#np.random.seed(seed=123)
tX = T.ftensor3('tX')
tH = T.fmatrix('tH')
tm = T.fmatrix('tm')
ty = T.imatrix('ty')
classifier = E_RNN(seqs=tX, h0s=tH, masks=tm, dim_x=909, dim_h=21, dim_y=21, wt_y=wt_y, Wx=Wx, Wh=Wh, W=W, bh=bh, b=b)
#classifier = E_RNN(seqs=tX, h0s=tH, masks=tm, dim_x=909, dim_h=21, dim_y=21, wt_y=wt_y)
tcost = classifier.negative_log_likelihood(ty)
tpred = classifier.pred_grosslabel()
terr = classifier.errors(ty)
tgrads = T.grad(cost=tcost, wrt=classifier.params)
f_grad_shared, f_update = adadelta(classifier.params, tgrads, tX, tH, tm, ty, tcost)
f_pred_gross = theano.function([tX, tH, tm], tpred,
on_unused_input='ignore')
f_pred_err = theano.function([tX, tH, tm, ty], terr,
on_unused_input='ignore')
f_watch = theano.function([tX, tH, tm, ty], classifier.watch_var(ty),
on_unused_input='ignore')
# endregion
# region Training Process
print 'Training...'
train_sz = X.shape[0]
dim_h=21,
dim_y=21,
wt_y=wt_y,
Wx=Wx,
Wh=Wh,
W=W,
bh=bh,
b=b)
#classifier = E_RNN(seqs=tX, h0s=tH, masks=tm, dim_x=909, dim_h=21, dim_y=21, wt_y=wt_y)
tcost = classifier.negative_log_likelihood(ty)
tpred = classifier.pred_grosslabel()
terr = classifier.errors(ty)
tgrads = T.grad(cost=tcost, wrt=classifier.params)
f_grad_shared, f_update = adadelta(classifier.params, tgrads, tX, tH, tm, ty,
tcost)
f_pred_gross = theano.function([tX, tH, tm], tpred, on_unused_input='ignore')
f_pred_err = theano.function([tX, tH, tm, ty], terr, on_unused_input='ignore')
f_watch = theano.function([tX, tH, tm, ty],
classifier.watch_var(ty),
on_unused_input='ignore')
# endregion
# region Training Process
print 'Training...'
train_sz = X.shape[0]
train_batch_sz = X.shape[0]
train_batch_n = train_sz / train_batch_sz