with open(corpus_file) as f:
sents = [[twp.split('|')[0].lower() for twp in line.split()] for line in f]
embeddings = helper.char_embeddings(sents)
print('init network and compile theano functions...')
h_size = hidden_layer_size # hidden size
W_xi, W_hi, W_ci, b_i, \
W_xf, W_hf, W_cf, b_f, \
W_xc, W_hc, b_c, \
W_xo, W_ho, W_co, b_o, \
W_hy, b_y = helper.load_states(weights_file_name, work_dir)
S_h = helper.init_zero_vec(h_size) # init values for hidden units
S_c = helper.init_zero_vec(h_size) # init values for cell units
S_x = T.matrix() # inputs
# BEGIN code inspired by Christian Herta
# http://christianherta.de/lehre/dataScience/machineLearning/neuralNetworks/LSTM.php
def step(S_x, S_h, S_c, W_xi, W_hi, W_ci, b_i, W_xf, W_hf, W_cf, b_f, W_xc,
W_hc, b_c, W_xo, W_ho, W_co, b_o, W_hy, b_y):
S_i = sigm(dot(S_x, W_xi) + dot(S_h, W_hi) + dot(S_c, W_ci) + b_i)
S_f = sigm(dot(S_x, W_xf) + dot(S_h, W_hf) + dot(S_c, W_cf) + b_f)
S_c = S_f * S_c + S_i * tanh(dot(S_x, W_xc) + dot(S_h, W_hc) + b_c)
S_o = sigm(dot(S_x, W_xo) + dot(S_h, W_ho) + dot(S_c, W_co) + b_o)
load_weights = None
print('init new states...')
print('timestamp: ', timestamp)
print()
# initialize lstm weights
io_size = len(embeddings) # input/output size
h_size = hidden_layer_size # hidden size
if not load_weights:
W_xi = helper.init_weights((io_size, h_size))
W_hi = helper.init_weights((h_size, h_size))
W_ci = helper.init_weights((h_size, h_size))
b_i = helper.init_zero_vec(h_size)
W_xf = helper.init_weights((io_size, h_size))
W_hf = helper.init_weights((h_size, h_size))
W_cf = helper.init_weights((h_size, h_size))
b_f = helper.init_zero_vec(h_size)
W_xc = helper.init_weights((io_size, h_size))
W_hc = helper.init_weights((h_size, h_size))
b_c = helper.init_zero_vec(h_size)
W_xo = helper.init_weights((io_size, h_size))
W_ho = helper.init_weights((h_size, h_size))
W_co = helper.init_weights((h_size, h_size))
b_o = helper.init_zero_vec(h_size)
load_weights = None
print('init new states...')
print('timestamp: ', timestamp)
print()
# initialize lstm weights
io_size = len(embeddings) # input/output size
h_size = hidden_layer_size # hidden size
if not load_weights:
W_xi = helper.init_weights((io_size, h_size))
W_hi = helper.init_weights((h_size, h_size))
W_ci = helper.init_weights((h_size, h_size))
b_i = helper.init_zero_vec(h_size)
W_xf = helper.init_weights((io_size, h_size))
W_hf = helper.init_weights((h_size, h_size))
W_cf = helper.init_weights((h_size, h_size))
b_f = helper.init_zero_vec(h_size)
W_xc = helper.init_weights((io_size, h_size))
W_hc = helper.init_weights((h_size, h_size))
b_c = helper.init_zero_vec(h_size)
W_xo = helper.init_weights((io_size, h_size))
W_ho = helper.init_weights((h_size, h_size))
W_co = helper.init_weights((h_size, h_size))
b_o = helper.init_zero_vec(h_size)
with open(corpus_file) as f:
sents = [[twp.split('|')[0].lower() for twp in line.split()] for line in f]
embeddings = helper.char_embeddings(sents)
print('init network and compile theano functions...')
h_size = hidden_layer_size # hidden size
W_xi, W_hi, W_ci, b_i, \
W_xf, W_hf, W_cf, b_f, \
W_xc, W_hc, b_c, \
W_xo, W_ho, W_co, b_o, \
W_hy, b_y = helper.load_states(weights_file_name, work_dir)
S_h = helper.init_zero_vec(h_size) # init values for hidden units
S_c = helper.init_zero_vec(h_size) # init values for cell units
S_x = T.matrix() # inputs
# BEGIN code inspired by Christian Herta
# http://christianherta.de/lehre/dataScience/machineLearning/neuralNetworks/LSTM.php
def step(S_x, S_h, S_c,
W_xi, W_hi, W_ci, b_i,
W_xf, W_hf, W_cf, b_f,
W_xc, W_hc, b_c,
W_xo, W_ho, W_co, b_o,
W_hy, b_y):
S_i = sigm(dot(S_x, W_xi) + dot(S_h, W_hi) + dot(S_c, W_ci) + b_i)
print("init new states...")
print("timestamp: ", timestamp)
print()
# initialize lstm weights
inp = w2v_embeddings.embeddings_size # input/output size
hid = hidden_layer_size # hidden size
out = token_embeddings.num_tokens
if not load_weights:
W_xi = helper.init_weights((inp, hid))
W_hi = helper.init_weights((hid, hid))
W_ci = helper.init_weights((hid, hid))
b_i = helper.init_zero_vec(hid)
W_xf = helper.init_weights((inp, hid))
W_hf = helper.init_weights((hid, hid))
W_cf = helper.init_weights((hid, hid))
b_f = helper.init_zero_vec(hid)
W_xc = helper.init_weights((inp, hid))
W_hc = helper.init_weights((hid, hid))
b_c = helper.init_zero_vec(hid)
W_xo = helper.init_weights((inp, hid))
W_ho = helper.init_weights((hid, hid))
W_co = helper.init_weights((hid, hid))
b_o = helper.init_zero_vec(hid)
