def __init__(self, train_raw, 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
# Process the input into its different parts and calculate the input mask
self.train_input = train_raw['inputs']
self.train_q = train_raw['questions']
self.train_answer = train_raw['answers']
self.train_input_mask=train_raw['input_masks']
self.test_input = test_raw['inputs']
self.test_q = test_raw['questions']
self.test_answer = test_raw['answers']
self.test_input_mask=test_raw['input_masks']
self.train_fact_count = [len(mask) for mask in self.train_input_mask]
self.test_fact_count = [len(mask) for mask in self.test_input_mask]
#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)
# Vocab size might only be used for the answer module and we only want to give that two choices
self.vocab_size = 2
print(np.array(self.train_answer).shape,
np.array(self.train_input[0]).shape, np.array(self.train_fact_count).shape,
np.array(self.train_input_mask).shape)
# print("facts", self.train_fact_count[:2])
# print("mask", self.train_input_mask[:2])
#raise
# Get the size of the word_vector from the data itself as this can be variable now
self.word_vector_size = np.array(self.train_input[0]).shape[1]
print(self.word_vector_size)
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, train_raw, test_raw, word2vec, word_vector_size, dim,
mode, answer_module, input_mask_mode, memory_hops, l2,
normalize_attention, num_cnn_layers, vocab_len,
maximum_doc_len, char_vocab, **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.final_num_layers = num_cnn_layers
self.vocab_length = vocab_len
self.max_doc_length = maximum_doc_len
self.char_vocab = char_vocab
self.cnn_layer_length = 100
# Process the input into its different parts and calculate the input mask
self.train_input_raw, self.train_q_raw, self.train_answer, self.train_input_mask = self._process_input(
train_raw)
self.test_input_raw, self.test_q_raw, self.test_answer, self.test_input_mask = self._process_input(
test_raw)
self.vocab_size = len(self.vocab)
print(type(self.train_input_raw), len(self.train_input_raw))
self.train_input = self.build_cnn(self.train_input_raw)
self.test_input = self.build_cnn(self.test_input_raw)
self.train_q = self.build_cnn(self.train_q_raw)
self.test_q = self.build_cnn(self.test_q_raw)
#print(self.train_input.shape.eval(), self.train_input.__getitem__(0).eval())
#print(type(self.train_input), len(self.train_input), len(self.train_input[1]))
#print(type(self.train_input), len(self.train_input), len(self.train_input[1]))
print(self.train_answer)
self.input_var = T.matrix('input_var') #previously matrix
self.q_var = T.matrix('question_var')
self.answer_var = T.iscalar('answer_var')
self.input_mask_var = T.ivector('input_mask_var')
#CNN
print("==> building input module")
self.W_inp_res_in = nn_utils.normal_param(
std=0.1, shape=(self.dim, self.cnn_layer_length))
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.cnn_layer_length))
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.cnn_layer_length))
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, ))
# self.input_var = self.build_cnn(self.input_var)
# self.q_var = self.build_cnn(self.q_var)
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, self.net_w
] #, self.net4_w]#, self.net_w]
#TODO add in the cnn params
#raise
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,
test_raw,
word2vec,
word_vector_size,
dim,
mode,
answer_module,
input_mask_mode,
memory_hops,
l2,
normalize_attention,
num_cnn_layers,
vocab_len,
maximum_doc_len,
char_vocab,
**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.final_num_layers = num_cnn_layers
self.vocab_length = vocab_len
self.max_doc_length = maximum_doc_len
self.char_vocab = char_vocab
self.cnn_layer_length = 100
# Process the input into its different parts and calculate the input mask
self.train_input_raw, self.train_q_raw, self.train_answer, self.train_input_mask = self._process_input(
train_raw
)
self.test_input_raw, self.test_q_raw, self.test_answer, self.test_input_mask = self._process_input(test_raw)
self.vocab_size = len(self.vocab)
print(type(self.train_input_raw), len(self.train_input_raw))
self.train_input = self.build_cnn(self.train_input_raw)
self.test_input = self.build_cnn(self.test_input_raw)
self.train_q = self.build_cnn(self.train_q_raw)
self.test_q = self.build_cnn(self.test_q_raw)
# print(self.train_input.shape.eval(), self.train_input.__getitem__(0).eval())
# print(type(self.train_input), len(self.train_input), len(self.train_input[1]))
# print(type(self.train_input), len(self.train_input), len(self.train_input[1]))
print(self.train_answer)
self.input_var = T.matrix("input_var") # previously matrix
self.q_var = T.matrix("question_var")
self.answer_var = T.iscalar("answer_var")
self.input_mask_var = T.ivector("input_mask_var")
# CNN
print("==> building input module")
self.W_inp_res_in = nn_utils.normal_param(std=0.1, shape=(self.dim, self.cnn_layer_length))
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.cnn_layer_length))
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.cnn_layer_length))
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,))
# self.input_var = self.build_cnn(self.input_var)
# self.q_var = self.build_cnn(self.q_var)
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,
self.net_w,
] # , self.net4_w]#, self.net_w]
# TODO add in the cnn params
# raise
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, 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.to_return = "word2vec" #TODO pass these in once updates are wrapped in
self.encoder_decoder = None
self.vocab_dict = {}
# Process the input into its different parts and calculate the input mask
self.train_input = train_raw['inputs']
self.train_q = train_raw['questions']
self.train_answer = train_raw['answers']
self.train_input_mask = train_raw['input_masks']
self.test_input = test_raw['inputs']
self.test_q = test_raw['questions']
self.test_answer = test_raw['answers']
self.test_input_mask = test_raw['input_masks']
# self.train_input, self.train_q, self.train_answer, self.train_input_mask = self._process_input(train_raw)
# self.test_input, self.test_q, self.test_answer, self.test_input_mask = self._process_input(test_raw)
self.vocab_size = 2 #vocab size might only be used for the answer module and we only want to give that two choices
# print(self.train_answer[0])
# print(self.train_input_mask[0])
# print(np.array(self.train_input[0]).shape)
# Get the size of the word_vector from the data itself as this can be variable now
self.word_vector_size = np.array(self.train_input[0]).shape[1]
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(len(self.train_answer), sum(self.train_answer),
len(self.test_answer), sum(self.test_answer))
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, 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.to_return = "word2vec" #TODO pass these in once updates are wrapped in
self.encoder_decoder = None
self.vocab_dict = {}
# Process the input into its different parts and calculate the input mask
self.train_input = train_raw['inputs']
self.train_q = train_raw['questions']
self.train_answer = train_raw['answers']
self.train_input_mask=train_raw['input_masks']
self.test_input = test_raw['inputs']
self.test_q = test_raw['questions']
self.test_answer = test_raw['answers']
self.test_input_mask=test_raw['input_masks']
# self.train_input, self.train_q, self.train_answer, self.train_input_mask = self._process_input(train_raw)
# self.test_input, self.test_q, self.test_answer, self.test_input_mask = self._process_input(test_raw)
self.vocab_size = 2 #vocab size might only be used for the answer module and we only want to give that two choices
# print(self.train_answer[0])
# print(self.train_input_mask[0])
# print(np.array(self.train_input[0]).shape)
# Get the size of the word_vector from the data itself as this can be variable now
self.word_vector_size = np.array(self.train_input[0]).shape[1]
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(len(self.train_answer), sum(self.train_answer), len(self.test_answer), sum(self.test_answer))
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)