def __init__(self, data_dir, word2vec, word_vector_size, truncate_gradient,
learning_rate, dim, cnn_dim, cnn_dim_fc, story_len, patches,
mode, answer_module, memory_hops, batch_size, l2,
normalize_attention, batch_norm, dropout, **kwargs):
print "==> not used params in DMN class:", kwargs.keys()
self.data_dir = data_dir
self.learning_rate = learning_rate
self.truncate_gradient = truncate_gradient
self.word2vec = word2vec
self.word_vector_size = word_vector_size
self.dim = dim
self.cnn_dim = cnn_dim
self.cnn_dim_fc = cnn_dim_fc
self.story_len = story_len
self.mode = mode
self.patches = patches
self.answer_module = answer_module
self.memory_hops = memory_hops
self.batch_size = batch_size
self.l2 = l2
self.normalize_attention = normalize_attention
self.batch_norm = batch_norm
self.dropout = dropout
#self.vocab, self.ivocab = self._load_vocab(self.data_dir)
self.vocab, self.ivocab = self._ext_vocab_from_word2vec()
self.train_story = None
self.test_story = None
self.train_dict_story, self.train_lmdb_env_fc, self.train_lmdb_env_conv = self._process_input_sind(
self.data_dir, 'train')
self.test_dict_story, self.test_lmdb_env_fc, self.test_lmdb_env_conv = self._process_input_sind(
self.data_dir, 'val')
self.train_story = self.train_dict_story.keys()
self.test_story = self.test_dict_story.keys()
self.vocab_size = len(self.vocab)
# Since this is pretty expensive, we will pass a story each time.
# We assume that the input has been processed such that the sequences of patches
# are snake like path.
self.input_var = T.tensor4(
'input_var') # (batch_size, seq_len, patches, cnn_dim)
self.q_var = T.matrix('q_var') # Now, it's a batch * image_sieze.
self.answer_var = T.imatrix(
'answer_var') # answer of example in minibatch
self.answer_mask = T.matrix('answer_mask')
self.answer_inp_var = T.tensor3(
'answer_inp_var') # answer of example in minibatch
print "==> building input module"
self.W_inp_emb_in = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.cnn_dim))
#self.b_inp_emb_in = nn_utils.constant_param(value=0.0, shape=(self.dim,))
# First, we embed the visual features before sending it to the bi-GRUs.
inp_rhp = T.reshape(
self.input_var,
(self.batch_size * self.story_len * self.patches, self.cnn_dim))
inp_rhp_dimshuffled = inp_rhp.dimshuffle(1, 0)
inp_rhp_emb = T.dot(self.W_inp_emb_in, inp_rhp_dimshuffled)
inp_rhp_emb_dimshuffled = inp_rhp_emb.dimshuffle(1, 0)
inp_emb_raw = T.reshape(
inp_rhp_emb_dimshuffled,
(self.batch_size, self.story_len, self.patches, self.cnn_dim))
inp_emb = T.tanh(
inp_emb_raw
) # Just follow the paper DMN for visual and textual QA.
# Now, we use a bi-directional GRU to produce the input.
# Forward GRU.
self.inp_dim = self.dim / 2 # since we have forward and backward
self.W_inpf_res_in = nn_utils.normal_param(std=0.1,
shape=(self.inp_dim,
self.cnn_dim))
self.W_inpf_res_hid = nn_utils.normal_param(std=0.1,
shape=(self.inp_dim,
self.inp_dim))
self.b_inpf_res = nn_utils.constant_param(value=0.0,
shape=(self.inp_dim, ))
self.W_inpf_upd_in = nn_utils.normal_param(std=0.1,
shape=(self.inp_dim,
self.cnn_dim))
self.W_inpf_upd_hid = nn_utils.normal_param(std=0.1,
shape=(self.inp_dim,
self.inp_dim))
self.b_inpf_upd = nn_utils.constant_param(value=0.0,
shape=(self.inp_dim, ))
self.W_inpf_hid_in = nn_utils.normal_param(std=0.1,
shape=(self.inp_dim,
self.cnn_dim))
self.W_inpf_hid_hid = nn_utils.normal_param(std=0.1,
shape=(self.inp_dim,
self.inp_dim))
self.b_inpf_hid = nn_utils.constant_param(value=0.0,
shape=(self.inp_dim, ))
# Backward GRU.
self.W_inpb_res_in = nn_utils.normal_param(std=0.1,
shape=(self.inp_dim,
self.cnn_dim))
self.W_inpb_res_hid = nn_utils.normal_param(std=0.1,
shape=(self.inp_dim,
self.inp_dim))
self.b_inpb_res = nn_utils.constant_param(value=0.0,
shape=(self.inp_dim, ))
self.W_inpb_upd_in = nn_utils.normal_param(std=0.1,
shape=(self.inp_dim,
self.cnn_dim))
self.W_inpb_upd_hid = nn_utils.normal_param(std=0.1,
shape=(self.inp_dim,
self.inp_dim))
self.b_inpb_upd = nn_utils.constant_param(value=0.0,
shape=(self.inp_dim, ))
self.W_inpb_hid_in = nn_utils.normal_param(std=0.1,
shape=(self.inp_dim,
self.cnn_dim))
self.W_inpb_hid_hid = nn_utils.normal_param(std=0.1,
shape=(self.inp_dim,
self.inp_dim))
self.b_inpb_hid = nn_utils.constant_param(value=0.0,
shape=(self.inp_dim, ))
# Now, we use the GRU to build the inputs.
# Two-level of nested scan is unnecessary. It will become too complicated. Just use this one.
inp_dummy = theano.shared(
np.zeros((self.inp_dim, self.story_len), dtype=floatX))
for i in range(self.batch_size):
if i == 0:
inp_1st_f, _ = theano.scan(
fn=self.input_gru_step_forward,
sequences=inp_emb[i, :].dimshuffle(1, 2, 0),
outputs_info=T.zeros_like(inp_dummy),
truncate_gradient=self.truncate_gradient)
inp_1st_b, _ = theano.scan(
fn=self.input_gru_step_backward,
sequences=inp_emb[i, :, ::-1, :].dimshuffle(1, 2, 0),
outputs_info=T.zeros_like(inp_dummy),
truncate_gradient=self.truncate_gradient)
# Now, combine them.
inp_1st = T.concatenate([
inp_1st_f.dimshuffle(2, 0, 1),
inp_1st_b.dimshuffle(2, 0, 1)
],
axis=-1)
self.inp_c = inp_1st.dimshuffle('x', 0, 1, 2)
else:
inp_f, _ = theano.scan(
fn=self.input_gru_step_forward,
sequences=inp_emb[i, :].dimshuffle(1, 2, 0),
outputs_info=T.zeros_like(inp_dummy),
truncate_gradient=self.truncate_gradient)
inp_b, _ = theano.scan(
fn=self.input_gru_step_backward,
sequences=inp_emb[i, :, ::-1, :].dimshuffle(1, 2, 0),
outputs_info=T.zeros_like(inp_dummy),
truncate_gradient=self.truncate_gradient)
# Now, combine them.
inp_fb = T.concatenate(
[inp_f.dimshuffle(2, 0, 1),
inp_b.dimshuffle(2, 0, 1)],
axis=-1)
self.inp_c = T.concatenate(
[self.inp_c, inp_fb.dimshuffle('x', 0, 1, 2)], axis=0)
# Done, now self.inp_c should be batch_size x story_len x patches x cnn_dim
# Eventually, we can flattern them.
# Now, the input dimension is 1024 because we have forward and backward.
inp_c_t = T.reshape(
self.inp_c,
(self.batch_size, self.story_len * self.patches, self.dim))
inp_c_t_dimshuffled = inp_c_t.dimshuffle(0, 'x', 1, 2)
inp_batch = T.repeat(inp_c_t_dimshuffled, self.story_len, axis=1)
# Now, its ready for all the 5 images in the same story.
# 50 * 980 * 512
self.inp_batch = T.reshape(inp_batch,
(inp_batch.shape[0] * inp_batch.shape[1],
inp_batch.shape[2], inp_batch.shape[3]))
self.inp_batch_dimshuffled = self.inp_batch.dimshuffle(
1, 2, 0) # 980 x 512 x 50
# It's very simple now, the input module just need to map from cnn_dim to dim.
logging.info('self.cnn_dim = %d', self.cnn_dim)
print "==> building question module"
# Now, share the parameter with the input module.
self.W_inp_emb_q = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.cnn_dim_fc))
self.b_inp_emb_q = nn_utils.normal_param(std=0.1, shape=(self.dim, ))
q_var_shuffled = self.q_var.dimshuffle(1, 0)
inp_q = T.dot(
self.W_inp_emb_q, q_var_shuffled) + self.b_inp_emb_q.dimshuffle(
0, 'x') # 512 x 50
self.q_q = T.tanh(
inp_q
) # Since this is used to initialize the memory, we need to make it tanh.
print "==> creating parameters for memory module"
self.W_mem_res_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.W_mem_res_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_mem_res = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_mem_upd_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.W_mem_upd_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_mem_upd = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_mem_hid_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.W_mem_hid_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_mem_hid = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
#self.W_b = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.W_1 = nn_utils.normal_param(std=0.1,
shape=(self.dim, 7 * self.dim + 0))
self.W_2 = nn_utils.normal_param(std=0.1, shape=(1, self.dim))
self.b_1 = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.b_2 = nn_utils.constant_param(value=0.0, shape=(1, ))
print "==> building episodic memory module (fixed number of steps: %d)" % self.memory_hops
memory = [self.q_q.copy()]
for iter in range(1, self.memory_hops + 1):
#m = printing.Print('mem')(memory[iter-1])
current_episode = self.new_episode(memory[iter - 1])
#current_episode = self.new_episode(m)
#current_episode = printing.Print('current_episode')(current_episode)
memory.append(
self.GRU_update(memory[iter - 1], current_episode,
self.W_mem_res_in, self.W_mem_res_hid,
self.b_mem_res, self.W_mem_upd_in,
self.W_mem_upd_hid, self.b_mem_upd,
self.W_mem_hid_in, self.W_mem_hid_hid,
self.b_mem_hid))
last_mem_raw = memory[-1].dimshuffle((1, 0))
net = layers.InputLayer(shape=(self.batch_size * self.story_len,
self.dim),
input_var=last_mem_raw)
if self.batch_norm:
net = layers.BatchNormLayer(incoming=net)
if self.dropout > 0 and self.mode == 'train':
net = layers.DropoutLayer(net, p=self.dropout)
last_mem = layers.get_output(net).dimshuffle((1, 0))
logging.info('last_mem size')
print last_mem.shape.eval({
self.input_var:
np.random.rand(10, 5, 196, 512).astype('float32'),
self.q_var:
np.random.rand(50, 4096).astype('float32')
})
print "==> building answer module"
answer_inp_var_shuffled = self.answer_inp_var.dimshuffle(1, 2, 0)
# Sounds good. Now, we need to map last_mem to a new space.
self.W_mem_emb = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim * 2))
self.W_inp_emb = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.word_vector_size))
def _dot2(x, W):
return T.dot(W, x)
answer_inp_var_shuffled_emb, _ = theano.scan(
fn=_dot2,
sequences=answer_inp_var_shuffled,
non_sequences=self.W_inp_emb) # seq x dim x batch
# Now, we also need to embed the image and use it to do the memory.
#q_q_shuffled = self.q_q.dimshuffle(1,0) # dim * batch.
init_ans = T.concatenate([self.q_q, last_mem], axis=0)
mem_ans = T.dot(self.W_mem_emb, init_ans) # dim x batchsize.
mem_ans_dim = mem_ans.dimshuffle('x', 0, 1)
answer_inp = T.concatenate([mem_ans_dim, answer_inp_var_shuffled_emb],
axis=0)
# Now, we have both embedding. We can let them go to the rnn.
# We also need to map the input layer as well.
dummy = theano.shared(
np.zeros((self.dim, self.batch_size * self.story_len),
dtype=floatX))
self.W_a = nn_utils.normal_param(std=0.1,
shape=(self.vocab_size + 1, self.dim))
self.W_ans_res_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.W_ans_res_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_ans_res = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_ans_upd_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.W_ans_upd_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_ans_upd = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_ans_hid_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.W_ans_hid_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_ans_hid = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
logging.info('answer_inp size')
#print answer_inp.shape.eval({self.input_var: np.random.rand(10,4,4096).astype('float32'),
# self.answer_inp_var: np.random.rand(10, 18, 8001).astype('float32'),
# self.q_var: np.random.rand(10, 4096).astype('float32')})
#last_mem = printing.Print('prob_sm')(last_mem)
results, _ = theano.scan(fn=self.answer_gru_step,
sequences=answer_inp,
outputs_info=[dummy])
# Assume there is a start token
#print results.shape.eval({self.input_var: np.random.rand(10,4,4096).astype('float32'),
# self.q_var: np.random.rand(10, 4096).astype('float32'),
# self.answer_inp_var: np.random.rand(10, 18, 8001).astype('float32')}, on_unused_input='ignore')
results = results[
1:
-1, :, :] # get rid of the last token as well as the first one (image)
#print results.shape.eval({self.input_var: np.random.rand(10,4,4096).astype('float32'),
# self.q_var: np.random.rand(10, 4096).astype('float32'),
# self.answer_inp_var: np.random.rand(10, 18, 8001).astype('float32')}, on_unused_input='ignore')
# Now, we need to transform it to the probabilities.
prob, _ = theano.scan(fn=lambda x, w: T.dot(w, x),
sequences=results,
non_sequences=self.W_a)
prob_shuffled = prob.dimshuffle(2, 0, 1) # b * len * vocab
logging.info("prob shape.")
#print prob.shape.eval({self.input_var: np.random.rand(10,4,4096).astype('float32'),
# self.q_var: np.random.rand(10, 4096).astype('float32'),
# self.answer_inp_var: np.random.rand(10, 18, 8001).astype('float32')})
n = prob_shuffled.shape[0] * prob_shuffled.shape[1]
prob_rhp = T.reshape(prob_shuffled, (n, prob_shuffled.shape[2]))
prob_sm = nn_utils.softmax_(prob_rhp)
self.prediction = prob_sm
mask = T.reshape(self.answer_mask, (n, ))
lbl = T.reshape(self.answer_var, (n, ))
self.params = [
self.W_inp_emb_in, #self.b_inp_emb_in,
self.W_inpf_res_in,
self.W_inpf_res_hid,
self.b_inpf_res,
self.W_inpf_upd_in,
self.W_inpf_upd_hid,
self.b_inpf_upd,
self.W_inpf_hid_in,
self.W_inpf_hid_hid,
self.b_inpf_hid,
self.W_inpb_res_in,
self.W_inpb_res_hid,
self.b_inpb_res,
self.W_inpb_upd_in,
self.W_inpb_upd_hid,
self.b_inpb_upd,
self.W_inpb_hid_in,
self.W_inpb_hid_hid,
self.b_inpb_hid,
self.W_inp_emb_q,
self.b_inp_emb_q,
self.W_mem_res_in,
self.W_mem_res_hid,
self.b_mem_res,
self.W_mem_upd_in,
self.W_mem_upd_hid,
self.b_mem_upd,
self.W_mem_hid_in,
self.W_mem_hid_hid,
self.b_mem_hid, #self.W_b
self.W_1,
self.W_2,
self.b_1,
self.b_2,
self.W_a,
self.W_mem_emb,
self.W_inp_emb,
self.W_ans_res_in,
self.W_ans_res_hid,
self.b_ans_res,
self.W_ans_upd_in,
self.W_ans_upd_hid,
self.b_ans_upd,
self.W_ans_hid_in,
self.W_ans_hid_hid,
self.b_ans_hid,
]
print "==> building loss layer and computing updates"
loss_vec = T.nnet.categorical_crossentropy(prob_sm, lbl)
self.loss_ce = (mask * loss_vec).sum() / mask.sum()
#self.loss_ce = T.nnet.categorical_crossentropy(results_rhp, lbl)
if self.l2 > 0:
self.loss_l2 = self.l2 * nn_utils.l2_reg(self.params)
else:
self.loss_l2 = 0
self.loss = self.loss_ce + self.loss_l2
#updates = lasagne.updates.adadelta(self.loss, self.params)
#updates = lasagne.updates.adam(self.loss, self.params, learning_rate = self.learning_rate)
updates = lasagne.updates.rmsprop(self.loss,
self.params,
learning_rate=self.learning_rate)
#updates = lasagne.updates.momentum(self.loss, self.params, learning_rate=0.001)
if self.mode == 'train':
print "==> compiling train_fn"
self.train_fn = theano.function(
inputs=[
self.input_var, self.q_var, self.answer_var,
self.answer_mask, self.answer_inp_var
],
outputs=[self.prediction, self.loss],
updates=updates)
#profile = True)
print "==> compiling test_fn"
self.test_fn = theano.function(inputs=[
self.input_var, self.q_var, self.answer_var, self.answer_mask,
self.answer_inp_var
],
outputs=[self.prediction, self.loss])
def __init__(self, babi_train_raw, babi_test_raw, word2vec,
word_vector_size, dim, mode, answer_module, input_mask_mode,
memory_hops, batch_size, l2, normalize_attention, **kwargs):
print "==> not used params in DMN class:", kwargs.keys()
self.vocab = {}
self.ivocab = {}
self.word2vec = word2vec
self.word_vector_size = word_vector_size
self.dim = dim
self.mode = mode
self.answer_module = answer_module
self.input_mask_mode = input_mask_mode
self.memory_hops = memory_hops
self.batch_size = batch_size
self.l2 = l2
self.normalize_attention = normalize_attention
self.max_fact_count = 0
self.train_input, self.train_q, self.train_answer, self.train_fact_count, self.train_input_mask = self._process_input(
babi_train_raw)
self.test_input, self.test_q, self.test_answer, self.test_fact_count, self.test_input_mask = self._process_input(
babi_test_raw)
self.vocab_size = len(self.vocab)
self.input_var = T.tensor3(
'input_var') # (batch_size, seq_len, glove_dim)
self.q_var = T.tensor3('question_var') # as self.input_var
self.answer_var = T.ivector(
'answer_var') # answer of example in minibatch
self.fact_count_var = T.ivector(
'fact_count_var') # number of facts in the example of minibatch
self.input_mask_var = T.imatrix(
'input_mask_var') # (batch_size, indices)
print "==> building input module"
self.W_inp_res_in = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.word_vector_size))
self.W_inp_res_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_inp_res = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_inp_upd_in = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.word_vector_size))
self.W_inp_upd_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_inp_upd = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_inp_hid_in = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.word_vector_size))
self.W_inp_hid_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_inp_hid = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
input_var_shuffled = self.input_var.dimshuffle(1, 2, 0)
inp_dummy = theano.shared(
np.zeros((self.dim, self.batch_size), dtype=floatX))
inp_c_history, _ = theano.scan(fn=self.input_gru_step,
sequences=input_var_shuffled,
outputs_info=T.zeros_like(inp_dummy))
inp_c_history_shuffled = inp_c_history.dimshuffle(2, 0, 1)
inp_c_list = []
inp_c_mask_list = []
for batch_index in range(self.batch_size):
taken = inp_c_history_shuffled[batch_index].take(
self.input_mask_var[
batch_index, :self.fact_count_var[batch_index]],
axis=0)
inp_c_list.append(
T.concatenate([
taken,
T.zeros((self.max_fact_count - taken.shape[0], self.dim),
floatX)
]))
inp_c_mask_list.append(
T.concatenate([
T.ones((taken.shape[0], ), np.int32),
T.zeros((self.max_fact_count - taken.shape[0], ), np.int32)
]))
self.inp_c = T.stack(inp_c_list).dimshuffle(1, 2, 0)
inp_c_mask = T.stack(inp_c_mask_list).dimshuffle(1, 0)
q_var_shuffled = self.q_var.dimshuffle(1, 2, 0)
q_dummy = theano.shared(
np.zeros((self.dim, self.batch_size), dtype=floatX))
q_q_history, _ = theano.scan(fn=self.input_gru_step,
sequences=q_var_shuffled,
outputs_info=T.zeros_like(q_dummy))
self.q_q = q_q_history[-1]
print "==> creating parameters for memory module"
self.W_mem_res_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.W_mem_res_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_mem_res = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_mem_upd_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.W_mem_upd_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_mem_upd = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_mem_hid_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.W_mem_hid_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_mem_hid = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_b = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.W_1 = nn_utils.normal_param(std=0.1,
shape=(self.dim, 7 * self.dim + 0))
self.W_2 = nn_utils.normal_param(std=0.1, shape=(1, self.dim))
self.b_1 = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.b_2 = nn_utils.constant_param(value=0.0, shape=(1, ))
print "==> building episodic memory module (fixed number of steps: %d)" % self.memory_hops
memory = [self.q_q.copy()]
for iter in range(1, self.memory_hops + 1):
current_episode = self.new_episode(memory[iter - 1])
memory.append(
self.GRU_update(memory[iter - 1], current_episode,
self.W_mem_res_in, self.W_mem_res_hid,
self.b_mem_res, self.W_mem_upd_in,
self.W_mem_upd_hid, self.b_mem_upd,
self.W_mem_hid_in, self.W_mem_hid_hid,
self.b_mem_hid))
last_mem = memory[-1]
print "==> building answer module"
self.W_a = nn_utils.normal_param(std=0.1,
shape=(self.vocab_size, self.dim))
if self.answer_module == 'feedforward':
self.prediction = nn_utils.softmax(T.dot(self.W_a, last_mem))
elif self.answer_module == 'recurrent':
self.W_ans_res_in = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.dim + self.vocab_size))
self.W_ans_res_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.dim))
self.b_ans_res = nn_utils.constant_param(value=0.0,
shape=(self.dim, ))
self.W_ans_upd_in = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.dim + self.vocab_size))
self.W_ans_upd_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.dim))
self.b_ans_upd = nn_utils.constant_param(value=0.0,
shape=(self.dim, ))
self.W_ans_hid_in = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.dim + self.vocab_size))
self.W_ans_hid_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.dim))
self.b_ans_hid = nn_utils.constant_param(value=0.0,
shape=(self.dim, ))
def answer_step(prev_a, prev_y):
a = self.GRU_update(prev_a, T.concatenate([prev_y, self.q_q]),
self.W_ans_res_in, self.W_ans_res_hid,
self.b_ans_res, self.W_ans_upd_in,
self.W_ans_upd_hid, self.b_ans_upd,
self.W_ans_hid_in, self.W_ans_hid_hid,
self.b_ans_hid)
y = nn_utils.softmax(T.dot(self.W_a, a))
return [a, y]
# TODO: add conditional ending
dummy = theano.shared(
np.zeros((self.vocab_size, self.batch_size), dtype=floatX))
results, updates = theano.scan(
fn=self.answer_step,
outputs_info=[last_mem, T.zeros_like(dummy)], #(last_mem, y)
n_steps=1)
self.prediction = results[1][-1]
else:
raise Exception("invalid answer_module")
self.prediction = self.prediction.dimshuffle(1, 0)
self.params = [
self.W_inp_res_in,
self.W_inp_res_hid,
self.b_inp_res,
self.W_inp_upd_in,
self.W_inp_upd_hid,
self.b_inp_upd,
self.W_inp_hid_in,
self.W_inp_hid_hid,
self.b_inp_hid,
self.W_mem_res_in,
self.W_mem_res_hid,
self.b_mem_res,
self.W_mem_upd_in,
self.W_mem_upd_hid,
self.b_mem_upd,
self.W_mem_hid_in,
self.W_mem_hid_hid,
self.b_mem_hid, #self.W_b
self.W_1,
self.W_2,
self.b_1,
self.b_2,
self.W_a
]
if self.answer_module == 'recurrent':
self.params = self.params + [
self.W_ans_res_in, self.W_ans_res_hid, self.b_ans_res,
self.W_ans_upd_in, self.W_ans_upd_hid, self.b_ans_upd,
self.W_ans_hid_in, self.W_ans_hid_hid, self.b_ans_hid
]
print "==> building loss layer and computing updates"
self.loss_ce = T.nnet.categorical_crossentropy(self.prediction,
self.answer_var).mean()
if self.l2 > 0:
self.loss_l2 = self.l2 * nn_utils.l2_reg(self.params)
else:
self.loss_l2 = 0
self.loss = self.loss_ce + self.loss_l2
updates = lasagne.updates.adadelta(self.loss, self.params)
if self.mode == 'train':
print "==> compiling train_fn"
self.train_fn = theano.function(
inputs=[
self.input_var, self.q_var, self.answer_var,
self.fact_count_var, self.input_mask_var
],
outputs=[self.prediction, self.loss],
updates=updates)
print "==> compiling test_fn"
self.test_fn = theano.function(inputs=[
self.input_var, self.q_var, self.answer_var, self.fact_count_var,
self.input_mask_var
],
outputs=[self.prediction, self.loss])
def __init__(self, data_dir, word2vec, truncate_gradient, learning_rate,
dim, cnn_dim, story_len, mode, answer_module, batch_size, l2,
dropout, **kwargs):
print "==> not used params in DMN class:", kwargs.keys()
self.data_dir = data_dir
self.learning_rate = learning_rate
self.word_vector_size = 300
self.truncate_gradient = truncate_gradient
self.word2vec = word2vec
self.dim = dim
self.cnn_dim = cnn_dim
self.story_len = story_len
self.mode = mode
self.answer_module = answer_module
self.batch_size = batch_size
self.l2 = l2
self.dropout = dropout
self.vocab, self.ivocab = self._load_vocab(self.data_dir)
self.train_story = None
self.test_story = None
self.train_dict_story, self.train_lmdb_env_fc = self._process_input_sind(
self.data_dir, 'train')
self.test_dict_story, self.test_lmdb_env_fc = self._process_input_sind(
self.data_dir, 'val')
self.train_story = self.train_dict_story.keys()
self.test_story = self.test_dict_story.keys()
self.vocab_size = len(self.vocab)
self.q_var = T.tensor3(
'q_var') # Now, it's a batch * story_len * image_sieze.
self.answer_var = T.imatrix(
'answer_var') # answer of example in minibatch
self.answer_mask = T.matrix('answer_mask')
self.answer_inp_var = T.tensor3(
'answer_inp_var') # answer of example in minibatch
q_shuffled = self.q_var.dimshuffle(
1, 2, 0) # now: story_len * image_size * batch_size
print "==> building input module"
# Now, we use a GRU to produce the input.
# Forward GRU.
self.W_inpf_res_in = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.cnn_dim))
self.W_inpf_res_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_inpf_res = nn_utils.constant_param(value=0.0,
shape=(self.dim, ))
self.W_inpf_upd_in = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.cnn_dim))
self.W_inpf_upd_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_inpf_upd = nn_utils.constant_param(value=0.0,
shape=(self.dim, ))
self.W_inpf_hid_in = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.cnn_dim))
self.W_inpf_hid_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_inpf_hid = nn_utils.constant_param(value=0.0,
shape=(self.dim, ))
# Now, we use the GRU to build the inputs.
# Two-level of nested scan is unnecessary. It will become too complicated. Just use this one.
inp_dummy = theano.shared(
np.zeros((self.dim, self.batch_size), dtype=floatX))
q_inp, _ = theano.scan(fn=self.input_gru_step_forward,
sequences=q_shuffled,
outputs_info=[T.zeros_like(inp_dummy)])
q_inp_shuffled = q_inp.dimshuffle(2, 0, 1)
q_inp_last = q_inp_shuffled[:, -1, :].dimshuffle(1,
0) #dim * batch_size
# Now, share the parameter with the input module.
self.W_inp_emb_q = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_inp_emb_q = nn_utils.normal_param(std=0.1, shape=(self.dim, ))
inp_q = T.dot(
self.W_inp_emb_q, q_inp_last) + self.b_inp_emb_q.dimshuffle(
0, 'x') # 512 x 50
self.q_q = T.tanh(
inp_q
) # Since this is used to initialize the memory, we need to make it tanh.
print "==> building answer module"
answer_inp_var_shuffled = self.answer_inp_var.dimshuffle(1, 2, 0)
# Sounds good. Now, we need to map last_mem to a new space.
self.W_inp_emb = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.vocab_size + 1))
def _dot2(x, W):
return T.dot(W, x)
answer_inp_var_shuffled_emb, _ = theano.scan(
fn=_dot2,
sequences=answer_inp_var_shuffled,
non_sequences=self.W_inp_emb) # seq x dim x batch
self.W_a = nn_utils.normal_param(std=0.1,
shape=(self.vocab_size + 1, self.dim))
self.W_ans_res_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.W_ans_res_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_ans_res = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_ans_upd_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.W_ans_upd_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_ans_upd = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_ans_hid_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.W_ans_hid_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_ans_hid = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
results, _ = theano.scan(fn=self.answer_gru_step,
sequences=answer_inp_var_shuffled_emb,
outputs_info=[self.q_q])
# Assume there is a start token
#print results.shape.eval({self.input_var: np.random.rand(10,4,4096).astype('float32'),
# self.q_var: np.random.rand(10, 4096).astype('float32'),
# self.answer_inp_var: np.random.rand(10, 18, 8001).astype('float32')}, on_unused_input='ignore')
#results = results[:-1,:,:] # get rid of the last token as well as the first one (image)
#print results.shape.eval({self.input_var: np.random.rand(10,4,4096).astype('float32'),
# self.q_var: np.random.rand(10, 4096).astype('float32'),
# self.answer_inp_var: np.random.rand(10, 18, 8001).astype('float32')}, on_unused_input='ignore')
# Now, we need to transform it to the probabilities.
prob_f, _ = theano.scan(fn=lambda x, w: T.dot(w, x),
sequences=results,
non_sequences=self.W_a)
prob = prob_f[:-1, :, :]
prob_shuffled = prob.dimshuffle(2, 0, 1) # b * len * vocab
prob_f_shuffled = prob_f.dimshuffle(2, 0, 1)
logging.info("prob shape.")
#print prob.shape.eval({self.input_var: np.random.rand(10,4,4096).astype('float32'),
# self.q_var: np.random.rand(10, 4096).astype('float32'),
# self.answer_inp_var: np.random.rand(10, 18, 8001).astype('float32')})
n = prob_shuffled.shape[0] * prob_shuffled.shape[1]
prob_rhp = T.reshape(prob_shuffled, (n, prob_shuffled.shape[2]))
prob_sm = nn_utils.softmax_(prob_rhp)
self.pred = prob_sm
n_f = prob_f_shuffled.shape[0] * prob_f_shuffled.shape[1]
prob_f_rhp = T.reshape(prob_f_shuffled,
(n_f, prob_f_shuffled.shape[2]))
prob_f_sm = nn_utils.softmax_(prob_f_rhp)
self.prediction = T.reshape(
prob_f_sm, (prob_f_shuffled.shape[0], prob_f_shuffled.shape[1],
prob_f_shuffled.shape[2]))
mask = T.reshape(self.answer_mask, (n, ))
lbl = T.reshape(self.answer_var, (n, ))
self.params = [
self.W_inpf_res_in,
self.W_inpf_res_hid,
self.b_inpf_res,
self.W_inpf_upd_in,
self.W_inpf_upd_hid,
self.b_inpf_upd,
self.W_inpf_hid_in,
self.W_inpf_hid_hid,
self.b_inpf_hid,
self.W_inp_emb_q,
self.b_inp_emb_q,
self.W_a,
self.W_inp_emb,
self.W_ans_res_in,
self.W_ans_res_hid,
self.b_ans_res,
self.W_ans_upd_in,
self.W_ans_upd_hid,
self.b_ans_upd,
self.W_ans_hid_in,
self.W_ans_hid_hid,
self.b_ans_hid,
]
print "==> building loss layer and computing updates"
loss_vec = T.nnet.categorical_crossentropy(prob_sm, lbl)
self.loss_ce = (mask * loss_vec).sum() / mask.sum()
#self.loss_ce = T.nnet.categorical_crossentropy(results_rhp, lbl)
if self.l2 > 0:
self.loss_l2 = self.l2 * nn_utils.l2_reg(self.params)
else:
self.loss_l2 = 0
self.loss = self.loss_ce + self.loss_l2
updates = lasagne.updates.adadelta(self.loss,
self.params,
learning_rate=self.learning_rate)
#updates = lasagne.updates.momentum(self.loss, self.params, learning_rate=0.001)
if self.mode == 'train':
print "==> compiling train_fn"
self.train_fn = theano.function(inputs=[
self.q_var, self.answer_var, self.answer_mask,
self.answer_inp_var
],
outputs=[self.pred, self.loss],
updates=updates)
print "==> compiling test_fn"
self.test_fn = theano.function(inputs=[
self.q_var, self.answer_var, self.answer_mask, self.answer_inp_var
],
outputs=[self.pred, self.loss])
print "==> compiling pred_fn"
self.pred_fn = theano.function(
inputs=[self.q_var, self.answer_inp_var],
outputs=[self.prediction])
def __init__(self, babi_train_raw, babi_test_raw, word2vec, word_vector_size, sent_vector_size,
dim, mode, answer_module, input_mask_mode, memory_hops, l2,
normalize_attention, batch_norm, dropout, dropout_in, **kwargs):
print "==> not used params in DMN class:", kwargs.keys()
self.vocab = {None: 0}
self.ivocab = {0: None}
self.word2vec = word2vec
self.word_vector_size = word_vector_size
self.sent_vector_size = sent_vector_size
self.dim = dim
self.mode = mode
self.answer_module = answer_module
self.input_mask_mode = input_mask_mode
self.memory_hops = memory_hops
self.l2 = l2
self.normalize_attention = normalize_attention
self.batch_norm = batch_norm
self.dropout = dropout
self.dropout_in = dropout_in
self.max_inp_sent_len = 0
self.max_q_len = 0
"""
#To Use All Vocab
self.vocab = {None: 0, 'jason': 134.0, 'office': 14.0, 'yellow': 78.0, 'bedroom': 24.0, 'go': 108.0, 'yes': 15.0, 'antoine': 138.0, 'milk': 139.0, 'before': 46.0, 'grabbed': 128.0, 'fit': 100.0, 'how': 105.0, 'swan': 73.0, 'than': 96.0, 'to': 13.0, 'does': 99.0, 's,e': 110.0, 'east': 102.0, 'rectangle': 82.0, 'gave': 149.0, 'then': 39.0, 'evening': 48.0, 'triangle': 79.0, 'garden': 37.0, 'get': 131.0, 'football,apple,milk': 179.0, 'they': 41.0, 'not': 178.0, 'bigger': 95.0, 'gray': 77.0, 'school': 6.0, 'apple': 142.0, 'did': 127.0, 'morning': 44.0, 'discarded': 146.0, 'julius': 72.0, 'she': 29.0, 'went': 11.0, 'where': 30.0, 'jeff': 152.0, 'square': 84.0, 'who': 153.0, 'tired': 124.0, 'there': 130.0, 'back': 12.0, 'lion': 70.0, 'are': 50.0, 'picked': 143.0, 'e,e': 119.0, 'pajamas': 129.0, 'Mary': 157.0, 'blue': 83.0, 'what': 63.0, 'container': 98.0, 'rhino': 76.0, 'daniel': 31.0, 'bernhard': 67.0, 'milk,football': 172.0, 'above': 80.0, 'got': 136.0, 'emily': 60.0, 'red': 88.0, 'either': 3.0, 'sheep': 58.0, 'football': 137.0, 'jessica': 61.0, 'do': 106.0, 'Bill': 155.0, 'football,apple': 168.0, 'fred': 1.0, 'winona': 59.0, 'objects': 161.0, 'put': 147.0, 'kitchen': 17.0, 'box': 90.0, 'received': 154.0, 'journeyed': 25.0, 'of': 52.0, 'wolf': 62.0, 'afternoon': 47.0, 'or': 7.0, 'south': 112.0, 's,w': 114.0, 'afterwards': 32.0, 'sumit': 123.0, 'color': 75.0, 'julie': 23.0, 'one': 163.0, 'down': 148.0, 'nothing': 167.0, 'n,n': 113.0, 'right': 86.0, 's,s': 116.0, 'gertrude': 54.0, 'bathroom': 26.0, 'from': 109.0, 'west': 104.0, 'chocolates': 91.0, 'two': 165.0, 'frog': 66.0, '.': 9.0, 'cats': 57.0, 'apple,milk,football': 175.0, 'passed': 158.0, 'apple,football,milk': 176.0, 'white': 71.0, 'john': 35.0, 'was': 45.0, 'mary': 10.0, 'apple,football': 170.0, 'north': 103.0, 'n,w': 111.0, 'that': 28.0, 'park': 8.0, 'took': 141.0, 'chocolate': 101.0, 'carrying': 162.0, 'n,e': 120.0, 'mice': 49.0, 'travelled': 22.0, 'he': 33.0, 'none': 164.0, 'bored': 133.0, 'e,n': 117.0, None: 0, 'Jeff': 159.0, 'this': 43.0, 'inside': 93.0, 'bill': 16.0, 'up': 144.0, 'cat': 64.0, 'will': 125.0, 'below': 87.0, 'greg': 74.0, 'three': 166.0, 'suitcase': 97.0, 'following': 36.0, 'e,s': 115.0, 'and': 40.0, 'thirsty': 135.0, 'cinema': 19.0, 'is': 2.0, 'moved': 18.0, 'yann': 132.0, 'sphere': 89.0, 'dropped': 145.0, 'in': 4.0, 'mouse': 56.0, 'football,milk': 171.0, 'pink': 81.0, 'afraid': 51.0, 'no': 20.0, 'Fred': 156.0, 'w,s': 121.0, 'handed': 151.0, 'w,w': 118.0, 'brian': 69.0, 'chest': 94.0, 'w,n': 122.0, 'you': 107.0, 'many': 160.0, 'lily': 65.0, 'hallway': 34.0, 'why': 126.0, 'after': 27.0, 'yesterday': 42.0, 'sandra': 38.0, 'fits': 92.0, 'milk,football,apple': 173.0, 'the': 5.0, 'milk,apple': 169.0, 'a': 55.0, 'give': 150.0, 'longer': 177.0, 'maybe': 21.0, 'hungry': 140.0, 'apple,milk': 174.0, 'green': 68.0, 'wolves': 53.0, 'left': 85.0}
self.ivocab = {0: None, 1: 'fred', 2: 'is', 3: 'either', 4: 'in', 5: 'the', 6: 'school', 7: 'or', 8: 'park', 9: '.', 10: 'mary', 11: 'went', 12: 'back', 13: 'to', 14: 'office', 15: 'yes', 16: 'bill', 17: 'kitchen', 18: 'moved', 19: 'cinema', 20: 'no', 21: 'maybe', 22: 'travelled', 23: 'julie', 24: 'bedroom', 25: 'journeyed', 26: 'bathroom', 27: 'after', 28: 'that', 29: 'she', 30: 'where', 31: 'daniel', 32: 'afterwards', 33: 'he', 34: 'hallway', 35: 'john', 36: 'following', 37: 'garden', 38: 'sandra', 39: 'then', 40: 'and', 41: 'they', 42: 'yesterday', 43: 'this', 44: 'morning', 45: 'was', 46: 'before', 47: 'afternoon', 48: 'evening', 49: 'mice', 50: 'are', 51: 'afraid', 52: 'of', 53: 'wolves', 54: 'gertrude', 55: 'a', 56: 'mouse', 57: 'cats', 58: 'sheep', 59: 'winona', 60: 'emily', 61: 'jessica', 62: 'wolf', 63: 'what', 64: 'cat', 65: 'lily', 66: 'frog', 67: 'bernhard', 68: 'green', 69: 'brian', 70: 'lion', 71: 'white', 72: 'julius', 73: 'swan', 74: 'greg', 75: 'color', 76: 'rhino', 77: 'gray', 78: 'yellow', 79: 'triangle', 80: 'above', 81: 'pink', 82: 'rectangle', 83: 'blue', 84: 'square', 85: 'left', 86: 'right', 87: 'below', 88: 'red', 89: 'sphere', 90: 'box', 91: 'chocolates', 92: 'fits', 93: 'inside', 94: 'chest', 95: 'bigger', 96: 'than', 97: 'suitcase', 98: 'container', 99: 'does', 100: 'fit', 101: 'chocolate', 102: 'east', 103: 'north', 104: 'west', 105: 'how', 106: 'do', 107: 'you', 108: 'go', 109: 'from', 110: 's,e', 111: 'n,w', 112: 'south', 113: 'n,n', 114: 's,w', 115: 'e,s', 116: 's,s', 117: 'e,n', 118: 'w,w', 119: 'e,e', 120: 'n,e', 121: 'w,s', 122: 'w,n', 123: 'sumit', 124: 'tired', 125: 'will', 126: 'why', 127: 'did', 128: 'grabbed', 129: 'pajamas', 130: 'there', 131: 'get', 132: 'yann', 133: 'bored', 134: 'jason', 135: 'thirsty', 136: 'got', 137: 'football', 138: 'antoine', 139: 'milk', 140: 'hungry', 141: 'took', 142: 'apple', 143: 'picked', 144: 'up', 145: 'dropped', 146: 'discarded', 147: 'put', 148: 'down', 149: 'gave', 150: 'give', 151: 'handed', 152: 'jeff', 153: 'who', 154: 'received', 155: 'Bill', 156: 'Fred', 157: 'Mary', 158: 'passed', 159: 'Jeff', 160: 'many', 161: 'objects', 162: 'carrying', 163: 'one', 164: 'none', 165: 'two', 166: 'three', 167: 'nothing', 168: 'football,apple', 169: 'milk,apple', 170: 'apple,football', 171: 'football,milk', 172: 'milk,football', 173: 'milk,football,apple', 174: 'apple,milk', 175: 'apple,milk,football', 176: 'apple,football,milk', 177: 'longer', 178: 'not', 179: 'football,apple,milk'}
#self.vocab = {'jason': 134.0, 'office': 14.0, 'yellow': 78.0, 'bedroom': 24.0, 'go': 108.0, 'yes': 15.0, 'antoine': 138.0, 'milk': 139.0, 'before': 46.0, 'grabbed': 128.0, 'fit': 100.0, 'how': 105.0, 'swan': 73.0, 'than': 96.0, 'to': 13.0, 'does': 99.0, 's,e': 110.0, 'east': 102.0, 'rectangle': 82.0, 'gave': 149.0, 'then': 39.0, 'evening': 48.0, 'triangle': 79.0, 'garden': 37.0, 'get': 131.0, 'football,apple,milk': 179.0, 'they': 41.0, 'not': 178.0, 'bigger': 95.0, 'gray': 77.0, 'school': 6.0, 'apple': 142.0, 'did': 127.0, 'morning': 44.0, 'discarded': 146.0, 'julius': 72.0, 'she': 29.0, 'went': 11.0, 'where': 30.0, 'jeff': 152.0, 'square': 84.0, 'who': 153.0, 'tired': 124.0, 'there': 130.0, 'back': 12.0, 'lion': 70.0, 'are': 50.0, 'picked': 143.0, 'e,e': 119.0, 'pajamas': 129.0, 'Mary': 157.0, 'blue': 83.0, 'what': 63.0, 'container': 98.0, 'rhino': 76.0, 'daniel': 31.0, 'bernhard': 67.0, 'milk,football': 172.0, 'above': 80.0, 'got': 136.0, 'emily': 60.0, 'red': 88.0, 'either': 3.0, 'sheep': 58.0, 'football': 137.0, 'jessica': 61.0, 'do': 106.0, 'Bill': 155.0, 'football,apple': 168.0, 'fred': 1.0, 'winona': 59.0, 'objects': 161.0, 'put': 147.0, 'kitchen': 17.0, 'box': 90.0, 'received': 154.0, 'journeyed': 25.0, 'of': 52.0, 'wolf': 62.0, 'afternoon': 47.0, 'or': 7.0, 'south': 112.0, 's,w': 114.0, 'afterwards': 32.0, 'sumit': 123.0, 'color': 75.0, 'julie': 23.0, 'one': 163.0, 'down': 148.0, 'nothing': 167.0, 'n,n': 113.0, 'right': 86.0, 's,s': 116.0, 'gertrude': 54.0, 'bathroom': 26.0, 'from': 109.0, 'west': 104.0, 'chocolates': 91.0, 'two': 165.0, 'frog': 66.0, '.': 9.0, 'cats': 57.0, 'apple,milk,football': 175.0, 'passed': 158.0, 'apple,football,milk': 176.0, 'white': 71.0, 'john': 35.0, 'was': 45.0, 'mary': 10.0, 'apple,football': 170.0, 'north': 103.0, 'n,w': 111.0, 'that': 28.0, 'park': 8.0, 'took': 141.0, 'chocolate': 101.0, 'carrying': 162.0, 'n,e': 120.0, 'mice': 49.0, 'travelled': 22.0, 'he': 33.0, 'none': 164.0, 'bored': 133.0, 'e,n': 117.0, None: 0, 'Jeff': 159.0, 'this': 43.0, 'inside': 93.0, 'bill': 16.0, 'up': 144.0, 'cat': 64.0, 'will': 125.0, 'below': 87.0, 'greg': 74.0, 'three': 166.0, 'suitcase': 97.0, 'following': 36.0, 'e,s': 115.0, 'and': 40.0, 'thirsty': 135.0, 'cinema': 19.0, 'is': 2.0, 'moved': 18.0, 'yann': 132.0, 'sphere': 89.0, 'dropped': 145.0, 'in': 4.0, 'mouse': 56.0, 'football,milk': 171.0, 'pink': 81.0, 'afraid': 51.0, 'no': 20.0, 'Fred': 156.0, 'w,s': 121.0, 'handed': 151.0, 'w,w': 118.0, 'brian': 69.0, 'chest': 94.0, 'w,n': 122.0, 'you': 107.0, 'many': 160.0, 'lily': 65.0, 'hallway': 34.0, 'why': 126.0, 'after': 27.0, 'yesterday': 42.0, 'sandra': 38.0, 'fits': 92.0, 'milk,football,apple': 173.0, 'the': 5.0, 'milk,apple': 169.0, 'a': 55.0, 'give': 150.0, 'longer': 177.0, 'maybe': 21.0, 'hungry': 140.0, 'apple,milk': 174.0, 'green': 68.0, 'wolves': 53.0, 'left': 85.0}
#self.ivocab = {1: 'fred', 2: 'is', 3: 'either', 4: 'in', 5: 'the', 6: 'school', 7: 'or', 8: 'park', 9: '.', 10: 'mary', 11: 'went', 12: 'back', 13: 'to', 14: 'office', 15: 'yes', 16: 'bill', 17: 'kitchen', 18: 'moved', 19: 'cinema', 20: 'no', 21: 'maybe', 22: 'travelled', 23: 'julie', 24: 'bedroom', 25: 'journeyed', 26: 'bathroom', 27: 'after', 28: 'that', 29: 'she', 30: 'where', 31: 'daniel', 32: 'afterwards', 33: 'he', 34: 'hallway', 35: 'john', 36: 'following', 37: 'garden', 38: 'sandra', 39: 'then', 40: 'and', 41: 'they', 42: 'yesterday', 43: 'this', 44: 'morning', 45: 'was', 46: 'before', 47: 'afternoon', 48: 'evening', 49: 'mice', 50: 'are', 51: 'afraid', 52: 'of', 53: 'wolves', 54: 'gertrude', 55: 'a', 56: 'mouse', 57: 'cats', 58: 'sheep', 59: 'winona', 60: 'emily', 61: 'jessica', 62: 'wolf', 63: 'what', 64: 'cat', 65: 'lily', 66: 'frog', 67: 'bernhard', 68: 'green', 69: 'brian', 70: 'lion', 71: 'white', 72: 'julius', 73: 'swan', 74: 'greg', 75: 'color', 76: 'rhino', 77: 'gray', 78: 'yellow', 79: 'triangle', 80: 'above', 81: 'pink', 82: 'rectangle', 83: 'blue', 84: 'square', 85: 'left', 86: 'right', 87: 'below', 88: 'red', 89: 'sphere', 90: 'box', 91: 'chocolates', 92: 'fits', 93: 'inside', 94: 'chest', 95: 'bigger', 96: 'than', 97: 'suitcase', 98: 'container', 99: 'does', 100: 'fit', 101: 'chocolate', 102: 'east', 103: 'north', 104: 'west', 105: 'how', 106: 'do', 107: 'you', 108: 'go', 109: 'from', 110: 's,e', 111: 'n,w', 112: 'south', 113: 'n,n', 114: 's,w', 115: 'e,s', 116: 's,s', 117: 'e,n', 118: 'w,w', 119: 'e,e', 120: 'n,e', 121: 'w,s', 122: 'w,n', 123: 'sumit', 124: 'tired', 125: 'will', 126: 'why', 127: 'did', 128: 'grabbed', 129: 'pajamas', 130: 'there', 131: 'get', 132: 'yann', 133: 'bored', 134: 'jason', 135: 'thirsty', 136: 'got', 137: 'football', 138: 'antoine', 139: 'milk', 140: 'hungry', 141: 'took', 142: 'apple', 143: 'picked', 144: 'up', 145: 'dropped', 146: 'discarded', 147: 'put', 148: 'down', 149: 'gave', 150: 'give', 151: 'handed', 152: 'jeff', 153: 'who', 154: 'received', 155: 'Bill', 156: 'Fred', 157: 'Mary', 158: 'passed', 159: 'Jeff', 160: 'many', 161: 'objects', 162: 'carrying', 163: 'one', 164: 'none', 165: 'two', 166: 'three', 167: 'nothing', 168: 'football,apple', 169: 'milk,apple', 170: 'apple,football', 171: 'football,milk', 172: 'milk,football', 173: 'milk,football,apple', 174: 'apple,milk', 175: 'apple,milk,football', 176: 'apple,football,milk', 177: 'longer', 178: 'not', 179: 'football,apple,milk'}
#"""
self.train_input, self.train_q, self.train_answer, self.train_input_mask = self._process_input(babi_train_raw)
self.test_input, self.test_q, self.test_answer, self.test_input_mask = self._process_input(babi_test_raw)
self.vocab_size = len(self.vocab)
self.input_var = T.imatrix('input_var')
self.q_var = T.ivector('question_var')
self.answer_var = T.iscalar('answer_var')
self.input_mask_var = T.ivector('input_mask_var')
self.attentions = []
self.pe_matrix_in = self.pe_matrix(self.max_inp_sent_len)
self.pe_matrix_q = self.pe_matrix(self.max_q_len)
print "==> building input module"
#positional encoder weights
self.W_pe = nn_utils.normal_param(std=0.1, shape=(self.vocab_size, self.dim))
#biGRU input fusion weights
self.W_inp_res_in_fwd = nn_utils.normal_param(std=0.1, shape=(self.dim, self.sent_vector_size))
self.W_inp_res_hid_fwd = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_inp_res_fwd = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_inp_upd_in_fwd = nn_utils.normal_param(std=0.1, shape=(self.dim, self.sent_vector_size))
self.W_inp_upd_hid_fwd = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_inp_upd_fwd = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_inp_hid_in_fwd = nn_utils.normal_param(std=0.1, shape=(self.dim, self.sent_vector_size))
self.W_inp_hid_hid_fwd = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_inp_hid_fwd = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_inp_res_in_bwd = nn_utils.normal_param(std=0.1, shape=(self.dim, self.sent_vector_size))
self.W_inp_res_hid_bwd = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_inp_res_bwd = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_inp_upd_in_bwd = nn_utils.normal_param(std=0.1, shape=(self.dim, self.sent_vector_size))
self.W_inp_upd_hid_bwd = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_inp_upd_bwd = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_inp_hid_in_bwd = nn_utils.normal_param(std=0.1, shape=(self.dim, self.sent_vector_size))
self.W_inp_hid_hid_bwd = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_inp_hid_bwd = nn_utils.constant_param(value=0.0, shape=(self.dim,))
#self.V_f = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
#self.V_b = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.inp_sent_reps, _ = theano.scan(
fn=self.sum_pos_encodings_in,
sequences=self.input_var)
self.inp_sent_reps_stacked = T.stacklists(self.inp_sent_reps)
#self.inp_c = self.input_module_full(self.inp_sent_reps_stacked)
self.inp_c = self.input_module_full(self.inp_sent_reps)
self.q_q = self.sum_pos_encodings_q(self.q_var)
print "==> creating parameters for memory module"
self.W_mem_res_in = nn_utils.normal_param(std=0.1, shape=(self.memory_hops, self.dim, self.dim))
self.W_mem_res_hid = nn_utils.normal_param(std=0.1, shape=(self.memory_hops, self.dim, self.dim))
self.b_mem_res = nn_utils.constant_param(value=0.0, shape=(self.memory_hops, self.dim,))
self.W_mem_upd_in = nn_utils.normal_param(std=0.1, shape=(self.memory_hops, self.dim, self.dim))
self.W_mem_upd_hid = nn_utils.normal_param(std=0.1, shape=(self.memory_hops, self.dim, self.dim))
self.b_mem_upd = nn_utils.constant_param(value=0.0, shape=(self.memory_hops, self.dim,))
self.W_mem_hid_in = nn_utils.normal_param(std=0.1, shape=(self.memory_hops, self.dim, self.dim))
self.W_mem_hid_hid = nn_utils.normal_param(std=0.1, shape=(self.memory_hops, self.dim, self.dim))
self.b_mem_hid = nn_utils.constant_param(value=0.0, shape=(self.memory_hops, self.dim,))
#self.W_b = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
#self.W_1 = nn_utils.normal_param(std=0.1, shape=(self.dim, 7 * self.dim + 0))
self.W_1 = nn_utils.normal_param(std=0.1, shape=(self.memory_hops, self.dim, 4 * self.dim + 0))
self.W_2 = nn_utils.normal_param(std=0.1, shape=(self.memory_hops, 1, self.dim))
self.b_1 = nn_utils.constant_param(value=0.0, shape=(self.memory_hops, self.dim,))
self.b_2 = nn_utils.constant_param(value=0.0, shape=(self.memory_hops, 1,))
print "==> building episodic memory module (fixed number of steps: %d)" % self.memory_hops
memory = [self.q_q.copy()]
for iter in range(1, self.memory_hops + 1):
self.mem_weight_num = int(iter - 1)
current_episode = self.new_episode(memory[iter - 1])
memory.append(self.GRU_update(memory[iter - 1], current_episode,
self.W_mem_res_in[self.mem_weight_num], self.W_mem_res_hid[self.mem_weight_num], self.b_mem_res[self.mem_weight_num],
self.W_mem_upd_in[self.mem_weight_num], self.W_mem_upd_hid[self.mem_weight_num], self.b_mem_upd[self.mem_weight_num],
self.W_mem_hid_in[self.mem_weight_num], self.W_mem_hid_hid[self.mem_weight_num], self.b_mem_hid[self.mem_weight_num]))
last_mem_raw = memory[-1].dimshuffle(('x', 0))
net = layers.InputLayer(shape=(1, self.dim), input_var=last_mem_raw)
if self.dropout > 0 and self.mode == 'train':
net = layers.DropoutLayer(net, p=self.dropout)
last_mem = layers.get_output(net)[0]
print "==> building answer module"
self.W_a = nn_utils.normal_param(std=0.1, shape=(self.vocab_size, self.dim))
if self.answer_module == 'feedforward':
self.prediction = nn_utils.softmax(T.dot(self.W_a, last_mem))
elif self.answer_module == 'recurrent':
self.W_ans_res_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim + self.vocab_size))
self.W_ans_res_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_ans_res = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_ans_upd_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim + self.vocab_size))
self.W_ans_upd_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_ans_upd = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_ans_hid_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim + self.vocab_size))
self.W_ans_hid_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_ans_hid = nn_utils.constant_param(value=0.0, shape=(self.dim,))
def answer_step(prev_a, prev_y):
a = self.GRU_update(prev_a, T.concatenate([prev_y, self.q_q]),
self.W_ans_res_in, self.W_ans_res_hid, self.b_ans_res,
self.W_ans_upd_in, self.W_ans_upd_hid, self.b_ans_upd,
self.W_ans_hid_in, self.W_ans_hid_hid, self.b_ans_hid)
y = nn_utils.softmax(T.dot(self.W_a, a))
return [a, y]
# add conditional ending?
dummy = theano.shared(np.zeros((self.vocab_size, ), dtype=floatX))
results, updates = theano.scan(fn=answer_step,
outputs_info=[last_mem, T.zeros_like(dummy)],
n_steps=1)
self.prediction = results[1][-1]
else:
raise Exception("invalid answer_module")
print "==> collecting all parameters"
self.params = [self.W_pe,
self.W_inp_res_in_fwd, self.W_inp_res_hid_fwd, self.b_inp_res_fwd,
self.W_inp_upd_in_fwd, self.W_inp_upd_hid_fwd, self.b_inp_upd_fwd,
self.W_inp_hid_in_fwd, self.W_inp_hid_hid_fwd, self.b_inp_hid_fwd,
self.W_inp_res_in_bwd, self.W_inp_res_hid_bwd, self.b_inp_res_bwd,
self.W_inp_upd_in_bwd, self.W_inp_upd_hid_bwd, self.b_inp_upd_bwd,
self.W_inp_hid_in_bwd, self.W_inp_hid_hid_bwd, self.b_inp_hid_bwd,
self.W_mem_res_in, self.W_mem_res_hid, self.b_mem_res,
self.W_mem_upd_in, self.W_mem_upd_hid, self.b_mem_upd,
self.W_mem_hid_in, self.W_mem_hid_hid, self.b_mem_hid, #self.W_b
self.W_1, self.W_2, self.b_1, self.b_2, self.W_a]
if self.answer_module == 'recurrent':
self.params = self.params + [self.W_ans_res_in, self.W_ans_res_hid, self.b_ans_res,
self.W_ans_upd_in, self.W_ans_upd_hid, self.b_ans_upd,
self.W_ans_hid_in, self.W_ans_hid_hid, self.b_ans_hid]
print "==> building loss layer and computing updates"
self.loss_ce = T.nnet.categorical_crossentropy(self.prediction.dimshuffle('x', 0),
T.stack([self.answer_var]))[0]
if self.l2 > 0:
self.loss_l2 = self.l2 * nn_utils.l2_reg(self.params)
else:
self.loss_l2 = 0
self.loss = self.loss_ce + self.loss_l2
#updates = lasagne.updates.adadelta(self.loss, self.params)
updates = lasagne.updates.adam(self.loss, self.params)
updates = lasagne.updates.adam(self.loss, self.params, learning_rate=0.0001, beta1=0.5) #from DCGAN paper
#updates = lasagne.updates.adadelta(self.loss, self.params, learning_rate=0.0005)
#updates = lasagne.updates.momentum(self.loss, self.params, learning_rate=0.0003)
self.attentions = T.stack(self.attentions)
if self.mode == 'train':
print "==> compiling train_fn"
self.train_fn = theano.function(inputs=[self.input_var, self.q_var, self.answer_var, self.input_mask_var],
outputs=[self.prediction, self.loss, self.attentions],
updates=updates,
on_unused_input='warn',
allow_input_downcast=True)
print "==> compiling test_fn"
self.test_fn = theano.function(inputs=[self.input_var, self.q_var, self.answer_var, self.input_mask_var],
outputs=[self.prediction, self.loss, self.attentions],
on_unused_input='warn',
allow_input_downcast=True)
def __init__(self, stories, QAs, batch_size, story_v, learning_rate,
word_vector_size, sent_vector_size, dim, mode, answer_module,
input_mask_mode, memory_hops, l2, story_source,
normalize_attention, batch_norm, dropout, dropout_in,
**kwargs):
#print "==> not used params in DMN class:", kwargs.keys()
self.learning_rate = learning_rate
self.rng = np.random
self.rng.seed(1234)
mqa = MovieQA.DataLoader()
### Load Word2Vec model
w2v_model = w2v.load(w2v_mqa_model_filename, kind='bin')
self.w2v = w2v_model
self.d_w2v = len(w2v_model.get_vector(w2v_model.vocab[1]))
self.word_thresh = 1
print "Loaded word2vec model: dim = %d | vocab-size = %d" % (
self.d_w2v, len(w2v_model.vocab))
### Create vocabulary-to-index and index-to-vocabulary
v2i = {'': 0, 'UNK': 1} # vocabulary to index
QA_words, v2i = self.create_vocabulary(
QAs,
stories,
v2i,
w2v_vocab=w2v_model.vocab.tolist(),
word_thresh=self.word_thresh)
i2v = {v: k for k, v in v2i.iteritems()}
self.vocab = v2i
self.ivocab = i2v
self.story_v = story_v
self.word2vec = w2v_model
self.word_vector_size = word_vector_size
self.sent_vector_size = sent_vector_size
self.dim = dim
self.batch_size = batch_size
self.mode = mode
self.answer_module = answer_module
self.input_mask_mode = input_mask_mode
self.memory_hops = memory_hops
self.l2 = l2
self.normalize_attention = normalize_attention
self.batch_norm = batch_norm
self.dropout = dropout
self.dropout_in = dropout_in
#self.max_inp_sent_len = 0
#self.max_q_len = 0
### Convert QAs and stories into numpy matrices (like in the bAbI data set)
# storyM - Dictionary - indexed by imdb_key. Values are [num-sentence X max-num-words]
# questionM - NP array - [num-question X max-num-words]
# answerM - NP array - [num-question X num-answer-options X max-num-words]
storyM, questionM, answerM = self.data_in_matrix_form(
stories, QA_words, v2i)
qinfo = self.associate_additional_QA_info(QAs)
### Split everything into train, val, and test data
#train_storyM = {k:v for k, v in storyM.iteritems() if k in mqa.data_split['train']}
#val_storyM = {k:v for k, v in storyM.iteritems() if k in mqa.data_split['val']}
#test_storyM = {k:v for k, v in storyM.iteritems() if k in mqa.data_split['test']}
def split_train_test(long_list, QAs, trnkey='train', tstkey='val'):
# Create train/val/test splits based on key
train_split = [
item for k, item in enumerate(long_list)
if QAs[k].qid.startswith('train')
]
val_split = [
item for k, item in enumerate(long_list)
if QAs[k].qid.startswith('val')
]
test_split = [
item for k, item in enumerate(long_list)
if QAs[k].qid.startswith('test')
]
if type(long_list) == np.ndarray:
return np.array(train_split), np.array(val_split), np.array(
test_split)
else:
return train_split, val_split, test_split
train_questionM, val_questionM, test_questionM = split_train_test(
questionM, QAs)
train_answerM, val_answerM, test_answerM, = split_train_test(
answerM, QAs)
train_qinfo, val_qinfo, test_qinfo = split_train_test(qinfo, QAs)
QA_train = [qa for qa in QAs if qa.qid.startswith('train:')]
QA_val = [qa for qa in QAs if qa.qid.startswith('val:')]
QA_test = [qa for qa in QAs if qa.qid.startswith('test:')]
#train_data = {'s':train_storyM, 'q':train_questionM, 'a':train_answerM, 'qinfo':train_qinfo}
#val_data = {'s':val_storyM, 'q':val_questionM, 'a':val_answerM, 'qinfo':val_qinfo}
#test_data = {'s':test_storyM, 'q':test_questionM, 'a':test_answerM, 'qinfo':test_qinfo}
with open('train_split.json') as fid:
trdev = json.load(fid)
s_key = self.story_v.keys()
self.train_range = [
k for k, qi in enumerate(qinfo)
if (qi['movie'] in trdev['train'] and qi['qid'] in s_key)
]
self.train_val_range = [
k for k, qi in enumerate(qinfo)
if (qi['movie'] in trdev['dev'] and qi['qid'] in s_key)
]
self.val_range = [
k for k, qi in enumerate(val_qinfo) if qi['qid'] in s_key
]
self.max_sent_len = max(
[sty.shape[0] for sty in self.story_v.values()])
self.train_input = self.story_v
self.train_val_input = self.story_v
self.test_input = self.story_v
self.train_q = train_questionM
self.train_answer = train_answerM
self.train_qinfo = train_qinfo
self.train_val_q = train_questionM
self.train_val_answer = train_answerM
self.train_val_qinfo = train_qinfo
self.test_q = val_questionM
self.test_answer = val_answerM
self.test_qinfo = val_qinfo
"""Setup some configuration parts of the model.
"""
self.v2i = v2i
self.vs = len(v2i)
self.d_lproj = 300
# define Look-Up-Table mask
np_mask = np.vstack(
(np.zeros(self.d_w2v), np.ones((self.vs - 1, self.d_w2v))))
T_mask = theano.shared(np_mask.astype(theano.config.floatX),
name='LUT_mask')
# setup Look-Up-Table to be Word2Vec
self.pca_mat = None
print "Initialize LUTs as word2vec and use linear projection layer"
self.LUT = np.zeros((self.vs, self.d_w2v), dtype='float32')
found_words = 0
for w, v in self.v2i.iteritems():
if w in self.w2v.vocab: # all valid words are already in vocab or 'UNK'
self.LUT[v] = self.w2v.get_vector(w)
found_words += 1
else:
# LUT[v] = np.zeros((self.d_w2v))
self.LUT[v] = self.rng.randn(self.d_w2v)
self.LUT[v] = self.LUT[v] / (np.linalg.norm(self.LUT[v]) +
1e-6)
print "Found %d / %d words" % (found_words, len(self.v2i))
# word 0 is blanked out, word 1 is 'UNK'
self.LUT[0] = np.zeros((self.d_w2v))
# if linear projection layer is not the same shape as LUT, then initialize with PCA
if self.d_lproj != self.LUT.shape[1]:
pca = PCA(n_components=self.d_lproj, whiten=True)
self.pca_mat = pca.fit_transform(self.LUT.T) # 300 x 100?
# setup LUT!
self.T_w2v = theano.shared(self.LUT.astype(theano.config.floatX))
self.train_input_mask = np_mask
self.test_input_mask = np_mask
#self.train_input, self.train_q, self.train_answer, self.train_input_mask = self._process_input(babi_train_raw)
#self.test_input, self.test_q, self.test_answer, self.test_input_mask = self._process_input(babi_test_raw)
self.vocab_size = len(self.vocab)
self.input_var = T.tensor3(
'input_var') # batch-size X sentences X 4096
self.q_var = T.matrix('question_var') # batch-size X 300
self.answer_var = T.tensor3(
'answer_var') # batch-size X multiple options X 300
self.input_mask_var = T.imatrix('input_mask_var')
self.target = T.ivector(
'target'
) # batch-size ('single': word index, 'multi_choice': correct option)
self.attentions = []
#self.pe_matrix_in = self.pe_matrix(self.max_inp_sent_len)
#self.pe_matrix_q = self.pe_matrix(self.max_q_len)
print "==> building input module"
#positional encoder weights
self.W_pe = nn_utils.normal_param(std=0.1,
shape=(self.vocab_size, self.dim))
#biGRU input fusion weights
self.W_inp_res_in_fwd = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.sent_vector_size))
self.W_inp_res_hid_fwd = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.dim))
self.b_inp_res_fwd = nn_utils.constant_param(value=0.0,
shape=(self.dim, ))
self.W_inp_upd_in_fwd = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.sent_vector_size))
self.W_inp_upd_hid_fwd = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.dim))
self.b_inp_upd_fwd = nn_utils.constant_param(value=0.0,
shape=(self.dim, ))
self.W_inp_hid_in_fwd = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.sent_vector_size))
self.W_inp_hid_hid_fwd = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.dim))
self.b_inp_hid_fwd = nn_utils.constant_param(value=0.0,
shape=(self.dim, ))
self.W_inp_res_in_bwd = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.sent_vector_size))
self.W_inp_res_hid_bwd = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.dim))
self.b_inp_res_bwd = nn_utils.constant_param(value=0.0,
shape=(self.dim, ))
self.W_inp_upd_in_bwd = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.sent_vector_size))
self.W_inp_upd_hid_bwd = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.dim))
self.b_inp_upd_bwd = nn_utils.constant_param(value=0.0,
shape=(self.dim, ))
self.W_inp_hid_in_bwd = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.sent_vector_size))
self.W_inp_hid_hid_bwd = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.dim))
self.b_inp_hid_bwd = nn_utils.constant_param(value=0.0,
shape=(self.dim, ))
#self.V_f = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
#self.V_b = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.inp_sent_reps = self.input_var
self.ans_reps = self.answer_var
self.inp_c = self.input_module_full(self.inp_sent_reps)
self.q_q = self.q_var
print "==> creating parameters for memory module"
self.W_mem_res_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.W_mem_res_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_mem_res = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_mem_upd_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.W_mem_upd_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_mem_upd = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_mem_hid_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.W_mem_hid_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_mem_hid = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
#self.W_b = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
#self.W_1 = nn_utils.normal_param(std=0.1, shape=(self.dim, 7 * self.dim + 0))
self.W_1 = nn_utils.normal_param(std=0.1,
shape=(self.dim, 4 * self.dim + 0))
self.W_2 = nn_utils.normal_param(std=0.1, shape=(1, self.dim))
self.b_1 = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.b_2 = nn_utils.constant_param(value=0.0, shape=(1, ))
print "==> building episodic memory module (fixed number of steps: %d)" % self.memory_hops
memory = [self.q_q.copy()]
for iter in range(1, self.memory_hops + 1):
current_episode = self.new_episode(memory[iter - 1])
memory.append(
self.GRU_update(memory[iter - 1], current_episode,
self.W_mem_res_in, self.W_mem_res_hid,
self.b_mem_res, self.W_mem_upd_in,
self.W_mem_upd_hid, self.b_mem_upd,
self.W_mem_hid_in, self.W_mem_hid_hid,
self.b_mem_hid))
#last_mem_raw = memory[-1].dimshuffle(('x', 0))
last_mem_raw = memory[-1]
net = layers.InputLayer(shape=(self.batch_size, self.dim),
input_var=last_mem_raw)
if self.dropout > 0 and self.mode == 'train':
net = layers.DropoutLayer(net, p=self.dropout)
last_mem = layers.get_output(net)[0]
print "==> building answer module"
self.W_a = nn_utils.normal_param(std=0.1, shape=(300, self.dim))
if self.answer_module == 'feedforward':
self.temp = T.dot(self.ans_reps, self.W_a)
self.prediction = nn_utils.softmax(T.dot(self.temp, last_mem))
elif self.answer_module == 'recurrent':
self.W_ans_res_in = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.dim + self.vocab_size))
self.W_ans_res_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.dim))
self.b_ans_res = nn_utils.constant_param(value=0.0,
shape=(self.dim, ))
self.W_ans_upd_in = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.dim + self.vocab_size))
self.W_ans_upd_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.dim))
self.b_ans_upd = nn_utils.constant_param(value=0.0,
shape=(self.dim, ))
self.W_ans_hid_in = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.dim + self.vocab_size))
self.W_ans_hid_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.dim))
self.b_ans_hid = nn_utils.constant_param(value=0.0,
shape=(self.dim, ))
def answer_step(prev_a, prev_y):
a = self.GRU_update(prev_a, T.concatenate([prev_y, self.q_q]),
self.W_ans_res_in, self.W_ans_res_hid,
self.b_ans_res, self.W_ans_upd_in,
self.W_ans_upd_hid, self.b_ans_upd,
self.W_ans_hid_in, self.W_ans_hid_hid,
self.b_ans_hid)
y = nn_utils.softmax(T.dot(self.W_a, a))
return [a, y]
# add conditional ending?
dummy = theano.shared(np.zeros((self.vocab_size, ), dtype=floatX))
results, updates = theano.scan(
fn=answer_step,
outputs_info=[last_mem, T.zeros_like(dummy)],
n_steps=1)
self.prediction = results[1][-1]
else:
raise Exception("invalid answer_module")
print "==> collecting all parameters"
self.params = [
self.W_pe,
self.W_inp_res_in_fwd,
self.W_inp_res_hid_fwd,
self.b_inp_res_fwd,
self.W_inp_upd_in_fwd,
self.W_inp_upd_hid_fwd,
self.b_inp_upd_fwd,
self.W_inp_hid_in_fwd,
self.W_inp_hid_hid_fwd,
self.b_inp_hid_fwd,
self.W_inp_res_in_bwd,
self.W_inp_res_hid_bwd,
self.b_inp_res_bwd,
self.W_inp_upd_in_bwd,
self.W_inp_upd_hid_bwd,
self.b_inp_upd_bwd,
self.W_inp_hid_in_bwd,
self.W_inp_hid_hid_bwd,
self.b_inp_hid_bwd,
self.W_mem_res_in,
self.W_mem_res_hid,
self.b_mem_res,
self.W_mem_upd_in,
self.W_mem_upd_hid,
self.b_mem_upd,
self.W_mem_hid_in,
self.W_mem_hid_hid,
self.b_mem_hid, #self.W_b
self.W_1,
self.W_2,
self.b_1,
self.b_2,
self.W_a
]
if self.answer_module == 'recurrent':
self.params = self.params + [
self.W_ans_res_in, self.W_ans_res_hid, self.b_ans_res,
self.W_ans_upd_in, self.W_ans_upd_hid, self.b_ans_upd,
self.W_ans_hid_in, self.W_ans_hid_hid, self.b_ans_hid
]
print "==> building loss layer and computing updates"
#tmp= self.prediction.dimshuffle(2,0,1)
#res, _ =theano.scan(fn = lambda inp: inp, sequences=tmp)
#self.prediction = res[-1]
self.loss_ce = T.nnet.categorical_crossentropy(self.prediction,
self.target)
if self.l2 > 0:
self.loss_l2 = self.l2 * nn_utils.l2_reg(self.params)
else:
self.loss_l2 = 0
self.loss = T.mean(self.loss_ce) + self.loss_l2
#updates = lasagne.updates.adadelta(self.loss, self.params)
#updates = lasagne.updates.adam(self.loss, self.params)
updates = lasagne.updates.adam(self.loss,
self.params,
learning_rate=self.learning_rate,
beta1=0.5) #from DCGAN paper
#updates = lasagne.updates.adadelta(self.loss, self.params, learning_rate=0.0005)
#updates = lasagne.updates.momentum(self.loss, self.params, learning_rate=0.0003)
self.attentions = T.stack(self.attentions)
if self.mode == 'train':
print "==> compiling train_fn"
self.train_fn = theano.function(
inputs=[
self.input_var, self.q_var, self.answer_var, self.target
],
outputs=[self.prediction, self.loss, self.attentions],
updates=updates,
on_unused_input='warn',
allow_input_downcast=True)
print "==> compiling test_fn"
self.test_fn = theano.function(
inputs=[self.input_var, self.q_var, self.answer_var, self.target],
outputs=[self.prediction, self.loss, self.attentions],
on_unused_input='warn',
allow_input_downcast=True)
def __init__(self,word2vec, word_vector_size, dim,
mode, answer_module, input_mask_mode, memory_hops, batch_size, l2,
normalize_attention, batch_norm, dropout,h5file,json_dict_file ,num_answers,img_vector_size,img_seq_len,
img_h5file_train,img_h5file_test,**kwargs):
print "==> not used params in DMN class:", kwargs.keys()
self.vocab = {}
self.ivocab = {}
self.lr=0.001
self.word2vec = word2vec
self.word_vector_size = word_vector_size
self.dim = dim
self.mode = mode
self.answer_module = answer_module
self.input_mask_mode = input_mask_mode
self.memory_hops = memory_hops
self.batch_size = batch_size
self.l2 = l2
self.normalize_attention = normalize_attention
self.batch_norm = batch_norm
self.dropout = dropout
self.h5file=h5py.File(h5file,"r")
self.img_h5file_train=h5py.File(img_h5file_train,"r")
self.img_h5file_test=h5py.File(img_h5file_test,"r")
self.img_seq_len=img_seq_len
self.img_vector_size=img_vector_size
with open (json_dict_file) as f:
self.json_dict=json.load(f)
#self.train_input, self.train_q, self.train_answer, self.train_fact_count, self.train_input_mask = self._process_input(babi_train_raw)
#self.test_input, self.test_q, self.test_answer, self.test_fact_count, self.test_input_mask = self._process_input(babi_test_raw)
#self.vocab_size = len(self.vocab)
self.vocab_size=num_answers
self.input_var = T.tensor3('input_var') # (batch_size, seq_len, glove_dim)
self.img_input_var=T.tensor3('img_input_var') # (batch_size * img_seq_len , img_vector_size)
self.q_var = T.tensor3('question_var') # as self.input_var
self.answer_var = T.ivector('answer_var') # answer of example in minibatch
self.fact_count_var = T.ivector('fact_count_var') # number of facts in the example of minibatch
self.input_mask_var = T.imatrix('input_mask_var') # (batch_size, indices)
print "==> building input module"
self.W_inp_res_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.word_vector_size))
self.W_inp_res_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_inp_res = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_inp_upd_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.word_vector_size))
self.W_inp_upd_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_inp_upd = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_inp_hid_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.word_vector_size))
self.W_inp_hid_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_inp_hid = nn_utils.constant_param(value=0.0, shape=(self.dim,))
input_var_shuffled = self.input_var.dimshuffle(1, 2, 0)
inp_dummy = theano.shared(np.zeros((self.dim, self.batch_size), dtype=floatX))
inp_c_history, _ = theano.scan(fn=self.input_gru_step,
sequences=input_var_shuffled,
outputs_info=T.zeros_like(inp_dummy))
inp_c_history_shuffled = inp_c_history.dimshuffle(2, 0, 1)
inp_c_list = []
inp_c_mask_list = []
for batch_index in range(self.batch_size):
taken = inp_c_history_shuffled[batch_index].take(self.input_mask_var[batch_index, :self.fact_count_var[batch_index]], axis=0)
inp_c_list.append(T.concatenate([taken, T.zeros((self.input_mask_var.shape[1] - taken.shape[0], self.dim), floatX)]))
inp_c_mask_list.append(T.concatenate([T.ones((taken.shape[0],), np.int32), T.zeros((self.input_mask_var.shape[1] - taken.shape[0],), np.int32)]))
self.inp_c = T.stack(inp_c_list).dimshuffle(1, 2, 0)
inp_c_mask = T.stack(inp_c_mask_list).dimshuffle(1, 0)
###################### Adding the Image Input Module
print "==> building image img_input module"
### Don't Really Need the GRU to reduce the sentences into vectors ###
self.img_input_var=T.reshape(self.img_input_var , ( self.batch_size * self.img_seq_len , self.img_vector_size ))
img_input_layer=layers.InputLayer( shape=(self.batch_size*self.img_seq_len, self.img_vector_size), input_var=self.img_input_var)
## Convert the img_vector_size to self.dim using a MLP ##
img_input_layer=layers.DenseLayer( img_input_layer , num_units=self.dim )
img_input_var_dim=layers.get_output(img_input_layer)
img_input_var_dim=T.reshape(img_input_var_dim ,(self.batch_size , self.img_seq_len , self.dim ) )
#self.img_inp_c = T.stack(img_input_var_dim).dimshuffle(1, 2, 0)
self.img_inp_c = img_input_var_dim.dimshuffle(1,2,0)
###################################################
q_var_shuffled = self.q_var.dimshuffle(1, 2, 0)
q_dummy = theano.shared(np.zeros((self.dim, self.batch_size), dtype=floatX))
q_q_history, _ = theano.scan(fn=self.input_gru_step,
sequences=q_var_shuffled,
outputs_info=T.zeros_like(q_dummy))
self.q_q = q_q_history[-1]
print "==> creating parameters for memory module"
self.W_mem_res_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.W_mem_res_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_mem_res = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_mem_upd_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.W_mem_upd_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_mem_upd = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_mem_hid_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.W_mem_hid_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_mem_hid = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_b = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.W_1 = nn_utils.normal_param(std=0.1, shape=(self.dim, 7 * self.dim + 0))
self.W_2 = nn_utils.normal_param(std=0.1, shape=(1, self.dim))
self.b_1 = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.b_2 = nn_utils.constant_param(value=0.0, shape=(1,))
print "==> building episodic memory module (fixed number of steps: %d)" % self.memory_hops
memory = [self.q_q.copy()]
for iter in range(1, self.memory_hops + 1):
current_episode = self.new_episode(memory[iter - 1])
memory.append(self.GRU_update(memory[iter - 1], current_episode,
self.W_mem_res_in, self.W_mem_res_hid, self.b_mem_res,
self.W_mem_upd_in, self.W_mem_upd_hid, self.b_mem_upd,
self.W_mem_hid_in, self.W_mem_hid_hid, self.b_mem_hid))
last_mem_raw = memory[-1].dimshuffle((1, 0))
################################# Episodic Memory Module for Image
print "==> creating parameters for image memory module"
self.W_img_mem_res_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.W_img_mem_res_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_img_mem_res = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_img_mem_upd_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.W_img_mem_upd_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_img_mem_upd = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_img_mem_hid_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.W_img_mem_hid_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_img_mem_hid = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_img_b = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.W_img_1 = nn_utils.normal_param(std=0.1, shape=(self.dim, 7 * self.dim + 0))
self.W_img_2 = nn_utils.normal_param(std=0.1, shape=(1, self.dim))
self.b_img_1 = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.b_img_2 = nn_utils.constant_param(value=0.0, shape=(1,))
print "==> building episodic img_memory module (fixed number of steps: %d)" % self.memory_hops
img_memory = [self.q_q.copy()]
for iter in range(1, self.memory_hops + 1):
current_episode = self.new_img_episode(img_memory[iter - 1])
img_memory.append(self.GRU_update(img_memory[iter - 1], current_episode,
self.W_img_mem_res_in, self.W_img_mem_res_hid, self.b_img_mem_res,
self.W_img_mem_upd_in, self.W_img_mem_upd_hid, self.b_img_mem_upd,
self.W_img_mem_hid_in, self.W_img_mem_hid_hid, self.b_img_mem_hid))
last_img_mem_raw = img_memory[-1].dimshuffle((1, 0))
#######################################################################
### Concatenating The 2 Memory Modules Representations Assuming the representation as self.batch_size x self.dim ###
combined_mem_raw=T.concatenate([last_mem_raw,last_img_mem_raw],axis=1)
#net = layers.InputLayer(shape=(self.batch_size, self.dim), input_var=last_mem_raw)
net = layers.InputLayer(shape=(self.batch_size, self.dim+self.dim), input_var=combined_mem_raw)
if self.batch_norm:
net = layers.BatchNormLayer(incoming=net)
if self.dropout > 0 and self.mode == 'train':
net = layers.DropoutLayer(net, p=self.dropout)
last_mem = layers.get_output(net).dimshuffle((1, 0))
print "==> building answer module"
#self.W_a = nn_utils.normal_param(std=0.1, shape=(self.vocab_size, self.dim))
self.W_a = nn_utils.normal_param(std=0.1, shape=(self.vocab_size, self.dim+self.dim))
if self.answer_module == 'feedforward':
self.prediction = nn_utils.softmax(T.dot(self.W_a, last_mem))
elif self.answer_module == 'recurrent':
self.W_ans_res_in = nn_utils.normal_param(std=0.1, shape=(2*self.dim, self.dim + self.vocab_size))
self.W_ans_res_hid = nn_utils.normal_param(std=0.1, shape=(2*self.dim, 2*self.dim))
self.b_ans_res = nn_utils.constant_param(value=0.0, shape=(2*self.dim,))
self.W_ans_upd_in = nn_utils.normal_param(std=0.1, shape=(2*self.dim, self.dim + self.vocab_size))
self.W_ans_upd_hid = nn_utils.normal_param(std=0.1, shape=(2*self.dim,2*self.dim))
self.b_ans_upd = nn_utils.constant_param(value=0.0, shape=(2*self.dim,))
self.W_ans_hid_in = nn_utils.normal_param(std=0.1, shape=(2*self.dim, self.dim + self.vocab_size))
self.W_ans_hid_hid = nn_utils.normal_param(std=0.1, shape=(2*self.dim, 2*self.dim))
self.b_ans_hid = nn_utils.constant_param(value=0.0, shape=(2*self.dim,))
def answer_step(prev_a, prev_y):
a = self.GRU_update(prev_a, T.concatenate([prev_y, self.q_q]),
self.W_ans_res_in, self.W_ans_res_hid, self.b_ans_res,
self.W_ans_upd_in, self.W_ans_upd_hid, self.b_ans_upd,
self.W_ans_hid_in, self.W_ans_hid_hid, self.b_ans_hid)
y = nn_utils.softmax(T.dot(self.W_a, a))
return [a, y]
# TODO: add conditional ending
dummy = theano.shared(np.zeros((self.vocab_size, self.batch_size), dtype=floatX))
results, updates = theano.scan(fn=answer_step,
outputs_info=[last_mem, T.zeros_like(dummy)], #(last_mem, y)
n_steps=1)
self.prediction = results[1][-1]
else:
raise Exception("invalid answer_module")
self.prediction = self.prediction.dimshuffle(1, 0)
self.params = [self.W_inp_res_in, self.W_inp_res_hid, self.b_inp_res,
self.W_inp_upd_in, self.W_inp_upd_hid, self.b_inp_upd,
self.W_inp_hid_in, self.W_inp_hid_hid, self.b_inp_hid,
self.W_mem_res_in, self.W_mem_res_hid, self.b_mem_res,
self.W_mem_upd_in, self.W_mem_upd_hid, self.b_mem_upd,
self.W_mem_hid_in, self.W_mem_hid_hid, self.b_mem_hid, #self.W_b
self.W_1, self.W_2, self.b_1, self.b_2, self.W_a, ## Add the parameters of the Image Input Module
self.W_img_mem_res_in, self.W_img_mem_res_hid, self.b_img_mem_res,
self.W_img_mem_upd_in, self.W_img_mem_upd_hid, self.b_img_mem_upd,
self.W_img_mem_hid_in, self.W_img_mem_hid_hid, self.b_img_mem_hid, #self.W_img_b_img
self.W_img_1, self.W_img_2, self.b_img_1, self.b_img_2] ## Add the parameters of the Image Input Module
dim_transform_mlp_params=layers.get_all_params(img_input_layer )
self.params=self.params+ dim_transform_mlp_params
if self.answer_module == 'recurrent':
self.params = self.params + [self.W_ans_res_in, self.W_ans_res_hid, self.b_ans_res,
self.W_ans_upd_in, self.W_ans_upd_hid, self.b_ans_upd,
self.W_ans_hid_in, self.W_ans_hid_hid, self.b_ans_hid]
print "==> building loss layer and computing updates"
self.loss_ce = T.nnet.categorical_crossentropy(self.prediction, self.answer_var).mean()
if self.l2 > 0:
self.loss_l2 = self.l2 * nn_utils.l2_reg(self.params)
else:
self.loss_l2 = 0
self.loss = self.loss_ce + self.loss_l2
self.learning_rate=T.scalar(name="learning_rate")
updates=lasagne.updates.adam(self.loss,self.params,learning_rate=self.learning_rate)
#updates = lasagne.updates.adadelta(self.loss, self.params)
#updates = lasagne.updates.momentum(self.loss, self.params, learning_rate=0.001)
if self.mode == 'train':
print "==> compiling train_fn"
self.train_fn = theano.function(inputs=[self.input_var, self.q_var, self.answer_var,
self.fact_count_var, self.input_mask_var,self.img_input_var,self.learning_rate],
outputs=[self.prediction, self.loss],
updates=updates)
print "==> compiling test_fn"
self.test_fn = theano.function(inputs=[self.input_var, self.q_var, self.answer_var,
self.fact_count_var, self.input_mask_var,self.img_input_var,self.learning_rate],on_unused_input='ignore',
outputs=[self.prediction, self.loss])
def __init__(self, babi_train_raw, babi_test_raw, word2vec,
word_vector_size, memory_hops, dim, mode, input_mask_mode, l2,
batch_norm, dropout, **kwargs):
print "==> not used params in DMN class:", kwargs.keys()
self.vocab = {}
self.ivocab = {}
self.word2vec = word2vec
self.word_vector_size = word_vector_size
self.dim = dim
self.mode = mode
self.input_mask_mode = input_mask_mode
self.l2 = l2
self.batch_norm = batch_norm
self.dropout = dropout
self.memory_hops = memory_hops
self.train_input, self.train_q, self.train_answer, self.train_input_mask, self.train_gates = self._process_input(
babi_train_raw)
self.test_input, self.test_q, self.test_answer, self.test_input_mask, self.test_gates = self._process_input(
babi_test_raw)
self.vocab_size = len(self.vocab)
print "Train size: ", len(self.train_input)
print "Test size: ", len(self.test_input)
print "Vocab size: ", self.vocab_size
self.input_var = T.matrix('input_var')
self.q_var = T.matrix('question_var')
self.answer_var = T.iscalar('answer_var')
self.input_mask_var = T.ivector('input_mask_var')
self.gates_var = T.ivector(
'gates_var') # attention gate (including end_reading)
self.attentions = []
print "==> building input module"
self.W_inp_res_in = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.word_vector_size))
self.W_inp_res_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_inp_res = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_inp_upd_in = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.word_vector_size))
self.W_inp_upd_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_inp_upd = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_inp_hid_in = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.word_vector_size))
self.W_inp_hid_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_inp_hid = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
inp_c_history, _ = theano.scan(fn=self.input_gru_step,
sequences=self.input_var,
outputs_info=T.zeros_like(
self.b_inp_hid))
self.end_reading = nn_utils.constant_param(value=0.0,
shape=(1, self.dim))
inp_c_tag = T.concatenate([inp_c_history, self.end_reading], axis=0)
self.inp_c = inp_c_tag.take(self.input_mask_var,
axis=0) #(facts_len,dim)
self.q_q, _ = theano.scan(fn=self.input_gru_step,
sequences=self.q_var,
outputs_info=T.zeros_like(self.b_inp_hid))
self.q_q = self.q_q[-1] #(1,dim)
print "==> creating parameters for memory module"
self.W_mem_res_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.W_mem_res_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_mem_res = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_mem_upd_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.W_mem_upd_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_mem_upd = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_mem_hid_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.W_mem_hid_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_mem_hid = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_b = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.W_1 = nn_utils.normal_param(std=0.1,
shape=(self.dim, 7 * self.dim + 2))
self.W_2 = nn_utils.normal_param(std=0.1, shape=(1, self.dim))
self.b_1 = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.b_2 = nn_utils.constant_param(value=0.0, shape=(1, ))
print "==> building episodic memory module (fixed number of steps: %d)" % self.memory_hops
memory = [self.q_q.copy()]
for iter in range(0, self.memory_hops):
current_episode, g = self.new_episode(memory[iter])
self.attentions.append(g)
memory.append(
self.GRU_update(memory[iter], current_episode,
self.W_mem_res_in, self.W_mem_res_hid,
self.b_mem_res, self.W_mem_upd_in,
self.W_mem_upd_hid, self.b_mem_upd,
self.W_mem_hid_in, self.W_mem_hid_hid,
self.b_mem_hid))
last_mem_raw = memory[-1].dimshuffle(('x', 0))
net = layers.InputLayer(shape=(1, self.dim), input_var=last_mem_raw)
if self.dropout > 0 and self.mode == 'train':
net = layers.DropoutLayer(net, p=self.dropout)
last_mem = layers.get_output(net)[0]
self.attentions = T.stack(self.attentions) #(memory_hops, fact_cnt)
print "==> building answer module"
self.W_a = nn_utils.normal_param(std=0.1,
shape=(self.vocab_size, self.dim))
self.prediction = nn_utils.softmax(T.dot(self.W_a, last_mem))
print "==> collecting all parameters"
self.params = [
self.W_inp_res_in, self.W_inp_res_hid, self.b_inp_res,
self.W_inp_upd_in, self.W_inp_upd_hid, self.b_inp_upd,
self.W_inp_hid_in, self.W_inp_hid_hid, self.b_inp_hid,
self.W_mem_res_in, self.W_mem_res_hid, self.b_mem_res,
self.W_mem_upd_in, self.W_mem_upd_hid, self.b_mem_upd,
self.W_mem_hid_in, self.W_mem_hid_hid, self.b_mem_hid, self.W_b,
self.W_1, self.W_2, self.b_1, self.b_2, self.W_a
]
print "==> building loss layer and computing updates"
self.loss_ce = T.nnet.categorical_crossentropy(
self.prediction.dimshuffle('x', 0), T.stack([self.answer_var]))[0]
if self.l2 > 0:
self.loss_l2 = self.l2 * nn_utils.l2_reg(self.params)
else:
self.loss_l2 = 0
self.loss_gate = T.nnet.categorical_crossentropy(
self.attentions, self.gates_var).mean()
self.loss = self.loss_ce + self.loss_l2 + self.loss_gate
updates = lasagne.updates.adam(self.loss, self.params)
#updates = lasagne.updates.momentum(self.loss, self.params, learning_rate=0.0003)
if self.mode == 'train':
print "==> compiling train_fn"
self.train_fn = theano.function(
inputs=[
self.input_var, self.q_var, self.answer_var,
self.input_mask_var, self.gates_var
],
allow_input_downcast=True,
outputs=[self.prediction, self.loss, self.attentions],
updates=updates)
print "==> compiling test_fn"
self.test_fn = theano.function(
inputs=[
self.input_var, self.q_var, self.answer_var,
self.input_mask_var, self.gates_var
],
allow_input_downcast=True,
outputs=[self.prediction, self.loss, self.attentions])
if self.mode == 'train':
print "==> computing gradients (for debugging)"
gradient = T.grad(self.loss, self.params)
self.get_gradient_fn = theano.function(inputs=[
self.input_var, self.q_var, self.answer_var,
self.input_mask_var, self.gates_var
],
allow_input_downcast=True,
outputs=gradient)
def __init__(self, train_raw, dev_raw, test_raw, word2vec,
word_vector_size, dim, mode, input_mask_mode, memory_hops, l2,
normalize_attention, dropout, **kwargs):
print "generate one-word answer for mctest"
print "==> not used params in DMN class:", kwargs.keys()
self.word2vec = word2vec
self.word_vector_size = word_vector_size
self.vocab_size = len(word2vec)
self.dim = dim # hidden state size
self.mode = mode
self.input_mask_mode = input_mask_mode
self.memory_hops = memory_hops
self.l2 = l2
self.normalize_attention = normalize_attention
self.dropout = dropout
self.train_input, self.train_q, self.train_answer, self.train_input_mask = self._process_input(
train_raw)
self.dev_input, self.dev_q, self.dev_answer, self.dev_input_mask = self._process_input(
dev_raw)
self.test_input, self.test_q, self.test_answer, self.test_input_mask = self._process_input(
test_raw)
self.input_var = T.matrix('input_var')
self.q_var = T.matrix('question_var')
self.answer_var = T.iscalar('answer_var')
self.input_mask_var = T.ivector('input_mask_var')
self.attentions = []
print "==> building input module"
self.W_inp_res_in = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.word_vector_size))
self.W_inp_res_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_inp_res = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_inp_upd_in = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.word_vector_size))
self.W_inp_upd_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_inp_upd = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_inp_hid_in = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.word_vector_size))
self.W_inp_hid_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_inp_hid = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
inp_c_history, _ = theano.scan(fn=self.input_gru_step,
sequences=self.input_var,
outputs_info=T.zeros_like(
self.b_inp_hid))
self.inp_c = inp_c_history.take(self.input_mask_var, axis=0)
self.q_q, _ = theano.scan(fn=self.input_gru_step,
sequences=self.q_var,
outputs_info=T.zeros_like(self.b_inp_hid))
self.q_q = self.q_q[-1]
print "==> creating parameters for memory module"
self.W_mem_res_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.W_mem_res_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_mem_res = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_mem_upd_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.W_mem_upd_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_mem_upd = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_mem_hid_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.W_mem_hid_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_mem_hid = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_b = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.W_1 = nn_utils.normal_param(std=0.1,
shape=(self.dim, 7 * self.dim + 2))
self.W_2 = nn_utils.normal_param(std=0.1, shape=(1, self.dim))
self.b_1 = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.b_2 = nn_utils.constant_param(value=0.0, shape=(1, ))
print "==> building episodic memory module (fixed number of steps: %d)" % self.memory_hops
memory = [self.q_q.copy()]
for iter in range(1, self.memory_hops + 1):
current_episode = self.new_episode(memory[iter - 1])
memory.append(
self.GRU_update(memory[iter - 1], current_episode,
self.W_mem_res_in, self.W_mem_res_hid,
self.b_mem_res, self.W_mem_upd_in,
self.W_mem_upd_hid, self.b_mem_upd,
self.W_mem_hid_in, self.W_mem_hid_hid,
self.b_mem_hid))
last_mem_raw = memory[-1].dimshuffle(('x', 0))
net = layers.InputLayer(shape=(1, self.dim), input_var=last_mem_raw)
if self.dropout > 0 and self.mode == 'train':
net = layers.DropoutLayer(net, p=self.dropout)
last_mem = layers.get_output(net)[0]
self.attentions = T.stack(self.attentions)
print "==> building answer module"
self.W_a = nn_utils.normal_param(std=0.1,
shape=(self.vocab_size, self.dim))
self.prediction = nn_utils.softmax(T.dot(self.W_a, last_mem))
print "==> collecting all parameters"
self.params = [
self.W_inp_res_in, self.W_inp_res_hid, self.b_inp_res,
self.W_inp_upd_in, self.W_inp_upd_hid, self.b_inp_upd,
self.W_inp_hid_in, self.W_inp_hid_hid, self.b_inp_hid,
self.W_mem_res_in, self.W_mem_res_hid, self.b_mem_res,
self.W_mem_upd_in, self.W_mem_upd_hid, self.b_mem_upd,
self.W_mem_hid_in, self.W_mem_hid_hid, self.b_mem_hid, self.W_b,
self.W_1, self.W_2, self.b_1, self.b_2, self.W_a
]
print "==> building loss layer and computing updates"
self.loss_ce = T.nnet.categorical_crossentropy(
self.prediction.dimshuffle('x', 0), T.stack([self.answer_var]))[0]
if self.l2 > 0:
self.loss_l2 = self.l2 * nn_utils.l2_reg(self.params)
else:
self.loss_l2 = 0
self.loss = self.loss_ce + self.loss_l2
updates = lasagne.updates.adam(self.loss, self.params)
#updates = lasagne.updates.momentum(self.loss, self.params, learning_rate=0.0003)
if self.mode == 'train':
print "==> compiling train_fn"
self.train_fn = theano.function(
inputs=[
self.input_var, self.q_var, self.answer_var,
self.input_mask_var
],
allow_input_downcast=True,
outputs=[self.prediction, self.loss],
updates=updates)
print "==> compiling test_fn"
self.test_fn = theano.function(
inputs=[
self.input_var, self.q_var, self.answer_var,
self.input_mask_var
],
allow_input_downcast=True,
outputs=[self.prediction, self.loss, self.attentions])
def __init__(self, babi_train_raw, babi_test_raw, word2vec, word_vector_size, dim,
mode, answer_module, input_mask_mode, memory_hops, batch_size, l2,
normalize_attention, batch_norm, dropout, **kwargs):
print "==> not used params in DMN class:", kwargs.keys()
self.vocab = {}
self.ivocab = {}
self.word2vec = word2vec
self.word_vector_size = word_vector_size
self.dim = dim
self.mode = mode
self.answer_module = answer_module
self.input_mask_mode = input_mask_mode
self.memory_hops = memory_hops
self.batch_size = batch_size
self.l2 = l2
self.normalize_attention = normalize_attention
self.batch_norm = batch_norm
self.dropout = dropout
self.train_input, self.train_q, self.train_answer, self.train_fact_count, self.train_input_mask = self._process_input(babi_train_raw)
self.test_input, self.test_q, self.test_answer, self.test_fact_count, self.test_input_mask = self._process_input(babi_test_raw)
self.vocab_size = len(self.vocab)
self.input_var = T.tensor3('input_var') # (batch_size, seq_len, glove_dim)
self.q_var = T.tensor3('question_var') # as self.input_var
self.answer_var = T.ivector('answer_var') # answer of example in minibatch
self.fact_count_var = T.ivector('fact_count_var') # number of facts in the example of minibatch
self.input_mask_var = T.imatrix('input_mask_var') # (batch_size, indices)
print "==> building input module"
self.W_inp_res_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.word_vector_size))
self.W_inp_res_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_inp_res = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_inp_upd_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.word_vector_size))
self.W_inp_upd_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_inp_upd = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_inp_hid_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.word_vector_size))
self.W_inp_hid_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_inp_hid = nn_utils.constant_param(value=0.0, shape=(self.dim,))
input_var_shuffled = self.input_var.dimshuffle(1, 2, 0)
inp_dummy = theano.shared(np.zeros((self.dim, self.batch_size), dtype=floatX))
inp_c_history, _ = theano.scan(fn=self.input_gru_step,
sequences=input_var_shuffled,
outputs_info=T.zeros_like(inp_dummy))
inp_c_history_shuffled = inp_c_history.dimshuffle(2, 0, 1)
inp_c_list = []
inp_c_mask_list = []
for batch_index in range(self.batch_size):
taken = inp_c_history_shuffled[batch_index].take(self.input_mask_var[batch_index, :self.fact_count_var[batch_index]], axis=0)
inp_c_list.append(T.concatenate([taken, T.zeros((self.input_mask_var.shape[1] - taken.shape[0], self.dim), floatX)]))
inp_c_mask_list.append(T.concatenate([T.ones((taken.shape[0],), np.int32), T.zeros((self.input_mask_var.shape[1] - taken.shape[0],), np.int32)]))
self.inp_c = T.stack(inp_c_list).dimshuffle(1, 2, 0)
inp_c_mask = T.stack(inp_c_mask_list).dimshuffle(1, 0)
q_var_shuffled = self.q_var.dimshuffle(1, 2, 0)
q_dummy = theano.shared(np.zeros((self.dim, self.batch_size), dtype=floatX))
q_q_history, _ = theano.scan(fn=self.input_gru_step,
sequences=q_var_shuffled,
outputs_info=T.zeros_like(q_dummy))
self.q_q = q_q_history[-1]
print "==> creating parameters for memory module"
self.W_mem_res_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.W_mem_res_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_mem_res = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_mem_upd_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.W_mem_upd_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_mem_upd = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_mem_hid_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.W_mem_hid_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_mem_hid = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_b = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.W_1 = nn_utils.normal_param(std=0.1, shape=(self.dim, 7 * self.dim + 0))
self.W_2 = nn_utils.normal_param(std=0.1, shape=(1, self.dim))
self.b_1 = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.b_2 = nn_utils.constant_param(value=0.0, shape=(1,))
print "==> building episodic memory module (fixed number of steps: %d)" % self.memory_hops
memory = [self.q_q.copy()]
for iter in range(1, self.memory_hops + 1):
current_episode = self.new_episode(memory[iter - 1])
memory.append(self.GRU_update(memory[iter - 1], current_episode,
self.W_mem_res_in, self.W_mem_res_hid, self.b_mem_res,
self.W_mem_upd_in, self.W_mem_upd_hid, self.b_mem_upd,
self.W_mem_hid_in, self.W_mem_hid_hid, self.b_mem_hid))
last_mem_raw = memory[-1].dimshuffle((1, 0))
net = layers.InputLayer(shape=(self.batch_size, self.dim), input_var=last_mem_raw)
if self.batch_norm:
net = layers.BatchNormLayer(incoming=net)
if self.dropout > 0 and self.mode == 'train':
net = layers.DropoutLayer(net, p=self.dropout)
last_mem = layers.get_output(net).dimshuffle((1, 0))
print "==> building answer module"
self.W_a = nn_utils.normal_param(std=0.1, shape=(self.vocab_size, self.dim))
if self.answer_module == 'feedforward':
self.prediction = nn_utils.softmax(T.dot(self.W_a, last_mem))
elif self.answer_module == 'recurrent':
self.W_ans_res_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim + self.vocab_size))
self.W_ans_res_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_ans_res = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_ans_upd_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim + self.vocab_size))
self.W_ans_upd_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_ans_upd = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_ans_hid_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim + self.vocab_size))
self.W_ans_hid_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_ans_hid = nn_utils.constant_param(value=0.0, shape=(self.dim,))
def answer_step(prev_a, prev_y):
a = self.GRU_update(prev_a, T.concatenate([prev_y, self.q_q]),
self.W_ans_res_in, self.W_ans_res_hid, self.b_ans_res,
self.W_ans_upd_in, self.W_ans_upd_hid, self.b_ans_upd,
self.W_ans_hid_in, self.W_ans_hid_hid, self.b_ans_hid)
y = nn_utils.softmax(T.dot(self.W_a, a))
return [a, y]
# TODO: add conditional ending
dummy = theano.shared(np.zeros((self.vocab_size, self.batch_size), dtype=floatX))
results, updates = theano.scan(fn=self.answer_step,
outputs_info=[last_mem, T.zeros_like(dummy)], #(last_mem, y)
n_steps=1)
self.prediction = results[1][-1]
else:
raise Exception("invalid answer_module")
self.prediction = self.prediction.dimshuffle(1, 0)
self.params = [self.W_inp_res_in, self.W_inp_res_hid, self.b_inp_res,
self.W_inp_upd_in, self.W_inp_upd_hid, self.b_inp_upd,
self.W_inp_hid_in, self.W_inp_hid_hid, self.b_inp_hid,
self.W_mem_res_in, self.W_mem_res_hid, self.b_mem_res,
self.W_mem_upd_in, self.W_mem_upd_hid, self.b_mem_upd,
self.W_mem_hid_in, self.W_mem_hid_hid, self.b_mem_hid, #self.W_b
self.W_1, self.W_2, self.b_1, self.b_2, self.W_a]
if self.answer_module == 'recurrent':
self.params = self.params + [self.W_ans_res_in, self.W_ans_res_hid, self.b_ans_res,
self.W_ans_upd_in, self.W_ans_upd_hid, self.b_ans_upd,
self.W_ans_hid_in, self.W_ans_hid_hid, self.b_ans_hid]
print "==> building loss layer and computing updates"
self.loss_ce = T.nnet.categorical_crossentropy(self.prediction, self.answer_var).mean()
if self.l2 > 0:
self.loss_l2 = self.l2 * nn_utils.l2_reg(self.params)
else:
self.loss_l2 = 0
self.loss = self.loss_ce + self.loss_l2
updates = lasagne.updates.adadelta(self.loss, self.params)
#updates = lasagne.updates.momentum(self.loss, self.params, learning_rate=0.001)
if self.mode == 'train':
print "==> compiling train_fn"
self.train_fn = theano.function(inputs=[self.input_var, self.q_var, self.answer_var,
self.fact_count_var, self.input_mask_var],
outputs=[self.prediction, self.loss],
updates=updates)
print "==> compiling test_fn"
self.test_fn = theano.function(inputs=[self.input_var, self.q_var, self.answer_var,
self.fact_count_var, self.input_mask_var],
outputs=[self.prediction, self.loss])
def __init__(self, babi_train_raw, babi_test_raw, word2vec,
word_vector_size, sent_vector_size, dim, mode, answer_module,
input_mask_mode, memory_hops, l2, normalize_attention,
batch_norm, dropout, dropout_in, **kwargs):
print "==> not used params in DMN class:", kwargs.keys()
self.vocab = {None: 0}
self.ivocab = {0: None}
self.word2vec = word2vec
self.word_vector_size = word_vector_size
self.sent_vector_size = sent_vector_size
self.dim = dim
self.mode = mode
self.answer_module = answer_module
self.input_mask_mode = input_mask_mode
self.memory_hops = memory_hops
self.l2 = l2
self.normalize_attention = normalize_attention
self.batch_norm = batch_norm
self.dropout = dropout
self.dropout_in = dropout_in
self.max_inp_sent_len = 0
self.max_q_len = 0
"""
#To Use All Vocab
self.vocab = {None: 0, 'jason': 134.0, 'office': 14.0, 'yellow': 78.0, 'bedroom': 24.0, 'go': 108.0, 'yes': 15.0, 'antoine': 138.0, 'milk': 139.0, 'before': 46.0, 'grabbed': 128.0, 'fit': 100.0, 'how': 105.0, 'swan': 73.0, 'than': 96.0, 'to': 13.0, 'does': 99.0, 's,e': 110.0, 'east': 102.0, 'rectangle': 82.0, 'gave': 149.0, 'then': 39.0, 'evening': 48.0, 'triangle': 79.0, 'garden': 37.0, 'get': 131.0, 'football,apple,milk': 179.0, 'they': 41.0, 'not': 178.0, 'bigger': 95.0, 'gray': 77.0, 'school': 6.0, 'apple': 142.0, 'did': 127.0, 'morning': 44.0, 'discarded': 146.0, 'julius': 72.0, 'she': 29.0, 'went': 11.0, 'where': 30.0, 'jeff': 152.0, 'square': 84.0, 'who': 153.0, 'tired': 124.0, 'there': 130.0, 'back': 12.0, 'lion': 70.0, 'are': 50.0, 'picked': 143.0, 'e,e': 119.0, 'pajamas': 129.0, 'Mary': 157.0, 'blue': 83.0, 'what': 63.0, 'container': 98.0, 'rhino': 76.0, 'daniel': 31.0, 'bernhard': 67.0, 'milk,football': 172.0, 'above': 80.0, 'got': 136.0, 'emily': 60.0, 'red': 88.0, 'either': 3.0, 'sheep': 58.0, 'football': 137.0, 'jessica': 61.0, 'do': 106.0, 'Bill': 155.0, 'football,apple': 168.0, 'fred': 1.0, 'winona': 59.0, 'objects': 161.0, 'put': 147.0, 'kitchen': 17.0, 'box': 90.0, 'received': 154.0, 'journeyed': 25.0, 'of': 52.0, 'wolf': 62.0, 'afternoon': 47.0, 'or': 7.0, 'south': 112.0, 's,w': 114.0, 'afterwards': 32.0, 'sumit': 123.0, 'color': 75.0, 'julie': 23.0, 'one': 163.0, 'down': 148.0, 'nothing': 167.0, 'n,n': 113.0, 'right': 86.0, 's,s': 116.0, 'gertrude': 54.0, 'bathroom': 26.0, 'from': 109.0, 'west': 104.0, 'chocolates': 91.0, 'two': 165.0, 'frog': 66.0, '.': 9.0, 'cats': 57.0, 'apple,milk,football': 175.0, 'passed': 158.0, 'apple,football,milk': 176.0, 'white': 71.0, 'john': 35.0, 'was': 45.0, 'mary': 10.0, 'apple,football': 170.0, 'north': 103.0, 'n,w': 111.0, 'that': 28.0, 'park': 8.0, 'took': 141.0, 'chocolate': 101.0, 'carrying': 162.0, 'n,e': 120.0, 'mice': 49.0, 'travelled': 22.0, 'he': 33.0, 'none': 164.0, 'bored': 133.0, 'e,n': 117.0, None: 0, 'Jeff': 159.0, 'this': 43.0, 'inside': 93.0, 'bill': 16.0, 'up': 144.0, 'cat': 64.0, 'will': 125.0, 'below': 87.0, 'greg': 74.0, 'three': 166.0, 'suitcase': 97.0, 'following': 36.0, 'e,s': 115.0, 'and': 40.0, 'thirsty': 135.0, 'cinema': 19.0, 'is': 2.0, 'moved': 18.0, 'yann': 132.0, 'sphere': 89.0, 'dropped': 145.0, 'in': 4.0, 'mouse': 56.0, 'football,milk': 171.0, 'pink': 81.0, 'afraid': 51.0, 'no': 20.0, 'Fred': 156.0, 'w,s': 121.0, 'handed': 151.0, 'w,w': 118.0, 'brian': 69.0, 'chest': 94.0, 'w,n': 122.0, 'you': 107.0, 'many': 160.0, 'lily': 65.0, 'hallway': 34.0, 'why': 126.0, 'after': 27.0, 'yesterday': 42.0, 'sandra': 38.0, 'fits': 92.0, 'milk,football,apple': 173.0, 'the': 5.0, 'milk,apple': 169.0, 'a': 55.0, 'give': 150.0, 'longer': 177.0, 'maybe': 21.0, 'hungry': 140.0, 'apple,milk': 174.0, 'green': 68.0, 'wolves': 53.0, 'left': 85.0}
self.ivocab = {0: None, 1: 'fred', 2: 'is', 3: 'either', 4: 'in', 5: 'the', 6: 'school', 7: 'or', 8: 'park', 9: '.', 10: 'mary', 11: 'went', 12: 'back', 13: 'to', 14: 'office', 15: 'yes', 16: 'bill', 17: 'kitchen', 18: 'moved', 19: 'cinema', 20: 'no', 21: 'maybe', 22: 'travelled', 23: 'julie', 24: 'bedroom', 25: 'journeyed', 26: 'bathroom', 27: 'after', 28: 'that', 29: 'she', 30: 'where', 31: 'daniel', 32: 'afterwards', 33: 'he', 34: 'hallway', 35: 'john', 36: 'following', 37: 'garden', 38: 'sandra', 39: 'then', 40: 'and', 41: 'they', 42: 'yesterday', 43: 'this', 44: 'morning', 45: 'was', 46: 'before', 47: 'afternoon', 48: 'evening', 49: 'mice', 50: 'are', 51: 'afraid', 52: 'of', 53: 'wolves', 54: 'gertrude', 55: 'a', 56: 'mouse', 57: 'cats', 58: 'sheep', 59: 'winona', 60: 'emily', 61: 'jessica', 62: 'wolf', 63: 'what', 64: 'cat', 65: 'lily', 66: 'frog', 67: 'bernhard', 68: 'green', 69: 'brian', 70: 'lion', 71: 'white', 72: 'julius', 73: 'swan', 74: 'greg', 75: 'color', 76: 'rhino', 77: 'gray', 78: 'yellow', 79: 'triangle', 80: 'above', 81: 'pink', 82: 'rectangle', 83: 'blue', 84: 'square', 85: 'left', 86: 'right', 87: 'below', 88: 'red', 89: 'sphere', 90: 'box', 91: 'chocolates', 92: 'fits', 93: 'inside', 94: 'chest', 95: 'bigger', 96: 'than', 97: 'suitcase', 98: 'container', 99: 'does', 100: 'fit', 101: 'chocolate', 102: 'east', 103: 'north', 104: 'west', 105: 'how', 106: 'do', 107: 'you', 108: 'go', 109: 'from', 110: 's,e', 111: 'n,w', 112: 'south', 113: 'n,n', 114: 's,w', 115: 'e,s', 116: 's,s', 117: 'e,n', 118: 'w,w', 119: 'e,e', 120: 'n,e', 121: 'w,s', 122: 'w,n', 123: 'sumit', 124: 'tired', 125: 'will', 126: 'why', 127: 'did', 128: 'grabbed', 129: 'pajamas', 130: 'there', 131: 'get', 132: 'yann', 133: 'bored', 134: 'jason', 135: 'thirsty', 136: 'got', 137: 'football', 138: 'antoine', 139: 'milk', 140: 'hungry', 141: 'took', 142: 'apple', 143: 'picked', 144: 'up', 145: 'dropped', 146: 'discarded', 147: 'put', 148: 'down', 149: 'gave', 150: 'give', 151: 'handed', 152: 'jeff', 153: 'who', 154: 'received', 155: 'Bill', 156: 'Fred', 157: 'Mary', 158: 'passed', 159: 'Jeff', 160: 'many', 161: 'objects', 162: 'carrying', 163: 'one', 164: 'none', 165: 'two', 166: 'three', 167: 'nothing', 168: 'football,apple', 169: 'milk,apple', 170: 'apple,football', 171: 'football,milk', 172: 'milk,football', 173: 'milk,football,apple', 174: 'apple,milk', 175: 'apple,milk,football', 176: 'apple,football,milk', 177: 'longer', 178: 'not', 179: 'football,apple,milk'}
#self.vocab = {'jason': 134.0, 'office': 14.0, 'yellow': 78.0, 'bedroom': 24.0, 'go': 108.0, 'yes': 15.0, 'antoine': 138.0, 'milk': 139.0, 'before': 46.0, 'grabbed': 128.0, 'fit': 100.0, 'how': 105.0, 'swan': 73.0, 'than': 96.0, 'to': 13.0, 'does': 99.0, 's,e': 110.0, 'east': 102.0, 'rectangle': 82.0, 'gave': 149.0, 'then': 39.0, 'evening': 48.0, 'triangle': 79.0, 'garden': 37.0, 'get': 131.0, 'football,apple,milk': 179.0, 'they': 41.0, 'not': 178.0, 'bigger': 95.0, 'gray': 77.0, 'school': 6.0, 'apple': 142.0, 'did': 127.0, 'morning': 44.0, 'discarded': 146.0, 'julius': 72.0, 'she': 29.0, 'went': 11.0, 'where': 30.0, 'jeff': 152.0, 'square': 84.0, 'who': 153.0, 'tired': 124.0, 'there': 130.0, 'back': 12.0, 'lion': 70.0, 'are': 50.0, 'picked': 143.0, 'e,e': 119.0, 'pajamas': 129.0, 'Mary': 157.0, 'blue': 83.0, 'what': 63.0, 'container': 98.0, 'rhino': 76.0, 'daniel': 31.0, 'bernhard': 67.0, 'milk,football': 172.0, 'above': 80.0, 'got': 136.0, 'emily': 60.0, 'red': 88.0, 'either': 3.0, 'sheep': 58.0, 'football': 137.0, 'jessica': 61.0, 'do': 106.0, 'Bill': 155.0, 'football,apple': 168.0, 'fred': 1.0, 'winona': 59.0, 'objects': 161.0, 'put': 147.0, 'kitchen': 17.0, 'box': 90.0, 'received': 154.0, 'journeyed': 25.0, 'of': 52.0, 'wolf': 62.0, 'afternoon': 47.0, 'or': 7.0, 'south': 112.0, 's,w': 114.0, 'afterwards': 32.0, 'sumit': 123.0, 'color': 75.0, 'julie': 23.0, 'one': 163.0, 'down': 148.0, 'nothing': 167.0, 'n,n': 113.0, 'right': 86.0, 's,s': 116.0, 'gertrude': 54.0, 'bathroom': 26.0, 'from': 109.0, 'west': 104.0, 'chocolates': 91.0, 'two': 165.0, 'frog': 66.0, '.': 9.0, 'cats': 57.0, 'apple,milk,football': 175.0, 'passed': 158.0, 'apple,football,milk': 176.0, 'white': 71.0, 'john': 35.0, 'was': 45.0, 'mary': 10.0, 'apple,football': 170.0, 'north': 103.0, 'n,w': 111.0, 'that': 28.0, 'park': 8.0, 'took': 141.0, 'chocolate': 101.0, 'carrying': 162.0, 'n,e': 120.0, 'mice': 49.0, 'travelled': 22.0, 'he': 33.0, 'none': 164.0, 'bored': 133.0, 'e,n': 117.0, None: 0, 'Jeff': 159.0, 'this': 43.0, 'inside': 93.0, 'bill': 16.0, 'up': 144.0, 'cat': 64.0, 'will': 125.0, 'below': 87.0, 'greg': 74.0, 'three': 166.0, 'suitcase': 97.0, 'following': 36.0, 'e,s': 115.0, 'and': 40.0, 'thirsty': 135.0, 'cinema': 19.0, 'is': 2.0, 'moved': 18.0, 'yann': 132.0, 'sphere': 89.0, 'dropped': 145.0, 'in': 4.0, 'mouse': 56.0, 'football,milk': 171.0, 'pink': 81.0, 'afraid': 51.0, 'no': 20.0, 'Fred': 156.0, 'w,s': 121.0, 'handed': 151.0, 'w,w': 118.0, 'brian': 69.0, 'chest': 94.0, 'w,n': 122.0, 'you': 107.0, 'many': 160.0, 'lily': 65.0, 'hallway': 34.0, 'why': 126.0, 'after': 27.0, 'yesterday': 42.0, 'sandra': 38.0, 'fits': 92.0, 'milk,football,apple': 173.0, 'the': 5.0, 'milk,apple': 169.0, 'a': 55.0, 'give': 150.0, 'longer': 177.0, 'maybe': 21.0, 'hungry': 140.0, 'apple,milk': 174.0, 'green': 68.0, 'wolves': 53.0, 'left': 85.0}
#self.ivocab = {1: 'fred', 2: 'is', 3: 'either', 4: 'in', 5: 'the', 6: 'school', 7: 'or', 8: 'park', 9: '.', 10: 'mary', 11: 'went', 12: 'back', 13: 'to', 14: 'office', 15: 'yes', 16: 'bill', 17: 'kitchen', 18: 'moved', 19: 'cinema', 20: 'no', 21: 'maybe', 22: 'travelled', 23: 'julie', 24: 'bedroom', 25: 'journeyed', 26: 'bathroom', 27: 'after', 28: 'that', 29: 'she', 30: 'where', 31: 'daniel', 32: 'afterwards', 33: 'he', 34: 'hallway', 35: 'john', 36: 'following', 37: 'garden', 38: 'sandra', 39: 'then', 40: 'and', 41: 'they', 42: 'yesterday', 43: 'this', 44: 'morning', 45: 'was', 46: 'before', 47: 'afternoon', 48: 'evening', 49: 'mice', 50: 'are', 51: 'afraid', 52: 'of', 53: 'wolves', 54: 'gertrude', 55: 'a', 56: 'mouse', 57: 'cats', 58: 'sheep', 59: 'winona', 60: 'emily', 61: 'jessica', 62: 'wolf', 63: 'what', 64: 'cat', 65: 'lily', 66: 'frog', 67: 'bernhard', 68: 'green', 69: 'brian', 70: 'lion', 71: 'white', 72: 'julius', 73: 'swan', 74: 'greg', 75: 'color', 76: 'rhino', 77: 'gray', 78: 'yellow', 79: 'triangle', 80: 'above', 81: 'pink', 82: 'rectangle', 83: 'blue', 84: 'square', 85: 'left', 86: 'right', 87: 'below', 88: 'red', 89: 'sphere', 90: 'box', 91: 'chocolates', 92: 'fits', 93: 'inside', 94: 'chest', 95: 'bigger', 96: 'than', 97: 'suitcase', 98: 'container', 99: 'does', 100: 'fit', 101: 'chocolate', 102: 'east', 103: 'north', 104: 'west', 105: 'how', 106: 'do', 107: 'you', 108: 'go', 109: 'from', 110: 's,e', 111: 'n,w', 112: 'south', 113: 'n,n', 114: 's,w', 115: 'e,s', 116: 's,s', 117: 'e,n', 118: 'w,w', 119: 'e,e', 120: 'n,e', 121: 'w,s', 122: 'w,n', 123: 'sumit', 124: 'tired', 125: 'will', 126: 'why', 127: 'did', 128: 'grabbed', 129: 'pajamas', 130: 'there', 131: 'get', 132: 'yann', 133: 'bored', 134: 'jason', 135: 'thirsty', 136: 'got', 137: 'football', 138: 'antoine', 139: 'milk', 140: 'hungry', 141: 'took', 142: 'apple', 143: 'picked', 144: 'up', 145: 'dropped', 146: 'discarded', 147: 'put', 148: 'down', 149: 'gave', 150: 'give', 151: 'handed', 152: 'jeff', 153: 'who', 154: 'received', 155: 'Bill', 156: 'Fred', 157: 'Mary', 158: 'passed', 159: 'Jeff', 160: 'many', 161: 'objects', 162: 'carrying', 163: 'one', 164: 'none', 165: 'two', 166: 'three', 167: 'nothing', 168: 'football,apple', 169: 'milk,apple', 170: 'apple,football', 171: 'football,milk', 172: 'milk,football', 173: 'milk,football,apple', 174: 'apple,milk', 175: 'apple,milk,football', 176: 'apple,football,milk', 177: 'longer', 178: 'not', 179: 'football,apple,milk'}
#"""
self.train_input, self.train_q, self.train_answer, self.train_input_mask = self._process_input(
babi_train_raw)
self.test_input, self.test_q, self.test_answer, self.test_input_mask = self._process_input(
babi_test_raw)
self.vocab_size = len(self.vocab)
self.input_var = T.imatrix('input_var')
self.q_var = T.ivector('question_var')
self.answer_var = T.iscalar('answer_var')
self.input_mask_var = T.ivector('input_mask_var')
self.attentions = []
self.pe_matrix_in = self.pe_matrix(self.max_inp_sent_len)
self.pe_matrix_q = self.pe_matrix(self.max_q_len)
print "==> building input module"
#positional encoder weights
self.W_pe = nn_utils.normal_param(std=0.1,
shape=(self.vocab_size, self.dim))
#biGRU input fusion weights
self.W_inp_res_in_fwd = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.sent_vector_size))
self.W_inp_res_hid_fwd = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.dim))
self.b_inp_res_fwd = nn_utils.constant_param(value=0.0,
shape=(self.dim, ))
self.W_inp_upd_in_fwd = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.sent_vector_size))
self.W_inp_upd_hid_fwd = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.dim))
self.b_inp_upd_fwd = nn_utils.constant_param(value=0.0,
shape=(self.dim, ))
self.W_inp_hid_in_fwd = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.sent_vector_size))
self.W_inp_hid_hid_fwd = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.dim))
self.b_inp_hid_fwd = nn_utils.constant_param(value=0.0,
shape=(self.dim, ))
self.W_inp_res_in_bwd = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.sent_vector_size))
self.W_inp_res_hid_bwd = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.dim))
self.b_inp_res_bwd = nn_utils.constant_param(value=0.0,
shape=(self.dim, ))
self.W_inp_upd_in_bwd = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.sent_vector_size))
self.W_inp_upd_hid_bwd = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.dim))
self.b_inp_upd_bwd = nn_utils.constant_param(value=0.0,
shape=(self.dim, ))
self.W_inp_hid_in_bwd = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.sent_vector_size))
self.W_inp_hid_hid_bwd = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.dim))
self.b_inp_hid_bwd = nn_utils.constant_param(value=0.0,
shape=(self.dim, ))
#self.V_f = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
#self.V_b = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.inp_sent_reps, _ = theano.scan(fn=self.sum_pos_encodings_in,
sequences=self.input_var)
self.inp_sent_reps_stacked = T.stacklists(self.inp_sent_reps)
#self.inp_c = self.input_module_full(self.inp_sent_reps_stacked)
self.inp_c = self.input_module_full(self.inp_sent_reps)
self.q_q = self.sum_pos_encodings_q(self.q_var)
print "==> creating parameters for memory module"
self.W_mem_res_in = nn_utils.normal_param(std=0.1,
shape=(self.memory_hops,
self.dim, self.dim))
self.W_mem_res_hid = nn_utils.normal_param(std=0.1,
shape=(self.memory_hops,
self.dim, self.dim))
self.b_mem_res = nn_utils.constant_param(value=0.0,
shape=(
self.memory_hops,
self.dim,
))
self.W_mem_upd_in = nn_utils.normal_param(std=0.1,
shape=(self.memory_hops,
self.dim, self.dim))
self.W_mem_upd_hid = nn_utils.normal_param(std=0.1,
shape=(self.memory_hops,
self.dim, self.dim))
self.b_mem_upd = nn_utils.constant_param(value=0.0,
shape=(
self.memory_hops,
self.dim,
))
self.W_mem_hid_in = nn_utils.normal_param(std=0.1,
shape=(self.memory_hops,
self.dim, self.dim))
self.W_mem_hid_hid = nn_utils.normal_param(std=0.1,
shape=(self.memory_hops,
self.dim, self.dim))
self.b_mem_hid = nn_utils.constant_param(value=0.0,
shape=(
self.memory_hops,
self.dim,
))
#self.W_b = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
#self.W_1 = nn_utils.normal_param(std=0.1, shape=(self.dim, 7 * self.dim + 0))
self.W_1 = nn_utils.normal_param(std=0.1,
shape=(self.memory_hops, self.dim,
4 * self.dim + 0))
self.W_2 = nn_utils.normal_param(std=0.1,
shape=(self.memory_hops, 1, self.dim))
self.b_1 = nn_utils.constant_param(value=0.0,
shape=(
self.memory_hops,
self.dim,
))
self.b_2 = nn_utils.constant_param(value=0.0,
shape=(
self.memory_hops,
1,
))
print "==> building episodic memory module (fixed number of steps: %d)" % self.memory_hops
memory = [self.q_q.copy()]
for iter in range(1, self.memory_hops + 1):
self.mem_weight_num = int(iter - 1)
current_episode = self.new_episode(memory[iter - 1])
memory.append(
self.GRU_update(memory[iter - 1], current_episode,
self.W_mem_res_in[self.mem_weight_num],
self.W_mem_res_hid[self.mem_weight_num],
self.b_mem_res[self.mem_weight_num],
self.W_mem_upd_in[self.mem_weight_num],
self.W_mem_upd_hid[self.mem_weight_num],
self.b_mem_upd[self.mem_weight_num],
self.W_mem_hid_in[self.mem_weight_num],
self.W_mem_hid_hid[self.mem_weight_num],
self.b_mem_hid[self.mem_weight_num]))
last_mem_raw = memory[-1].dimshuffle(('x', 0))
net = layers.InputLayer(shape=(1, self.dim), input_var=last_mem_raw)
if self.dropout > 0 and self.mode == 'train':
net = layers.DropoutLayer(net, p=self.dropout)
last_mem = layers.get_output(net)[0]
print "==> building answer module"
self.W_a = nn_utils.normal_param(std=0.1,
shape=(self.vocab_size, self.dim))
if self.answer_module == 'feedforward':
self.prediction = nn_utils.softmax(T.dot(self.W_a, last_mem))
elif self.answer_module == 'recurrent':
self.W_ans_res_in = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.dim + self.vocab_size))
self.W_ans_res_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.dim))
self.b_ans_res = nn_utils.constant_param(value=0.0,
shape=(self.dim, ))
self.W_ans_upd_in = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.dim + self.vocab_size))
self.W_ans_upd_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.dim))
self.b_ans_upd = nn_utils.constant_param(value=0.0,
shape=(self.dim, ))
self.W_ans_hid_in = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.dim + self.vocab_size))
self.W_ans_hid_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.dim))
self.b_ans_hid = nn_utils.constant_param(value=0.0,
shape=(self.dim, ))
def answer_step(prev_a, prev_y):
a = self.GRU_update(prev_a, T.concatenate([prev_y, self.q_q]),
self.W_ans_res_in, self.W_ans_res_hid,
self.b_ans_res, self.W_ans_upd_in,
self.W_ans_upd_hid, self.b_ans_upd,
self.W_ans_hid_in, self.W_ans_hid_hid,
self.b_ans_hid)
y = nn_utils.softmax(T.dot(self.W_a, a))
return [a, y]
# add conditional ending?
dummy = theano.shared(np.zeros((self.vocab_size, ), dtype=floatX))
results, updates = theano.scan(
fn=answer_step,
outputs_info=[last_mem, T.zeros_like(dummy)],
n_steps=1)
self.prediction = results[1][-1]
else:
raise Exception("invalid answer_module")
print "==> collecting all parameters"
self.params = [
self.W_pe,
self.W_inp_res_in_fwd,
self.W_inp_res_hid_fwd,
self.b_inp_res_fwd,
self.W_inp_upd_in_fwd,
self.W_inp_upd_hid_fwd,
self.b_inp_upd_fwd,
self.W_inp_hid_in_fwd,
self.W_inp_hid_hid_fwd,
self.b_inp_hid_fwd,
self.W_inp_res_in_bwd,
self.W_inp_res_hid_bwd,
self.b_inp_res_bwd,
self.W_inp_upd_in_bwd,
self.W_inp_upd_hid_bwd,
self.b_inp_upd_bwd,
self.W_inp_hid_in_bwd,
self.W_inp_hid_hid_bwd,
self.b_inp_hid_bwd,
self.W_mem_res_in,
self.W_mem_res_hid,
self.b_mem_res,
self.W_mem_upd_in,
self.W_mem_upd_hid,
self.b_mem_upd,
self.W_mem_hid_in,
self.W_mem_hid_hid,
self.b_mem_hid, #self.W_b
self.W_1,
self.W_2,
self.b_1,
self.b_2,
self.W_a
]
if self.answer_module == 'recurrent':
self.params = self.params + [
self.W_ans_res_in, self.W_ans_res_hid, self.b_ans_res,
self.W_ans_upd_in, self.W_ans_upd_hid, self.b_ans_upd,
self.W_ans_hid_in, self.W_ans_hid_hid, self.b_ans_hid
]
print "==> building loss layer and computing updates"
self.loss_ce = T.nnet.categorical_crossentropy(
self.prediction.dimshuffle('x', 0), T.stack([self.answer_var]))[0]
if self.l2 > 0:
self.loss_l2 = self.l2 * nn_utils.l2_reg(self.params)
else:
self.loss_l2 = 0
self.loss = self.loss_ce + self.loss_l2
#updates = lasagne.updates.adadelta(self.loss, self.params)
updates = lasagne.updates.adam(self.loss, self.params)
updates = lasagne.updates.adam(self.loss,
self.params,
learning_rate=0.0001,
beta1=0.5) #from DCGAN paper
#updates = lasagne.updates.adadelta(self.loss, self.params, learning_rate=0.0005)
#updates = lasagne.updates.momentum(self.loss, self.params, learning_rate=0.0003)
self.attentions = T.stack(self.attentions)
if self.mode == 'train':
print "==> compiling train_fn"
self.train_fn = theano.function(
inputs=[
self.input_var, self.q_var, self.answer_var,
self.input_mask_var
],
outputs=[self.prediction, self.loss, self.attentions],
updates=updates,
on_unused_input='warn',
allow_input_downcast=True)
print "==> compiling test_fn"
self.test_fn = theano.function(
inputs=[
self.input_var, self.q_var, self.answer_var,
self.input_mask_var
],
outputs=[self.prediction, self.loss, self.attentions],
on_unused_input='warn',
allow_input_downcast=True)
def __init__(self, data_dir, word2vec, word_vector_size, dim, mode,
answer_module, memory_hops, batch_size, l2,
normalize_attention, batch_norm, dropout, **kwargs):
print "==> not used params in DMN class:", kwargs.keys()
self.data_dir = data_dir
self.word2vec = word2vec
self.word_vector_size = word_vector_size
self.dim = dim
self.mode = mode
self.answer_module = answer_module
self.memory_hops = memory_hops
self.batch_size = batch_size
self.l2 = l2
self.normalize_attention = normalize_attention
self.batch_norm = batch_norm
self.dropout = dropout
self.vocab, self.ivocab = self._load_vocab(self.data_dir)
self.train_story = None
self.test_story = None
self.train_dict_story, self.train_features, self.train_fns_dict, self.train_num_imgs = self._process_input_sind(
self.data_dir, 'train')
self.test_dict_story, self.test_features, self.test_fns_dict, self.test_num_imgs = self._process_input_sind(
self.data_dir, 'val')
self.train_story = self.train_dict_story.keys()
self.test_story = self.test_dict_story.keys()
self.vocab_size = len(self.vocab)
self.input_var = T.tensor3(
'input_var') # (batch_size, seq_len, cnn_dim)
self.q_var = T.matrix('q_var') # Now, it's a batch * image_sieze.
self.answer_var = T.imatrix(
'answer_var') # answer of example in minibatch
self.answer_mask = T.matrix('answer_mask')
self.answer_inp_var = T.tensor3(
'answer_inp_var') # answer of example in minibatch
print "==> building input module"
# It's very simple now, the input module just need to map from cnn_dim to dim.
logging.info('self.cnn_dim = %d', self.cnn_dim)
self.W_inp_emb_in = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.cnn_dim))
self.b_inp_emb_in = nn_utils.constant_param(value=0.0,
shape=(self.dim, ))
#inp_c_hist = T.dot(self.W_inp_emb_in, self.input_var) + self.b_inp_emb_in
inp_var_shuffled = self.input_var.dimshuffle(1, 2, 0)
print inp_var_shuffled.shape.eval(
{self.input_var: np.random.rand(10, 4, 4096).astype('float32')})
def _dot(x, W, b):
return T.dot(W, x) + b.dimshuffle(0, 'x')
inp_c_hist, _ = theano.scan(
fn=_dot,
sequences=inp_var_shuffled,
non_sequences=[self.W_inp_emb_in, self.b_inp_emb_in])
#inp_c_hist,_ = theano.scan(fn = _dot, sequences=self.input_var, non_sequences = [self.W_inp_emb_in, self.b_inp_emb_in])
#self.inp_c = inp_c_hist.dimshuffle(2,0,1) # b x len x fea
self.inp_c = inp_c_hist
print "==> building question module"
# Now, share the parameter with the input module.
q_var_shuffled = self.q_var.dimshuffle(1, 0)
q_hist = T.dot(self.W_inp_emb_in,
q_var_shuffled) + self.b_inp_emb_in.dimshuffle(0, 'x')
self.q_q = q_hist.dimshuffle(0, 1) # batch x dim
print "==> creating parameters for memory module"
self.W_mem_res_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.W_mem_res_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_mem_res = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_mem_upd_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.W_mem_upd_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_mem_upd = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_mem_hid_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.W_mem_hid_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_mem_hid = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_b = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.W_1 = nn_utils.normal_param(std=0.1,
shape=(self.dim, 7 * self.dim + 0))
self.W_2 = nn_utils.normal_param(std=0.1, shape=(1, self.dim))
self.b_1 = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.b_2 = nn_utils.constant_param(value=0.0, shape=(1, ))
print "==> building episodic memory module (fixed number of steps: %d)" % self.memory_hops
memory = [self.q_q.copy()]
for iter in range(1, self.memory_hops + 1):
#m = printing.Print('mem')(memory[iter-1])
current_episode = self.new_episode(memory[iter - 1])
#current_episode = self.new_episode(m)
#current_episode = printing.Print('current_episode')(current_episode)
memory.append(
self.GRU_update(memory[iter - 1], current_episode,
self.W_mem_res_in, self.W_mem_res_hid,
self.b_mem_res, self.W_mem_upd_in,
self.W_mem_upd_hid, self.b_mem_upd,
self.W_mem_hid_in, self.W_mem_hid_hid,
self.b_mem_hid))
last_mem_raw = memory[-1].dimshuffle((1, 0))
net = layers.InputLayer(shape=(self.batch_size, self.dim),
input_var=last_mem_raw)
if self.batch_norm:
net = layers.BatchNormLayer(incoming=net)
if self.dropout > 0 and self.mode == 'train':
net = layers.DropoutLayer(net, p=self.dropout)
last_mem = layers.get_output(net).dimshuffle((1, 0))
logging.info('last_mem size')
print last_mem.shape.eval({
self.input_var:
np.random.rand(10, 4, 4096).astype('float32'),
self.q_var:
np.random.rand(10, 4096).astype('float32')
})
print "==> building answer module"
answer_inp_var_shuffled = self.answer_inp_var.dimshuffle(1, 2, 0)
# because we have the additional #start token. Thus, we need to add this +1 for all the parameters as well.
dummy = theano.shared(
np.zeros((self.vocab_size + 1, self.batch_size), dtype=floatX))
self.W_a = nn_utils.normal_param(std=0.1,
shape=(self.vocab_size + 1, self.dim))
self.W_ans_res_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim +
self.vocab_size + 1))
self.W_ans_res_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_ans_res = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_ans_upd_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim +
self.vocab_size + 1))
self.W_ans_upd_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_ans_upd = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_ans_hid_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim +
self.vocab_size + 1))
self.W_ans_hid_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_ans_hid = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
logging.info('answer_inp_var_shuffled size')
print answer_inp_var_shuffled.shape.eval({
self.answer_inp_var:
np.random.rand(10, 18, 8001).astype('float32')
})
#last_mem = printing.Print('prob_sm')(last_mem)
results, _ = theano.scan(fn=self.answer_gru_step,
sequences=answer_inp_var_shuffled,
outputs_info=[last_mem])
# Assume there is a start token
print results.shape.eval({
self.input_var:
np.random.rand(10, 4, 4096).astype('float32'),
self.q_var:
np.random.rand(10, 4096).astype('float32'),
self.answer_inp_var:
np.random.rand(10, 18, 8001).astype('float32')
})
results = results[0:-1, :, :] # get rid of the last token.
print results.shape.eval({
self.input_var:
np.random.rand(10, 4, 4096).astype('float32'),
self.q_var:
np.random.rand(10, 4096).astype('float32'),
self.answer_inp_var:
np.random.rand(10, 18, 8001).astype('float32')
})
# Now, we need to transform it to the probabilities.
prob, _ = theano.scan(fn=lambda x, w: T.dot(w, x),
sequences=results,
non_sequences=self.W_a)
prob_shuffled = prob.dimshuffle(2, 0, 1) # b * len * vocab
logging.info("prob shape.")
print prob.shape.eval({
self.input_var:
np.random.rand(10, 4, 4096).astype('float32'),
self.q_var:
np.random.rand(10, 4096).astype('float32'),
self.answer_inp_var:
np.random.rand(10, 18, 8001).astype('float32')
})
n = prob_shuffled.shape[0] * prob_shuffled.shape[1]
prob_rhp = T.reshape(prob_shuffled, (n, prob_shuffled.shape[2]))
prob_sm = nn_utils.softmax(prob_rhp)
self.prediction = prob_sm
mask = T.reshape(self.answer_mask, (n, ))
lbl = T.reshape(self.answer_var, (n, ))
self.params = [
self.W_inp_emb_in,
self.b_inp_emb_in,
self.W_mem_res_in,
self.W_mem_res_hid,
self.b_mem_res,
self.W_mem_upd_in,
self.W_mem_upd_hid,
self.b_mem_upd,
self.W_mem_hid_in,
self.W_mem_hid_hid,
self.b_mem_hid, #self.W_b
self.W_1,
self.W_2,
self.b_1,
self.b_2,
self.W_a
]
self.params = self.params + [
self.W_ans_res_in, self.W_ans_res_hid, self.b_ans_res,
self.W_ans_upd_in, self.W_ans_upd_hid, self.b_ans_upd,
self.W_ans_hid_in, self.W_ans_hid_hid, self.b_ans_hid
]
print "==> building loss layer and computing updates"
loss_vec = T.nnet.categorical_crossentropy(prob_sm, lbl)
self.loss_ce = (mask * loss_vec).sum() / mask.sum()
#self.loss_ce = T.nnet.categorical_crossentropy(results_rhp, lbl)
if self.l2 > 0:
self.loss_l2 = self.l2 * nn_utils.l2_reg(self.params)
else:
self.loss_l2 = 0
self.loss = self.loss_ce + self.loss_l2
updates = lasagne.updates.adadelta(self.loss, self.params)
#updates = lasagne.updates.momentum(self.loss, self.params, learning_rate=0.001)
if self.mode == 'train':
print "==> compiling train_fn"
self.train_fn = theano.function(
inputs=[
self.input_var, self.q_var, self.answer_var,
self.answer_mask, self.answer_inp_var
],
outputs=[self.prediction, self.loss],
updates=updates)
print "==> compiling test_fn"
self.test_fn = theano.function(inputs=[
self.input_var, self.q_var, self.answer_var, self.answer_mask,
self.answer_inp_var
],
outputs=[self.prediction, self.loss])
def __init__(self, train_raw, dev_raw, test_raw, word2vec,
word_vector_size, answer_module, dim, mode, input_mask_mode,
memory_hops, l2, normalize_attention, dropout, **kwargs):
print "generate sentence answer for mctest"
print "==> not used params in DMN class:", kwargs.keys()
self.word2vec = word2vec
self.word_vector_size = word_vector_size
# add eng_of_sentence tag for answer generation
#self.end_tag = len(word2vec)
#self.vocab_size = self.end_tag+1
self.vocab_size = len(word2vec)
self.dim = dim # hidden state size
self.mode = mode
self.input_mask_mode = input_mask_mode
self.memory_hops = memory_hops
self.answer_module = answer_module
self.l2 = l2
self.normalize_attention = normalize_attention
self.dropout = dropout
self.train_input, self.train_q, self.train_answer, self.train_input_mask, self.train_max_n = self._process_input(
train_raw)
self.dev_input, self.dev_q, self.dev_answer, self.dev_input_mask, self.dev_max_n = self._process_input(
dev_raw)
self.test_input, self.test_q, self.test_answer, self.test_input_mask, self.test_max_n = self._process_input(
test_raw)
self.input_var = T.matrix('input_var')
self.q_var = T.matrix('question_var')
self.answer_var = T.ivector('answer_var')
self.input_mask_var = T.ivector('input_mask_var')
self.max_n = T.iscalar('max_n')
self.attentions = []
print "==> building input module"
self.W_inp_res_in = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.word_vector_size))
self.W_inp_res_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_inp_res = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_inp_upd_in = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.word_vector_size))
self.W_inp_upd_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_inp_upd = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_inp_hid_in = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.word_vector_size))
self.W_inp_hid_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_inp_hid = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
inp_c_history, _ = theano.scan(fn=self.input_gru_step,
sequences=self.input_var,
outputs_info=T.zeros_like(
self.b_inp_hid))
self.inp_c = inp_c_history.take(self.input_mask_var, axis=0)
self.q_q, _ = theano.scan(fn=self.input_gru_step,
sequences=self.q_var,
outputs_info=T.zeros_like(self.b_inp_hid))
self.q_q = self.q_q[-1]
print "==> creating parameters for memory module"
self.W_mem_res_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.W_mem_res_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_mem_res = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_mem_upd_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.W_mem_upd_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_mem_upd = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_mem_hid_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.W_mem_hid_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_mem_hid = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_b = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.W_1 = nn_utils.normal_param(std=0.1,
shape=(self.dim, 7 * self.dim + 2))
self.W_2 = nn_utils.normal_param(std=0.1, shape=(1, self.dim))
self.b_1 = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.b_2 = nn_utils.constant_param(value=0.0, shape=(1, ))
print "==> building episodic memory module (fixed number of steps: %d)" % self.memory_hops
memory = [self.q_q.copy()]
for iter in range(1, self.memory_hops + 1):
current_episode = self.new_episode(memory[iter - 1])
memory.append(
self.GRU_update(memory[iter - 1], current_episode,
self.W_mem_res_in, self.W_mem_res_hid,
self.b_mem_res, self.W_mem_upd_in,
self.W_mem_upd_hid, self.b_mem_upd,
self.W_mem_hid_in, self.W_mem_hid_hid,
self.b_mem_hid))
last_mem_raw = memory[-1].dimshuffle(('x', 0))
net = layers.InputLayer(shape=(1, self.dim), input_var=last_mem_raw)
if self.dropout > 0 and self.mode == 'train':
net = layers.DropoutLayer(net, p=self.dropout)
last_mem = layers.get_output(net)[0]
self.attentions = T.stack(self.attentions)
print "==> building answer module"
self.W_a = nn_utils.normal_param(std=0.1,
shape=(self.vocab_size, self.dim))
if self.answer_module == 'feedforward':
self.prediction = nn_utils.softmax(T.dot(self.W_a, last_mem))
self.prediction = self.prediction.dimshuffle('x', 0)
elif self.answer_module == 'recurrent':
self.W_ans_res_in = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.dim + self.vocab_size))
self.W_ans_res_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.dim))
self.b_ans_res = nn_utils.constant_param(value=0.0,
shape=(self.dim, ))
self.W_ans_upd_in = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.dim + self.vocab_size))
self.W_ans_upd_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.dim))
self.b_ans_upd = nn_utils.constant_param(value=0.0,
shape=(self.dim, ))
self.W_ans_hid_in = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.dim + self.vocab_size))
self.W_ans_hid_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.dim))
self.b_ans_hid = nn_utils.constant_param(value=0.0,
shape=(self.dim, ))
def answer_step(prev_a, prev_y):
a = self.GRU_update(prev_a, T.concatenate([prev_y, self.q_q]),
self.W_ans_res_in, self.W_ans_res_hid,
self.b_ans_res, self.W_ans_upd_in,
self.W_ans_upd_hid, self.b_ans_upd,
self.W_ans_hid_in, self.W_ans_hid_hid,
self.b_ans_hid)
y = nn_utils.softmax(T.dot(self.W_a, a))
return [
a, y
] #, theano.scan_module.until(n>=max_n)) # or argmax==self.end_tag)
dummy = theano.shared(np.zeros((self.vocab_size, ), dtype=floatX))
results, updates = theano.scan(
fn=answer_step,
outputs_info=[last_mem, T.zeros_like(dummy)],
n_steps=self.max_n)
self.prediction = results[1]
else:
raise Exception("invalid answer_module")
print "==> collecting all parameters"
self.params = [
self.W_inp_res_in, self.W_inp_res_hid, self.b_inp_res,
self.W_inp_upd_in, self.W_inp_upd_hid, self.b_inp_upd,
self.W_inp_hid_in, self.W_inp_hid_hid, self.b_inp_hid,
self.W_mem_res_in, self.W_mem_res_hid, self.b_mem_res,
self.W_mem_upd_in, self.W_mem_upd_hid, self.b_mem_upd,
self.W_mem_hid_in, self.W_mem_hid_hid, self.b_mem_hid, self.W_b,
self.W_1, self.W_2, self.b_1, self.b_2, self.W_a
]
if self.answer_module == 'recurrent': #feedforward':
self.params = self.params + [
self.W_ans_res_in, self.W_ans_res_hid, self.b_ans_res,
self.W_ans_upd_in, self.W_ans_upd_hid, self.b_ans_upd,
self.W_ans_hid_in, self.W_ans_hid_hid, self.b_ans_hid
]
print "==> building loss layer and computing updates"
self.loss_ce = T.nnet.categorical_crossentropy(self.prediction,
self.answer_var).sum()
if self.l2 > 0:
self.loss_l2 = self.l2 * nn_utils.l2_reg(self.params)
else:
self.loss_l2 = 0
self.loss = self.loss_ce + self.loss_l2
updates = lasagne.updates.adam(self.loss, self.params)
#updates = lasagne.updates.momentum(self.loss, self.params, learning_rate=0.0003)
if self.mode == 'train':
print "==> compiling train_fn"
self.train_fn = theano.function(
inputs=[
self.input_var, self.q_var, self.answer_var,
self.input_mask_var, self.max_n
],
allow_input_downcast=True,
outputs=[self.prediction, self.loss],
updates=updates)
print "==> compiling test_fn"
self.test_fn = theano.function(
inputs=[
self.input_var, self.q_var, self.answer_var,
self.input_mask_var, self.max_n
],
allow_input_downcast=True,
outputs=[self.prediction, self.loss, self.attentions])
def __init__(self, babi_train_raw, babi_test_raw, word2vec, word_vector_size,
memory_hops, dim, mode, input_mask_mode, l2, batch_norm, dropout, **kwargs):
print "==> not used params in DMN class:", kwargs.keys()
self.vocab = {}
self.ivocab = {}
self.word2vec = word2vec
self.word_vector_size = word_vector_size
self.dim = dim
self.mode = mode
self.input_mask_mode = input_mask_mode
self.l2 = l2
self.batch_norm = batch_norm
self.dropout = dropout
self.memory_hops = memory_hops
self.train_input, self.train_q, self.train_answer, self.train_input_mask, self.train_gates = self._process_input(babi_train_raw)
self.test_input, self.test_q, self.test_answer, self.test_input_mask, self.test_gates = self._process_input(babi_test_raw)
self.vocab_size = len(self.vocab)
print "Train size: ", len(self.train_input)
print "Test size: ", len(self.test_input)
print "Vocab size: ", self.vocab_size
self.input_var = T.matrix('input_var')
self.q_var = T.matrix('question_var')
self.answer_var = T.iscalar('answer_var')
self.input_mask_var = T.ivector('input_mask_var')
self.gates_var = T.ivector('gates_var') # attention gate (including end_reading)
self.attentions = []
print "==> building input module"
self.W_inp_res_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.word_vector_size))
self.W_inp_res_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_inp_res = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_inp_upd_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.word_vector_size))
self.W_inp_upd_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_inp_upd = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_inp_hid_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.word_vector_size))
self.W_inp_hid_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_inp_hid = nn_utils.constant_param(value=0.0, shape=(self.dim,))
inp_c_history, _ = theano.scan(fn=self.input_gru_step,
sequences=self.input_var,
outputs_info=T.zeros_like(self.b_inp_hid))
self.end_reading = nn_utils.constant_param(value=0.0,shape=(1,self.dim))
inp_c_tag = T.concatenate([inp_c_history,self.end_reading],axis=0)
self.inp_c = inp_c_tag.take(self.input_mask_var, axis=0) #(facts_len,dim)
self.q_q, _ = theano.scan(fn=self.input_gru_step,
sequences=self.q_var,
outputs_info=T.zeros_like(self.b_inp_hid))
self.q_q = self.q_q[-1] #(1,dim)
print "==> creating parameters for memory module"
self.W_mem_res_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.W_mem_res_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_mem_res = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_mem_upd_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.W_mem_upd_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_mem_upd = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_mem_hid_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.W_mem_hid_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_mem_hid = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_b = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.W_1 = nn_utils.normal_param(std=0.1, shape=(self.dim, 7 * self.dim + 2))
self.W_2 = nn_utils.normal_param(std=0.1, shape=(1, self.dim))
self.b_1 = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.b_2 = nn_utils.constant_param(value=0.0, shape=(1,))
print "==> building episodic memory module (fixed number of steps: %d)" % self.memory_hops
memory = [self.q_q.copy()]
for iter in range(0, self.memory_hops):
current_episode, g = self.new_episode(memory[iter])
self.attentions.append(g)
memory.append(self.GRU_update(memory[iter], current_episode,
self.W_mem_res_in, self.W_mem_res_hid, self.b_mem_res,
self.W_mem_upd_in, self.W_mem_upd_hid, self.b_mem_upd,
self.W_mem_hid_in, self.W_mem_hid_hid, self.b_mem_hid))
last_mem_raw = memory[-1].dimshuffle(('x', 0))
net = layers.InputLayer(shape=(1, self.dim), input_var=last_mem_raw)
if self.dropout > 0 and self.mode == 'train':
net = layers.DropoutLayer(net, p=self.dropout)
last_mem = layers.get_output(net)[0]
self.attentions = T.stack(self.attentions) #(memory_hops, fact_cnt)
print "==> building answer module"
self.W_a = nn_utils.normal_param(std=0.1, shape=(self.vocab_size, self.dim))
self.prediction = nn_utils.softmax(T.dot(self.W_a, last_mem))
print "==> collecting all parameters"
self.params = [self.W_inp_res_in, self.W_inp_res_hid, self.b_inp_res,
self.W_inp_upd_in, self.W_inp_upd_hid, self.b_inp_upd,
self.W_inp_hid_in, self.W_inp_hid_hid, self.b_inp_hid,
self.W_mem_res_in, self.W_mem_res_hid, self.b_mem_res,
self.W_mem_upd_in, self.W_mem_upd_hid, self.b_mem_upd,
self.W_mem_hid_in, self.W_mem_hid_hid, self.b_mem_hid, self.W_b,
self.W_1, self.W_2, self.b_1, self.b_2, self.W_a]
print "==> building loss layer and computing updates"
self.loss_ce = T.nnet.categorical_crossentropy(self.prediction.dimshuffle('x', 0),
T.stack([self.answer_var]))[0]
if self.l2 > 0:
self.loss_l2 = self.l2 * nn_utils.l2_reg(self.params)
else:
self.loss_l2 = 0
self.loss_gate = T.nnet.categorical_crossentropy(self.attentions, self.gates_var).mean()
self.loss = self.loss_ce + self.loss_l2 + self.loss_gate
updates = lasagne.updates.adam(self.loss, self.params)
#updates = lasagne.updates.momentum(self.loss, self.params, learning_rate=0.0003)
if self.mode == 'train':
print "==> compiling train_fn"
self.train_fn = theano.function(inputs=[self.input_var, self.q_var, self.answer_var, self.input_mask_var, self.gates_var],
allow_input_downcast = True,
outputs=[self.prediction, self.loss, self.attentions],
updates=updates)
print "==> compiling test_fn"
self.test_fn = theano.function(inputs=[self.input_var, self.q_var, self.answer_var, self.input_mask_var, self.gates_var],
allow_input_downcast = True,
outputs=[self.prediction, self.loss, self.attentions])
if self.mode == 'train':
print "==> computing gradients (for debugging)"
gradient = T.grad(self.loss, self.params)
self.get_gradient_fn = theano.function(inputs=[self.input_var, self.q_var, self.answer_var, self.input_mask_var, self.gates_var],
allow_input_downcast = True, outputs=gradient)
def __init__(self, data_dir, word2vec, word_vector_size, dim, cnn_dim, story_len,
patches,cnn_dim_fc,truncate_gradient, learning_rate,
mode, answer_module, memory_hops, batch_size, l2,
normalize_attention, batch_norm, dropout, **kwargs):
print "==> not used params in DMN class:", kwargs.keys()
self.data_dir = data_dir
self.truncate_gradient = truncate_gradient
self.learning_rate = learning_rate
self.trng = RandomStreams(1234)
self.word2vec = word2vec
self.word_vector_size = word_vector_size
self.dim = dim
self.cnn_dim = cnn_dim
self.cnn_dim_fc = cnn_dim_fc
self.story_len = story_len
self.patches = patches
self.mode = mode
self.answer_module = answer_module
self.memory_hops = memory_hops
self.batch_size = batch_size
self.l2 = l2
self.normalize_attention = normalize_attention
self.batch_norm = batch_norm
self.dropout = dropout
self.vocab, self.ivocab = self._load_vocab(self.data_dir)
self.train_story = None
self.test_story = None
self.train_dict_story, self.train_lmdb_env_fc, self.train_lmdb_env_conv = self._process_input_sind_lmdb(self.data_dir, 'train')
self.test_dict_story, self.test_lmdb_env_fc, self.test_lmdb_env_conv = self._process_input_sind_lmdb(self.data_dir, 'val')
self.train_story = self.train_dict_story.keys()
self.test_story = self.test_dict_story.keys()
self.vocab_size = len(self.vocab)
self.alpha_entropy_c = 0.02 # for hard attention.
# This is the local patch of each image.
self.input_var = T.tensor4('input_var') # (batch_size, seq_len, patches, cnn_dim)
self.q_var = T.tensor3('q_var') # Now, it's a batch * story_len * image_sieze.
self.answer_var = T.ivector('answer_var') # answer of example in minibatch
self.answer_mask = T.matrix('answer_mask')
self.answer_idx = T.imatrix('answer_idx') # batch x seq
self.answer_inp_var = T.tensor3('answer_inp_var') # answer of example in minibatch
print "==> building input module"
# It's very simple now, the input module just need to map from cnn_dim to dim.
logging.info('self.cnn_dim = %d', self.cnn_dim)
logging.info('self.cnn_dim_fc = %d', self.cnn_dim_fc)
logging.info('self.dim = %d', self.dim)
self.W_q_emb_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.cnn_dim_fc))
self.b_q_emb_in = nn_utils.constant_param(value=0.0, shape=(self.dim,))
logging.info('Building the glob attention model')
self.W_glb_att_1 = nn_utils.normal_param(std = 0.1, shape = (self.dim, 2 * self.dim))
self.W_glb_att_2 = nn_utils.normal_param(std = 0.1, shape = (1, self.dim))
self.b_glb_att_1 = nn_utils.constant_param(value = 0.0, shape = (self.dim,))
self.b_glb_att_2 = nn_utils.constant_param(value = 0.0, shape = (1,))
q_var_shuffled = self.q_var.dimshuffle(1,2,0) # seq x cnn x batch.
def _dot(x, W, b):
return T.tanh( T.dot(W, x) + b.dimshuffle(0, 'x'))
q_var_shuffled_emb,_ = theano.scan(fn = _dot, sequences= q_var_shuffled, non_sequences = [self.W_q_emb_in, self.b_q_emb_in])
print 'q_var_shuffled_emb', q_var_shuffled_emb.shape.eval({self.q_var:np.random.rand(2,5,4096).astype('float32')})
q_var_emb = q_var_shuffled_emb.dimshuffle(2,0,1) # batch x seq x emb_size
q_var_emb_ext = q_var_emb.dimshuffle(0,'x',1,2)
q_var_emb_ext = T.repeat(q_var_emb_ext, q_var_emb.shape[1],1) # batch x seq x seq x emb_size
q_var_emb_rhp = T.reshape( q_var_emb, (q_var_emb.shape[0] * q_var_emb.shape[1], q_var_emb.shape[2]))
q_var_emb_ext_rhp = T.reshape(q_var_emb_ext, (q_var_emb_ext.shape[0] * q_var_emb_ext.shape[1],q_var_emb_ext.shape[2], q_var_emb_ext.shape[3]))
q_var_emb_ext_rhp = q_var_emb_ext_rhp.dimshuffle(0,2,1)
q_idx = T.arange(self.story_len).dimshuffle('x',0)
q_idx = T.repeat(q_idx,self.batch_size, axis = 0)
q_idx = T.reshape(q_idx, (q_idx.shape[0]* q_idx.shape[1],))
print q_idx.eval()
print 'q_var_emb_rhp.shape', q_var_emb_rhp.shape.eval({self.q_var:np.random.rand(3,5,4096).astype('float32')})
print 'q_var_emb_ext_rhp.shape', q_var_emb_ext_rhp.shape.eval({self.q_var:np.random.rand(3,5,4096).astype('float32')})
#att_alpha,_ = theano.scan(fn = self.new_attention_step_glob, sequences = [q_var_emb_rhp, q_var_emb_ext_rhp, q_idx] )
alpha,_ = theano.scan(fn = self.new_attention_step_glob, sequences = [q_var_emb_rhp, q_var_emb_ext_rhp, q_idx] )
att_alpha = alpha[1]
att_alpha_a = alpha[0]
#print results.shape.eval({self.input_var: np.random.rand(3,4,4096).astype('float32'),
print att_alpha.shape.eval({self.q_var:np.random.rand(3,5,4096).astype('float32')})
# att_alpha: (batch x seq) x seq)
self.W_inp_emb_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.cnn_dim))
self.b_inp_emb_in = nn_utils.constant_param(value=0.0, shape=(self.dim,))
inp_rhp = T.reshape(self.input_var, (self.batch_size* self.story_len* self.patches, self.cnn_dim))
inp_rhp_dimshuffled = inp_rhp.dimshuffle(1,0)
inp_rhp_emb = T.dot(self.W_inp_emb_in, inp_rhp_dimshuffled) + self.b_inp_emb_in.dimshuffle(0,'x')
inp_rhp_emb_dimshuffled = inp_rhp_emb.dimshuffle(1,0)
inp_emb_raw = T.reshape(inp_rhp_emb_dimshuffled, (self.batch_size, self.story_len, self.patches, self.cnn_dim))
inp_emb = T.tanh(inp_emb_raw) # Just follow the paper DMN for visual and textual QA.
#inp_emb = inp_emb.dimshuffle(0,'x', 1,2)
#inp_emb = T.repeat(inp_emb, self.story_len, 1)
#print inp_emb.shape.eval({self.input_var:np.random.rand(3,5,196, 4096).astype('float32')})
att_alpha_sample = self.trng.multinomial(pvals = att_alpha, dtype=theano.config.floatX)
att_mask = att_alpha_sample.argmax(1)
print 'att_mask.shape', att_mask.shape.eval({self.q_var:np.random.rand(2,5,4096).astype('float32')})
print 'att_mask', att_mask.eval({self.q_var:np.random.rand(2,5,4096).astype('float32')})
# No time to fix the hard attention, now we use the soft attention.
idx_t = T.repeat(T.arange(self.input_var.shape[0]), self.input_var.shape[1])
print 'idx_t', idx_t.eval({self.input_var:np.random.rand(2,5,196,512).astype('float32')})
att_input = inp_emb[idx_t, att_mask,:,:] # (batch x seq) x batches x emb_size
att_input = T.reshape(att_input, (self.batch_size, self.story_len, self.patches, self.dim))
print 'att_input', att_input.shape.eval({self.input_var:np.random.rand(2,5,196,512).astype('float32'),self.q_var:np.random.rand(2,5,4096).astype('float32')})
# Now, it's the same size with the input_var, but we have only one image for each one of input.
# Now, we can use the rnn on these local imgs to learn the
# Now, we use a bi-directional GRU to produce the input.
# Forward GRU.
self.inp_c = T.reshape(att_input, (att_input.shape[0] * att_input.shape[1], att_input.shape[2], att_input.shape[3]))
self.inp_c = self.inp_c.dimshuffle(1,2,0)
#print 'inp_c', self.inp_c.shape.eval({att_input:np.random.rand(2,5,196,512).astype('float32')})
print "==> building question module"
self.W_qf_res_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.W_qf_res_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_qf_res = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_qf_upd_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.W_qf_upd_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_qf_upd = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_qf_hid_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.W_qf_hid_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_qf_hid = nn_utils.constant_param(value=0.0, shape=(self.dim,))
inp_dummy = theano.shared(np.zeros((self.dim, self.batch_size), dtype = floatX))
#print 'q_var_shuffled_emb', q_var_shuffled_emb.shape.eval({self.q_var:np.random.rand(2,5,4096).astype('float32')})
q_glb,_ = theano.scan(fn = self.q_gru_step_forward,
sequences = q_var_shuffled_emb,
outputs_info = [T.zeros_like(inp_dummy)])
q_glb_shuffled = q_glb.dimshuffle(2,0,1) # batch_size * seq_len * dim
#print 'q_glb_shuffled', q_glb_shuffled.shape.eval({self.q_var:np.random.rand(2,5,4096).astype('float32')})
q_glb_last = q_glb_shuffled[:,-1,:] # batch_size * dim
#print 'q_glb_last', q_glb_last.shape.eval({self.q_var:np.random.rand(2,5,4096).astype('float32')})
q_net = layers.InputLayer(shape=(self.batch_size*self.story_len, self.dim), input_var=q_var_emb_rhp)
if self.batch_norm:
q_net = layers.BatchNormLayer(incoming=q_net)
if self.dropout > 0 and self.mode == 'train':
q_net = layers.DropoutLayer(q_net, p=self.dropout)
self.q_q = layers.get_output(q_net).dimshuffle(1,0)
print "==> creating parameters for memory module"
self.W_mem_res_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.W_mem_res_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_mem_res = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_mem_upd_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.W_mem_upd_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_mem_upd = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_mem_hid_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.W_mem_hid_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_mem_hid = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_mem_update1 = nn_utils.normal_param(std=0.1, shape=(self.dim , self.dim* 2))
self.b_mem_upd1 = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_mem_update2 = nn_utils.normal_param(std=0.1, shape=(self.dim,self.dim*2))
self.b_mem_upd2 = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_mem_update3 = nn_utils.normal_param(std=0.1, shape=(self.dim , self.dim*2))
self.b_mem_upd3 = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_mem_update = [self.W_mem_update1,self.W_mem_update2,self.W_mem_update3]
self.b_mem_update = [self.b_mem_upd1,self.b_mem_upd2, self.b_mem_upd3]
self.W_1 = nn_utils.normal_param(std=0.1, shape=(self.dim, 7 * self.dim + 0))
self.W_2 = nn_utils.normal_param(std=0.1, shape=(1, self.dim))
self.b_1 = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.b_2 = nn_utils.constant_param(value=0.0, shape=(1,))
print "==> building episodic memory module (fixed number of steps: %d)" % self.memory_hops
memory = [self.q_q.copy()]
for iter in range(1, self.memory_hops + 1):
#m = printing.Print('mem')(memory[iter-1])
current_episode = self.new_episode(memory[iter - 1])
# Replace GRU with ReLU activation + MLP.
c = T.concatenate([memory[iter - 1], current_episode], axis = 0)
cur_mem = T.dot(self.W_mem_update[iter-1], c) + self.b_mem_update[iter-1].dimshuffle(0,'x')
memory.append(T.nnet.relu(cur_mem))
last_mem_raw = memory[-1].dimshuffle((1, 0))
net = layers.InputLayer(shape=(self.batch_size * self.story_len, self.dim), input_var=last_mem_raw)
if self.batch_norm:
net = layers.BatchNormLayer(incoming=net)
if self.dropout > 0 and self.mode == 'train':
net = layers.DropoutLayer(net, p=self.dropout)
last_mem = layers.get_output(net).dimshuffle((1, 0))
print "==> building answer module"
answer_inp_var_shuffled = self.answer_inp_var.dimshuffle(1,2,0)
# Sounds good. Now, we need to map last_mem to a new space.
self.W_mem_emb = nn_utils.normal_param(std = 0.1, shape = (self.dim, self.dim * 2))
self.b_mem_emb = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_inp_emb = nn_utils.normal_param(std = 0.1, shape = (self.dim, self.vocab_size + 1))
self.b_inp_emb = nn_utils.constant_param(value=0.0, shape=(self.dim,))
def _dot2(x, W, b):
#return T.tanh(T.dot(W, x) + b.dimshuffle(0,'x'))
return T.dot(W, x) + b.dimshuffle(0,'x')
answer_inp_var_shuffled_emb,_ = theano.scan(fn = _dot2, sequences = answer_inp_var_shuffled,
non_sequences = [self.W_inp_emb,self.b_inp_emb] ) # seq x dim x batch
#print 'answer_inp_var_shuffled_emb', answer_inp_var_shuffled_emb.shape.eval({self.answer_inp_var:np.random.rand(2,5,8900).astype('float32')})
# dim x batch_size * 5
q_glb_dim = q_glb_last.dimshuffle(0,'x', 1) # batch_size * 1 * dim
#print 'q_glb_dim', q_glb_dim.shape.eval({self.q_var:np.random.rand(2,5,4096).astype('float32')})
q_glb_repmat = T.repeat(q_glb_dim, self.story_len, 1) # batch_size * len * dim
#print 'q_glb_repmat', q_glb_repmat.shape.eval({self.q_var:np.random.rand(2,5,4096).astype('float32')})
q_glb_rhp = T.reshape(q_glb_repmat, (q_glb_repmat.shape[0] * q_glb_repmat.shape[1], q_glb_repmat.shape[2]))
#print 'q_glb_rhp', q_glb_rhp.shape.eval({q_glb_last:np.random.rand(2,512).astype('float32')})
init_ans = T.concatenate([self.q_q, last_mem], axis = 0)
#print 'init_ans', init_ans.shape.eval({self.q_var:np.random.rand(2,5,4096).astype('float32'), self.input_var:np.random.rand(2,5,196, 512).astype('float32')})
mem_ans = T.dot(self.W_mem_emb, init_ans) + self.b_mem_emb.dimshuffle(0,'x') # dim x batchsize
mem_ans_dim = mem_ans.dimshuffle('x',0,1)
answer_inp = T.concatenate([mem_ans_dim, answer_inp_var_shuffled_emb], axis = 0)
q_glb_rhp = q_glb_rhp.dimshuffle(1,0)
q_glb_rhp = q_glb_rhp.dimshuffle('x', 0, 1)
q_glb_step = T.repeat(q_glb_rhp, answer_inp.shape[0], 0)
#mem_ans = T.tanh(T.dot(self.W_mem_emb, init_ans) + self.b_mem_emb.dimshuffle(0,'x')) # dim x batchsize.
# seq + 1 x dim x batch
answer_inp = T.concatenate([answer_inp, q_glb_step], axis = 1)
dummy = theano.shared(np.zeros((self.dim, self.batch_size * self.story_len), dtype=floatX))
self.W_a = nn_utils.normal_param(std=0.1, shape=(self.vocab_size + 1, self.dim))
self.W_ans_res_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim * 2))
self.W_ans_res_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_ans_res = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_ans_upd_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim * 2))
self.W_ans_upd_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_ans_upd = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_ans_hid_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim * 2))
self.W_ans_hid_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_ans_hid = nn_utils.constant_param(value=0.0, shape=(self.dim,))
results, _ = theano.scan(fn = self.answer_gru_step,
sequences = answer_inp,
outputs_info = [ dummy ])
#results = None
#r = None
#for i in range(self.story_len):
# answer_inp_i = answer_inp[i,:]
#
# if i == 0:
# # results: seq + 1 x dim x batch_size
# r, _ = theano.scan(fn = self.answer_gru_step,
# sequences = answer_inp_i,
# truncate_gradient = self.truncate_gradient,
# outputs_info = [ dummy ])
# #print 'r', r.shape.eval({answer_inp_i:np.random.rand(23,512,2).astype('float32')})
# results = r.dimshuffle('x', 0, 1,2)
# else:
# prev_h = r[self.answer_idx[:,i],:,T.arange(self.batch_size)]
# #print 'prev_h', prev_h.shape.eval({answer_inp_i:np.random.rand(23,512,2).astype('float32'), self.answer_idx: np.asarray([[1,1,1,1,1],[2,2,2,2,2]]).astype('int32')},on_unused_input='warn' )
# #print 'prev_h', prev_h.shape.eval({r:np.random.rand(23,512,2).astype('float32'), self.answer_idx: np.asarray([[1,1,1,1,1],[2,2,2,2,2]]).astype('int32')})
# r,_ = theano.scan(fn = self.answer_gru_step,
# sequences = answer_inp_i,
# truncate_gradient = self.truncate_gradient,
# outputs_info = [ prev_h.dimshuffle(1,0) ])
# results = T.concatenate([results, r.dimshuffle('x', 0, 1, 2)])
## results: story_len x seq+1 x dim x batch_size
#results = results.dimshuffle(3,0,1,2)
#results = T.reshape(results, (self.batch_size * self.story_len, results.shape[2], results.shape[3]))
#results = results.dimshuffle(1,2,0) # seq_len x dim x (batch x seq)
# Assume there is a start token
#print 'results', results.shape.eval({self.input_var: np.random.rand(2,5,196,512).astype('float32'),
# self.q_var: np.random.rand(2,5, 4096).astype('float32'),
# self.answer_idx: np.asarray([[1,1,1,1,1],[2,2,2,2,2]]).astype('int32'),
# self.answer_inp_var: np.random.rand(5, 18, 8001).astype('float32')})
#results = results[1:-1,:,:] # get rid of the last token as well as the first one (image)
#print results.shape.eval({self.input_var: np.random.rand(3,4,4096).astype('float32'),
# self.q_var: np.random.rand(3, 4096).astype('float32'),
# self.answer_inp_var: np.random.rand(3, 18, 8001).astype('float32')}, on_unused_input='ignore')
# Now, we need to transform it to the probabilities.
prob,_ = theano.scan(fn = lambda x, w: T.dot(w, x), sequences = results, non_sequences = self.W_a )
#print 'prob', prob.shape.eval({self.input_var: np.random.rand(2,5,196,512).astype('float32'),
# self.q_var: np.random.rand(2,5, 4096).astype('float32'),
# self.answer_idx: np.asarray([[1,1,1,1,1],[2,2,2,2,2]]).astype('int32'),
# self.answer_inp_var: np.random.rand(5, 18, 8001).astype('float32')})
preds = prob[1:,:,:]
prob = prob[1:-1,:,:]
prob_shuffled = prob.dimshuffle(2,0,1) # b * len * vocab
preds_shuffled = preds.dimshuffle(2,0,1)
logging.info("prob shape.")
#print prob.shape.eval({self.input_var: np.random.rand(3,4,4096).astype('float32'),
# self.q_var: np.random.rand(3, 4096).astype('float32'),
# self.answer_inp_var: np.random.rand(3, 18, 8001).astype('float32')})
n = prob_shuffled.shape[0] * prob_shuffled.shape[1]
n_preds = preds_shuffled.shape[0] * preds_shuffled.shape[1]
prob_rhp = T.reshape(prob_shuffled, (n, prob_shuffled.shape[2]))
preds_rhp = T.reshape(preds_shuffled, (n_preds, preds_shuffled.shape[2]))
prob_sm = nn_utils.softmax_(prob_rhp)
preds_sm = nn_utils.softmax_(preds_rhp)
self.prediction = prob_sm # this one is for the training.
#print 'prob_sm', prob_sm.shape.eval({prob: np.random.rand(19,8897,3).astype('float32')})
#print 'lbl', loss_vec.shape.eval({prob: np.random.rand(19,8897,3).astype('float32')})
# This one is for the beamsearch.
self.pred = T.reshape(preds_sm, (preds_shuffled.shape[0], preds_shuffled.shape[1], preds_shuffled.shape[2]))
mask = T.reshape(self.answer_mask, (n,))
lbl = T.reshape(self.answer_var, (n,))
self.params = [self.W_inp_emb_in, self.b_inp_emb_in,
self.W_q_emb_in, self.b_q_emb_in,
self.W_glb_att_1, self.W_glb_att_2, self.b_glb_att_1, self.b_glb_att_2,
self.W_qf_res_in, self.W_qf_res_hid, self.b_qf_res,
self.W_qf_upd_in, self.W_qf_upd_hid, self.b_qf_upd,
self.W_qf_hid_in, self.W_qf_hid_hid, self.b_qf_hid,
self.W_mem_emb, self.W_inp_emb,self.b_mem_emb, self.b_inp_emb,
self.W_mem_res_in, self.W_mem_res_hid, self.b_mem_res,
self.W_mem_upd_in, self.W_mem_upd_hid, self.b_mem_upd,
self.W_mem_hid_in, self.W_mem_hid_hid, self.b_mem_hid, #self.W_b
#self.W_mem_emb, self.W_inp_emb,self.b_mem_emb, self.b_inp_emb,
self.W_1, self.W_2, self.b_1, self.b_2, self.W_a,
self.W_ans_res_in, self.W_ans_res_hid, self.b_ans_res,
self.W_ans_upd_in, self.W_ans_upd_hid, self.b_ans_upd,
self.W_ans_hid_in, self.W_ans_hid_hid, self.b_ans_hid,
]
self.params += self.W_mem_update
self.params += self.b_mem_update
print "==> building loss layer and computing updates"
reward_prob = prob_sm[T.arange(n), lbl]
reward_prob = T.reshape(reward_prob, (prob_shuffled.shape[0], prob_shuffled.shape[1]))
#reward_prob = printing.Print('mean_r')(reward_prob)
loss_vec = T.nnet.categorical_crossentropy(prob_sm, lbl)
#loss_vec = T.nnet.categorical_crossentropy(prob_sm, T.flatten(self.answer_var))
#print 'loss_vec', loss_vec.shape.eval({prob_sm: np.random.rand(39,8900).astype('float32'),
# lbl: np.random.rand(39,).astype('int32')})
self.loss_ce = (mask * loss_vec ).sum() / mask.sum()
print 'loss_ce', self.loss_ce.eval({prob_sm: np.random.rand(39,8900).astype('float32'),
lbl: np.random.rand(39,).astype('int32'), mask: np.random.rand(39,).astype('float32')})
if self.l2 > 0:
self.loss_l2 = self.l2 * nn_utils.l2_reg(self.params)
else:
self.loss_l2 = 0
self.loss = self.loss_ce + self.loss_l2
self.baseline_time = theano.shared(np.float32(0.), name='baseline_time')
alpha_entropy_c = theano.shared(np.float32(self.alpha_entropy_c), name='alpha_entropy_c')
#mean_r = ( mask * reward_prob).sum() / mask.sum() # or just fixed it as 1.
#mean_r = 1
mean_r = (self.answer_mask * reward_prob).sum(1) / self.answer_mask.sum(1) # or just fixed it as 1.
mean_r = mean_r[0,None]
grads = T.grad(self.loss, wrt=self.params,
disconnected_inputs='raise',
known_grads={att_alpha_a:(mean_r - self.baseline_time)*
(att_alpha_sample/(att_alpha_a + 1e-10)) + alpha_entropy_c*(T.log(att_alpha_a + 1e-10) + 1)})
updates = lasagne.updates.adadelta(grads, self.params, learning_rate = self.learning_rate)
updates[self.baseline_time] = self.baseline_time * 0.9 + 0.1 * mean_r.mean()
#updates = lasagne.updates.momentum(self.loss, self.params, learning_rate=0.001)
if self.mode == 'train':
logging.info("compiling train_fn")
self.train_fn = theano.function(inputs=[self.input_var, self.q_var, self.answer_var, self.answer_mask, self.answer_inp_var],
outputs=[self.prediction, self.loss],
updates=updates)
logging.info("compiling test_fn")
self.test_fn = theano.function(inputs=[self.input_var, self.q_var, self.answer_var, self.answer_mask, self.answer_inp_var],
outputs=[self.prediction, self.loss])
logging.info("compiling pred_fn")
self.pred_fn= theano.function(inputs=[self.input_var, self.q_var, self.answer_inp_var],
outputs=[self.pred])
def __init__(self, data_dir, word2vec, word_vector_size, dim, cnn_dim,
story_len, mode, answer_module, memory_hops, batch_size, l2,
normalize_attention, batch_norm, dropout, **kwargs):
print "==> not used params in DMN class:", kwargs.keys()
self.data_dir = data_dir
self.word2vec = word2vec
self.word_vector_size = word_vector_size
self.dim = dim
self.cnn_dim = cnn_dim
self.story_len = story_len
self.mode = mode
self.answer_module = answer_module
self.memory_hops = memory_hops
self.batch_size = batch_size
self.l2 = l2
self.normalize_attention = normalize_attention
self.batch_norm = batch_norm
self.dropout = dropout
self.vocab, self.ivocab = self._load_vocab(self.data_dir)
self.train_story = None
self.test_story = None
self.train_dict_story, self.train_lmdb_env_fc = self._process_input_sind_lmdb(
self.data_dir, 'train')
self.val_dict_story, self.val_lmdb_env_fc = self._process_input_sind_lmdb(
self.data_dir, 'val')
self.test_dict_story, self.test_lmdb_env_fc = self._process_input_sind_lmdb(
self.data_dir, 'test')
self.train_story = self.train_dict_story.keys()
self.val_story = self.val_dict_story.keys()
self.test_story = self.test_dict_story.keys()
self.vocab_size = len(self.vocab)
self.q_var = T.tensor3(
'q_var') # Now, it's a batch * story_len * image_sieze.
self.answer_var = T.imatrix(
'answer_var') # answer of example in minibatch
self.answer_mask = T.matrix('answer_mask')
self.answer_inp_var = T.tensor3(
'answer_inp_var') # answer of example in minibatch
print "==> building input module"
# It's very simple now, the input module just need to map from cnn_dim to dim.
logging.info('self.cnn_dim = %d', self.cnn_dim)
self.W_inp_emb_in = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.cnn_dim))
self.b_inp_emb_in = nn_utils.constant_param(value=0.0,
shape=(self.dim, ))
q_seq = self.q_var.dimshuffle(0, 'x', 1, 2)
q_seq_rpt = T.repeat(q_seq, self.story_len, 1)
q_seq_rhp = T.reshape(q_seq_rpt,
(q_seq_rpt.shape[0] * q_seq_rpt.shape[1],
q_seq_rpt.shape[2], q_seq_rpt.shape[3]))
inp_var_shuffled = q_seq_rhp.dimshuffle(1, 2, 0) #seq x cnn x batch
def _dot(x, W, b):
return T.dot(W, x) + b.dimshuffle(0, 'x')
inp_c_hist, _ = theano.scan(
fn=_dot,
sequences=inp_var_shuffled,
non_sequences=[self.W_inp_emb_in, self.b_inp_emb_in])
#inp_c_hist,_ = theano.scan(fn = _dot, sequences=self.input_var, non_sequences = [self.W_inp_emb_in, self.b_inp_emb_in])
self.inp_c = inp_c_hist # seq x emb x batch
print "==> building question module"
# Now, share the parameter with the input module.
q_var_shuffled = self.q_var.dimshuffle(
1, 2, 0) # now: story_len * image_size * batch_size
# This is the RNN used to produce the Global Glimpse
self.W_inpf_res_in = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.cnn_dim))
self.W_inpf_res_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_inpf_res = nn_utils.constant_param(value=0.0,
shape=(self.dim, ))
self.W_inpf_upd_in = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.cnn_dim))
self.W_inpf_upd_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_inpf_upd = nn_utils.constant_param(value=0.0,
shape=(self.dim, ))
self.W_inpf_hid_in = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.cnn_dim))
self.W_inpf_hid_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_inpf_hid = nn_utils.constant_param(value=0.0,
shape=(self.dim, ))
inp_dummy = theano.shared(
np.zeros((self.dim, self.batch_size), dtype=floatX))
q_glb, _ = theano.scan(fn=self.input_gru_step_forward,
sequences=q_var_shuffled,
outputs_info=[T.zeros_like(inp_dummy)])
q_glb_shuffled = q_glb.dimshuffle(2, 0,
1) # batch_size * seq_len * dim
q_glb_last = q_glb_shuffled[:, -1, :] # batch_size * dim
# Now, we also need to build the individual model.
#q_var_shuffled = self.q_var.dimshuffle(1,0)
q_single = T.reshape(
self.q_var,
(self.q_var.shape[0] * self.q_var.shape[1], self.q_var.shape[2]))
q_single_shuffled = q_single.dimshuffle(1,
0) #cnn_dim x batch_size * 5
# batch_size * 5 x dim
q_hist = T.dot(self.W_inp_emb_in,
q_single_shuffled) + self.b_inp_emb_in.dimshuffle(
0, 'x')
q_hist_shuffled = q_hist.dimshuffle(1, 0) # batch_size * 5 x dim
if self.batch_norm:
logging.info("Using batch normalization.")
q_net = layers.InputLayer(shape=(self.batch_size * self.story_len,
self.dim),
input_var=q_hist_shuffled)
if self.batch_norm:
q_net = layers.BatchNormLayer(incoming=q_net)
if self.dropout > 0 and self.mode == 'train':
q_net = layers.DropoutLayer(q_net, p=self.dropout)
#last_mem = layers.get_output(q_net).dimshuffle((1, 0))
self.q_q = layers.get_output(q_net).dimshuffle(1, 0)
print "==> creating parameters for memory module"
self.W_mem_res_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.W_mem_res_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_mem_res = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_mem_upd_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.W_mem_upd_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_mem_upd = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_mem_hid_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.W_mem_hid_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_mem_hid = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_b = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.W_1 = nn_utils.normal_param(std=0.1,
shape=(self.dim, 7 * self.dim + 0))
self.W_2 = nn_utils.normal_param(std=0.1, shape=(1, self.dim))
self.b_1 = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.b_2 = nn_utils.constant_param(value=0.0, shape=(1, ))
print "==> building episodic memory module (fixed number of steps: %d)" % self.memory_hops
memory = [self.q_q.copy()]
for iter in range(1, self.memory_hops + 1):
#m = printing.Print('mem')(memory[iter-1])
current_episode = self.new_episode(memory[iter - 1])
#current_episode = self.new_episode(m)
#current_episode = printing.Print('current_episode')(current_episode)
memory.append(
self.GRU_update(memory[iter - 1], current_episode,
self.W_mem_res_in, self.W_mem_res_hid,
self.b_mem_res, self.W_mem_upd_in,
self.W_mem_upd_hid, self.b_mem_upd,
self.W_mem_hid_in, self.W_mem_hid_hid,
self.b_mem_hid))
last_mem_raw = memory[-1].dimshuffle((1, 0))
net = layers.InputLayer(shape=(self.batch_size * self.story_len,
self.dim),
input_var=last_mem_raw)
if self.batch_norm:
net = layers.BatchNormLayer(incoming=net)
if self.dropout > 0 and self.mode == 'train':
net = layers.DropoutLayer(net, p=self.dropout)
last_mem = layers.get_output(net).dimshuffle((1, 0))
print "==> building answer module"
answer_inp_var_shuffled = self.answer_inp_var.dimshuffle(1, 2, 0)
# Sounds good. Now, we need to map last_mem to a new space.
self.W_mem_emb = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim * 3))
self.W_inp_emb = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.vocab_size + 1))
def _dot2(x, W):
return T.dot(W, x)
answer_inp_var_shuffled_emb, _ = theano.scan(
fn=_dot2,
sequences=answer_inp_var_shuffled,
non_sequences=self.W_inp_emb) # seq x dim x batch
# dim x batch_size * 5
q_glb_dim = q_glb_last.dimshuffle(0, 'x', 1) # batch_size * 1 * dim
q_glb_repmat = T.repeat(q_glb_dim, self.story_len,
1) # batch_size * len * dim
q_glb_rhp = T.reshape(q_glb_repmat,
(q_glb_repmat.shape[0] * q_glb_repmat.shape[1],
q_glb_repmat.shape[2]))
init_ans = T.concatenate(
[self.q_q, last_mem,
q_glb_rhp.dimshuffle(1, 0)], axis=0)
mem_ans = T.dot(self.W_mem_emb, init_ans) # dim x batchsize.
mem_ans_dim = mem_ans.dimshuffle('x', 0, 1)
answer_inp = T.concatenate([mem_ans_dim, answer_inp_var_shuffled_emb],
axis=0)
dummy = theano.shared(
np.zeros((self.dim, self.batch_size * self.story_len),
dtype=floatX))
self.W_a = nn_utils.normal_param(std=0.1,
shape=(self.vocab_size + 1, self.dim))
self.W_ans_res_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.W_ans_res_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_ans_res = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_ans_upd_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.W_ans_upd_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_ans_upd = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_ans_hid_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.W_ans_hid_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_ans_hid = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
results, _ = theano.scan(fn=self.answer_gru_step,
sequences=answer_inp,
outputs_info=[dummy])
# Assume there is a start token
#print results.shape.eval({self.input_var: np.random.rand(10,4,4096).astype('float32'),
# self.q_var: np.random.rand(10, 4096).astype('float32'),
# self.answer_inp_var: np.random.rand(10, 18, 8001).astype('float32')}, on_unused_input='ignore')
#results = results[1:-1,:,:] # get rid of the last token as well as the first one (image)
#print results.shape.eval({self.input_var: np.random.rand(10,4,4096).astype('float32'),
# self.q_var: np.random.rand(10, 4096).astype('float32'),
# self.answer_inp_var: np.random.rand(10, 18, 8001).astype('float32')}, on_unused_input='ignore')
# Now, we need to transform it to the probabilities.
prob, _ = theano.scan(fn=lambda x, w: T.dot(w, x),
sequences=results,
non_sequences=self.W_a)
preds = prob[1:, :, :]
prob = prob[1:-1, :, :]
prob_shuffled = prob.dimshuffle(2, 0, 1) # b * len * vocab
preds_shuffled = preds.dimshuffle(2, 0, 1)
logging.info("prob shape.")
#print prob.shape.eval({self.input_var: np.random.rand(10,4,4096).astype('float32'),
# self.q_var: np.random.rand(10, 4096).astype('float32'),
# self.answer_inp_var: np.random.rand(10, 18, 8001).astype('float32')})
n = prob_shuffled.shape[0] * prob_shuffled.shape[1]
n_preds = preds_shuffled.shape[0] * preds_shuffled.shape[1]
prob_rhp = T.reshape(prob_shuffled, (n, prob_shuffled.shape[2]))
preds_rhp = T.reshape(preds_shuffled,
(n_preds, preds_shuffled.shape[2]))
prob_sm = nn_utils.softmax_(prob_rhp)
preds_sm = nn_utils.softmax_(preds_rhp)
self.prediction = prob_sm # this one is for the training.
# This one is for the beamsearch.
self.pred = T.reshape(
preds_sm, (preds_shuffled.shape[0], preds_shuffled.shape[1],
preds_shuffled.shape[2]))
mask = T.reshape(self.answer_mask, (n, ))
lbl = T.reshape(self.answer_var, (n, ))
self.params = [
self.W_inp_emb_in,
self.b_inp_emb_in,
self.W_mem_res_in,
self.W_mem_res_hid,
self.b_mem_res,
self.W_mem_upd_in,
self.W_mem_upd_hid,
self.b_mem_upd,
self.W_mem_hid_in,
self.W_mem_hid_hid,
self.b_mem_hid, #self.W_b
self.W_1,
self.W_2,
self.b_1,
self.b_2,
self.W_a
]
self.params = self.params + [
self.W_ans_res_in, self.W_ans_res_hid, self.b_ans_res,
self.W_ans_upd_in, self.W_ans_upd_hid, self.b_ans_upd,
self.W_ans_hid_in, self.W_ans_hid_hid, self.b_ans_hid,
self.W_mem_emb, self.W_inp_emb
]
print "==> building loss layer and computing updates"
loss_vec = T.nnet.categorical_crossentropy(prob_sm, lbl)
self.loss_ce = (mask * loss_vec).sum() / mask.sum()
#self.loss_ce = T.nnet.categorical_crossentropy(results_rhp, lbl)
if self.l2 > 0:
self.loss_l2 = self.l2 * nn_utils.l2_reg(self.params)
else:
self.loss_l2 = 0
self.loss = self.loss_ce + self.loss_l2
updates = lasagne.updates.adadelta(self.loss, self.params)
#updates = lasagne.updates.momentum(self.loss, self.params, learning_rate=0.001)
if self.mode == 'train':
print "==> compiling train_fn"
self.train_fn = theano.function(
inputs=[
self.q_var, self.answer_var, self.answer_mask,
self.answer_inp_var
],
outputs=[self.prediction, self.loss],
updates=updates)
print "==> compiling test_fn"
self.test_fn = theano.function(inputs=[
self.q_var, self.answer_var, self.answer_mask, self.answer_inp_var
],
outputs=[self.prediction, self.loss])
print "==> compiling pred_fn"
self.pred_fn = theano.function(
inputs=[self.q_var, self.answer_inp_var], outputs=[self.pred])
def __init__(self, data_dir, word2vec, word_vector_size, dim, cnn_dim,
story_len, patches, cnn_dim_fc, truncate_gradient,
learning_rate, mode, answer_module, memory_hops, batch_size,
l2, normalize_attention, batch_norm, dropout, **kwargs):
print "==> not used params in DMN class:", kwargs.keys()
self.data_dir = data_dir
self.truncate_gradient = truncate_gradient
self.learning_rate = learning_rate
self.trng = RandomStreams(1234)
self.word2vec = word2vec
self.word_vector_size = word_vector_size
self.dim = dim
self.cnn_dim = cnn_dim
self.cnn_dim_fc = cnn_dim_fc
self.story_len = story_len
self.patches = patches
self.mode = mode
self.answer_module = answer_module
self.memory_hops = memory_hops
self.batch_size = batch_size
self.l2 = l2
self.normalize_attention = normalize_attention
self.batch_norm = batch_norm
self.dropout = dropout
self.vocab, self.ivocab = self._load_vocab(self.data_dir)
self.train_story = None
self.test_story = None
self.train_dict_story, self.train_lmdb_env_fc, self.train_lmdb_env_conv = self._process_input_sind_lmdb(
self.data_dir, 'train')
self.test_dict_story, self.test_lmdb_env_fc, self.test_lmdb_env_conv = self._process_input_sind_lmdb(
self.data_dir, 'val')
self.train_story = self.train_dict_story.keys()
self.test_story = self.test_dict_story.keys()
self.vocab_size = len(self.vocab)
# This is the local patch of each image.
self.input_var = T.tensor4(
'input_var') # (batch_size, seq_len, patches, cnn_dim)
self.q_var = T.tensor3(
'q_var') # Now, it's a batch * story_len * image_sieze.
self.answer_var = T.ivector(
'answer_var') # answer of example in minibatch
self.answer_mask = T.matrix('answer_mask')
self.answer_idx = T.imatrix('answer_idx') # batch x seq
self.answer_inp_var = T.tensor3(
'answer_inp_var') # answer of example in minibatch
print "==> building input module"
# It's very simple now, the input module just need to map from cnn_dim to dim.
logging.info('self.cnn_dim = %d', self.cnn_dim)
logging.info('self.cnn_dim_fc = %d', self.cnn_dim_fc)
logging.info('self.dim = %d', self.dim)
self.W_q_emb_in = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.cnn_dim_fc))
self.b_q_emb_in = nn_utils.constant_param(value=0.0,
shape=(self.dim, ))
q_var_shuffled = self.q_var.dimshuffle(1, 2, 0) # seq x cnn x batch.
def _dot(x, W, b):
return T.tanh(T.dot(W, x) + b.dimshuffle(0, 'x'))
q_var_shuffled_emb, _ = theano.scan(
fn=_dot,
sequences=q_var_shuffled,
non_sequences=[self.W_q_emb_in, self.b_q_emb_in])
#print 'q_var_shuffled_emb', q_var_shuffled_emb.shape.eval({self.q_var:np.random.rand(2,5,4096).astype('float32')})
q_var_emb = q_var_shuffled_emb.dimshuffle(2, 0,
1) # batch x seq x emb_size
q_var_emb_ext = q_var_emb.dimshuffle(0, 'x', 1, 2)
q_var_emb_ext = T.repeat(q_var_emb_ext, q_var_emb.shape[1],
1) # batch x seq x seq x emb_size
q_var_emb_rhp = T.reshape(
q_var_emb,
(q_var_emb.shape[0] * q_var_emb.shape[1], q_var_emb.shape[2]))
q_var_emb_ext_rhp = T.reshape(
q_var_emb_ext, (q_var_emb_ext.shape[0] * q_var_emb_ext.shape[1],
q_var_emb_ext.shape[2], q_var_emb_ext.shape[3]))
q_var_emb_ext_rhp = q_var_emb_ext_rhp.dimshuffle(0, 2, 1)
q_idx = T.arange(self.story_len).dimshuffle('x', 0)
q_idx = T.repeat(q_idx, self.batch_size, axis=0)
q_idx = T.reshape(q_idx, (q_idx.shape[0] * q_idx.shape[1], ))
self.W_inp_emb_in = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.cnn_dim))
self.b_inp_emb_in = nn_utils.constant_param(value=0.0,
shape=(self.dim, ))
inp_rhp = T.reshape(
self.input_var,
(self.batch_size * self.story_len * self.patches, self.cnn_dim))
inp_rhp_dimshuffled = inp_rhp.dimshuffle(1, 0)
inp_rhp_emb = T.dot(
self.W_inp_emb_in,
inp_rhp_dimshuffled) + self.b_inp_emb_in.dimshuffle(0, 'x')
inp_rhp_emb_dimshuffled = inp_rhp_emb.dimshuffle(1, 0)
inp_emb_raw = T.reshape(
inp_rhp_emb_dimshuffled,
(self.batch_size * self.story_len, self.patches, self.cnn_dim))
inp_emb = T.tanh(
inp_emb_raw
) # Just follow the paper DMN for visual and textual QA.
self.inp_c = inp_emb.dimshuffle(1, 2, 0)
logging.info('building question module')
self.W_qf_res_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.W_qf_res_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_qf_res = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_qf_upd_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.W_qf_upd_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_qf_upd = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_qf_hid_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.W_qf_hid_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_qf_hid = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
inp_dummy = theano.shared(
np.zeros((self.dim, self.batch_size), dtype=floatX))
q_var_shuffled_emb_reversed = q_var_shuffled_emb[::
-1, :, :] # seq x emb_size x batch
q_glb, _ = theano.scan(fn=self.q_gru_step_forward,
sequences=q_var_shuffled_emb_reversed,
outputs_info=[T.zeros_like(inp_dummy)])
q_glb_shuffled = q_glb.dimshuffle(2, 0,
1) # batch_size * seq_len * dim
q_glb_last = q_glb_shuffled[:, -1, :] # batch_size * dim
q_net = layers.InputLayer(shape=(self.batch_size * self.story_len,
self.dim),
input_var=q_var_emb_rhp)
if self.batch_norm:
q_net = layers.BatchNormLayer(incoming=q_net)
if self.dropout > 0 and self.mode == 'train':
q_net = layers.DropoutLayer(q_net, p=self.dropout)
self.q_q = layers.get_output(q_net).dimshuffle(1, 0)
#print "==> creating parameters for memory module"
logging.info('creating parameters for memory module')
self.W_mem_res_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.W_mem_res_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_mem_res = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_mem_upd_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.W_mem_upd_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_mem_upd = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_mem_hid_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.W_mem_hid_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_mem_hid = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_mem_update1 = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.dim * 2))
self.b_mem_upd1 = nn_utils.constant_param(value=0.0,
shape=(self.dim, ))
self.W_mem_update2 = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.dim * 2))
self.b_mem_upd2 = nn_utils.constant_param(value=0.0,
shape=(self.dim, ))
self.W_mem_update3 = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.dim * 2))
self.b_mem_upd3 = nn_utils.constant_param(value=0.0,
shape=(self.dim, ))
self.W_mem_update = [
self.W_mem_update1, self.W_mem_update2, self.W_mem_update3
]
self.b_mem_update = [self.b_mem_upd1, self.b_mem_upd2, self.b_mem_upd3]
self.W_1 = nn_utils.normal_param(std=0.1,
shape=(self.dim, 7 * self.dim + 0))
self.W_2 = nn_utils.normal_param(std=0.1, shape=(1, self.dim))
self.b_1 = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.b_2 = nn_utils.constant_param(value=0.0, shape=(1, ))
logging.info(
'==> building episodic memory module (fixed number of steps: %d)',
self.memory_hops)
memory = [self.q_q.copy()]
for iter in range(1, self.memory_hops + 1):
#m = printing.Print('mem')(memory[iter-1])
current_episode = self.new_episode(memory[iter - 1])
# Replace GRU with ReLU activation + MLP.
c = T.concatenate([memory[iter - 1], current_episode], axis=0)
cur_mem = T.dot(self.W_mem_update[iter - 1],
c) + self.b_mem_update[iter - 1].dimshuffle(
0, 'x')
memory.append(T.nnet.relu(cur_mem))
last_mem_raw = memory[-1].dimshuffle((1, 0))
net = layers.InputLayer(shape=(self.batch_size * self.story_len,
self.dim),
input_var=last_mem_raw)
if self.batch_norm:
net = layers.BatchNormLayer(incoming=net)
if self.dropout > 0 and self.mode == 'train':
net = layers.DropoutLayer(net, p=self.dropout)
last_mem = layers.get_output(net).dimshuffle((1, 0))
print "==> building answer module"
answer_inp_var_shuffled = self.answer_inp_var.dimshuffle(1, 2, 0)
# Sounds good. Now, we need to map last_mem to a new space.
self.W_mem_emb = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim * 2))
self.b_mem_emb = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_inp_emb = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.vocab_size + 1))
self.b_inp_emb = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
def _dot2(x, W, b):
#return T.tanh(T.dot(W, x) + b.dimshuffle(0,'x'))
return T.dot(W, x) + b.dimshuffle(0, 'x')
answer_inp_var_shuffled_emb, _ = theano.scan(
fn=_dot2,
sequences=answer_inp_var_shuffled,
non_sequences=[self.W_inp_emb,
self.b_inp_emb]) # seq x dim x batch
init_ans = T.concatenate([self.q_q, last_mem],
axis=0) # dim x (batch x self.story_len)
mem_ans = T.dot(self.W_mem_emb, init_ans) + self.b_mem_emb.dimshuffle(
0, 'x') # dim x (batchsize x self.story_len)
#mem_ans_dim = mem_ans.dimshuffle('x',0,1)
mem_ans_rhp = T.reshape(mem_ans.dimshuffle(
1, 0), (self.batch_size, self.story_len, mem_ans.shape[0]))
mem_ans_dim = mem_ans_rhp.dimshuffle(1, 2, 0)
answer_inp = answer_inp_var_shuffled_emb
#answer_inp = T.concatenate([mem_ans_dim, answer_inp_var_shuffled_emb], axis = 0) #seq + 1 x dim x (batch-size x self.story+len)
# Now, each answer got its input, our next step is to obtain the sequences.
answer_inp_shu = answer_inp.dimshuffle(2, 0, 1)
answer_inp_shu_rhp = T.reshape(answer_inp_shu, (self.batch_size, self.story_len, answer_inp_shu.shape[1],\
answer_inp_shu.shape[2]))
answer_inp = answer_inp_shu_rhp.dimshuffle(
1, 2, 3, 0) # story_len x seq + 1 x dim x batch_size
self.W_a = nn_utils.normal_param(std=0.1,
shape=(self.vocab_size + 1, self.dim))
self.W_ans_res_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.W_ans_res_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_ans_res = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_ans_upd_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.W_ans_upd_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_ans_upd = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_ans_hid_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.W_ans_hid_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_ans_hid = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_ans_map = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim * 2))
self.b_ans_map = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
results = None
r = None
dummy = theano.shared(
np.zeros((self.dim, self.batch_size), dtype=floatX))
for i in range(self.story_len):
answer_inp_i = answer_inp[i, :] # seq + 1 x dim x batch_size
mem_ans_dim_i = mem_ans_dim[i, :] # dim x batch_size
if i == 0:
q_glb_inp = q_glb_last.dimshuffle('x', 1,
0) #1 x dim x batch_size
answer_inp_i = T.concatenate([q_glb_inp, answer_inp_i], axis=0)
init_h = T.concatenate([dummy, mem_ans_dim_i], axis=0)
init_h = T.dot(self.W_ans_map,
init_h) + self.b_ans_map.dimshuffle(0, 'x')
init_h = T.tanh(init_h)
r, _ = theano.scan(fn=self.answer_gru_step,
sequences=answer_inp_i,
truncate_gradient=self.truncate_gradient,
outputs_info=[init_h])
r = r[1:, :] # get rid of the first glob one.
results = r.dimshuffle('x', 0, 1, 2)
else:
prev_h = r[self.answer_idx[:, i], :, T.arange(self.batch_size)]
h_ = T.concatenate([prev_h.dimshuffle(1, 0), mem_ans_dim_i],
axis=0)
h_ = T.dot(self.W_ans_map, h_) + self.b_ans_map.dimshuffle(
0, 'x')
h_ = T.tanh(h_)
r, _ = theano.scan(fn=self.answer_gru_step,
sequences=answer_inp_i,
truncate_gradient=self.truncate_gradient,
outputs_info=[h_])
results = T.concatenate([results, r.dimshuffle('x', 0, 1, 2)])
## results: story_len x seq+1 x dim x batch_size
results = results.dimshuffle(3, 0, 1, 2)
results = T.reshape(results, (self.batch_size * self.story_len,
results.shape[2], results.shape[3]))
results = results.dimshuffle(1, 2, 0) # seq_len x dim x (batch x seq)
# Assume there is a start token
#print 'results', results.shape.eval({self.input_var: np.random.rand(2,5,196,512).astype('float32'),
# self.q_var: np.random.rand(2,5, 4096).astype('float32'),
# self.answer_idx: np.asarray([[1,1,1,1,1],[2,2,2,2,2]]).astype('int32'),
# self.answer_inp_var: np.random.rand(5, 18, 8001).astype('float32')})
#results = results[1:-1,:,:] # get rid of the last token as well as the first one (image)
#print results.shape.eval({self.input_var: np.random.rand(3,4,4096).astype('float32'),
# self.q_var: np.random.rand(3, 4096).astype('float32'),
# self.answer_inp_var: np.random.rand(3, 18, 8001).astype('float32')}, on_unused_input='ignore')
# Now, we need to transform it to the probabilities.
prob, _ = theano.scan(fn=lambda x, w: T.dot(w, x),
sequences=results,
non_sequences=self.W_a)
#print 'prob', prob.shape.eval({self.input_var: np.random.rand(2,5,196,512).astype('float32'),
# self.q_var: np.random.rand(2,5, 4096).astype('float32'),
# self.answer_idx: np.asarray([[1,1,1,1,1],[2,2,2,2,2]]).astype('int32'),
# self.answer_inp_var: np.random.rand(5, 18, 8001).astype('float32')})
#preds = prob[1:,:,:]
#prob = prob[1:-1,:,:]
preds = prob
prob = prob[:-1, :, :]
prob_shuffled = prob.dimshuffle(2, 0, 1) # b * len * vocab
preds_shuffled = preds.dimshuffle(2, 0, 1)
logging.info("prob shape.")
#print prob.shape.eval({self.input_var: np.random.rand(3,4,4096).astype('float32'),
# self.q_var: np.random.rand(3, 4096).astype('float32'),
# self.answer_inp_var: np.random.rand(3, 18, 8001).astype('float32')})
n = prob_shuffled.shape[0] * prob_shuffled.shape[1]
n_preds = preds_shuffled.shape[0] * preds_shuffled.shape[1]
prob_rhp = T.reshape(prob_shuffled, (n, prob_shuffled.shape[2]))
preds_rhp = T.reshape(preds_shuffled,
(n_preds, preds_shuffled.shape[2]))
prob_sm = nn_utils.softmax_(prob_rhp)
preds_sm = nn_utils.softmax_(preds_rhp)
self.prediction = prob_sm # this one is for the training.
#print 'prob_sm', prob_sm.shape.eval({prob: np.random.rand(19,8897,3).astype('float32')})
#print 'lbl', loss_vec.shape.eval({prob: np.random.rand(19,8897,3).astype('float32')})
# This one is for the beamsearch.
self.pred = T.reshape(
preds_sm, (preds_shuffled.shape[0], preds_shuffled.shape[1],
preds_shuffled.shape[2]))
mask = T.reshape(self.answer_mask, (n, ))
lbl = T.reshape(self.answer_var, (n, ))
self.params = [
self.W_inp_emb_in,
self.b_inp_emb_in,
self.W_q_emb_in,
self.b_q_emb_in,
#self.W_glb_att_1, self.W_glb_att_2, self.b_glb_att_1, self.b_glb_att_2,
self.W_qf_res_in,
self.W_qf_res_hid,
self.b_qf_res,
self.W_qf_upd_in,
self.W_qf_upd_hid,
self.b_qf_upd,
self.W_qf_hid_in,
self.W_qf_hid_hid,
self.b_qf_hid,
self.W_mem_emb,
self.W_inp_emb,
self.b_mem_emb,
self.b_inp_emb,
self.W_mem_res_in,
self.W_mem_res_hid,
self.b_mem_res,
self.W_mem_upd_in,
self.W_mem_upd_hid,
self.b_mem_upd,
self.W_mem_hid_in,
self.W_mem_hid_hid,
self.b_mem_hid, #self.W_b
#self.W_mem_emb, self.W_inp_emb,self.b_mem_emb, self.b_inp_emb,
self.W_1,
self.W_2,
self.b_1,
self.b_2,
self.W_a,
self.W_ans_res_in,
self.W_ans_res_hid,
self.b_ans_res,
self.W_ans_upd_in,
self.W_ans_upd_hid,
self.b_ans_upd,
self.W_ans_hid_in,
self.W_ans_hid_hid,
self.b_ans_hid,
self.W_ans_map,
self.b_ans_map,
]
self.params += self.W_mem_update
self.params += self.b_mem_update
print "==> building loss layer and computing updates"
reward_prob = prob_sm[T.arange(n), lbl]
reward_prob = T.reshape(
reward_prob, (prob_shuffled.shape[0], prob_shuffled.shape[1]))
#reward_prob = printing.Print('mean_r')(reward_prob)
loss_vec = T.nnet.categorical_crossentropy(prob_sm, lbl)
#loss_vec = T.nnet.categorical_crossentropy(prob_sm, T.flatten(self.answer_var))
#print 'loss_vec', loss_vec.shape.eval({prob_sm: np.random.rand(39,8900).astype('float32'),
# lbl: np.random.rand(39,).astype('int32')})
self.loss_ce = (mask * loss_vec).sum() / mask.sum()
print 'loss_ce', self.loss_ce.eval({
prob_sm:
np.random.rand(39, 8900).astype('float32'),
lbl:
np.random.rand(39, ).astype('int32'),
mask:
np.random.rand(39, ).astype('float32')
})
if self.l2 > 0:
self.loss_l2 = self.l2 * nn_utils.l2_reg(self.params)
else:
self.loss_l2 = 0
self.loss = self.loss_ce + self.loss_l2
grads = T.grad(self.loss, wrt=self.params, disconnected_inputs='raise')
updates = lasagne.updates.adadelta(grads,
self.params,
learning_rate=self.learning_rate)
if self.mode == 'train':
logging.info("compiling train_fn")
self.train_fn = theano.function(
inputs=[
self.input_var, self.q_var, self.answer_var,
self.answer_mask, self.answer_inp_var, self.answer_idx
],
outputs=[self.prediction, self.loss],
updates=updates)
logging.info("compiling test_fn")
self.test_fn = theano.function(inputs=[
self.input_var, self.q_var, self.answer_var, self.answer_mask,
self.answer_inp_var, self.answer_idx
],
outputs=[self.prediction, self.loss])
logging.info("compiling pred_fn")
self.pred_fn = theano.function(inputs=[
self.input_var, self.q_var, self.answer_inp_var, self.answer_idx
],
outputs=[self.pred])
def __init__(self, babi_train_raw, babi_test_raw, word2vec,
word_vector_size, dim, mode, answer_module, input_mask_mode,
memory_hops, l2, normalize_attention, **kwargs):
print "==> not used params in DMN class:", kwargs.keys()
self.vocab = {}
self.ivocab = {}
self.word2vec = word2vec
self.word_vector_size = word_vector_size
self.dim = dim
self.mode = mode
self.answer_module = answer_module
self.input_mask_mode = input_mask_mode
self.memory_hops = memory_hops
self.l2 = l2
self.normalize_attention = normalize_attention
self.train_input, self.train_q, self.train_answer, self.train_input_mask = self._process_input(
babi_train_raw)
self.test_input, self.test_q, self.test_answer, self.test_input_mask = self._process_input(
babi_test_raw)
self.vocab_size = len(self.vocab)
self.input_var = T.matrix('input_var')
self.q_var = T.matrix('question_var')
self.answer_var = T.iscalar('answer_var')
self.input_mask_var = T.ivector('input_mask_var')
print "==> building input module"
self.W_inp_res_in = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.word_vector_size))
self.W_inp_res_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_inp_res = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_inp_upd_in = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.word_vector_size))
self.W_inp_upd_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_inp_upd = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_inp_hid_in = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.word_vector_size))
self.W_inp_hid_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_inp_hid = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
inp_c_history, _ = theano.scan(fn=self.input_gru_step,
sequences=self.input_var,
outputs_info=T.zeros_like(
self.b_inp_hid))
self.inp_c = inp_c_history.take(self.input_mask_var, axis=0)
self.q_q, _ = theano.scan(fn=self.input_gru_step,
sequences=self.q_var,
outputs_info=T.zeros_like(self.b_inp_hid))
self.q_q = self.q_q[-1]
print "==> creating parameters for memory module"
self.W_mem_res_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.W_mem_res_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_mem_res = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_mem_upd_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.W_mem_upd_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_mem_upd = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_mem_hid_in = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.W_mem_hid_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim, self.dim))
self.b_mem_hid = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.W_b = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.W_1 = nn_utils.normal_param(std=0.1,
shape=(self.dim, 7 * self.dim + 2))
self.W_2 = nn_utils.normal_param(std=0.1, shape=(1, self.dim))
self.b_1 = nn_utils.constant_param(value=0.0, shape=(self.dim, ))
self.b_2 = nn_utils.constant_param(value=0.0, shape=(1, ))
print "==> building episodic memory module (fixed number of steps: %d)" % self.memory_hops
memory = [self.q_q.copy()]
for iter in range(1, self.memory_hops + 1):
current_episode = self.new_episode(memory[iter - 1])
memory.append(
self.GRU_update(memory[iter - 1], current_episode,
self.W_mem_res_in, self.W_mem_res_hid,
self.b_mem_res, self.W_mem_upd_in,
self.W_mem_upd_hid, self.b_mem_upd,
self.W_mem_hid_in, self.W_mem_hid_hid,
self.b_mem_hid))
last_mem = memory[-1]
print "==> building answer module"
self.W_a = nn_utils.normal_param(std=0.1,
shape=(self.vocab_size, self.dim))
if self.answer_module == 'feedforward':
self.prediction = nn_utils.softmax(T.dot(self.W_a, last_mem))
elif self.answer_module == 'recurrent':
self.W_ans_res_in = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.dim + self.vocab_size))
self.W_ans_res_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.dim))
self.b_ans_res = nn_utils.constant_param(value=0.0,
shape=(self.dim, ))
self.W_ans_upd_in = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.dim + self.vocab_size))
self.W_ans_upd_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.dim))
self.b_ans_upd = nn_utils.constant_param(value=0.0,
shape=(self.dim, ))
self.W_ans_hid_in = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.dim + self.vocab_size))
self.W_ans_hid_hid = nn_utils.normal_param(std=0.1,
shape=(self.dim,
self.dim))
self.b_ans_hid = nn_utils.constant_param(value=0.0,
shape=(self.dim, ))
def answer_step(prev_a, prev_y):
a = self.GRU_update(prev_a, T.concatenate([prev_y, self.q_q]),
self.W_ans_res_in, self.W_ans_res_hid,
self.b_ans_res, self.W_ans_upd_in,
self.W_ans_upd_hid, self.b_ans_upd,
self.W_ans_hid_in, self.W_ans_hid_hid,
self.b_ans_hid)
y = nn_utils.softmax(T.dot(self.W_a, a))
return [a, y]
# TODO: add conditional ending
dummy = theano.shared(np.zeros((self.vocab_size, ), dtype=floatX))
results, updates = theano.scan(
fn=answer_step,
outputs_info=[last_mem, T.zeros_like(dummy)],
n_steps=1)
self.prediction = results[1][-1]
else:
raise Exception("invalid answer_module")
print "==> collecting all parameters"
self.params = [
self.W_inp_res_in, self.W_inp_res_hid, self.b_inp_res,
self.W_inp_upd_in, self.W_inp_upd_hid, self.b_inp_upd,
self.W_inp_hid_in, self.W_inp_hid_hid, self.b_inp_hid,
self.W_mem_res_in, self.W_mem_res_hid, self.b_mem_res,
self.W_mem_upd_in, self.W_mem_upd_hid, self.b_mem_upd,
self.W_mem_hid_in, self.W_mem_hid_hid, self.b_mem_hid, self.W_b,
self.W_1, self.W_2, self.b_1, self.b_2, self.W_a
]
if self.answer_module == 'recurrent':
self.params = self.params + [
self.W_ans_res_in, self.W_ans_res_hid, self.b_ans_res,
self.W_ans_upd_in, self.W_ans_upd_hid, self.b_ans_upd,
self.W_ans_hid_in, self.W_ans_hid_hid, self.b_ans_hid
]
print "==> building loss layer and computing updates"
self.loss_ce = T.nnet.categorical_crossentropy(
self.prediction.dimshuffle('x', 0), T.stack([self.answer_var]))[0]
if self.l2 > 0:
self.loss_l2 = self.l2 * nn_utils.l2_reg(self.params)
else:
self.loss_l2 = 0
self.loss = self.loss_ce + self.loss_l2
updates = lasagne.updates.adadelta(self.loss, self.params)
if self.mode == 'train':
print "==> compiling train_fn"
self.train_fn = theano.function(
inputs=[
self.input_var, self.q_var, self.answer_var,
self.input_mask_var
],
outputs=[self.prediction, self.loss],
updates=updates)
print "==> compiling test_fn"
self.test_fn = theano.function(inputs=[
self.input_var, self.q_var, self.answer_var, self.input_mask_var
],
outputs=[
self.prediction, self.loss,
self.inp_c, self.q_q, last_mem
])
if self.mode == 'train':
print "==> computing gradients (for debugging)"
gradient = T.grad(self.loss, self.params)
self.get_gradient_fn = theano.function(inputs=[
self.input_var, self.q_var, self.answer_var,
self.input_mask_var
],
outputs=gradient)
def __init__(self, train_raw, dev_raw, test_raw, word2vec, word_vector_size,
dim, mode, input_mask_mode, memory_hops, l2, normalize_attention, dropout, **kwargs):
print "generate one-word answer for mctest"
print "==> not used params in DMN class:", kwargs.keys()
self.word2vec = word2vec
self.word_vector_size = word_vector_size
self.vocab_size = len(word2vec)
self.dim = dim # hidden state size
self.mode = mode
self.input_mask_mode = input_mask_mode
self.memory_hops = memory_hops
self.l2 = l2
self.normalize_attention = normalize_attention
self.dropout = dropout
self.train_input, self.train_q, self.train_answer, self.train_input_mask = self._process_input(train_raw)
self.dev_input, self.dev_q, self.dev_answer, self.dev_input_mask = self._process_input(dev_raw)
self.test_input, self.test_q, self.test_answer, self.test_input_mask = self._process_input(test_raw)
self.input_var = T.matrix('input_var')
self.q_var = T.matrix('question_var')
self.answer_var = T.iscalar('answer_var')
self.input_mask_var = T.ivector('input_mask_var')
self.attentions = []
print "==> building input module"
self.W_inp_res_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.word_vector_size))
self.W_inp_res_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_inp_res = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_inp_upd_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.word_vector_size))
self.W_inp_upd_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_inp_upd = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_inp_hid_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.word_vector_size))
self.W_inp_hid_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_inp_hid = nn_utils.constant_param(value=0.0, shape=(self.dim,))
inp_c_history, _ = theano.scan(fn=self.input_gru_step,
sequences=self.input_var,
outputs_info=T.zeros_like(self.b_inp_hid))
self.inp_c = inp_c_history.take(self.input_mask_var, axis=0)
self.q_q, _ = theano.scan(fn=self.input_gru_step,
sequences=self.q_var,
outputs_info=T.zeros_like(self.b_inp_hid))
self.q_q = self.q_q[-1]
print "==> creating parameters for memory module"
self.W_mem_res_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.W_mem_res_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_mem_res = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_mem_upd_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.W_mem_upd_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_mem_upd = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_mem_hid_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.W_mem_hid_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_mem_hid = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_b = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.W_1 = nn_utils.normal_param(std=0.1, shape=(self.dim, 7 * self.dim + 2))
self.W_2 = nn_utils.normal_param(std=0.1, shape=(1, self.dim))
self.b_1 = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.b_2 = nn_utils.constant_param(value=0.0, shape=(1,))
print "==> building episodic memory module (fixed number of steps: %d)" % self.memory_hops
memory = [self.q_q.copy()]
for iter in range(1, self.memory_hops + 1):
current_episode = self.new_episode(memory[iter - 1])
memory.append(self.GRU_update(memory[iter - 1], current_episode,
self.W_mem_res_in, self.W_mem_res_hid, self.b_mem_res,
self.W_mem_upd_in, self.W_mem_upd_hid, self.b_mem_upd,
self.W_mem_hid_in, self.W_mem_hid_hid, self.b_mem_hid))
last_mem_raw = memory[-1].dimshuffle(('x', 0))
net = layers.InputLayer(shape=(1, self.dim), input_var=last_mem_raw)
if self.dropout > 0 and self.mode == 'train':
net = layers.DropoutLayer(net, p=self.dropout)
last_mem = layers.get_output(net)[0]
self.attentions = T.stack(self.attentions)
print "==> building answer module"
self.W_a = nn_utils.normal_param(std=0.1, shape=(self.vocab_size, self.dim))
self.prediction = nn_utils.softmax(T.dot(self.W_a, last_mem))
print "==> collecting all parameters"
self.params = [self.W_inp_res_in, self.W_inp_res_hid, self.b_inp_res,
self.W_inp_upd_in, self.W_inp_upd_hid, self.b_inp_upd,
self.W_inp_hid_in, self.W_inp_hid_hid, self.b_inp_hid,
self.W_mem_res_in, self.W_mem_res_hid, self.b_mem_res,
self.W_mem_upd_in, self.W_mem_upd_hid, self.b_mem_upd,
self.W_mem_hid_in, self.W_mem_hid_hid, self.b_mem_hid, self.W_b,
self.W_1, self.W_2, self.b_1, self.b_2, self.W_a]
print "==> building loss layer and computing updates"
self.loss_ce = T.nnet.categorical_crossentropy(self.prediction.dimshuffle('x',0),T.stack([self.answer_var]))[0]
if self.l2 > 0:
self.loss_l2 = self.l2 * nn_utils.l2_reg(self.params)
else:
self.loss_l2 = 0
self.loss = self.loss_ce + self.loss_l2
updates = lasagne.updates.adam(self.loss, self.params)
#updates = lasagne.updates.momentum(self.loss, self.params, learning_rate=0.0003)
if self.mode == 'train':
print "==> compiling train_fn"
self.train_fn = theano.function(inputs=[self.input_var, self.q_var, self.answer_var, self.input_mask_var],
allow_input_downcast = True,
outputs=[self.prediction, self.loss],
updates=updates)
print "==> compiling test_fn"
self.test_fn = theano.function(inputs=[self.input_var, self.q_var, self.answer_var, self.input_mask_var],
allow_input_downcast = True,
outputs=[self.prediction, self.loss, self.attentions])
def __init__(self, babi_train_raw, babi_test_raw, word2vec, word_vector_size,
dim, mode, answer_module, input_mask_mode, memory_hops, l2,
normalize_attention, **kwargs):
print "==> not used params in DMN class:", kwargs.keys()
self.vocab = {}
self.ivocab = {}
self.word2vec = word2vec
self.word_vector_size = word_vector_size
self.dim = dim
self.mode = mode
self.answer_module = answer_module
self.input_mask_mode = input_mask_mode
self.memory_hops = memory_hops
self.l2 = l2
self.normalize_attention = normalize_attention
self.train_input, self.train_q, self.train_answer, self.train_input_mask = self._process_input(babi_train_raw)
self.test_input, self.test_q, self.test_answer, self.test_input_mask = self._process_input(babi_test_raw)
self.vocab_size = len(self.vocab)
self.input_var = T.matrix('input_var')
self.q_var = T.matrix('question_var')
self.answer_var = T.iscalar('answer_var')
self.input_mask_var = T.ivector('input_mask_var')
print "==> building input module"
self.W_inp_res_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.word_vector_size))
self.W_inp_res_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_inp_res = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_inp_upd_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.word_vector_size))
self.W_inp_upd_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_inp_upd = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_inp_hid_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.word_vector_size))
self.W_inp_hid_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_inp_hid = nn_utils.constant_param(value=0.0, shape=(self.dim,))
inp_c_history, _ = theano.scan(fn=self.input_gru_step,
sequences=self.input_var,
outputs_info=T.zeros_like(self.b_inp_hid))
self.inp_c = inp_c_history.take(self.input_mask_var, axis=0)
self.q_q, _ = theano.scan(fn=self.input_gru_step,
sequences=self.q_var,
outputs_info=T.zeros_like(self.b_inp_hid))
self.q_q = self.q_q[-1]
print "==> creating parameters for memory module"
self.W_mem_res_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.W_mem_res_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_mem_res = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_mem_upd_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.W_mem_upd_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_mem_upd = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_mem_hid_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.W_mem_hid_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_mem_hid = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_b = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.W_1 = nn_utils.normal_param(std=0.1, shape=(self.dim, 7 * self.dim + 2))
self.W_2 = nn_utils.normal_param(std=0.1, shape=(1, self.dim))
self.b_1 = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.b_2 = nn_utils.constant_param(value=0.0, shape=(1,))
print "==> building episodic memory module (fixed number of steps: %d)" % self.memory_hops
memory = [self.q_q.copy()]
for iter in range(1, self.memory_hops + 1):
current_episode = self.new_episode(memory[iter - 1])
memory.append(self.GRU_update(memory[iter - 1], current_episode,
self.W_mem_res_in, self.W_mem_res_hid, self.b_mem_res,
self.W_mem_upd_in, self.W_mem_upd_hid, self.b_mem_upd,
self.W_mem_hid_in, self.W_mem_hid_hid, self.b_mem_hid))
last_mem = memory[-1]
print "==> building answer module"
self.W_a = nn_utils.normal_param(std=0.1, shape=(self.vocab_size, self.dim))
if self.answer_module == 'feedforward':
self.prediction = nn_utils.softmax(T.dot(self.W_a, last_mem))
elif self.answer_module == 'recurrent':
self.W_ans_res_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim + self.vocab_size))
self.W_ans_res_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_ans_res = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_ans_upd_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim + self.vocab_size))
self.W_ans_upd_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_ans_upd = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_ans_hid_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim + self.vocab_size))
self.W_ans_hid_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_ans_hid = nn_utils.constant_param(value=0.0, shape=(self.dim,))
def answer_step(prev_a, prev_y):
a = self.GRU_update(prev_a, T.concatenate([prev_y, self.q_q]),
self.W_ans_res_in, self.W_ans_res_hid, self.b_ans_res,
self.W_ans_upd_in, self.W_ans_upd_hid, self.b_ans_upd,
self.W_ans_hid_in, self.W_ans_hid_hid, self.b_ans_hid)
y = nn_utils.softmax(T.dot(self.W_a, a))
return [a, y]
# TODO: add conditional ending
dummy = theano.shared(np.zeros((self.vocab_size, ), dtype=floatX))
results, updates = theano.scan(fn=answer_step,
outputs_info=[last_mem, T.zeros_like(dummy)],
n_steps=1)
self.prediction = results[1][-1]
else:
raise Exception("invalid answer_module")
print "==> collecting all parameters"
self.params = [self.W_inp_res_in, self.W_inp_res_hid, self.b_inp_res,
self.W_inp_upd_in, self.W_inp_upd_hid, self.b_inp_upd,
self.W_inp_hid_in, self.W_inp_hid_hid, self.b_inp_hid,
self.W_mem_res_in, self.W_mem_res_hid, self.b_mem_res,
self.W_mem_upd_in, self.W_mem_upd_hid, self.b_mem_upd,
self.W_mem_hid_in, self.W_mem_hid_hid, self.b_mem_hid,
self.W_b, self.W_1, self.W_2, self.b_1, self.b_2, self.W_a]
if self.answer_module == 'recurrent':
self.params = self.params + [self.W_ans_res_in, self.W_ans_res_hid, self.b_ans_res,
self.W_ans_upd_in, self.W_ans_upd_hid, self.b_ans_upd,
self.W_ans_hid_in, self.W_ans_hid_hid, self.b_ans_hid]
print "==> building loss layer and computing updates"
self.loss_ce = T.nnet.categorical_crossentropy(self.prediction.dimshuffle('x', 0), T.stack([self.answer_var]))[0]
if self.l2 > 0:
self.loss_l2 = self.l2 * nn_utils.l2_reg(self.params)
else:
self.loss_l2 = 0
self.loss = self.loss_ce + self.loss_l2
updates = lasagne.updates.adadelta(self.loss, self.params)
if self.mode == 'train':
print "==> compiling train_fn"
self.train_fn = theano.function(inputs=[self.input_var, self.q_var, self.answer_var, self.input_mask_var],
outputs=[self.prediction, self.loss],
updates=updates)
print "==> compiling test_fn"
self.test_fn = theano.function(inputs=[self.input_var, self.q_var, self.answer_var, self.input_mask_var],
outputs=[self.prediction, self.loss, self.inp_c, self.q_q, last_mem])
if self.mode == 'train':
print "==> computing gradients (for debugging)"
gradient = T.grad(self.loss, self.params)
self.get_gradient_fn = theano.function(inputs=[self.input_var, self.q_var, self.answer_var, self.input_mask_var], outputs=gradient)
def __init__(self, data_dir, word2vec, word_vector_size, dim,
mode, answer_module, memory_hops, batch_size, l2,
normalize_attention, batch_norm, dropout, **kwargs):
print "==> not used params in DMN class:", kwargs.keys()
self.data_dir = data_dir
self.word2vec = word2vec
self.word_vector_size = word_vector_size
self.dim = dim
self.mode = mode
self.answer_module = answer_module
self.memory_hops = memory_hops
self.batch_size = batch_size
self.l2 = l2
self.normalize_attention = normalize_attention
self.batch_norm = batch_norm
self.dropout = dropout
self.vocab, self.ivocab = self._load_vocab(self.data_dir)
self.train_story = None
self.test_story = None
self.train_dict_story, self.train_features, self.train_fns_dict, self.train_num_imgs = self._process_input_sind(self.data_dir, 'train')
self.test_dict_story, self.test_features, self.test_fns_dict, self.test_num_imgs = self._process_input_sind(self.data_dir, 'val')
self.train_story = self.train_dict_story.keys()
self.test_story = self.test_dict_story.keys()
self.vocab_size = len(self.vocab)
self.q_var = T.matrix('q_var') # Now, it's a batch * image_sieze.
self.answer_var = T.imatrix('answer_var') # answer of example in minibatch
self.answer_mask = T.matrix('answer_mask')
self.answer_inp_var = T.tensor3('answer_inp_var') # answer of example in minibatch
print "==> building question module"
# Now, share the parameter with the input module.
q_var_shuffled = self.q_var.dimshuffle(1,0)
self.W_inp_emb_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.cnn_dim))
self.b_inp_emb_in = nn_utils.constant_param(value=0.0, shape=(self.dim,))
q_hist = T.dot(self.W_inp_emb_in, q_var_shuffled) + self.b_inp_emb_in.dimshuffle(0,'x')
q_hist_shuffled = q_hist.dimshuffle(1,0)
if self.batch_norm:
logging.info("Using batch normalization.")
q_net = layers.InputLayer(shape=(self.batch_size, self.dim), input_var=q_hist_shuffled)
if self.batch_norm:
q_net = layers.BatchNormLayer(incoming=q_net)
if self.dropout > 0 and self.mode == 'train':
q_net = layers.DropoutLayer(q_net, p=self.dropout)
#last_mem = layers.get_output(q_net).dimshuffle((1, 0))
self.q_q = layers.get_output(q_net).dimshuffle(1,0)
print "==> building answer module"
answer_inp_var_shuffled = self.answer_inp_var.dimshuffle(1,2,0)
#self.W_mem_emb = nn_utils.normal_param(std = 0.1, shape = (self.dim, self.dim))
self.W_inp_emb = nn_utils.normal_param(std = 0.1, shape = (self.dim, self.vocab_size + 1))
def _dot2(x, W):
return T.dot(W, x)
answer_inp_var_shuffled_emb,_ = theano.scan(fn = _dot2, sequences = answer_inp_var_shuffled,
non_sequences = self.W_inp_emb ) # seq x dim x batch
mem_ans = self.q_q
mem_ans_dim = mem_ans.dimshuffle('x',0,1)
answer_inp = T.concatenate([mem_ans_dim, answer_inp_var_shuffled_emb], axis = 0)
dummy = theano.shared(np.zeros((self.dim, self.batch_size), dtype=floatX))
self.W_a = nn_utils.normal_param(std=0.1, shape=(self.vocab_size + 1, self.dim))
self.W_ans_res_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.W_ans_res_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_ans_res = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_ans_upd_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.W_ans_upd_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_ans_upd = nn_utils.constant_param(value=0.0, shape=(self.dim,))
self.W_ans_hid_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.W_ans_hid_hid = nn_utils.normal_param(std=0.1, shape=(self.dim, self.dim))
self.b_ans_hid = nn_utils.constant_param(value=0.0, shape=(self.dim,))
logging.info('answer_inp size')
#last_mem = printing.Print('prob_sm')(last_mem)
results, _ = theano.scan(fn = self.answer_gru_step,
sequences = answer_inp,
outputs_info = [ dummy ])
prob,_ = theano.scan(fn = lambda x, w: T.dot(w, x), sequences = results, non_sequences = self.W_a )
preds = prob[1:,:,:]
prob = prob[1:-1,:,:]
prob_shuffled = prob.dimshuffle(2,0,1) # b * len * vocab
preds_shuffled = preds.dimshuffle(2,0,1)
logging.info("prob shape.")
#print prob.shape.eval({self.input_var: np.random.rand(10,4,4096).astype('float32'),
# self.q_var: np.random.rand(10, 4096).astype('float32'),
# self.answer_inp_var: np.random.rand(10, 18, 8001).astype('float32')})
n = prob_shuffled.shape[0] * prob_shuffled.shape[1]
n_preds = preds_shuffled.shape[0] * preds_shuffled.shape[1]
prob_rhp = T.reshape(prob_shuffled, (n, prob_shuffled.shape[2]))
preds_rhp = T.reshape(preds_shuffled, (n_preds, preds_shuffled.shape[2]))
prob_sm = nn_utils.softmax_(prob_rhp)
preds_sm = nn_utils.softmax_(preds_rhp)
self.prediction = prob_sm # this one is for the training.
# This one is for the beamsearch.
self.pred = T.reshape(preds_sm, (preds_shuffled.shape[0], preds_shuffled.shape[1], preds_shuffled.shape[2]))
mask = T.reshape(self.answer_mask, (n,))
lbl = T.reshape(self.answer_var, (n,))
self.params = [self.W_a,self.W_ans_res_in, self.W_ans_res_hid, self.b_ans_res,
self.W_ans_upd_in, self.W_ans_upd_hid, self.b_ans_upd,
self.W_ans_hid_in, self.W_ans_hid_hid, self.b_ans_hid,
self.W_inp_emb_in, self.b_inp_emb_in,
self.W_inp_emb]
print "==> building loss layer and computing updates"
loss_vec = T.nnet.categorical_crossentropy(prob_sm, lbl)
self.loss_ce = (mask * loss_vec ).sum() / mask.sum()
#self.loss_ce = T.nnet.categorical_crossentropy(results_rhp, lbl)
if self.l2 > 0:
self.loss_l2 = self.l2 * nn_utils.l2_reg(self.params)
else:
self.loss_l2 = 0
self.loss = self.loss_ce + self.loss_l2
grad = T.grad(self.loss, self.params)
#scaled_grad = lasagne.updates.norm_constraint(grad, max_norm = 1e4)
updates = lasagne.updates.adadelta(self.loss, self.params, learning_rate = 0.01)
#updates = lasagne.updates.momentum(self.loss, self.params, learning_rate=0.001)
if self.mode == 'train':
print "==> compiling train_fn"
self.train_fn = theano.function(inputs=[self.q_var, self.answer_var, self.answer_mask, self.answer_inp_var],
outputs=[self.prediction, self.loss],
updates=updates)
print "==> compiling test_fn"
self.test_fn = theano.function(inputs=[self.q_var, self.answer_var, self.answer_mask, self.answer_inp_var],
outputs=[self.prediction, self.loss])
print "==> compiling pred_fn"
self.pred_fn= theano.function(inputs=[self.q_var, self.answer_inp_var],
outputs=[self.pred])
