我们从Python开源项目中，提取了以下18个代码示例，用于说明如何使用utils.uniform_weight()。
def param_init_gru(options, param, prefix='gru', nin=None, dim=None): param[prefix + '_W'] = numpy.concatenate( [ uniform_weight(nin, dim), uniform_weight(nin, dim) ], axis=1) param[prefix + '_U'] = numpy.concatenate( [ ortho_weight(dim), ortho_weight(dim) ], axis=1) param[prefix + '_b'] = zero_vector(2 * dim) param[prefix + '_Wx'] = uniform_weight(nin, dim) param[prefix + '_Ux'] = ortho_weight(dim) param[prefix + '_bx'] = zero_vector(dim) return param
def param_init_encoder(options, params, prefix='lstm_encoder'): n_x = options['n_x'] n_h = options['n_h'] W = np.concatenate([uniform_weight(n_x,n_h), uniform_weight(n_x,n_h), uniform_weight(n_x,n_h), uniform_weight(n_x,n_h)], axis=1) params[_p(prefix, 'W')] = W U = np.concatenate([ortho_weight(n_h), ortho_weight(n_h), ortho_weight(n_h), ortho_weight(n_h)], axis=1) params[_p(prefix, 'U')] = U params[_p(prefix,'b')] = zero_bias(4*n_h) # It is observed that setting a high initial forget gate bias for LSTMs can # give slighly better results (Le et al., 2015). Hence, the initial forget # gate bias is set to 3. params[_p(prefix, 'b')][n_h:2*n_h] = 3*np.ones((n_h,)).astype(theano.config.floatX) return params
def param_init_encoder(options, params, prefix='gru_encoder'): n_x = options['n_x'] n_h = options['n_h'] W = np.concatenate([uniform_weight(n_x,n_h), uniform_weight(n_x,n_h)], axis=1) params[_p(prefix,'W')] = W U = np.concatenate([ortho_weight(n_h), ortho_weight(n_h)], axis=1) params[_p(prefix,'U')] = U params[_p(prefix,'b')] = zero_bias(2*n_h) Wx = uniform_weight(n_x, n_h) params[_p(prefix,'Wx')] = Wx Ux = ortho_weight(n_h) params[_p(prefix,'Ux')] = Ux params[_p(prefix,'bx')] = zero_bias(n_h) return params
def init_params(options,W): 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) length = len(options['filter_shapes']) for idx in range(length): params = param_init_encoder(options['filter_shapes'][idx],params,prefix=_p('cnn_encoder',idx)) n_h = options['feature_maps'] * length params['Wy'] = uniform_weight(n_h,options['n_y']) params['by'] = zero_bias(options['n_y']) 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="gru_encoder") params = param_init_encoder(options,params,prefix="gru_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_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 param_init_encoder(options, params, prefix='encoder'): n_x = options['n_x'] n_h = options['n_h'] W = np.concatenate([uniform_weight(n_x,n_h), uniform_weight(n_x,n_h), uniform_weight(n_x,n_h), uniform_weight(n_x,n_h)], axis=1) params[_p(prefix, 'W')] = W U = np.concatenate([ortho_weight(n_h), ortho_weight(n_h), ortho_weight(n_h), ortho_weight(n_h)], axis=1) params[_p(prefix, 'U')] = U params[_p(prefix,'b')] = zero_bias(4*n_h) params[_p(prefix, 'b')][n_h:2*n_h] = 3*np.ones((n_h,)).astype(theano.config.floatX) return params
def init_params(options,W): n_words = options['n_words'] n_x = options['n_x'] n_h = options['n_h'] params = OrderedDict() # word embedding # params['Wemb'] = uniform_weight(n_words,n_x) params['Wemb'] = W.astype(config.floatX) params = param_init_decoder(options,params) params['Vhid'] = uniform_weight(n_h,n_x) params['bhid'] = zero_bias(n_words) return params
def init_params(options): n_words = options['n_words'] n_x = options['n_x'] n_h = options['n_h'] params = OrderedDict() params['Wemb'] = uniform_weight(n_words,n_x) params = param_init_decoder(options,params,prefix='decoder_h1') options['n_x'] = n_h params = param_init_decoder(options,params,prefix='decoder_h2') options['n_x'] = n_x params['Vhid'] = uniform_weight(n_h,n_words) params['bhid'] = zero_bias(n_words) return params
def param_init_fflayer(options, param, prefix='ff', nin=None, nout=None, ortho=True): param[prefix + '_W'] = uniform_weight(nin, nout) param[prefix + '_b'] = zero_vector(nout) return param
def param_init_decoder(options, params, prefix='decoder_gru'): n_x = options['n_x'] n_h = options['n_h'] W = np.concatenate([uniform_weight(n_x,n_h), uniform_weight(n_x,n_h)], axis=1) params[_p(prefix,'W')] = W U = np.concatenate([ortho_weight(n_h), ortho_weight(n_h)], axis=1) params[_p(prefix,'U')] = U params[_p(prefix,'b')] = zero_bias(2*n_h) Wx = uniform_weight(n_x, n_h) params[_p(prefix,'Wx')] = Wx Ux = ortho_weight(n_h) params[_p(prefix,'Ux')] = Ux params[_p(prefix,'bx')] = zero_bias(n_h) params[_p(prefix,'b0')] = zero_bias(n_h) return params
def init_params(options): n_x = options['n_x'] n_h = options['n_h'] params = OrderedDict() params = param_init_decoder(options,params) params['Vhid'] = uniform_weight(n_h,n_x) params['bhid'] = zero_bias(n_x) return params
def param_init_decoder(options, params, prefix='decoder'): n_x = options['n_x'] n_h = options['n_h'] n_z = options['n_z'] W = np.concatenate([uniform_weight(n_x,n_h), uniform_weight(n_x,n_h), uniform_weight(n_x,n_h), uniform_weight(n_x,n_h)], axis=1) params[_p(prefix, 'W')] = W U = np.concatenate([ortho_weight(n_h), ortho_weight(n_h), ortho_weight(n_h), ortho_weight(n_h)], axis=1) params[_p(prefix, 'U')] = U C = np.concatenate([uniform_weight(n_z,n_h), uniform_weight(n_z,n_h), uniform_weight(n_z,n_h), uniform_weight(n_z,n_h)], axis=1) params[_p(prefix,'C')] = C params[_p(prefix,'b')] = zero_bias(4*n_h) params[_p(prefix, 'b')][n_h:2*n_h] = 3*np.ones((n_h,)).astype(theano.config.floatX) C0 = uniform_weight(n_z, n_h) params[_p(prefix,'C0')] = C0 params[_p(prefix,'b0')] = zero_bias(n_h) params[_p(prefix,'b_y')] = zero_bias(n_x) # 48 return params
def param_init_decoder(options, params, prefix='decoder'): n_x = options['n_x'] n_h = options['n_h'] n_z = options['n_z'] W = np.concatenate([uniform_weight(n_x,n_h), uniform_weight(n_x,n_h), uniform_weight(n_x,n_h), uniform_weight(n_x,n_h)], axis=1) params[_p(prefix, 'W')] = W U = np.concatenate([ortho_weight(n_h), ortho_weight(n_h), ortho_weight(n_h), ortho_weight(n_h)], axis=1) params[_p(prefix, 'U')] = U C = np.concatenate([uniform_weight(n_z,n_h), uniform_weight(n_z,n_h), uniform_weight(n_z,n_h), uniform_weight(n_z,n_h)], axis=1) params[_p(prefix,'C')] = C params[_p(prefix,'b')] = zero_bias(4*n_h) params[_p(prefix, 'b')][n_h:2*n_h] = 3*np.ones((n_h,)).astype(theano.config.floatX) C0 = uniform_weight(n_z, n_h) params[_p(prefix,'C0')] = C0 params[_p(prefix,'b0')] = zero_bias(n_h) #params[_p(prefix,'b_y')] = zero_bias(n_x) # 48 return params
def param_init_decoder(options, params, prefix='decoder_lstm'): n_x = options['n_x'] n_h = options['n_h'] n_z = options['n_z'] W = np.concatenate([uniform_weight(n_x,n_h), uniform_weight(n_x,n_h), uniform_weight(n_x,n_h), uniform_weight(n_x,n_h)], axis=1) params[_p(prefix,'W')] = W U = np.concatenate([ortho_weight(n_h), ortho_weight(n_h), ortho_weight(n_h), ortho_weight(n_h)], axis=1) params[_p(prefix,'U')] = U #C = np.concatenate([uniform_weight(n_z,n_h), # uniform_weight(n_z,n_h), # uniform_weight(n_z,n_h), # uniform_weight(n_z,n_h)], axis=1) #params[_p(prefix,'C')] = C params[_p(prefix,'b')] = zero_bias(4*n_h) #params[_p(prefix, 'b')][n_h:2*n_h] = 3*np.ones((n_h,)).astype(theano.config.floatX) C0 = uniform_weight(n_z, n_h) params[_p(prefix,'C0')] = C0 params[_p(prefix,'b0')] = zero_bias(n_h) return params
