def add_model(self, fname, mtype, n_inputs):
rnn = RNN(self.model_dir + fname)
rnn.build_model(self.maxlen, self.embedding_matrix, mtype, n_inputs)
self.models[fname] = rnn
def train(inputs_train, inputs_test, hidden_dim=None, update='adam2', sq_loss=False,
lamb=0., drop=0., model='lstml', share=False, category=False, num_epoch=40, num_class=None, early_stop=False):
Xs_train, y_train, start_batches_train, end_batches_train, len_batches_train = inputs_train
Xs_test, y_test, start_batches_test, end_batches_test, len_batches_test = inputs_test
n_train = Xs_train.shape[0]
input_dim = Xs_train.shape[2]
Xs_train = Xs_train.transpose([1, 0, 2])
Xs_test = Xs_test.transpose([1, 0, 2])
Xs_train_shared = theano.shared(Xs_train, borrow=True)
y_train_shared = theano.shared(y_train, borrow=True)
Xs_test_shared = theano.shared(Xs_test, borrow=True)
y_test_shared = theano.shared(y_test, borrow=True)
if category:
n_class = num_class
else:
n_class = 1
ra = RNN(input_dim, hidden_dim, [n_class], lamb=lamb, model=model, share=share, update=update, drop=drop,
sq_loss=sq_loss)
symbols = ra.build_model()
X_batch, y_batch, is_train = symbols['X_batch'], symbols['y_batch'], symbols['is_train']
att, pred, loss = symbols['att'], symbols['pred'], symbols['loss']
cost, updates = symbols['cost'], symbols['updates']
loss_krip, acc = symbols['loss_krip'], symbols['acc']
print 'Compiling function'
start_symbol, end_symbol = T.lscalar(), T.lscalar()
len_symbol = T.iscalar()
if category:
outputs = [cost, acc, pred]
else:
outputs = [cost, loss_krip, pred]
train_model = theano.function(inputs=[start_symbol, end_symbol, len_symbol, is_train],
outputs=outputs, updates=updates,
givens={
X_batch: Xs_train_shared[:len_symbol, start_symbol: end_symbol, :],
y_batch: y_train_shared[start_symbol: end_symbol]},
on_unused_input='ignore', mode='FAST_RUN')
print 'Compilation done 1'
test_model = theano.function(inputs=[start_symbol, end_symbol, len_symbol, is_train],
outputs=outputs,
givens={
X_batch: Xs_test_shared[:len_symbol, start_symbol: end_symbol, :],
y_batch: y_test_shared[start_symbol: end_symbol]},
on_unused_input='ignore', mode='FAST_RUN')
print 'Compilation done 2'
costs_train, costs_test = [], []
best_acc = None
best_actual_test = None
best_pred_test = None
best_epoch = 0
num_iter = len(start_batches_train)
for epoch_index in xrange(num_epoch):
cost_avg = 0.
all_actual, all_pred = [], []
print 'Epoch = %d' % (epoch_index + 1)
for iter_index in xrange(num_iter):
start, end = start_batches_train[iter_index], end_batches_train[iter_index]
length = len_batches_train[iter_index]
if length == 0:
continue
cost, tmp, pred = train_model(start, end, length, 1)
cost_avg += cost * (end - start)
all_actual += y_train[start: end].tolist()
all_pred += pred.tolist()
if (iter_index + 1) % 100 == 0:
print iter_index + 1, '/', num_iter
cost_avg /= n_train
costs_train.append(cost_avg)
y_actual = numpy.asarray(all_actual)
y_predicted = numpy.asarray(all_pred)
acc = eval(y_actual, y_predicted, category=category)
print '\tTrain cost = %f,\tAccuracy = %f' % (cost_avg, acc)
acc_test, actual_test, pred_test = test(test_model, start_batches_test, end_batches_test, len_batches_test,
y_test, costs_test, category=category)
if best_acc is None:
best_acc = acc_test
best_actual_test = actual_test
best_pred_test = pred_test
best_epoch = epoch_index
elif acc_test > best_acc:
best_acc = acc_test
best_actual_test = actual_test
best_pred_test = pred_test
best_epoch = epoch_index
# Early Stopping
if early_stop and epoch_index - best_epoch >= 3 and epoch_index >= num_epoch // 4:
break
print 'Best Epoch = %d, Best ACC in Test = %f' % (best_epoch + 1, best_acc)
return best_actual_test, best_pred_test
