本文整理汇总了Python中theano_toolkit.parameters.Parameters.V方法的典型用法代码示例。如果您正苦于以下问题：Python Parameters.V方法的具体用法？Python Parameters.V怎么用？Python Parameters.V使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类theano_toolkit.parameters.Parameters的用法示例。
在下文中一共展示了Parameters.V方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: create_model
# 需要导入模块: from theano_toolkit.parameters import Parameters [as 别名]
# 或者: from theano_toolkit.parameters.Parameters import V [as 别名]
def create_model(ids,vocab2id,size):
word_vector_size = size
hidden_state_size = size
P = Parameters()
P.V = create_vocab_vectors(P,vocab2id,word_vector_size)
P.W_predict = np.zeros(P.V.get_value().shape).T
P.b_predict = np.zeros((P.V.get_value().shape[0],))
X = P.V[ids]
step = build_lstm_step(P,word_vector_size,hidden_state_size)
[states,_],_ = theano.scan(
step,
sequences = [X],
outputs_info = [P.init_h,P.init_c]
)
scores = T.dot(states,P.W_predict) + P.b_predict
scores = T.nnet.softmax(scores)
log_likelihood, cross_ent = word_cost(scores[:-1],ids[1:])
cost = log_likelihood #+ 1e-4 * sum( T.sum(abs(w)) for w in P.values() )
obv_cost = cross_ent
return scores, cost, obv_cost, P示例2: Parameters
# 需要导入模块: from theano_toolkit.parameters import Parameters [as 别名]
# 或者: from theano_toolkit.parameters.Parameters import V [as 别名]
cell = forget_gate * prev_cell + in_gate * cell_updates
out_lin = x_o + h_o + b_o + T.dot(cell, V_o)
out_gate = T.nnet.sigmoid(out_lin)
hid = out_gate * T.tanh(cell)
return cell, hid
return step
if __name__ == "__main__":
P = Parameters()
X = T.ivector("X")
P.V = np.zeros((8, 8), dtype=np.int32)
X_rep = P.V[X]
P.W_output = np.zeros((15, 8), dtype=np.int32)
lstm_layer = build(P, name="test", input_size=8, hidden_size=15)
_, hidden = lstm_layer(X_rep)
output = T.nnet.softmax(T.dot(hidden, P.W_output))
delay = 5
label = X[:-delay]
predicted = output[delay:]
cost = -T.sum(T.log(predicted[T.arange(predicted.shape[0]), label]))
params = P.values()
gradients = T.grad(cost, wrt=params)