def init_params(options):
n_chars = options['n_chars']
n_x = options['n_x']
params = OrderedDict()
# character embedding
params['Wemb'] = uniform_weight(n_chars, n_x)
# encoding characters into words
params = param_init_encoder(options, params, prefix='encoder_f')
params = param_init_encoder(options, params, prefix='encoder_b')
return params
def init_params(options,W):
n_h = options['n_h']
n_y = options['n_y']
params = OrderedDict()
# W is initialized by the pretrained word embedding
params['Wemb'] = W.astype(config.floatX)
# otherwise, W will be initialized randomly
# n_words = options['n_words']
# n_x = options['n_x']
# params['Wemb'] = uniform_weight(n_words,n_x)
# bidirectional LSTM
params = param_init_encoder(options,params,prefix="lstm_encoder")
params = param_init_encoder(options,params,prefix="lstm_encoder_rev")
params['Wy'] = uniform_weight(2*n_h,n_y)
params['by'] = zero_bias(n_y)
return params
def init_params(options, W):
n_p = options['n_p']
n_h = options['n_h']
n_y = options['n_y']
params = OrderedDict()
# W is initialized by the pretrained word embedding
params['Wemb'] = W.astype(config.floatX)
# otherwise, W will be initialized randomly
# n_words = options['n_words']
# n_x = options['n_x']
# params['Wemb'] = uniform_weight(n_words,n_x)
# bidirectional LSTM
#params = param_init_encoder(options,params, prefix="lstm_encoder")
#params = param_init_encoder(options,params, prefix="lstm_encoder_rev")
params = param_init_encoder(options, params, prefix="gru_encoder")
params = param_init_encoder(options, params, prefix="gru_encoder_rev")
#params['Wy'] = uniform_weight(n_p, 2*n_h+1, scale=0.1)
params['Wy'] = np.squeeze(uniform_weight(n_p, 2 * n_h, 1))
return params
def init_params(options, W):
n_words = options['n_words']
n_x = options['n_x']
n_h = options['n_h']
n_z = options['n_z']
params = OrderedDict()
# word embedding
# params['Wemb'] = uniform_weight(n_words,n_x)
params['Wemb'] = W.astype(config.floatX)
params = param_init_encoder(options, params)
params['Vhid'] = uniform_weight(n_h, n_x)
params['bhid'] = zero_bias(n_words)
params['C0'] = uniform_weight(n_z, n_x)
return params