def build_fn(args, embeddings):
"""
Build training and testing functions.
"""
in_x1 = T.imatrix('x1')
in_x2 = T.imatrix('x2')
in_x3 = T.imatrix('x3')
in_mask1 = T.matrix('mask1')
in_mask2 = T.matrix('mask2')
in_mask3 = T.matrix('mask3')
in_y = T.ivector('y')
#batch x word_num x mea_num ( extra input)
in_x4 = T.ftensor3('x4')
l_in1 = lasagne.layers.InputLayer((None, None), in_x1)
l_mask1 = lasagne.layers.InputLayer((None, None), in_mask1)
l_emb1 = lasagne.layers.EmbeddingLayer(l_in1,
args.vocab_size,
args.embedding_size,
W=embeddings)
l_in2 = lasagne.layers.InputLayer((None, None), in_x2)
l_mask2 = lasagne.layers.InputLayer((None, None), in_mask2)
l_emb2 = lasagne.layers.EmbeddingLayer(l_in2,
args.vocab_size,
args.embedding_size,
W=l_emb1.W)
l_in3 = lasagne.layers.InputLayer((None, None), in_x3)
l_mask3 = lasagne.layers.InputLayer((None, None), in_mask3)
l_emb3 = lasagne.layers.EmbeddingLayer(l_in3,
args.vocab_size,
args.embedding_size,
W=l_emb1.W)
# extra
l_in4 = lasagne.layers.InputLayer((None, None, args.mea_num), in_x4)
if not args.tune_embedding:
l_emb1.params[l_emb1.W].remove('trainable')
l_emb2.params[l_emb2.W].remove('trainable')
l_emb3.params[l_emb3.W].remove('trainable')
args.rnn_output_size = args.hidden_size * 2 if args.bidir else args.hidden_size
assert args.model is None
network1 = nn_layers.stack_rnn(l_emb1,
l_mask1,
args.num_layers,
args.hidden_size,
grad_clipping=args.grad_clipping,
dropout_rate=args.dropout_rate,
only_return_final=(args.att_func == 'last'),
bidir=args.bidir,
name='d',
rnn_layer=args.rnn_layer)
network2 = nn_layers.stack_rnn(l_emb2,
l_mask2,
args.num_layers,
args.hidden_size,
grad_clipping=args.grad_clipping,
dropout_rate=args.dropout_rate,
only_return_final=True,
bidir=args.bidir,
name='q',
rnn_layer=args.rnn_layer)
if args.att_func == 'mlp':
att = nn_layers.MLPAttentionLayer([network1, network2],
args.rnn_output_size,
mask_input=l_mask1)
elif args.att_func == 'bilinear':
att = nn_layers.BilinearAttentionLayer([network1, network2],
args.rnn_output_size,
mask_input=l_mask1)
att_weightLayer = nn_layers.BilinearAttentionWeightLayer(
[network1, network2], args.rnn_output_size, mask_input=l_mask1)
elif args.att_func == 'avg':
att = nn_layers.AveragePoolingLayer(network1, mask_input=l_mask1)
elif args.att_func == 'last':
att = network1
elif args.att_func == 'dot':
att = nn_layers.DotProductAttentionLayer([network1, network2],
mask_input=l_mask1)
else:
raise NotImplementedError('att_func = %s' % args.att_func)
#weighted mean: passage embedding
l_weight = lasagne.layers.DenseLayer(l_in4, 1, num_leading_axes=-1)
att = nn_layers.WeightedAverageLayer([l_emb1, l_weight, l_mask1])
network3 = nn_layers.stack_rnn(l_emb3,
l_mask3,
args.num_layers,
args.hidden_size,
grad_clipping=args.grad_clipping,
dropout_rate=args.dropout_rate,
only_return_final=True,
bidir=args.bidir,
name='o',
rnn_layer=args.rnn_layer)
network3 = lasagne.layers.ReshapeLayer(
network3, (in_x1.shape[0], 4, args.rnn_output_size))
network = nn_layers.BilinearDotLayer([network3, att], args.rnn_output_size)
if args.pre_trained is not None:
dic = utils.load_params(args.pre_trained)
lasagne.layers.set_all_param_values(network, dic['params'])
del dic['params']
logging.info('Loaded pre-trained model: %s' % args.pre_trained)
for dic_param in dic.iteritems():
logging.info(dic_param)
logging.info('#params: %d' %
lasagne.layers.count_params(network, trainable=True))
logging.info('#fixed params: %d' %
lasagne.layers.count_params(network, trainable=False))
for layer in lasagne.layers.get_all_layers(network):
logging.info(layer)
# Test functions
test_prob = lasagne.layers.get_output(network, deterministic=True)
test_prediction = T.argmax(test_prob, axis=-1)
acc = T.sum(T.eq(test_prediction, in_y))
test_fn = theano.function([in_x1, in_mask1, in_x3, in_mask3, in_y, in_x4],
[acc, test_prediction],
on_unused_input='warn')
# Train functions
train_prediction = lasagne.layers.get_output(network)
train_prediction = T.clip(train_prediction, 1e-7, 1.0 - 1e-7)
loss = lasagne.objectives.categorical_crossentropy(train_prediction,
in_y).mean()
# Attention functions
att_weight = lasagne.layers.get_output(att_weightLayer, deterministic=True)
attention_fn = theano.function([in_x1, in_mask1, in_x2, in_mask2],
att_weight,
on_unused_input='warn')
# TODO: lasagne.regularization.regularize_network_params(network, lasagne.regularization.l2)
# params = lasagne.layers.get_all_params(network)#, trainable=True)
params = lasagne.layers.get_all_params(network, trainable=True)
all_params = lasagne.layers.get_all_params(network)
if args.optimizer == 'sgd':
updates = lasagne.updates.sgd(loss, params, args.learning_rate)
elif args.optimizer == 'adam':
updates = lasagne.updates.adam(loss,
params,
learning_rate=args.learning_rate)
elif args.optimizer == 'rmsprop':
updates = lasagne.updates.rmsprop(loss,
params,
learning_rate=args.learning_rate)
else:
raise NotImplementedError('optimizer = %s' % args.optimizer)
train_fn = theano.function([in_x1, in_mask1, in_x3, in_mask3, in_y, in_x4],
loss,
updates=updates,
on_unused_input='warn')
return train_fn, test_fn, params, all_params
def build_fn(args, embeddings):
"""
Build training and testing functions.
"""
in_x1 = T.imatrix('x1')
in_x2 = T.imatrix('x2')
in_mask1 = T.matrix('mask1')
in_mask2 = T.matrix('mask2')
in_l = T.matrix('l')
in_y = T.ivector('y')
l_in1 = lasagne.layers.InputLayer((None, None), in_x1)
l_mask1 = lasagne.layers.InputLayer((None, None), in_mask1)
l_emb1 = lasagne.layers.EmbeddingLayer(l_in1,
args.vocab_size,
args.embedding_size,
W=embeddings)
l_in2 = lasagne.layers.InputLayer((None, None), in_x2)
l_mask2 = lasagne.layers.InputLayer((None, None), in_mask2)
l_emb2 = lasagne.layers.EmbeddingLayer(l_in2,
args.vocab_size,
args.embedding_size,
W=l_emb1.W)
network1 = nn_layers.stack_rnn(l_emb1,
l_mask1,
args.num_layers,
args.hidden_size,
grad_clipping=args.grad_clipping,
dropout_rate=args.dropout_rate,
only_return_final=(args.att_func == 'last'),
bidir=args.bidir,
name='d',
rnn_layer=args.rnn_layer)
network2 = nn_layers.stack_rnn(l_emb2,
l_mask2,
args.num_layers,
args.hidden_size,
grad_clipping=args.grad_clipping,
dropout_rate=args.dropout_rate,
only_return_final=True,
bidir=args.bidir,
name='q',
rnn_layer=args.rnn_layer)
args.rnn_output_size = args.hidden_size * 2 if args.bidir else args.hidden_size
if args.att_func == 'mlp':
att = nn_layers.MLPAttentionLayer([network1, network2],
args.rnn_output_size,
mask_input=l_mask1)
elif args.att_func == 'bilinear':
att = nn_layers.BilinearAttentionLayer([network1, network2],
args.rnn_output_size,
mask_input=l_mask1)
elif args.att_func == 'avg':
att = nn_layers.AveragePoolingLayer(network1, mask_input=l_mask1)
elif args.att_func == 'last':
att = network1
elif args.att_func == 'dot':
att = nn_layers.DotProductAttentionLayer([network1, network2],
mask_input=l_mask1)
else:
raise NotImplementedError('att_func = %s' % args.att_func)
network = lasagne.layers.DenseLayer(
att, args.num_labels, nonlinearity=lasagne.nonlinearities.softmax)
if args.pre_trained is not None:
dic = utils.load_params(args.pre_trained)
lasagne.layers.set_all_param_values(network,
dic['params'],
trainable=True)
del dic['params']
logging.info('Loaded pre-trained model: %s' % args.pre_trained)
for dic_param in dic.iteritems():
logging.info(dic_param)
logging.info('#params: %d' %
lasagne.layers.count_params(network, trainable=True))
for layer in lasagne.layers.get_all_layers(network):
logging.info(layer)
# Test functions
test_prob = lasagne.layers.get_output(network, deterministic=True) * in_l
test_prediction = T.argmax(test_prob, axis=-1)
acc = T.sum(T.eq(test_prediction, in_y))
test_fn = theano.function([in_x1, in_mask1, in_x2, in_mask2, in_l, in_y],
acc)
# Train functions
train_prediction = lasagne.layers.get_output(network) * in_l
train_prediction = train_prediction / \
train_prediction.sum(axis=1).reshape((train_prediction.shape[0], 1))
train_prediction = T.clip(train_prediction, 1e-7, 1.0 - 1e-7)
loss = lasagne.objectives.categorical_crossentropy(train_prediction,
in_y).mean()
# TODO: lasagne.regularization.regularize_network_params(network, lasagne.regularization.l2)
params = lasagne.layers.get_all_params(network, trainable=True)
if args.optimizer == 'sgd':
updates = lasagne.updates.sgd(loss, params, args.learning_rate)
elif args.optimizer == 'adam':
updates = lasagne.updates.adam(loss, params)
elif args.optimizer == 'rmsprop':
updates = lasagne.updates.rmsprop(loss, params)
else:
raise NotImplementedError('optimizer = %s' % args.optimizer)
train_fn = theano.function([in_x1, in_mask1, in_x2, in_mask2, in_l, in_y],
loss,
updates=updates)
return train_fn, test_fn, params
def build_fn(args, embeddings):
"""
Build training and testing functions.
"""
in_x1 = T.imatrix('x1')
in_x2 = T.imatrix('x2')
in_x3 = T.imatrix('x3')
in_mask1 = T.matrix('mask1')
in_mask2 = T.matrix('mask2')
in_mask3 = T.matrix('mask3')
in_y = T.ivector('y')
l_in1 = lasagne.layers.InputLayer((None, None), in_x1)
l_mask1 = lasagne.layers.InputLayer((None, None), in_mask1)
l_emb1 = lasagne.layers.EmbeddingLayer(l_in1,
args.vocab_size,
args.embedding_size,
W=embeddings)
l_in2 = lasagne.layers.InputLayer((None, None), in_x2)
l_mask2 = lasagne.layers.InputLayer((None, None), in_mask2)
l_emb2 = lasagne.layers.EmbeddingLayer(l_in2,
args.vocab_size,
args.embedding_size,
W=l_emb1.W)
l_in3 = lasagne.layers.InputLayer((None, None), in_x3)
l_mask3 = lasagne.layers.InputLayer((None, None), in_mask3)
l_emb3 = lasagne.layers.EmbeddingLayer(l_in3,
args.vocab_size,
args.embedding_size,
W=l_emb1.W)
if not args.tune_embedding:
l_emb1.params[l_emb1.W].remove('trainable')
l_emb2.params[l_emb2.W].remove('trainable')
l_emb3.params[l_emb3.W].remove('trainable')
args.rnn_output_size = args.hidden_size * 2 if args.bidir else args.hidden_size
if args.model == "GA":
l_d = l_emb1
# NOTE: This implementation slightly differs from the original GA reader. Specifically:
# 1. The query GRU is shared across hops.
# 2. Dropout is applied to all hops (including the initial hop).
# 3. Gated-attention is applied at the final layer as well.
# 4. No character-level embeddings are used.
l_q = nn_layers.stack_rnn(l_emb2,
l_mask2,
1,
args.hidden_size,
grad_clipping=args.grad_clipping,
dropout_rate=args.dropout_rate,
only_return_final=False,
bidir=args.bidir,
name='q',
rnn_layer=args.rnn_layer)
q_length = nn_layers.LengthLayer(l_mask2)
network2 = QuerySliceLayer([l_q, q_length])
for layer_num in xrange(args.num_GA_layers):
l_d = nn_layers.stack_rnn(l_d,
l_mask1,
1,
args.hidden_size,
grad_clipping=args.grad_clipping,
dropout_rate=args.dropout_rate,
only_return_final=False,
bidir=args.bidir,
name='d' + str(layer_num),
rnn_layer=args.rnn_layer)
l_d = GatedAttentionLayerWithQueryAttention([l_d, l_q, l_mask2])
network1 = l_d
else:
assert args.model is None
network1 = nn_layers.stack_rnn(
l_emb1,
l_mask1,
args.num_layers,
args.hidden_size,
grad_clipping=args.grad_clipping,
dropout_rate=args.dropout_rate,
only_return_final=(args.att_func == 'last'),
bidir=args.bidir,
name='d',
rnn_layer=args.rnn_layer)
network2 = nn_layers.stack_rnn(l_emb2,
l_mask2,
args.num_layers,
args.hidden_size,
grad_clipping=args.grad_clipping,
dropout_rate=args.dropout_rate,
only_return_final=True,
bidir=args.bidir,
name='q',
rnn_layer=args.rnn_layer)
if args.att_func == 'mlp':
att = nn_layers.MLPAttentionLayer([network1, network2],
args.rnn_output_size,
mask_input=l_mask1)
elif args.att_func == 'bilinear':
att = nn_layers.BilinearAttentionLayer([network1, network2],
args.rnn_output_size,
mask_input=l_mask1)
elif args.att_func == 'avg':
att = nn_layers.AveragePoolingLayer(network1, mask_input=l_mask1)
elif args.att_func == 'last':
att = network1
elif args.att_func == 'dot':
att = nn_layers.DotProductAttentionLayer([network1, network2],
mask_input=l_mask1)
else:
raise NotImplementedError('att_func = %s' % args.att_func)
network3 = nn_layers.stack_rnn(l_emb3,
l_mask3,
args.num_layers,
args.hidden_size,
grad_clipping=args.grad_clipping,
dropout_rate=args.dropout_rate,
only_return_final=True,
bidir=args.bidir,
name='o',
rnn_layer=args.rnn_layer)
network3 = lasagne.layers.ReshapeLayer(
network3, (in_x1.shape[0], 4, args.rnn_output_size))
network = nn_layers.BilinearDotLayer([network3, att], args.rnn_output_size)
if args.pre_trained is not None:
dic = utils.load_params(args.pre_trained)
lasagne.layers.set_all_param_values(network, dic['params'])
del dic['params']
logging.info('Loaded pre-trained model: %s' % args.pre_trained)
for dic_param in dic.iteritems():
logging.info(dic_param)
logging.info('#params: %d' %
lasagne.layers.count_params(network, trainable=True))
logging.info('#fixed params: %d' %
lasagne.layers.count_params(network, trainable=False))
for layer in lasagne.layers.get_all_layers(network):
logging.info(layer)
# Test functions
test_prob = lasagne.layers.get_output(network, deterministic=True)
test_prediction = T.argmax(test_prob, axis=-1)
acc = T.sum(T.eq(test_prediction, in_y))
test_fn = theano.function(
[in_x1, in_mask1, in_x2, in_mask2, in_x3, in_mask3, in_y],
[acc, test_prediction],
on_unused_input='warn')
# Train functions
train_prediction = lasagne.layers.get_output(network)
train_prediction = T.clip(train_prediction, 1e-7, 1.0 - 1e-7)
loss = lasagne.objectives.categorical_crossentropy(train_prediction,
in_y).mean()
# TODO: lasagne.regularization.regularize_network_params(network, lasagne.regularization.l2)
params = lasagne.layers.get_all_params(network) #, trainable=True)
all_params = lasagne.layers.get_all_params(network)
if args.optimizer == 'sgd':
updates = lasagne.updates.sgd(loss, params, args.learning_rate)
elif args.optimizer == 'adam':
updates = lasagne.updates.adam(loss,
params,
learning_rate=args.learning_rate)
elif args.optimizer == 'rmsprop':
updates = lasagne.updates.rmsprop(loss,
params,
learning_rate=args.learning_rate)
else:
raise NotImplementedError('optimizer = %s' % args.optimizer)
train_fn = theano.function(
[in_x1, in_mask1, in_x2, in_mask2, in_x3, in_mask3, in_y],
loss,
updates=updates,
on_unused_input='warn')
return train_fn, test_fn, params, all_params
def build_fn(args, embeddings):
"""
Build training and testing functions.
"""
in_x1 = T.imatrix('x1')
in_x3 = T.imatrix('x3')
in_mask1 = T.matrix('mask1')
in_mask3 = T.matrix('mask3')
in_y = T.ivector('y')
#batch x word_num x mea_num
in_x4 = T.ftensor3('x4')
l_in1 = lasagne.layers.InputLayer((None, None), in_x1)
l_mask1 = lasagne.layers.InputLayer((None, None), in_mask1)
l_emb1 = lasagne.layers.EmbeddingLayer(l_in1,
args.vocab_size,
args.embedding_size,
W=embeddings)
l_in3 = lasagne.layers.InputLayer((None, None), in_x3)
l_mask3 = lasagne.layers.InputLayer((None, None), in_mask3)
l_emb3 = lasagne.layers.EmbeddingLayer(l_in3,
args.vocab_size,
args.embedding_size,
W=l_emb1.W)
l_in4 = lasagne.layers.InputLayer((None, None, args.mea_num), in_x4)
if not args.tune_embedding:
l_emb1.params[l_emb1.W].remove('trainable')
l_emb3.params[l_emb3.W].remove('trainable')
assert args.model is None
#weighted mean: passage embedding
if args.freezeMlP:
weight_mlp_np = np.array([[1.]])
b_mlp = np.array([0.])
l_weight = lasagne.layers.DenseLayer(l_in4,
1,
num_leading_axes=-1,
W=weight_mlp_np,
b=b_mlp,
nonlinearity=None)
l_weight.params[l_weight.W].remove('trainable')
l_weight.params[l_weight.b].remove('trainable')
else:
# weight_mlp_np = np.zeros((args.mea_num, 1)) + 0.01*np.random.randn(args.mea_num, 1)
weight_mlp_np = np.zeros((args.mea_num, 1))
weight_mlp_np[-5] = 1.
b_mlp = np.array([0.])
# l_weight = lasagne.layers.DenseLayer(l_in4, 1, num_leading_axes=-1,
# nonlinearity=args.actiMlP)
# l_weight = lasagne.layers.DenseLayer(l_in4, 1, num_leading_axes=-1,
# W=weight_mlp_np, b=b_mlp,
# nonlinearity=None)
# l_weight = lasagne.layers.DenseLayer(l_in4, 1, num_leading_axes=-1,
# nonlinearity=None)
l_weight1 = lasagne.layers.DenseLayer(l_in4,
1,
num_leading_axes=-1,
W=weight_mlp_np,
b=b_mlp,
nonlinearity=None)
l_weight = nn_layers.WeightedNormLayer(l_weight1)
# l_weight.params[l_weight.W].remove('trainable')
# l_weight.params[l_weight.b].remove('trainable')
# l_weight = lasagne.layers.DenseLayer(l_in4, 1, num_leading_axes=-1,
# W=lasagne.init.Constant(0.), b=lasagne.init.Constant(1.),
# nonlinearity=args.actiMlP)
# l_weight.params[l_weight.W].remove('trainable')
# weight_mlp_np = np.zeros((15, 1))
# weight_mlp_np[-2] = 1.
# weight_mlp_np = np.array([[1.]])
# b_mlp = np.array([0.])
# l_weight = lasagne.layers.DenseLayer(l_in4, 1, num_leading_axes=-1,
# W=weight_mlp_np, b=b_mlp, nonlinearity=None)
# l_weight1 = lasagne.layers.DenseLayer(l_in4, 2, num_leading_axes=-1, nonlinearity=LeakyRectify(0.1))
# l_weight = lasagne.layers.DenseLayer(l_weight1, 1, num_leading_axes=-1, nonlinearity=sigmoid)
# l_weight.params[l_weight.W].remove('trainable')
# l_weight.params[l_weight.b].remove('trainable')
att = nn_layers.WeightedAverageLayer([l_emb1, l_weight, l_mask1])
#mean: option embedding
network3 = nn_layers.AveragePoolingLayer(l_emb3, mask_input=l_mask3)
network3 = lasagne.layers.ReshapeLayer(
network3, (in_x1.shape[0], 4, args.embedding_size))
#predict answer
network = nn_layers.DotLayer([network3, att], args.embedding_size)
if args.pre_trained is not None:
dic = utils.load_params(args.pre_trained)
lasagne.layers.set_all_param_values(network, dic['params'])
del dic['params']
logging.info('Loaded pre-trained model: %s' % args.pre_trained)
for dic_param in dic.iteritems():
logging.info(dic_param)
logging.info('#params: %d' %
lasagne.layers.count_params(network, trainable=True))
logging.info('#fixed params: %d' %
lasagne.layers.count_params(network, trainable=False))
for layer in lasagne.layers.get_all_layers(network):
logging.info(layer)
# Test functions
weight = lasagne.layers.get_output(l_weight, deterministic=True)
test_prob = lasagne.layers.get_output(network, deterministic=True)
loss_test = lasagne.objectives.categorical_crossentropy(test_prob,
in_y).mean()
test_prediction = T.argmax(test_prob, axis=-1)
acc = T.sum(T.eq(test_prediction, in_y))
# test_fn = theano.function([in_x1, in_mask1, in_x3, in_mask3, in_y, in_x4],
# [acc, test_prediction, test_prob], on_unused_input='warn',
# mode=NanGuardMode(nan_is_error=True, inf_is_error=True, big_is_error=True))
test_fn = theano.function(
[in_x1, in_mask1, in_x3, in_mask3, in_y, in_x4],
[acc, test_prediction, test_prob, weight, loss_test],
on_unused_input='warn')
# Train functions
train_prediction = lasagne.layers.get_output(network)
train_prediction = T.clip(train_prediction, 1e-7, 1.0 - 1e-7)
loss = lasagne.objectives.categorical_crossentropy(train_prediction,
in_y).mean()
# TODO: lasagne.regularization.regularize_network_params(network, lasagne.regularization.l2)
# l1_penalty = regularize_layer_params(l_weight, l1) * 1e-4
# loss = loss + l1_penalty
# params = lasagne.layers.get_all_params(network)#, trainable=True)
params = lasagne.layers.get_all_params(network, trainable=True)
all_params = lasagne.layers.get_all_params(network)
if args.optimizer == 'sgd':
updates = lasagne.updates.sgd(loss, params, args.learning_rate)
elif args.optimizer == 'adam':
updates = lasagne.updates.adam(loss,
params,
learning_rate=args.learning_rate)
elif args.optimizer == 'rmsprop':
updates = lasagne.updates.rmsprop(loss,
params,
learning_rate=args.learning_rate)
else:
raise NotImplementedError('optimizer = %s' % args.optimizer)
# train_fn = theano.function([in_x1, in_mask1, in_x3, in_mask3, in_y, in_x4],
# loss, updates=updates, on_unused_input='warn',
# mode=NanGuardMode(nan_is_error=True, inf_is_error=True, big_is_error=True))
train_fn = theano.function([in_x1, in_mask1, in_x3, in_mask3, in_y, in_x4],
loss,
updates=updates,
on_unused_input='warn')
return train_fn, test_fn, params, all_params