize_reconstruction(test_X[:36, :], output[:36, :], shape=(26, 44))
window = T.iscalar('theta')
dct = T.tensor3('dct', dtype='float32')
inputs = T.tensor3('inputs', dtype='float32')
inputs_diff = T.tensor3('inputs_diff', dtype='float32')
mask = T.matrix('mask', dtype='uint8')
targets = T.ivector('targets')
lr = theano.shared(np.array(learning_rate, dtype=theano.config.floatX), name='learning_rate')
lr_decay = np.array(decay_rate, dtype=theano.config.floatX)
print('constructing end to end model...')
network, l_fuse = adenet_v3.create_model(ae, ae_diff, (None, None, 1500), inputs,
(None, None), mask,
(None, None, 90), dct,
(None, None, 1500), inputs_diff,
250, window, 10, fusiontype)
print_network(network)
# draw_to_file(las.layers.get_all_layers(network), 'network.png')
print('compiling model...')
predictions = las.layers.get_output(network, deterministic=False)
all_params = las.layers.get_all_params(network, trainable=True)
cost = T.mean(las.objectives.categorical_crossentropy(predictions, targets))
updates = adadelta(cost, all_params, learning_rate=lr)
# updates = adagrad(cost, all_params, learning_rate=lr)
train = theano.function(
[inputs, targets, mask, dct, inputs_diff, window],
cost, updates=updates, allow_input_downcast=True)
compute_train_cost = theano.function([inputs, targets, mask, dct, inputs_diff, window],
cost, allow_input_downcast=True)
test_predictions = las.layers.get_output(network, deterministic=True)
test_cost = T.mean(las.objectives.categorical_crossentropy(test_predictions, targets))
compute_test_cost = theano.function(
[inputs, targets, mask, dct, inputs_diff, window], test_cost, allow_input_downcast=True)
val_fn = theano.function([inputs, mask, dct, inputs_diff, window], test_predictions, allow_input_downcast=True)
# We'll train the network with 10 epochs of 30 minibatches each
print('begin training...')
cost_train = []
cost_val = []
class_rate = []
NUM_EPOCHS = 30
EPOCH_SIZE = 45
BATCH_SIZE = 20
WINDOW_SIZE = 9
STRIP_SIZE = 3
MAX_LOSS = 0.2
VALIDATION_WINDOW = 4
val_window = circular_list(VALIDATION_WINDOW)
train_strip = np.zeros((STRIP_SIZE,))
best_val = float('inf')
best_conf = None
best_cr = 0.0
datagen = gen_lstm_batch_random(train_X, train_y, train_vidlens, batchsize=BATCH_SIZE)
integral_lens = compute_integral_len(train_vidlens)
val_datagen = gen_lstm_batch_random(val_X, val_y, val_vidlens, batchsize=len(val_vidlens))
test_datagen = gen_lstm_batch_random(test_X, test_y, test_vidlens, batchsize=len(test_vidlens))
# We'll use this "validation set" to periodically check progress
X_val, y_val, mask_val, idxs_val = next(val_datagen)
integral_lens_val = compute_integral_len(val_vidlens)
dct_val = gen_seq_batch_from_idx(val_dct, idxs_val, val_vidlens, integral_lens_val, np.max(val_vidlens))
X_diff_val = gen_seq_batch_from_idx(val_X_diff, idxs_val, val_vidlens, integral_lens_val, np.max(val_vidlens))
# we use the test set to check final classification rate
X_test, y_test, mask_test, idxs_test = next(test_datagen)
integral_lens_test = compute_integral_len(test_vidlens)
dct_test = gen_seq_batch_from_idx(test_dct, idxs_test, test_vidlens, integral_lens_test, np.max(test_vidlens))
X_diff_test = gen_seq_batch_from_idx(test_X_diff, idxs_test, test_vidlens, integral_lens_test, np.max(test_vidlens))
def early_stop(cost_window):
if len(cost_window) < 2:
return False
else:
curr = cost_window[0]
for idx, cost in enumerate(cost_window):
if curr < cost or idx == 0:
curr = cost
else:
return False
return True
for epoch in range(NUM_EPOCHS):
time_start = time.time()
for i in range(EPOCH_SIZE):
X, y, m, batch_idxs = next(datagen)
d = gen_seq_batch_from_idx(train_dct, batch_idxs,
train_vidlens, integral_lens, np.max(train_vidlens))
X_diff = gen_seq_batch_from_idx(train_X_diff, batch_idxs,
train_vidlens, integral_lens, np.max(train_vidlens))
print_str = 'Epoch {} batch {}/{}: {} examples at learning rate = {:.4f}'.format(
epoch + 1, i + 1, EPOCH_SIZE, len(X), float(lr.get_value()))
print(print_str, end='')
sys.stdout.flush()
train(X, y, m, d, X_diff, WINDOW_SIZE)
print('\r', end='')
cost = compute_train_cost(X, y, m, d, X_diff, WINDOW_SIZE)
val_cost = compute_test_cost(X_val, y_val, mask_val, dct_val, X_diff_val, WINDOW_SIZE)
cost_train.append(cost)
cost_val.append(val_cost)
train_strip[epoch % STRIP_SIZE] = cost
val_window.push(val_cost)
gl = 100 * (cost_val[-1] / np.min(cost_val) - 1)
pk = 1000 * (np.sum(train_strip) / (STRIP_SIZE * np.min(train_strip)) - 1)
pq = gl / pk
cr, val_conf = evaluate_model(X_val, y_val, mask_val, dct_val, X_diff_val, WINDOW_SIZE, val_fn)
class_rate.append(cr)
if val_cost < best_val:
best_val = val_cost
best_cr = cr
if fusiontype == 'adasum':
adascale_param = las.layers.get_all_param_values(l_fuse, scaling_param=True)
test_cr, test_conf = evaluate_model(X_test, y_test, mask_test,
dct_test, X_diff_test, WINDOW_SIZE, val_fn)
print("Epoch {} train cost = {}, val cost = {}, "
"GL loss = {:.3f}, GQ = {:.3f}, CR = {:.3f}, Test CR= {:.3f} ({:.1f}sec)"
.format(epoch + 1, cost_train[-1], cost_val[-1], gl, pq, cr, test_cr, time.time() - time_start))
else:
print("Epoch {} train cost = {}, val cost = {}, "
"GL loss = {:.3f}, GQ = {:.3f}, CR = {:.3f} ({:.1f}sec)"
.format(epoch + 1, cost_train[-1], cost_val[-1], gl, pq, cr, time.time() - time_start))
if epoch >= VALIDATION_WINDOW and early_stop(val_window):
break
# learning rate decay
if epoch + 1 >= decay_start:
lr.set_value(lr.get_value() * lr_decay)
numbers = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
print('Final Model')
print('CR: {}, val loss: {}, Test CR: {}'.format(best_cr, best_val, test_cr))
if fusiontype == 'adasum':
print("final scaling params: {}".format(adascale_param))
print('confusion matrix: ')
plot_confusion_matrix(test_conf, numbers, fmt='latex')
plot_validation_cost(cost_train, cost_val, savefilename='valid_cost')
if options['write_results']:
results_file = options['write_results']
with open(results_file, mode='a') as f:
f.write('{},{},{}\n'.format(fusiontype, test_cr, best_val))
def main():
def signal_handler(signal, frame):
global terminate
terminate = True
print('terminating...'.format(terminate))
signal.signal(signal.SIGINT, signal_handler)
configure_theano()
options = parse_options()
X, X_val = generate_data()
# X = np.reshape(X, (-1, 1, 30, 40))[:-5]
print('X type and shape:', X.dtype, X.shape)
print('X.min():', X.min())
print('X.max():', X.max())
# X_val = np.reshape(X_val, (-1, 1, 30, 40))[:-1]
print('X_val type and shape:', X_val.dtype, X_val.shape)
print('X_val.min():', X_val.min())
print('X_val.max():', X_val.max())
# we need our target to be 1 dimensional
X_out = X.reshape((X.shape[0], -1))
X_val_out = X_val.reshape((X_val.shape[0], -1))
print('X_out:', X_out.dtype, X_out.shape)
print('X_val_out', X_val_out.dtype, X_val_out.shape)
# X_noisy = apply_gaussian_noise(X_out)
# visualize_reconstruction(X_noisy[0:25], X_out[0:25], shape=(28, 28))
# X = np.reshape(X_noisy, (-1, 1, 28, 28))
print('constructing and compiling model...')
# input_var = T.tensor4('input', dtype='float32')
input_var = T.tensor3('input', dtype='float32')
target_var = T.matrix('output', dtype='float32')
lr = theano.shared(np.array(0.8, dtype=theano.config.floatX),
name='learning_rate')
lr_decay = np.array(0.9, dtype=theano.config.floatX)
# try building a reshaping layer
# network = create_model(input_var, (None, 1, 30, 40), options)
l_input = InputLayer((None, None, 1200), input_var, name='input')
l_input = ReshapeLayer(l_input, (-1, 1, 30, 40), name='reshape_input')
# l_input = InputLayer((None, 1, 30, 40), input_var, name='input')
if options['MODEL'] == 'normal':
network, encoder = avletters_convae.create_model(l_input, options)
if options['MODEL'] == 'batchnorm':
network, encoder = avletters_convae_bn.create_model(l_input, options)
if options['MODEL'] == 'dropout':
network, encoder = avletters_convae_drop.create_model(l_input, options)
if options['MODEL'] == 'bn+dropout':
network, encoder = avletters_convae_bndrop.create_model(
l_input, options)
print('AE Network architecture: {}'.format(options['MODEL']))
print_network(network)
recon = las.layers.get_output(network, deterministic=False)
all_params = las.layers.get_all_params(network, trainable=True)
cost = T.mean(squared_error(recon, target_var))
updates = adadelta(cost, all_params, lr)
# updates = las.updates.apply_nesterov_momentum(updates, all_params, momentum=0.90)
use_max_constraint = False
print('apply max norm constraint: {}'.format(use_max_constraint))
if use_max_constraint:
MAX_NORM = 4
for param in las.layers.get_all_params(network, regularizable=True):
if param.ndim > 1: # only apply to dimensions larger than 1, exclude biases
# updates[param] = norm_constraint(param, MAX_NORM * las.utils.compute_norms(param.get_value()).mean())
updates[param] = norm_constraint(param, MAX_NORM)
train = theano.function([input_var, target_var],
recon,
updates=updates,
allow_input_downcast=True)
train_cost_fn = theano.function([input_var, target_var],
cost,
allow_input_downcast=True)
eval_recon = las.layers.get_output(network, deterministic=True)
eval_cost = T.mean(las.objectives.squared_error(eval_recon, target_var))
eval_cost_fn = theano.function([input_var, target_var],
eval_cost,
allow_input_downcast=True)
recon_fn = theano.function([input_var],
eval_recon,
allow_input_downcast=True)
if terminate:
exit()
NUM_EPOCHS = options['NUM_EPOCHS']
EPOCH_SIZE = options['EPOCH_SIZE']
NO_STRIDES = options['NO_STRIDES']
VAL_NO_STRIDES = options['VAL_NO_STRIDES']
print('begin training for {} epochs...'.format(NUM_EPOCHS))
datagen = batch_iterator(X, X_out, 128)
costs = []
val_costs = []
for epoch in range(NUM_EPOCHS):
time_start = time.time()
for i in range(EPOCH_SIZE):
batch_X, batch_y = next(datagen)
print_str = 'Epoch {} batch {}/{}: {} examples at learning rate = {:.4f}'.format(
epoch + 1, i + 1, EPOCH_SIZE, len(batch_X), lr.get_value())
print(print_str, end='')
sys.stdout.flush()
batch_X = batch_X.reshape((-1, 1, 1200))
train(batch_X, batch_y)
print('\r', end='')
if terminate:
break
if terminate:
break
cost = batch_compute_cost(X, X_out, NO_STRIDES, train_cost_fn)
val_cost = batch_compute_cost(X_val, X_val_out, VAL_NO_STRIDES,
eval_cost_fn)
costs.append(cost)
val_costs.append(val_cost)
print("Epoch {} train cost = {}, validation cost = {} ({:.1f}sec) ".
format(epoch + 1, cost, val_cost,
time.time() - time_start))
if epoch > 10:
lr.set_value(lr.get_value() * lr_decay)
X_val_recon = recon_fn(X_val)
visualize_reconstruction(X_val_out[450:550],
X_val_recon[450:550],
shape=(30, 40),
savefilename='avletters')
plot_validation_cost(costs, val_costs, None, savefilename='valid_cost')
conv2d1 = las.layers.get_all_layers(network)[2]
visualize.plot_conv_weights(conv2d1, (15, 14)).savefig('conv2d1.png')
print('saving encoder...')
save_model(encoder, 'models/conv_encoder.dat')
save_model(network, 'models/conv_ae.dat')
def build_network_2dconv(
args, input1_var, input1_mask_var, input2_var, intut2_mask_var, target_var, wordEmbeddings, maxlen=36
):
print ("Building model with 2D Convolution")
vocab_size = wordEmbeddings.shape[1]
wordDim = wordEmbeddings.shape[0]
num_filters = 100
stride = 1
# CNN_sentence config
filter_size = (3, wordDim)
pool_size = (maxlen - 3 + 1, 1)
# two conv pool layer
# filter_size=(10, 100)
# pool_size=(4,4)
input_1 = InputLayer((None, maxlen), input_var=input1_var)
batchsize, seqlen = input_1.input_var.shape
# input_1_mask = InputLayer((None, maxlen),input_var=input1_mask_var)
emb_1 = EmbeddingLayer(input_1, input_size=vocab_size, output_size=wordDim, W=wordEmbeddings.T)
emb_1.params[emb_1.W].remove("trainable") # (batchsize, maxlen, wordDim)
reshape_1 = ReshapeLayer(emb_1, (batchsize, 1, maxlen, wordDim))
conv2d_1 = Conv2DLayer(
reshape_1,
num_filters=num_filters,
filter_size=(filter_size),
stride=stride,
nonlinearity=rectify,
W=GlorotUniform(),
) # (None, 100, 34, 1)
maxpool_1 = MaxPool2DLayer(conv2d_1, pool_size=pool_size) # (None, 100, 1, 1)
"""
filter_size_2=(4, 10)
pool_size_2=(2,2)
conv2d_1 = Conv2DLayer(maxpool_1, num_filters=num_filters, filter_size=filter_size_2, stride=stride,
nonlinearity=rectify,W=GlorotUniform()) #(None, 100, 34, 1)
maxpool_1 = MaxPool2DLayer(conv2d_1, pool_size=pool_size_2) #(None, 100, 1, 1) (None, 100, 1, 20)
"""
forward_1 = FlattenLayer(maxpool_1) # (None, 100) #(None, 50400)
input_2 = InputLayer((None, maxlen), input_var=input2_var)
# input_2_mask = InputLayer((None, maxlen),input_var=input2_mask_var)
emb_2 = EmbeddingLayer(input_2, input_size=vocab_size, output_size=wordDim, W=wordEmbeddings.T)
emb_2.params[emb_2.W].remove("trainable")
reshape_2 = ReshapeLayer(emb_2, (batchsize, 1, maxlen, wordDim))
conv2d_2 = Conv2DLayer(
reshape_2,
num_filters=num_filters,
filter_size=filter_size,
stride=stride,
nonlinearity=rectify,
W=GlorotUniform(),
) # (None, 100, 34, 1)
maxpool_2 = MaxPool2DLayer(conv2d_2, pool_size=pool_size) # (None, 100, 1, 1)
"""
conv2d_2 = Conv2DLayer(maxpool_2, num_filters=num_filters, filter_size=filter_size_2, stride=stride,
nonlinearity=rectify,W=GlorotUniform()) #(None, 100, 34, 1)
maxpool_2 = MaxPool2DLayer(conv2d_2, pool_size=pool_size_2) #(None, 100, 1, 1)
"""
forward_2 = FlattenLayer(maxpool_2) # (None, 100)
# elementwisemerge need fix the sequence length
mul = ElemwiseMergeLayer([forward_1, forward_2], merge_function=T.mul)
sub = AbsSubLayer([forward_1, forward_2], merge_function=T.sub)
concat = ConcatLayer([mul, sub])
concat = ConcatLayer([forward_1, forward_2])
hid = DenseLayer(concat, num_units=args.hiddenDim, nonlinearity=sigmoid)
if args.task == "sts":
network = DenseLayer(hid, num_units=5, nonlinearity=softmax)
elif args.task == "ent":
network = DenseLayer(hid, num_units=3, nonlinearity=softmax)
# prediction = get_output(network, {input_1:input1_var, input_2:input2_var})
prediction = get_output(network)
loss = T.mean(categorical_crossentropy(prediction, target_var))
lambda_val = 0.5 * 1e-4
layers = {conv2d_1: lambda_val, hid: lambda_val, network: lambda_val}
penalty = regularize_layer_params_weighted(layers, l2)
loss = loss + penalty
params = get_all_params(network, trainable=True)
if args.optimizer == "sgd":
updates = sgd(loss, params, learning_rate=args.step)
elif args.optimizer == "adagrad":
updates = adagrad(loss, params, learning_rate=args.step)
elif args.optimizer == "adadelta":
updates = adadelta(loss, params, learning_rate=args.step)
elif args.optimizer == "nesterov":
updates = nesterov_momentum(loss, params, learning_rate=args.step)
elif args.optimizer == "rms":
updates = rmsprop(loss, params, learning_rate=args.step)
elif args.optimizer == "adam":
updates = adam(loss, params, learning_rate=args.step)
else:
raise "Need set optimizer correctly"
# test_prediction = get_output(network, {input_1:input1_var, input_2:input2_var}, deterministic=True)
test_prediction = get_output(network, deterministic=True)
test_loss = T.mean(categorical_crossentropy(test_prediction, target_var))
"""
train_fn = theano.function([input1_var, input1_mask_var, input2_var, intut2_mask_var, target_var],
loss, updates=updates, allow_input_downcast=True)
"""
train_fn = theano.function([input1_var, input2_var, target_var], loss, updates=updates, allow_input_downcast=True)
if args.task == "sts":
"""
val_fn = theano.function([input1_var, input1_mask_var, input2_var, intut2_mask_var, target_var],
[test_loss, test_prediction], allow_input_downcast=True)
"""
val_fn = theano.function(
[input1_var, input2_var, target_var], [test_loss, test_prediction], allow_input_downcast=True
)
elif args.task == "ent":
# test_acc = T.mean(T.eq(T.argmax(test_prediction, axis=1), target_var), dtype=theano.config.floatX)
test_acc = T.mean(categorical_accuracy(test_prediction, target_var))
"""
val_fn = theano.function([input1_var, input1_mask_var, input2_var, intut2_mask_var, target_var],
[test_loss, test_acc], allow_input_downcast=True)
"""
val_fn = theano.function([input1_var, input2_var, target_var], [test_loss, test_acc], allow_input_downcast=True)
return train_fn, val_fn
def main():
configure_theano()
config_file = 'config/trimodal.ini'
print('loading config file: {}'.format(config_file))
config = ConfigParser.ConfigParser()
config.read(config_file)
print('Reading Config File: {}...'.format(config_file))
print(config.items('data'))
print(config.items('models'))
print(config.items('training'))
print('preprocessing dataset...')
data = load_mat_file(config.get('data', 'images'))
dct_data = load_mat_file(config.get('data', 'dct'))
ae_pretrained = config.get('models', 'pretrained')
ae_finetuned = config.get('models', 'finetuned')
ae_finetuned_diff = config.get('models', 'finetuned_diff')
use_adascale = config.getboolean('models', 'use_adascale')
learning_rate = float(config.get('training', 'learning_rate'))
decay_rate = float(config.get('training', 'decay_rate'))
decay_start = int(config.get('training', 'decay_start'))
do_finetune = config.getboolean('training', 'do_finetune')
save_finetune = config.getboolean('training', 'save_finetune')
load_finetune = config.getboolean('training', 'load_finetune')
load_finetune_diff = config.getboolean('training', 'load_finetune_diff')
# 53 subjects, 70 utterances, 5 view angles
# s[x]_v[y]_u[z].mp4
# resized, height, width = (26, 44)
# ['dataMatrix', 'targetH', 'targetsPerVideoVec', 'videoLengthVec', '__header__', 'targetsVec',
# '__globals__', 'iterVec', 'filenamesVec', 'dataMatrixCells', 'subjectsVec', 'targetW', '__version__']
print(data.keys())
X = data['dataMatrix'].astype('float32')
y = data['targetsVec'].astype('int32')
y = y.reshape((len(y),))
dct_feats = dct_data['dctFeatures'].astype('float32')
uniques = np.unique(y)
print('number of classifications: {}'.format(len(uniques)))
subjects = data['subjectsVec'].astype('int')
subjects = subjects.reshape((len(subjects),))
video_lens = data['videoLengthVec'].astype('int')
video_lens = video_lens.reshape((len(video_lens,)))
# X = reorder_data(X, (26, 44), 'f', 'c')
# print('performing sequencewise mean image removal...')
# X = sequencewise_mean_image_subtraction(X, video_lens)
# visualize_images(X[550:650], (26, 44))
X_diff = compute_diff_images(X, video_lens)
# mean remove dct features
dct_feats = sequencewise_mean_image_subtraction(dct_feats, video_lens)
train_subject_ids = read_data_split_file('data/train_val.txt')
test_subject_ids = read_data_split_file('data/test.txt')
print(train_subject_ids)
print(test_subject_ids)
train_X, train_y, train_dct, train_X_diff, train_vidlens, train_subjects, \
test_X, test_y, test_dct, test_X_diff, test_vidlens, test_subjects = \
split_data(X, y, dct_feats, X_diff, subjects, video_lens, train_subject_ids, test_subject_ids)
assert train_X.shape[0] + test_X.shape[0] == len(X)
assert train_y.shape[0] + test_y.shape[0] == len(y)
assert train_vidlens.shape[0] + test_vidlens.shape[0] == len(video_lens)
assert train_subjects.shape[0] + test_subjects.shape[0] == len(subjects)
train_X = normalize_input(train_X, centralize=True)
test_X = normalize_input(test_X, centralize=True)
# featurewise normalize dct features
train_dct, dct_mean, dct_std = featurewise_normalize_sequence(train_dct)
test_dct = (test_dct - dct_mean) / dct_std
if do_finetune:
print('performing finetuning on pretrained encoder: {}'.format(ae_pretrained))
ae = load_dbn(ae_pretrained)
ae.initialize()
ae.fit(train_X, train_X)
if save_finetune:
print('saving finetuned encoder: {}...'.format(ae_finetuned))
pickle.dump(ae, open(ae_finetuned, 'wb'))
if load_finetune:
print('loading finetuned encoder: {}...'.format(ae_finetuned))
ae = pickle.load(open(ae_finetuned, 'rb'))
ae.initialize()
if load_finetune_diff:
print('loading finetuned encoder: {}...'.format(ae_finetuned_diff))
ae_diff = pickle.load(open(ae_finetuned_diff, 'rb'))
ae_diff.initialize()
# IMPT: the encoder was trained with fortan ordered images, so to visualize
# convert all the images to C order using reshape_images_order()
# output = dbn.predict(test_X)
# test_X = reshape_images_order(test_X, (26, 44))
# output = reshape_images_order(output, (26, 44))
# visualize_reconstruction(test_X[:36, :], output[:36, :], shape=(26, 44))
window = T.iscalar('theta')
dct = T.tensor3('dct', dtype='float32')
inputs = T.tensor3('inputs', dtype='float32')
inputs_diff = T.tensor3('inputs_diff', dtype='float32')
mask = T.matrix('mask', dtype='uint8')
targets = T.ivector('targets')
lr = theano.shared(np.array(learning_rate, dtype=theano.config.floatX), name='learning_rate')
lr_decay = np.array(decay_rate, dtype=theano.config.floatX)
print('constructing end to end model...')
'''
network = create_end_to_end_model(dbn, (None, None, 1144), inputs,
(None, None), mask, 250, window)
'''
network, adascale = adenet_v5.create_model(ae, ae_diff, (None, None, 1144), inputs,
(None, None), mask,
(None, None, 90), dct,
(None, None, 1144), inputs_diff,
250, window, 10, use_adascale)
print_network(network)
print('compiling model...')
predictions = las.layers.get_output(network, deterministic=False)
all_params = las.layers.get_all_params(network, trainable=True)
cost = T.mean(las.objectives.categorical_crossentropy(predictions, targets))
updates = adadelta(cost, all_params, learning_rate=lr)
# updates = adagrad(cost, all_params, learning_rate=lr)
use_max_constraint = False
if use_max_constraint:
MAX_NORM = 4
for param in las.layers.get_all_params(network, regularizable=True):
if param.ndim > 1: # only apply to dimensions larger than 1, exclude biases
updates[param] = norm_constraint(param, MAX_NORM * las.utils.compute_norms(param.get_value()).mean())
train = theano.function(
[inputs, targets, mask, dct, inputs_diff, window],
cost, updates=updates, allow_input_downcast=True)
compute_train_cost = theano.function([inputs, targets, mask, dct, inputs_diff, window],
cost, allow_input_downcast=True)
test_predictions = las.layers.get_output(network, deterministic=True)
test_cost = T.mean(las.objectives.categorical_crossentropy(test_predictions, targets))
compute_test_cost = theano.function(
[inputs, targets, mask, dct, inputs_diff, window], test_cost, allow_input_downcast=True)
val_fn = theano.function([inputs, mask, dct, inputs_diff, window], test_predictions, allow_input_downcast=True)
# We'll train the network with 10 epochs of 30 minibatches each
print('begin training...')
cost_train = []
cost_val = []
class_rate = []
NUM_EPOCHS = 30
EPOCH_SIZE = 120
BATCH_SIZE = 10
WINDOW_SIZE = 9
STRIP_SIZE = 3
MAX_LOSS = 0.2
VALIDATION_WINDOW = 4
val_window = circular_list(VALIDATION_WINDOW)
train_strip = np.zeros((STRIP_SIZE,))
best_val = float('inf')
best_conf = None
best_cr = 0.0
datagen = gen_lstm_batch_random(train_X, train_y, train_vidlens, batchsize=BATCH_SIZE)
val_datagen = gen_lstm_batch_random(test_X, test_y, test_vidlens,
batchsize=len(test_vidlens))
integral_lens = compute_integral_len(train_vidlens)
# We'll use this "validation set" to periodically check progress
X_val, y_val, mask_val, idxs_val = next(val_datagen)
integral_lens_val = compute_integral_len(test_vidlens)
dct_val = gen_seq_batch_from_idx(test_dct, idxs_val, test_vidlens, integral_lens_val, np.max(test_vidlens))
X_diff_val = gen_seq_batch_from_idx(test_X_diff, idxs_val, test_vidlens, integral_lens_val, np.max(test_vidlens))
def early_stop(cost_window):
if len(cost_window) < 2:
return False
else:
curr = cost_window[0]
for idx, cost in enumerate(cost_window):
if curr < cost or idx == 0:
curr = cost
else:
return False
return True
for epoch in range(NUM_EPOCHS):
time_start = time.time()
for i in range(EPOCH_SIZE):
X, y, m, batch_idxs = next(datagen)
d = gen_seq_batch_from_idx(train_dct, batch_idxs,
train_vidlens, integral_lens, np.max(train_vidlens))
X_diff = gen_seq_batch_from_idx(train_X_diff, batch_idxs,
train_vidlens, integral_lens, np.max(train_vidlens))
print_str = 'Epoch {} batch {}/{}: {} examples at learning rate = {:.4f}'.format(
epoch + 1, i + 1, EPOCH_SIZE, len(X), float(lr.get_value()))
print(print_str, end='')
sys.stdout.flush()
train(X, y, m, d, X_diff, WINDOW_SIZE)
print('\r', end='')
cost = compute_train_cost(X, y, m, d, X_diff, WINDOW_SIZE)
val_cost = compute_test_cost(X_val, y_val, mask_val, dct_val, X_diff_val, WINDOW_SIZE)
cost_train.append(cost)
cost_val.append(val_cost)
train_strip[epoch % STRIP_SIZE] = cost
val_window.push(val_cost)
gl = 100 * (cost_val[-1] / np.min(cost_val) - 1)
pk = 1000 * (np.sum(train_strip) / (STRIP_SIZE * np.min(train_strip)) - 1)
pq = gl / pk
cr, val_conf = evaluate_model(X_val, y_val, mask_val, dct_val, X_diff_val, WINDOW_SIZE, val_fn)
class_rate.append(cr)
print("Epoch {} train cost = {}, validation cost = {}, "
"generalization loss = {:.3f}, GQ = {:.3f}, classification rate = {:.3f} ({:.1f}sec)"
.format(epoch + 1, cost_train[-1], cost_val[-1], gl, pq, cr, time.time() - time_start))
if val_cost < best_val:
best_val = val_cost
best_conf = val_conf
best_cr = cr
if use_adascale:
adascale_param = las.layers.get_all_param_values(adascale, scaling_param=True)
if epoch >= VALIDATION_WINDOW and early_stop(val_window):
break
# learning rate decay
if epoch >= decay_start - 1:
lr.set_value(lr.get_value() * lr_decay)
phrases = ['p1', 'p2', 'p3', 'p4', 'p5', 'p6', 'p7', 'p8', 'p9', 'p10']
print('Final Model')
print('classification rate: {}, validation loss: {}'.format(best_cr, best_val))
if use_adascale:
print("final scaling params: {}".format(adascale_param))
print('confusion matrix: ')
plot_confusion_matrix(best_conf, phrases, fmt='grid')
plot_validation_cost(cost_train, cost_val, class_rate, savefilename='valid_cost')
def construct_lstm(input_size, lstm_size, output_size, train_data_gen, val_data_gen):
# All gates have initializers for the input-to-gate and hidden state-to-gate
# weight matrices, the cell-to-gate weight vector, the bias vector, and the nonlinearity.
# The convention is that gates use the standard sigmoid nonlinearity,
# which is the default for the Gate class.
gate_parameters = Gate(
W_in=las.init.Orthogonal(), W_hid=las.init.Orthogonal(),
b=las.init.Constant(0.))
cell_parameters = Gate(
W_in=las.init.Orthogonal(), W_hid=las.init.Orthogonal(),
# Setting W_cell to None denotes that no cell connection will be used.
W_cell=None, b=las.init.Constant(0.),
# By convention, the cell nonlinearity is tanh in an LSTM.
nonlinearity=tanh)
# prepare the input layers
# By setting the first and second dimensions to None, we allow
# arbitrary minibatch sizes with arbitrary sequence lengths.
# The number of feature dimensions is 150, as described above.
l_in = InputLayer(shape=(None, None, input_size), name='input')
# This input will be used to provide the network with masks.
# Masks are expected to be matrices of shape (n_batch, n_time_steps);
# both of these dimensions are variable for us so we will use
# an input shape of (None, None)
l_mask = InputLayer(shape=(None, None), name='mask')
# Our LSTM will have 250 hidden/cell units
N_HIDDEN = lstm_size
l_lstm = LSTMLayer(
l_in, N_HIDDEN,
# We need to specify a separate input for masks
mask_input=l_mask,
# Here, we supply the gate parameters for each gate
ingate=gate_parameters, forgetgate=gate_parameters,
cell=cell_parameters, outgate=gate_parameters,
# We'll learn the initialization and use gradient clipping
learn_init=True, grad_clipping=5., name='lstm1')
'''
# The "backwards" layer is the same as the first,
# except that the backwards argument is set to True.
l_lstm_back = LSTMLayer(
l_in, N_HIDDEN, ingate=gate_parameters,
mask_input=l_mask, forgetgate=gate_parameters,
cell=cell_parameters, outgate=gate_parameters,
learn_init=True, grad_clipping=5., backwards=True)
# We'll combine the forward and backward layer output by summing.
# Merge layers take in lists of layers to merge as input.
l_sum = ElemwiseSumLayer([l_lstm, l_lstm_back])
# implement drop-out regularization
l_dropout = DropoutLayer(l_sum)
l_lstm2 = LSTMLayer(
l_dropout, N_HIDDEN,
# We need to specify a separate input for masks
mask_input=l_mask,
# Here, we supply the gate parameters for each gate
ingate=gate_parameters, forgetgate=gate_parameters,
cell=cell_parameters, outgate=gate_parameters,
# We'll learn the initialization and use gradient clipping
learn_init=True, grad_clipping=5.)
# The "backwards" layer is the same as the first,
# except that the backwards argument is set to True.
l_lstm_back2 = LSTMLayer(
l_dropout, N_HIDDEN, ingate=gate_parameters,
mask_input=l_mask, forgetgate=gate_parameters,
cell=cell_parameters, outgate=gate_parameters,
learn_init=True, grad_clipping=5., backwards=True)
# We'll combine the forward and backward layer output by summing.
# Merge layers take in lists of layers to merge as input.
l_sum2 = ElemwiseSumLayer([l_lstm2, l_lstm_back2])
'''
# The l_forward layer creates an output of dimension (batch_size, SEQ_LENGTH, N_HIDDEN)
# Since we are only interested in the final prediction, we isolate that quantity and feed it to the next layer.
# The output of the sliced layer will then be of size (batch_size, N_HIDDEN)
l_forward_slice = SliceLayer(l_lstm, -1, 1, name='slice')
# Now, we can apply feed-forward layers as usual.
# We want the network to predict a classification for the sequence,
# so we'll use a the number of classes.
l_out = DenseLayer(
l_forward_slice, num_units=output_size, nonlinearity=las.nonlinearities.softmax, name='output')
print_network(l_out)
# draw_to_file(las.layers.get_all_layers(l_out), 'network.png')
# Symbolic variable for the target network output.
# It will be of shape n_batch, because there's only 1 target value per sequence.
target_values = T.ivector('target_output')
# This matrix will tell the network the length of each sequences.
# The actual values will be supplied by the gen_data function.
mask = T.matrix('mask')
# lasagne.layers.get_output produces an expression for the output of the net
prediction = las.layers.get_output(l_out)
# The value we care about is the final value produced for each sequence
# so we simply slice it out.
# predicted_values = network_output[:, -1]
# Our cost will be categorical cross entropy error
cost = T.mean(las.objectives.categorical_crossentropy(prediction, target_values))
# cost = T.mean((predicted_values - target_values) ** 2)
# Retrieve all parameters from the network
all_params = las.layers.get_all_params(l_out, trainable=True)
# Compute adam updates for training
# updates = las.updates.adam(cost, all_params)
updates = adadelta(cost, all_params)
# Theano functions for training and computing cost
train = theano.function(
[l_in.input_var, target_values, l_mask.input_var],
cost, updates=updates, allow_input_downcast=True)
compute_train_cost = theano.function(
[l_in.input_var, target_values, l_mask.input_var], cost, allow_input_downcast=True)
test_prediction = las.layers.get_output(l_out, deterministic=True)
test_cost = T.mean(las.objectives.categorical_crossentropy(test_prediction, target_values))
compute_val_cost = theano.function([l_in.input_var, target_values, l_mask.input_var],
test_cost, allow_input_downcast=True)
val_fn = theano.function([l_in.input_var, l_mask.input_var], test_prediction, allow_input_downcast=True)
# We'll use this "validation set" to periodically check progress
X_val, y_val, mask_val = next(val_data_gen)
# We'll train the network with 10 epochs of 100 minibatches each
cost_train = []
cost_val = []
class_rate = []
best_val = float('inf')
best_conf = None
best_cr = 0.0
NUM_EPOCHS = 30
EPOCH_SIZE = 26
STRIP_SIZE = 3
MAX_LOSS = 0.05
VALIDATION_WINDOW = 4
val_window = circular_list(VALIDATION_WINDOW)
train_strip = np.zeros((STRIP_SIZE,))
def early_stop(cost_window):
if len(cost_window) < 2:
return False
else:
curr = cost_window[0]
for idx, cost in enumerate(cost_window):
if curr < cost or idx == 0:
curr = cost
else:
return False
return True
for epoch in range(NUM_EPOCHS):
time_start = time.time()
for _ in range(EPOCH_SIZE):
X, y, m, _ = next(train_data_gen)
train(X, y, m)
train_cost = compute_train_cost(X, y, m)
val_cost = compute_val_cost(X_val, y_val, mask_val)
cr, conf = evaluate_model(X_val, y_val, mask_val, val_fn)
cost_train.append(train_cost)
cost_val.append(val_cost)
class_rate.append(cr)
train_strip[epoch % STRIP_SIZE] = train_cost
val_window.push(val_cost)
gl = 100 * (cost_val[-1] / np.min(cost_val) - 1)
pk = 1000 * (np.sum(train_strip) / (STRIP_SIZE * np.min(train_strip)) - 1)
pq = gl / pk
print("Epoch {} train cost = {}, validation cost = {}, "
"generalization loss = {:.3f}, GQ = {:.3f}, classification rate = {:.3f} ({:.1f}sec)"
.format(epoch + 1, cost_train[-1], cost_val[-1], gl, pq, cr, time.time() - time_start))
if val_cost < best_val:
best_val = val_cost
best_cr = cr
best_conf = conf
if epoch >= VALIDATION_WINDOW and early_stop(val_window):
break
letters = ['a', 'b', 'c', 'd', 'e', 'f', 'g',
'h', 'i', 'j', 'k', 'l', 'm', 'n',
'o', 'p', 'q', 'r', 's', 't', 'u',
'v', 'w', 'x', 'y', 'z']
print('Final Model')
print('classification rate: {}'.format(best_cr))
print('validation loss: {}'.format(best_val))
print('confusion matrix: ')
plot_confusion_matrix(best_conf, letters, fmt='grid')
plot_validation_cost(cost_train, cost_val, class_rate)
def main():
configure_theano()
config_file = 'config/separate_train.ini'
print('loading config file: {}'.format(config_file))
config = ConfigParser.ConfigParser()
config.read(config_file)
print('preprocessing dataset...')
data = load_mat_file(config.get('data', 'images'))
ae_pretrained = config.get('models', 'pretrained')
ae_finetuned = config.get('models', 'finetuned')
learning_rate = float(config.get('training', 'learning_rate'))
decay_rate = float(config.get('training', 'decay_rate'))
decay_start = int(config.get('training', 'decay_start'))
lstm_units = int(config.get('training', 'lstm_units'))
output_units = int(config.get('training', 'output_units'))
do_finetune = config.getboolean('training', 'do_finetune')
save_finetune = config.getboolean('training', 'save_finetune')
load_finetune = config.getboolean('training', 'load_finetune')
# 53 subjects, 70 utterances, 5 view angles
# s[x]_v[y]_u[z].mp4
# resized, height, width = (26, 44)
# ['dataMatrix', 'targetH', 'targetsPerVideoVec', 'videoLengthVec', '__header__', 'targetsVec',
# '__globals__', 'iterVec', 'filenamesVec', 'dataMatrixCells', 'subjectsVec', 'targetW', '__version__']
print(data.keys())
X = data['dataMatrix'].astype(
'float32') # .reshape((-1, 26, 44), order='f').reshape((-1, 26 * 44))
y = data['targetsVec'].astype('int32')
y = y.reshape((len(y), ))
uniques = np.unique(y)
print('number of classifications: {}'.format(len(uniques)))
subjects = data['subjectsVec'].astype('int')
subjects = subjects.reshape((len(subjects), ))
video_lens = data['videoLengthVec'].astype('int')
video_lens = video_lens.reshape((len(video_lens, )))
train_subject_ids = read_data_split_file('data/train.txt')
val_subject_ids = read_data_split_file('data/val.txt')
test_subject_ids = read_data_split_file('data/test.txt')
print('Train: {}'.format(train_subject_ids))
print('Validation: {}'.format(val_subject_ids))
print('Test: {}'.format(test_subject_ids))
train_X, train_y, train_vidlens, train_subjects, \
val_X, val_y, val_vidlens, val_subjects, \
test_X, test_y, test_vidlens, test_subjects = \
split_data(X, y, subjects, video_lens, train_subject_ids, val_subject_ids, test_subject_ids)
assert train_X.shape[0] + val_X.shape[0] + test_X.shape[0] == len(X)
assert train_y.shape[0] + val_y.shape[0] + test_y.shape[0] == len(y)
assert train_vidlens.shape[0] + val_vidlens.shape[0] + test_vidlens.shape[
0] == len(video_lens)
assert train_subjects.shape[0] + val_subjects.shape[
0] + test_subjects.shape[0] == len(subjects)
train_X = normalize_input(train_X, centralize=True)
test_X = normalize_input(test_X, centralize=True)
if do_finetune:
dbn = load_dbn(ae_pretrained)
dbn.initialize()
dbn.fit(train_X, train_X)
recon = dbn.predict(test_X)
visualize_reconstruction(reorder_data(test_X[800:864], (26, 44)),
reorder_data(recon[800:864], (26, 44)),
shape=(26, 44))
if save_finetune:
pickle.dump(dbn, open(ae_finetuned, 'wb'))
if load_finetune:
print('loading pre-trained encoding layers...')
dbn = pickle.load(open(ae_finetuned, 'rb'))
dbn.initialize()
# recon = dbn.predict(test_X)
# visualize_reconstruction(reorder_data(test_X[800:864], (26, 44)),
# reorder_data(recon[800:864], (26, 44)),
# shape=(26, 44))
encoder = extract_encoder(dbn)
train_X = encoder.predict(train_X)
val_X = encoder.predict(val_X)
test_X = encoder.predict(test_X)
# train_X = concat_first_second_deltas(train_X, train_vidlens)
# val_X = concat_first_second_deltas(val_X, val_vidlens)
# test_X = concat_first_second_deltas(test_X, test_vidlens)
# featurewise normalize
train_X, mean, std = featurewise_normalize_sequence(train_X)
val_X = (val_X - mean) / std
test_X = (test_X - mean) / std
# recon = dbn.predict(test_X)
# visualize_reconstruction(test_X[550:650], recon[550:650], (26, 44))
# exit()
# IMPT: the encoder was trained with fortan ordered images, so to visualize
# convert all the images to C order using reshape_images_order()
# output = dbn.predict(test_X)
# test_X = reshape_images_order(test_X, (26, 44))
# output = reshape_images_order(output, (26, 44))
# visualize_reconstruction(test_X[:36, :], output[:36, :], shape=(26, 44))
inputs = T.tensor3('inputs', dtype='float32')
mask = T.matrix('mask', dtype='uint8')
targets = T.ivector('targets')
lr = theano.shared(np.array(learning_rate, dtype=theano.config.floatX),
name='learning_rate')
lr_decay = np.array(decay_rate, dtype=theano.config.floatX)
print('constructing lstm classifier...')
network = lstm_classifier_baseline.create_model(
(None, None, 50), inputs, (None, None), mask, lstm_units, output_units)
print_network(network)
print('compiling model...')
predictions = las.layers.get_output(network, deterministic=False)
all_params = las.layers.get_all_params(network, trainable=True)
cost = T.mean(las.objectives.categorical_crossentropy(
predictions, targets))
updates = adadelta(cost, all_params, learning_rate=lr)
# updates = las.updates.apply_momentum(sgd(cost, all_params, learning_rate=lr), all_params, 0.1)
use_max_constraint = False
if use_max_constraint:
MAX_NORM = 4
for param in las.layers.get_all_params(network, regularizable=True):
if param.ndim > 1: # only apply to dimensions larger than 1, exclude biases
updates[param] = norm_constraint(
param,
MAX_NORM *
las.utils.compute_norms(param.get_value()).mean())
train = theano.function([inputs, targets, mask],
cost,
updates=updates,
allow_input_downcast=True)
compute_train_cost = theano.function([inputs, targets, mask],
cost,
allow_input_downcast=True)
test_predictions = las.layers.get_output(network, deterministic=True)
test_cost = T.mean(
las.objectives.categorical_crossentropy(test_predictions, targets))
compute_test_cost = theano.function([inputs, targets, mask],
test_cost,
allow_input_downcast=True)
val_fn = theano.function([inputs, mask],
test_predictions,
allow_input_downcast=True)
# We'll train the network with 10 epochs of 30 minibatches each
print('begin training...')
cost_train = []
cost_val = []
class_rate = []
NUM_EPOCHS = 30
EPOCH_SIZE = 120
BATCH_SIZE = 10
STRIP_SIZE = 3
MAX_LOSS = 0.2
VALIDATION_WINDOW = 10
val_window = circular_list(VALIDATION_WINDOW)
train_strip = np.zeros((STRIP_SIZE, ))
best_val = float('inf')
best_conf = None
best_cr = 0.0
datagen = gen_lstm_batch_random(train_X,
train_y,
train_vidlens,
batchsize=BATCH_SIZE)
val_datagen = gen_lstm_batch_random(val_X,
val_y,
val_vidlens,
batchsize=len(val_vidlens))
test_datagen = gen_lstm_batch_random(test_X,
test_y,
test_vidlens,
batchsize=len(test_vidlens))
# We'll use this "validation set" to periodically check progress
X_val, y_val, mask_val, _ = next(val_datagen)
X_test, y_test, mask_test, _ = next(test_datagen)
def early_stop(cost_window):
if len(cost_window) < 2:
return False
else:
curr = cost_window[0]
for idx, cost in enumerate(cost_window):
if curr < cost or idx == 0:
curr = cost
else:
return False
return True
for epoch in range(NUM_EPOCHS):
time_start = time.time()
for i in range(EPOCH_SIZE):
X, y, m, _ = next(datagen)
print_str = 'Epoch {} batch {}/{}: {} examples at learning rate = {:.4f}'.format(
epoch + 1, i + 1, EPOCH_SIZE, len(X), float(lr.get_value()))
print(print_str, end='')
sys.stdout.flush()
train(X, y, m)
print('\r', end='')
cost = compute_train_cost(X, y, m)
val_cost = compute_test_cost(X_val, y_val, mask_val)
cost_train.append(cost)
cost_val.append(val_cost)
train_strip[epoch % STRIP_SIZE] = cost
val_window.push(val_cost)
gl = 100 * (cost_val[-1] / np.min(cost_val) - 1)
pk = 1000 * (np.sum(train_strip) /
(STRIP_SIZE * np.min(train_strip)) - 1)
pq = gl / pk
cr, val_conf = evaluate_model(X_val, y_val, mask_val, val_fn)
class_rate.append(cr)
if val_cost < best_val:
best_val = val_cost
best_conf = val_conf
best_cr = cr
test_cr, test_conf = evaluate_model(X_test, y_test, mask_test,
val_fn)
print(
"Epoch {} train cost = {}, val cost = {}, "
"GL loss = {:.3f}, GQ = {:.3f}, CR = {:.3f}, Test CR= {:.3f} ({:.1f}sec)"
.format(epoch + 1, cost_train[-1], cost_val[-1], gl, pq, cr,
test_cr,
time.time() - time_start))
else:
print("Epoch {} train cost = {}, val cost = {}, "
"GL loss = {:.3f}, GQ = {:.3f}, CR = {:.3f} ({:.1f}sec)".
format(epoch + 1, cost_train[-1], cost_val[-1], gl, pq, cr,
time.time() - time_start))
if epoch >= VALIDATION_WINDOW and early_stop(val_window):
break
# learning rate decay
if epoch > decay_start:
lr.set_value(lr.get_value() * lr_decay)
phrases = ['p1', 'p2', 'p3', 'p4', 'p5', 'p6', 'p7', 'p8', 'p9', 'p10']
print('Final Model')
print('CR: {}, val loss: {}, Test CR: {}'.format(best_cr, best_val,
test_cr))
print('confusion matrix: ')
plot_confusion_matrix(test_conf, phrases, fmt='grid')
plot_validation_cost(cost_train, cost_val, savefilename='valid_cost')
def model_class(ds, paths, param_arch, param_cost, param_updates, param_train):
# create a log file containing the architecture configuration
formatter = logging.Formatter('%(message)s')
logger = logging.getLogger('log_config')
if 'start_from_epoch' in param_train:
name_tmp = 'config_from_epoch=%04d.log' % (
param_train['start_from_epoch'])
else:
name_tmp = 'config.log'
path_tmp = os.path.join(paths['exp'], name_tmp)
if not os.path.isfile(path_tmp):
handler = logging.FileHandler(
path_tmp,
mode='w') # to append at the end of the file use: mode='a'
else:
raise Exception('[e] the log file ', name_tmp, ' already exists!')
handler.setFormatter(formatter)
handler.setLevel(logging.INFO)
logger.addHandler(handler)
logger.setLevel(logging.INFO)
# input dimensions
dim_desc = ds.descs_train[0].shape[1]
dim_labels = ds.labels_train[0].shape[0]
print(dim_labels)
# architecture definition:
print(("[i] architecture definition... "), end=' ')
tic = time.time()
if param_arch['type'] == 0:
desc, patch_op, cla, net, logger = arch_class_00(
dim_desc, dim_labels, param_arch, logger)
elif param_arch['type'] == 1:
desc, patch_op, cla, net, logger = arch_class_01(
dim_desc, dim_labels, param_arch, logger)
elif param_arch['type'] == 2:
desc, patch_op, cla, net, logger = arch_class_02(
dim_desc, dim_labels, param_arch, logger)
else:
raise Exception('[e] architecture not supported!')
print(("%02.2fs" % (time.time() - tic)))
# cost function definition:
print(("[i] cost function definition... "), end=' ')
tic = time.time()
pred = LL.get_output(cla, deterministic=True) # in case we use dropout
feat = LL.get_output(net)
target = T.ivector('target')
# data term
if param_cost['cost_func'] == 'cross_entropy':
if param_arch['non_linearity'] == 'softmax':
cost_dataterm = T.mean(
LO.categorical_crossentropy(pred, target)
) # in the original code we were using *.mean() instead of T.mean(*)
elif param_arch['non_linearity'] == 'log_softmax':
cost_dataterm = T.mean(
categorical_crossentropy_logdomain(pred, target))
elif param_cost['cost_func'] == 'cross_entropy_stable':
if param_arch['non_linearity'] == 'softmax':
cost_dataterm = T.mean(
categorical_crossentropy_stable(pred, target))
else:
raise Exception(
'[e] the chosen cost function is not implemented for the chosen non-linearity!'
)
else:
raise Exception('[e] the chosen cost function is not supported!')
# classification accuracy
acc = LO.categorical_accuracy(pred, target).mean()
# regularization
cost_reg = param_cost['mu'] * LR.regularize_network_params(cla, LR.l2)
# cost function
cost = cost_dataterm + cost_reg
# get params
params = LL.get_all_params(cla)
# gradient definition
grad = T.grad(cost, params)
grad_norm = T.nlinalg.norm(T.concatenate([g.flatten() for g in grad]), 2)
print(("%02.2fs" % (time.time() - tic)))
# updates definition:
print(("[i] gradient updates definition... "), end=' ')
tic = time.time()
if param_updates['method'] == 'momentum':
if param_updates.get('learning_rate') is not None:
learning_rate = param_updates['learning_rate'] # default: 1.0
else:
raise Exception('[e] missing learning_rate parameter!')
if param_updates.get('momentum') is not None:
momentum = param_updates['momentum'] # default: 0.9
else:
raise Exception('[e] missing learning_rate parameter!')
updates = LU.momentum(grad, params, learning_rate, momentum)
elif param_updates['method'] == 'adagrad':
if param_updates.get('learning_rate') is not None:
learning_rate = param_updates['learning_rate'] # default: 1.0
else:
raise Exception('[e] missing learning_rate parameter!')
updates = LU.adagrad(grad, params, learning_rate)
elif param_updates['method'] == 'adadelta':
if param_updates.get('learning_rate') is not None:
learning_rate = param_updates['learning_rate'] # default: 1.0
else:
raise Exception('[e] missing learning_rate parameter!')
updates = LU.adadelta(grad, params, learning_rate)
elif param_updates['method'] == 'adam':
if param_updates.get('learning_rate') is not None:
learning_rate = param_updates['learning_rate'] # default: 1e-03
else:
raise Exception('[e] missing learning_rate parameter!')
if param_updates.get('beta1') is not None:
beta1 = param_updates['beta1'] # default: 0.9
else:
raise Exception('[e] missing beta1 parameter!')
if param_updates.get('beta2') is not None:
beta2 = param_updates['beta2'] # default: 0.999
else:
raise Exception('[e] missing beta2 parameter!')
if param_updates.get('epsilon') is not None:
epsilon = param_updates['epsilon'] # default: 1e-08
else:
raise Exception('[e] missing epsilon parameter!')
updates = LU.adam(grad, params, learning_rate, beta1, beta2, epsilon)
else:
raise Exception('[e] updates method not supported!')
print(("%02.2fs" % (time.time() - tic)))
# train / test functions:
funcs = dict()
print(("[i] compiling function 'train'... "), end=' ')
tic = time.time()
funcs['train'] = theano.function(
[desc.input_var, patch_op.input_var, target],
[cost, cost_dataterm, cost_reg, grad_norm, acc],
updates=updates,
allow_input_downcast=True,
on_unused_input='warn')
print(("%02.2fs" % (time.time() - tic)))
print(("[i] compiling function 'fwd'... "), end=' ')
tic = time.time()
funcs['fwd'] = theano.function(
[desc.input_var, patch_op.input_var, target], [cost, grad_norm, acc],
allow_input_downcast=True,
on_unused_input='ignore')
print(("%02.2fs" % (time.time() - tic)))
print(("[i] compiling function 'pred'... "), end=' ')
tic = time.time()
funcs['pred'] = theano.function(
[desc.input_var, patch_op.input_var, target], [pred],
allow_input_downcast=True,
on_unused_input='ignore')
print(("%02.2fs" % (time.time() - tic)))
print(("[i] compiling function 'feat'... "), end=' ')
tic = time.time()
funcs['feat'] = theano.function(
[desc.input_var, patch_op.input_var, target], [feat],
allow_input_downcast=True,
on_unused_input='ignore')
print(("%02.2fs" % (time.time() - tic)))
# save cost function parameters to a config file
logger.info('\nCost function parameters:')
logger.info(' cost function = %s' % param_cost['cost_func'])
logger.info(' mu = %e' % param_cost['mu'])
# save updates parameters to a config file
logger.info('\nUpdates parameters:')
logger.info(' method = %s' % param_updates['method'])
logger.info(' learning rate = %e' % param_updates['learning_rate'])
if param_updates['method'] == 'momentum':
logger.info(' momentum = %e' % param_updates['momentum'])
if param_updates['method'] == 'adam':
logger.info(' beta1 = %e' % param_updates['beta1'])
logger.info(' beta2 = %e' % param_updates['beta2'])
logger.info(' epsilon = %e' % param_updates['epsilon'])
# save training parameters to a config file
logger.info('\nTraining parameters:')
logger.info(' epoch size = %d' % ds.epoch_size)
return funcs, cla, updates
def __init__(self,
retina_model,
seeder_model,
n_seeds,
n_steps,
n_units=100,
normalization_coefs=None,
loss_coefs=None,
alpha=1.0,
threshold=1.0):
self.seeder_model = seeder_model
self.n_seeds = n_seeds
self.n_steps = n_steps
self.threshold = threshold
self.retina = retina_model
event_shareds = retina_model.get_event_variables()
self.seeder = self.seeder_model(retina_model)
if normalization_coefs is None:
normalization_coefs = np.ones(shape=retina_model.model_nparams,
dtype='float32')
else:
normalization_coefs = np.array(normalization_coefs,
dtype='float32')
### params + sigma
self.inputs = retina_model.alloc_model_params()
self.input_layer, self.out_layer, self.reg = self.build_nn(
retina_model.model_nparams, n_units=n_units)
print 'Linking to Retina Model'
iterations = [self.inputs]
responses = []
for i in xrange(self.n_steps):
print 'Iteration %d' % i
prev = iterations[i]
r, grads = retina_model.grad_for(*event_shareds + prev)
normed_params = [p * c for p, c in zip(prev, normalization_coefs)]
normed_grads = [g * c for g, c in zip(grads, normalization_coefs)]
out = self.get_update_for(normed_params, r, normed_grads)
param_updates = [out[:, i] for i in range(len(self.inputs))]
track_param_updates, sigma_update = param_updates[:
-1], param_updates[
-1]
### sigma (last parameter) is updated simply by replacing
### previous variable
update = [
var + upd * alpha
for var, upd in zip(prev[:-1], track_param_updates)
] + [T.exp(-sigma_update)]
for var, upd, new in zip(prev[:-1], track_param_updates, update):
print ' -', new, '=', var, '+ %.2e' % alpha, upd
iterations.append(update)
responses.append(r)
prediction = iterations[-1]
sigma_train = T.fscalar('sigma_train')
### Except sigma
self.true_parameters_shareds = [
theano.shared(np.ndarray(shape=(0, ), dtype='float32'), name=name)
for name in retina_model.model_params_names[:-1]
]
### predictions without sigma
print 'Constucting loss:'
print ' - Loss coefs:', loss_coefs
print ' - True params shared:', self.true_parameters_shareds
print ' - Predictions:', prediction[:-1]
print ' - Sigma:', sigma_train
pure_response, rmse = retina_model.parameter_response(
loss_coefs,
*self.true_parameters_shareds + prediction[:-1] + [sigma_train])
pure_loss = 1.0 - pure_response
initial_response, initial_rmse = retina_model.parameter_response(
loss_coefs,
*self.true_parameters_shareds + self.inputs[:-1] + [sigma_train])
initial_loss = 1.0 - initial_response
reg_c = T.fscalar('reg_c')
alpha_rmse = T.fscalar('reg_c')
loss = (1.0 -
alpha_rmse) * pure_loss + alpha_rmse * rmse + reg_c * self.reg
params = layers.get_all_params(self.out_layer)
learning_rate = T.fscalar('learning rate')
net_updates = updates.adadelta(loss,
params,
learning_rate=learning_rate)
self._train = theano.function(
self.inputs + [sigma_train, learning_rate, reg_c, alpha_rmse],
[pure_loss, rmse, self.reg, loss, initial_loss, initial_rmse],
updates=net_updates)
self._loss = theano.function(self.inputs + [sigma_train], pure_loss)
outputs = [v for it in iterations for v in it]
self.ndim = len(self.inputs)
self.predictions = theano.function(self.inputs, responses + outputs)
self.responses = None
self.traces = None
self.seeds = None
w_L3 = T.sum(alphas_L3[:, :, :, None, None] * basis_L3[None, None, :, :, :],
axis=2)
w_L4 = init_weights((3136, 10))
#-------------------------
# Set up function
#-------------------------
noise_l1, noise_l2, noise_l3, noise_py_x = model(X, w_L1, w_L2, w_L3, w_L4,
0.2, 0.7)
l1, l2, l3, py_x = model(X, w_L1, w_L2, w_L3, w_L4, 0., 0.)
y_x = T.argmax(py_x, axis=1)
cost = T.mean(T.nnet.categorical_crossentropy(noise_py_x, Y))
params = [alphas_L1, alphas_L2, alphas_L3, w_L4]
updates = adadelta(cost, params, learning_rate=lr, rho=0.95, epsilon=1e-6)
train = theano.function(inputs=[X, Y, lr],
outputs=cost,
updates=updates,
allow_input_downcast=True)
predict = theano.function(inputs=[X], outputs=y_x, allow_input_downcast=True)
#-------------------------
# Train model
#-------------------------
d = {}
batch_size = 25
epochs = args.epochs[0]
epoch_count = np.array(args.epochs).astype(int)
def build_treatment_model(self, n_vars, **kwargs):
input_vars = TT.matrix()
instrument_vars = TT.matrix()
targets = TT.vector()
inputs = layers.InputLayer((None, n_vars), input_vars)
inputs = layers.DropoutLayer(inputs, p=0.2)
dense_layer = layers.DenseLayer(inputs,
2 * kwargs['dense_size'],
nonlinearity=nonlinearities.rectify)
dense_layer = layers.batch_norm(dense_layer)
dense_layer = layers.DropoutLayer(dense_layer, p=0.2)
for _ in xrange(kwargs['n_dense_layers'] - 1):
dense_layer = layers.DenseLayer(
dense_layer,
kwargs['dense_size'],
nonlinearity=nonlinearities.rectify)
dense_layer = layers.batch_norm(dense_layer)
self.treatment_output = layers.DenseLayer(
dense_layer, 1, nonlinearity=nonlinearities.linear)
init_params = layers.get_all_param_values(self.treatment_output)
prediction = layers.get_output(self.treatment_output,
deterministic=False)
test_prediction = layers.get_output(self.treatment_output,
deterministic=True)
l2_cost = regularization.regularize_network_params(
self.treatment_output, regularization.l2)
loss = gmm_loss(prediction, targets, instrument_vars) + 1e-4 * l2_cost
params = layers.get_all_params(self.treatment_output, trainable=True)
param_updates = updates.adadelta(loss, params)
self._train_fn = theano.function([
input_vars,
targets,
instrument_vars,
],
loss,
updates=param_updates)
self._loss_fn = theano.function(
[
input_vars,
targets,
instrument_vars,
],
loss,
)
self._output_fn = theano.function(
[
input_vars,
],
test_prediction,
)
return init_params
def __init__(self, full_length, output_size, meta_size, depth=2, encoder_size=64, decoder_size=64):
latent_size = 16
input_var = TT.tensor3(dtype='float32')
meta_var = TT.tensor3(dtype='float32')
target_var = TT.matrix()
cut_weights = TT.vector(dtype='float32')
input_layer = layers.InputLayer((None, None, output_size), input_var=input_var)
meta_layer = layers.InputLayer((None, None, meta_size), input_var=meta_var)
meta_layer = layers.DropoutLayer(meta_layer, p=0.2)
concat_input_layer = layers.ConcatLayer([input_layer, meta_layer], axis=-1)
# encoder
lstm_layer = layers.RecurrentLayer(concat_input_layer, encoder_size / 2, learn_init=True)
lstm_layer = layers.RecurrentLayer(lstm_layer, encoder_size / 2, learn_init=True)
lstm_layer = layers.ReshapeLayer(lstm_layer, (-1, encoder_size / 2))
encoded = layers.DenseLayer(lstm_layer, latent_size)
encoded = layers.batch_norm(encoded)
dense = encoded
for idx in xrange(depth):
dense = layers.DenseLayer(dense, decoder_size)
dense = layers.batch_norm(dense)
mu_and_logvar_x_layer = layers.DenseLayer(dense, full_length * 2, nonlinearity=nonlinearities.linear)
mu_x_layer = layers.SliceLayer(mu_and_logvar_x_layer, slice(0, full_length), axis=1)
mu_x_layer = layers.ReshapeLayer(mu_x_layer, (-1, full_length, full_length))
logvar_x_layer = layers.SliceLayer(mu_and_logvar_x_layer, slice(full_length, None), axis=1)
logvar_x_layer = layers.ReshapeLayer(logvar_x_layer, (-1, full_length, full_length))
l2_norm = regularization.regularize_network_params(mu_and_logvar_x_layer, regularization.l2)
loss = neg_log_likelihood(
target_var,
layers.get_output(mu_x_layer, deterministic=False),
layers.get_output(logvar_x_layer, deterministic=False),
cut_weights
) + 1e-4 * l2_norm
test_loss = neg_log_likelihood(
target_var,
layers.get_output(mu_x_layer, deterministic=False),
layers.get_output(logvar_x_layer, deterministic=False),
cut_weights
) + 1e-4 * l2_norm
params = layers.get_all_params(mu_and_logvar_x_layer, trainable=True)
param_updates = updates.adadelta(loss.mean(), params)
self._train_fn = theano.function(
[input_var, meta_var, target_var, cut_weights],
updates=param_updates,
outputs=loss.mean()
)
self._loss_fn = theano.function(
[input_var, meta_var, target_var, cut_weights],
outputs=test_loss.mean()
)
self._predict_fn = theano.function(
[input_var, meta_var],
outputs=[
layers.get_output(mu_x_layer, deterministic=True),
layers.get_output(logvar_x_layer, deterministic=True)
]
)
