def predict(word_indices, model, builder, target):
"""
predict demographic label
:param word_indices:
:param model:
:param builder:
:return: tag and label predictions
"""
forward_states = build_tagging_graph(word_indices, model, builder)
final_state = forward_states[-1]
H = pycnn.parameter(pH)
bias_H = pycnn.parameter(biasH)
H2 = pycnn.parameter(pH2)
bias_H2 = pycnn.parameter(biasH2)
if target in ['age', 'both', 'joint']:
O = pycnn.parameter(pOutAge)
bias_O = pycnn.parameter(biasOutAge)
elif target == 'gender':
O = pycnn.parameter(pOutGender)
bias_O = pycnn.parameter(biasOutGender)
if target == 'both':
O2 = pycnn.parameter(pOutGender)
bias_O2 = pycnn.parameter(biasOutGender)
if target == 'both':
# hidden = bias_H2 + pycnn.tanh(H2 * (bias_H + pycnn.tanh(H * final_state)))
hidden = bias_H + pycnn.rectify(H * final_state)
r_age = bias_O + (O * hidden)
r_gender = bias_O2 + (O2 * hidden)
out_age = pycnn.softmax(r_age)
out_gender = pycnn.softmax(r_gender)
return [np.argmax(out_age.npvalue()), np.argmax(out_gender.npvalue())]
else:
# r_t = bias_O + (O * (bias_H2 + pycnn.tanh(H2 * (bias_H + pycnn.tanh(H * final_state)))))
r_t = bias_O + (O * (bias_H + pycnn.rectify(H * final_state)))
out = pycnn.softmax(r_t)
chosen = np.argmax(out.npvalue())
return chosen
def fit(word_indices, label, model, builder, target):
"""
compute joint error of the
:param word_indices: list of indices
:param label: index
:param model: current model to access parameters
:param builder: builder to create state combinations
:return: joint error
"""
forward_states = build_tagging_graph(word_indices, model, builder)
# retrieve model parameters
final_state = forward_states[-1]
# final_state = pycnn.dropout(final_state, 0.1)
# print("final state", final_state, file=sys.stderr)
H = pycnn.parameter(pH)
bias_H = pycnn.parameter(biasH)
H2 = pycnn.parameter(pH2)
bias_H2 = pycnn.parameter(biasH2)
if target in ['age', 'joint']:
O = pycnn.parameter(pOutAge)
bias_O = pycnn.parameter(biasOutAge)
elif target == 'gender':
O = pycnn.parameter(pOutGender)
bias_O = pycnn.parameter(biasOutGender)
# print(pycnn.cg().PrintGraphviz())
# if target == 'both':
# hidden = bias_H + pycnn.tanh(H * final_state)
# r_age = bias_O + (O * hidden)
# r_gender = bias_O2 + (O2 * hidden)
# return pycnn.esum([pycnn.pickneglogsoftmax(r_age, label[0]), pycnn.pickneglogsoftmax(r_gender, label[1])])
# r_t = bias_O + (O * (bias_H2 + pycnn.tanh(H2 * (bias_H + pycnn.tanh(H * final_state)))))
r_t = bias_O + (O * (bias_H + pycnn.rectify(H * final_state)))
# return pycnn.pick(r_t, label)
return pycnn.pickneglogsoftmax(r_t, label)
