本文整理匯總了Python中theano_toolkit.parameters.Parameters.parameter_count方法的典型用法代碼示例。如果您正苦於以下問題：Python Parameters.parameter_count方法的具體用法？Python Parameters.parameter_count怎麽用？Python Parameters.parameter_count使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類theano_toolkit.parameters.Parameters的用法示例。
在下文中一共展示了Parameters.parameter_count方法的2個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: make_functions
# 需要導入模塊: from theano_toolkit.parameters import Parameters [as 別名]
# 或者: from theano_toolkit.parameters.Parameters import parameter_count [as 別名]
def make_functions(
input_size, output_size, mem_size, mem_width, hidden_sizes=[100]):
start_time = time.time()
input_seqs = T.btensor3('input_sequences')
output_seqs = T.btensor3('output_sequences')
P = Parameters()
process = model.build(P,
input_size, output_size, mem_size, mem_width, hidden_sizes[0])
outputs = process(T.cast(input_seqs,'float32'))
output_length = (input_seqs.shape[1] - 2) // 2
Y = output_seqs[:,-output_length:,:-2]
Y_hat = T.nnet.sigmoid(outputs[:,-output_length:,:-2])
cross_entropy = T.mean(T.nnet.binary_crossentropy(Y_hat,Y))
bits_loss = cross_entropy * (Y.shape[1] * Y.shape[2]) / T.log(2)
params = P.values()
cost = cross_entropy # + 1e-5 * sum(T.sum(T.sqr(w)) for w in params)
print "Computing gradients",
grads = T.grad(cost, wrt=params)
grads = updates.clip_deltas(grads, np.float32(clip_length))
print "Done. (%0.3f s)"%(time.time() - start_time)
start_time = time.time()
print "Compiling function",
P_learn = Parameters()
update_pairs = updates.rmsprop(
params, grads,
learning_rate=1e-4,
P=P_learn
)
train = theano.function(
inputs=[input_seqs, output_seqs],
outputs=cross_entropy,
updates=update_pairs,
)
test = theano.function(
inputs=[input_seqs, output_seqs],
outputs=bits_loss
)
print "Done. (%0.3f s)"%(time.time() - start_time)
print P.parameter_count()
return P, P_learn, train, test示例2: attention
# 需要導入模塊: from theano_toolkit.parameters import Parameters [as 別名]
# 或者: from theano_toolkit.parameters.Parameters import parameter_count [as 別名]
word_rep_size = 128,
stmt_hidden_size = 128,
diag_hidden_size = 128,
vocab_size = vocab_size,
output_size = vocab_size,
map_fun_size = 128,
evidence_count = evidence_count
)
output_evds,output_ans = attention(story,idxs,qstn)
cross_entropy = -T.log(output_ans[ans_lbl]) \
+ -T.log(output_evds[0][ans_evds[0]]) \
+ -T.log(output_evds[1][ans_evds[1]])
#cost += -T.log(ordered_probs(output_evds,ans_e.vds))
print "Done."
print "Parameter count:", P.parameter_count()
print "Calculating gradient expression...",
params = P.values()
cost = cross_entropy
grads = T.grad(cost,wrt=params)
print "Done."
inputs = [story,idxs,qstn,ans_lbl,ans_evds]
outputs = cross_entropy
pickle.dump(
(inputs,outputs,params,grads),
open("compute_tree.pkl","wb"),2
)
print "Compiling native...",