model.add(Dense(512, batch_input_shape=(None, C)))
model.add(Dropout(0.02))
model.add(Dense(nb_classes, activation='softmax'))
# load weights
model.load_weights(best_model_path)
# Compile model (required to make predictions)
model.compile(loss='categorical_crossentropy',
optimizer='Adadelta',
metrics=['accuracy'])
dev_pred = model.predict_classes([dev_x], batch_size=batch_size, verbose=1)
test_pred = model.predict_classes([test_x],
batch_size=batch_size,
verbose=1)
######Dev
accu, P, R, F1, wAUC, AUC, report = performance.performance_measure_tf(
dev_y, dev_pred, [], dev_le, labels, dev_file)
wauc = wAUC * 100
auc = AUC * 100
precision = P * 100
recall = R * 100
f1_score = F1 * 100
print(
str("{0:.2f}".format(auc)) + "\t" + str("{0:.2f}".format(wauc)) +
"\t" + str("{0:.2f}".format(precision)) + "\t" +
str("{0:.2f}".format(recall)) + "\t" +
str("{0:.2f}".format(f1_score)) + "\n")
print(report)
######Test
accu, P, R, F1, wAUC, AUC, report = performance.performance_measure_tf(
test_y, test_pred, [], le, labels, test_file)
best_valid_epoch = 0
best_weights = None
train_history = {}
train_history['f1'] = list()
train_history['epoch'] = list()
#for epoch in range(num_epochs):
epoch = 0
#while True:
for epoch in range(num_epochs):
#m.step_train(max_iter = 100, iter_graph = 10, iter_inst = 10, iter_label = 10) # perform a training step
m.step_train_minibatch(iter_label=10, epoch=epoch)
tpy = m.predict(dev_x) # predict the dev set
accu = comp_accu(tpy, dev_y) # compute the accuracy on the dev set
iter_cnt += 1
accu, P, R, F1, wAUC, AUC, report = performance.performance_measure_tf(
dev_y, tpy, dev_le, dev_labels)
wauc = wAUC * 100
auc = AUC * 100
precision = P * 100
recall = R * 100
f1_score = F1 * 100
current_valid = train_history['f1'].append(f1_score)
current_epoch = train_history['epoch'].append(epoch)
# print (epoch)
if (epoch % 10 == 0):
current_valid = train_history['f1'][-1]
print("f1-cur " + str(current_valid))
current_epoch = train_history['epoch'][-1]
if current_valid > best_valid:
print("f1-best " + str(best_valid))
best_valid = current_valid
domain_tr_y = y.astype('int32')
train_err += train2(inputs, domain_tr_y)
# print ("error2 "+str(train_err))
train_batches += 1
print("Epoch {} of {} took {:.3f}s".format(epoch + 1, num_epochs,
time.time() - start_time))
print(" training loss:\t\t{:.6f}".format(train_err / train_batches))
dev_pred = pred_fn(dev_x)
dev_pred = np.argmax(dev_pred, axis=1)
# print (dev_pred)
# accu,P,R,F1,wAUC,AUC,report =performance.performance_measure_tf(dev_y,pred,dev_le,dev_labels)
y_prob = [0]
devoutFile = "dev.txt"
accu, P, R, F1, wAUC, AUC, report = performance.performance_measure_tf(
dev_y, dev_pred, y_prob, dev_le, dev_labels, devoutFile)
wauc = wAUC * 100
auc = AUC * 100
precision = P * 100
recall = R * 100
f1_score = F1 * 100
result = str("{0:.2f}".format(auc)) + "\t" + str(
"{0:.2f}".format(wauc)) + "\t" + str(
"{0:.2f}".format(precision)) + "\t" + str(
"{0:.2f}".format(recall)) + "\t" + str(
"{0:.2f}".format(f1_score)) + "\n"
print(result)
# print (report)
current_valid = train_history['valid_loss'].append(f1_score)
current_epoch = train_history['epoch'].append(epoch)
