def train(epoch):
avg_loss = 0.0
epoch_time = 0
progbar = Progbar(len(train_loader.dataset) // c.batch_size)
for num_iter, batch in enumerate(train_loader):
start_time = time.time()
wav = batch[0].unsqueeze(1)
mel = batch[1].transpose(1, 2)
lens = batch[2]
target = batch[3]
if use_cuda:
wav = wav.cuda()
mel = mel.cuda()
target = target.cuda()
current_step = num_iter + epoch * len(train_loader) + 1
optimizer.zero_grad()
out = model(wav, mel)
loss, fp, tp = criterion(out, target, lens)
loss.backward()
grad_norm, skip_flag = check_update(model, 5, 100)
if skip_flag:
optimizer.zero_grad()
print(" | > Iteration skipped!!")
continue
optimizer.step()
step_time = time.time() - start_time
epoch_time += step_time
# update
progbar.update(num_iter+1, values=[('total_loss', loss.item()),
('grad_norm', grad_norm.item()),
('fp', fp),
('tp', tp)
])
avg_loss += loss.item()
def evaluate(epoch, ema):
avg_loss = 0.0
epoch_time = 0
progbar = Progbar(len(val_loader.dataset) // c.eval_batch_size)
ema_model = FFTNetModel(hid_channels=256,
out_channels=256,
n_layers=c.num_quant,
cond_channels=80)
ema_model = ema.assign_ema_model(model, ema_model, use_cuda)
ema_model.eval()
with torch.no_grad():
for num_iter, batch in enumerate(train_loader):
start_time = time.time()
wav = batch[0].unsqueeze(1)
mel = batch[1].transpose(1, 2)
lens = batch[2]
target = batch[3]
if use_cuda:
wav = wav.cuda()
mel = mel.cuda()
target = target.cuda()
current_step = num_iter + epoch * len(train_loader) + 1
out = ema_model(wav, mel)
loss, fp, tp = criterion(out, target, lens)
step_time = time.time() - start_time
epoch_time += step_time
# update
progbar.update(num_iter + 1,
values=[('total_loss', loss.item()), ('fp', fp),
('tp', tp)])
avg_loss += loss.item()
def evaluate(epoch):
avg_loss = 0.0
epoch_time = 0
progbar = Progbar(len(val_loader.dataset) // c.eval_batch_size)
with torch.no_grad():
for num_iter, batch in enumerate(train_loader):
start_time = time.time()
wav = batch[0].unsqueeze(1)
mel = batch[1].transpose(1, 2)
lens = batch[2]
target = batch[3]
if use_cuda:
wav = wav.cuda()
mel = mel.cuda()
target = target.cuda()
current_step = num_iter + epoch * len(train_loader) + 1
out = model(wav, mel)
loss, fp, tp = criterion(out, target, lens)
step_time = time.time() - start_time
epoch_time += step_time
# update
progbar.update(num_iter+1, values=[('total_loss', loss.item()),
('grad_norm', grad_norm.item()),
('fp', fp),
('tp', tp)
])
avg_loss += loss.item()
def dl_progress(count, block_size, total_size):
global progbar
if total_size < 1000000:
return
if progbar is None:
progbar = Progbar(total_size)
else:
progbar.update(count * block_size)
def train(epoch):
avg_loss = 0.0
epoch_time = 0
progbar = Progbar(len(train_loader.dataset) // c.batch_size)
if c.ema_decay > 0:
ema = EMA(c.ema_decay)
for name, param in model.named_parameters():
if param.requires_grad:
ema.register(name, param)
else:
ema = None
model.train()
for num_iter, batch in enumerate(train_loader):
start_time = time.time()
wav = batch[0].unsqueeze(1)
mel = batch[1].transpose(1, 2)
lens = batch[2]
target = batch[3]
if use_cuda:
wav = wav.cuda()
mel = mel.cuda()
target = target.cuda()
current_step = num_iter + epoch * len(train_loader) + 1
optimizer.zero_grad()
# out = torch.nn.parallel.data_parallel(model, (wav, mel))
out = model(wav, mel)
loss, fp, tp = criterion(out, target, lens)
loss.backward()
grad_norm, skip_flag = check_update(model, 5, 100)
if skip_flag:
optimizer.zero_grad()
print(" | > Iteration skipped!!")
continue
optimizer.step()
# model ema
if ema is not None:
for name, param in model.named_parameters():
if name in ema.shadow:
ema.update(name, param.data)
step_time = time.time() - start_time
epoch_time += step_time
# update
progbar.update(num_iter + 1,
values=[('total_loss', loss.item()),
('grad_norm', grad_norm.item()), ('fp', fp),
('tp', tp)])
avg_loss += loss.item()
return ema, avg_loss
def mainmodel(dro=0.4, lr=0.00005, threshold = 0.5, fn_weights = 1.0, model=modeltype, mini_batch=20, num_epochs=500, preload=preload):
print("model:%s minibatch:%d num_epochs:%d dropout:%f learingrate:%f threshold:%f fn_weights:%f\n"
%(model,mini_batch,num_epochs,dro,lr,threshold,fn_weights))
print('Preprocessing data...')
for i in range(X_train.shape[0]):
X_train[i,0] = preprocessing.scale(X_train[i,0])
X_train[i,1] = preprocessing.scale(X_train[i,1])
X_train[i,2] = preprocessing.scale(X_train[i,2])
for i in range(X_val.shape[0]):
X_val[i,0] = preprocessing.scale(X_val[i,0])
X_val[i,1] = preprocessing.scale(X_val[i,1])
X_val[i,2] = preprocessing.scale(X_val[i,2])
print ('The shape of training data is' + str(X_train.shape))
print ('The shape of test data is' + str(X_val.shape))
# Prepare Theano variables for inputs and targets
input_var = T.tensor4('input')
target_var = T.matrix('targets')
# target_var = T.matrix('targets')
# Create neural network model (depending on first command line parameter)
print("Building model and compiling functions...")
# add input_var when needed in transfer learning model
network = build_model(input_var=input_var,dro=dro)
if preload:
if model=='vgg':
read_model_param(network['fc7'],modelloaddir, preload)
else:
read_model_param(network['pool5/7x7_s1'],modelloaddir, preload)
print('pretrained model loaded')
start_time = time.time()
networkout = network['prob']
prediction = lasagne.layers.get_output(networkout)
# disable dropout
test_prediction = lasagne.layers.get_output(networkout, deterministic=True)
# loss = lasagne.objectives.categorical_crossentropy(prediction, target_var)
loss = fas_neg_entrop(prediction, target_var, fn_weights)
loss = loss.mean()
params = lasagne.layers.get_all_params(networkout, trainable=True)
updates = lasagne.updates.adagrad(
loss, params, learning_rate=lr, epsilon=1e-06)
train_fn = theano.function([input_var, target_var], loss, updates=updates)
getpred_fn = theano.function([input_var], prediction)
getpred_test_fn = theano.function([input_var], test_prediction)
print("Starting training...")
time.time()-start_time
# We iterate over epochs:
# log the accuracy of each epoch
fout = open('../log/xin_log_dro_' + str(dro) +'_lr_'+ str(lr) +'_ts_'+ str(threshold) + '.log', 'w+')
list_sort = []#postive,negtive,epoch
for epoch in range(num_epochs):
# In each epoch, we do a full pass over the training data:
fout.write('Epoch'+str(epoch)+'\n')
print('Epoch',epoch)
train_batches = 0
progbarcount = 0
progbar=Progbar(30)
# log the probabilities of prediction in each epoch
output_path = '../log/prediction_epoch_' + str(epoch) + '.out'
output_path_train = '../log/train_epoch_' + str(epoch) + '.out'
with file(output_path_train, 'wb') as outfile_train:
for batch in iterate_minibatches(X_train, Y_train, mini_batch, shuffle=True):
inputs, targets = batch
# reshape to fit the loss function calculation
targets = targets.reshape((targets.shape[0],1))
progbarcount = progbarcount + np.float(len(targets))/len(Y_train)*25
batch_pred_train = getpred_fn(inputs)
if train_batches == 0:
pred_train = batch_pred_train
y_label_train = targets
train_batches += 1
else:
pred_train = np.concatenate((pred_train,batch_pred_train))
y_label_train = np.concatenate((y_label_train,targets))
train_batches += 1
np.savetxt(outfile_train, batch_pred_train, fmt='%-7.2f')
err = train_fn(inputs, targets)
outfile_train.close()
progbar.update(progbarcount,values=[('train_batches',train_batches)])
# And a full pass over the validation data:
val_batches = 0
with file(output_path, 'wb') as outfile:
pred_val = []
for batch in iterate_minibatches(X_val, Y_val, np.min([mini_batch,len(Y_val)]), shuffle=True):
inputs, targets = batch
batch_pred_val = getpred_test_fn(inputs)
if val_batches == 0:
pred_val = batch_pred_val
y_label_val = targets
else:
pred_val = np.concatenate((pred_val,batch_pred_val))
y_label_val = np.concatenate((y_label_val,targets))
np.savetxt(outfile, batch_pred_val, fmt='%-7.2f')
val_batches += 1
outfile.close()
progbar.update(30,values=[('val_batches',val_batches)])
## For softmax
# train_acc = get_acc(y_label_train, pred_train[:,1], threshold)
# val_acc = get_acc(y_label_val, pred_val[:,1], threshold)
# For sigmoid
train_acc = get_acc(y_label_train, pred_train, threshold)
val_acc = get_acc(y_label_val, pred_val, threshold)
# img_save_path = '../log/curve_epoch_' + str(epoch) + '.png'
# plot_recall_curve(y_label_val,pred_val,img_save_path)
# all_param_values = lasagne.layers.get_all_param_values(networkout)
# print ('Accuracy : {:.2f} %'.format(100*train_acc[0][0]))
# print ('Precision : {:.2f} %'.format(100*train_acc[0][3]))
# print ('Positive Recall: {:.2f} %'.format(100*train_acc[0][1]))
# print ('Negtive Recall : {:.2f} %'.format(100*train_acc[0][2]))
#
# print ('Val Accuracy : {:.2f} %'.format(100*val_acc[0]))
# print ('Val Precision : {:.2f} %'.format(100*val_acc[3]))
# print ('Val Positive Recall: {:.2f} %'.format(100*val_acc[1]))
# print ('Val Negtive Recall : {:.2f} %'.format(100*val_acc[2]))
print ('Accuracy : {:.2f}% {:.2f}%'.format(100*train_acc[0][0], 100*val_acc[0]))
print ('Precision : {:.2f}% {:.2f}%'.format(100*train_acc[0][3], 100*val_acc[3]))
print ('Positive Recall: {:.2f}% {:.2f}%'.format(100*train_acc[0][1], 100*val_acc[1]))
print ('Negtive Recall : {:.2f}% {:.2f}%'.format(100*train_acc[0][2], 100*val_acc[2]))
if 0 == epoch:
list_sort.append([val_acc[0],val_acc[3],val_acc[1],val_acc[2],epoch])
saveflag = True
i = 0
while i< len(list_sort) and i < 30:
if 1.0 == val_acc[1] and val_acc[2] < 0.1:
saveflag = False
break
elif val_acc[1] > list_sort[i][2] and abs(train_acc[0][0]-val_acc[0])> 0.05 and train_acc[0][0] < 0.99:
list_sort.insert(i, [val_acc[0],val_acc[3],val_acc[1],val_acc[2],epoch])
break
elif val_acc[1] == list_sort[i][2] and val_acc[2] > list_sort[i][3] and \
abs(train_acc[0][0]-val_acc[0])> 0.05 and train_acc[0][0] < 0.99:
list_sort.insert(i, [val_acc[0],val_acc[3],val_acc[1],val_acc[2],epoch])
break
else:
i +=1
if 30 == i:
saveflag = False
fout.write('Accuracy : {:.2f} %'.format(100*train_acc[0][0]))
fout.write('Precision : {:.2f} %'.format(100*train_acc[0][3]))
fout.write('Positive Recall: {:.2f} %'.format(100*train_acc[0][1]))
fout.write('Negtive Recall : {:.2f} %'.format(100*train_acc[0][2]))
fout.write('Val Accuracy : {:.2f} %'.format(100*val_acc[0]))
fout.write('Val Precision : {:.2f} %'.format(100*val_acc[3]))
fout.write('Val Positive Recall: {:.2f} %'.format(100*val_acc[1]))
fout.write('Val Negtive Recall : {:.2f} %'.format(100*val_acc[2]))
if True == saveflag:
saveto = ('1w_zzz_jjy_qt_SavedModels_Epoch_' + str(epoch) +
'_dro_' + str(dro) +'_lr_'+ str(lr) +'_ts_'+ str(threshold)+ '.params')
write_model_param(networkout,modelsavedir,saveto)
# After training, we compute and print the test error:
for one in list_sort[:30]:
print(one)
fout.close()
print ("Training Completed!")
return 0
def mainmodel(model=modeltype,
mini_batch=20,
num_epochs=30,
dro=0.7,
lr=0.0001,
preload=preload,
saveto=saveto):
print("model:%s minibatch:%d num_epochs:%d dropout:%f learingrate:%f\n" %
(model, mini_batch, num_epochs, dro, lr))
# Load the dataset
print("Loading data...")
X_train, y_train, X_val, y_val, X_test, y_test = load_data()
# Prepare Theano variables for inputs and targets
input_var = T.tensor4('input')
target_var = T.ivector('targets')
# Create neural network model (depending on first command line parameter)
print("Building model and compiling functions...")
## add input_var when needed in transfer learning model
network = build_model(input_var=input_var, dro=dro)
if preload:
# with open(preload, 'r') as f:
# data = pickle.load(f)
# print(data)
# lasagne.layers.set_all_param_values(model, data)
if model == 'vgg':
read_model_param(network['fc7'], modelloaddir, preload)
else:
read_model_param(network['pool5/7x7_s1'], modelloaddir, preload)
print('pretrained model loaded')
start_time = time.time()
networkout = network['prob']
#networkout=network
prediction = lasagne.layers.get_output(networkout)
loss = lasagne.objectives.categorical_crossentropy(prediction, target_var)
loss = loss.mean()
acc = T.mean(T.eq(T.argmax(prediction, axis=1), target_var),
dtype=theano.config.floatX)
params = lasagne.layers.get_all_params(networkout, trainable=True)
updates = lasagne.updates.adagrad(loss,
params,
learning_rate=lr,
epsilon=1e-06)
test_prediction = lasagne.layers.get_output(networkout, deterministic=True)
test_loss = lasagne.objectives.categorical_crossentropy(
test_prediction, target_var)
test_loss = test_loss.mean()
test_acc = T.mean(T.eq(T.argmax(test_prediction, axis=1), target_var),
dtype=theano.config.floatX)
train_fn = theano.function([input_var, target_var], [loss, acc],
updates=updates)
# Compile a second function computing the validation loss and accuracy:
val_fn = theano.function([input_var, target_var], [test_loss, test_acc])
# Finally, launch the training loop.
print("Starting training...")
time.time() - start_time
# We iterate over epochs:
train_accuracy = []
train_losses = []
val_accuracy = []
val_losses = []
time_id = str(int(time.time()))
#outfilename='./benchmark/'+model+'_'+str(mini_batch)+'_'+str(num_epochs)+'_'+str(dro)+'_'+str(lr)+'_'+time_id+'.txt'
outfilename = './benchmark/' + model + '_' + str(mini_batch) + '_' + str(
num_epochs) + '_' + str(dro) + '_' + str(lr) + '.txt'
print(outfilename)
outfile = open(outfilename, 'w')
#outfile.write("model:%s mini_batch:%s num_epochs:%s dro:%s learningrate:%s\n" % (model,mini_batch,num_epochs,dro,lr))
for epoch in range(num_epochs):
# In each epoch, we do a full pass over the training data:
print('Epoch', epoch)
train_err = 0
train_acc = 0
train_batches = 0
progbarcount = 0
progbar = Progbar(30)
for batch in iterate_minibatches(X_train,
y_train,
mini_batch,
shuffle=True):
inputs, targets = batch
progbarcount = progbarcount + np.float(
len(targets)) / len(y_train) * 25
err, acc = train_fn(inputs, targets)
train_err += err
train_acc += acc
train_batches += 1
#print(train_batches)
progbar.update(progbarcount,
values=[('acc', round(train_acc / train_batches,
3)),
('loss', round(train_err / train_batches,
3))])
train_accuracy.append(round(train_acc / train_batches, 3))
train_losses.append(round(train_err / train_batches, 3))
# And a full pass over the validation data:
val_err = 0
val_acc = 0
val_batches = 0
for batch in iterate_minibatches(X_val,
y_val,
np.min([mini_batch,
len(y_val)]),
shuffle=False):
inputs, targets = batch
err, acc = val_fn(inputs, targets)
val_err += err
val_acc += acc
val_batches += 1
#print(val_acc,val_err,val_batches)
progbar.update(30,
values=[('val_acc', round(val_acc / val_batches, 3)),
('val_loss', round(val_err / val_batches, 3))])
val_accuracy.append(round(val_acc / val_batches, 3))
val_losses.append(round(val_err / val_batches, 3))
# After training, we compute and print the test error:
print(val_losses)
'''
outfile.write('\n')
outfile.write('train_acc: ')
for it in train_accuracy:
outfile.write('%s,' % it)
outfile.write('\n')
outfile.write('train_loss: ')
for it in train_losses:
outfile.write('%s,' % it)
outfile.write('\n')
outfile.write('val_acc: ')
for it in val_accuracy:
outfile.write('%s,' % it)
outfile.write('\n')
outfile.write('vall_loss: ')
for it in val_losses:
outfile.write('%s,' % it)
outfile.write('\n')
'''
test_err = 0
test_acc = 0
test_batches = 0
for batch in iterate_minibatches(X_val,
y_val,
np.min([mini_batch,
len(y_val)]),
shuffle=False):
inputs, targets = batch
err, acc = val_fn(inputs, targets)
test_err += err
test_acc += acc
test_batches += 1
print("")
print("Final results:")
print(" test loss:\t\t\t{:.6f}".format(test_err / test_batches))
print(" test accuracy:\t\t{:.2f} %".format(test_acc / test_batches * 100))
#outfile.write('\n')
#outfile.write("test accuracy:\t\t{:.2f} %".format(test_acc / test_batches * 100))
outfile.write("test accuracy:\t{:.2f}".format(test_acc / test_batches))
outfile.close()
write_model_param(networkout, modelsavedir, saveto)
return train_err / train_batches, train_acc / train_batches * 100,
val_err / val_batches, val_acc / val_batches * 100
def mainmodel(dro=0.4, lr=0.00005, threshold = 0.5, fn_weights = 1.0, model=modeltype, mini_batch=20,num_epochs=100, preload=preload):
print("model:%s minibatch:%d num_epochs:%d dropout:%f learingrate:%f threshold:%f fn_weights:%f\n"
%(model,mini_batch,num_epochs,dro,lr,threshold,fn_weights))
print('Preprocessing data...')
for i in range(X_train.shape[0]):
X_train[i,0] = preprocessing.scale(X_train[i,0])
X_train[i,1] = preprocessing.scale(X_train[i,1])
X_train[i,2] = preprocessing.scale(X_train[i,2])
for i in range(X_val.shape[0]):
X_val[i,0] = preprocessing.scale(X_val[i,0])
X_val[i,1] = preprocessing.scale(X_val[i,1])
X_val[i,2] = preprocessing.scale(X_val[i,2])
print ('The shape of training data is' + str(X_train.shape))
print ('The shape of test data is' + str(X_val.shape))
# Prepare Theano variables for inputs and targets
input_var = T.tensor4('input')
target_var = T.matrix('targets')
# target_var = T.matrix('targets')
# Create neural network model (depending on first command line parameter)
print("Building model and compiling functions...")
# add input_var when needed in transfer learning model
network = build_model(input_var=input_var,dro=dro)
if preload:
if model=='vgg':
read_model_param(network['fc7'],modelloaddir, preload)
else:
read_model_param(network['pool5/7x7_s1'],modelloaddir, preload)
print('pretrained model loaded')
start_time = time.time()
networkout = network['prob']
prediction = lasagne.layers.get_output(networkout)
# disable dropout
test_prediction = lasagne.layers.get_output(networkout, deterministic=True)
# loss = lasagne.objectives.categorical_crossentropy(prediction, target_var)
loss = fas_neg_entrop(prediction, target_var, fn_weights)
loss = loss.mean()
params = lasagne.layers.get_all_params(networkout, trainable=True)
updates = lasagne.updates.adagrad(
loss, params, learning_rate=lr, epsilon=1e-06)
train_fn = theano.function([input_var, target_var], loss, updates=updates)
getpred_fn = theano.function([input_var], prediction)
getpred_test_fn = theano.function([input_var], test_prediction)
print("Starting training...")
time.time()-start_time
# We iterate over epochs:
# log the accuracy of each epoch
fout = open('../log/xin_log_dro_' + str(dro) +'_lr_'+ str(lr) +'_ts_'+ str(threshold) + '.log', 'w+')
list_sort = []#postive,negtive,epoch
list_sort.append([0,0,0,0,0])
for epoch in range(num_epochs):
# In each epoch, we do a full pass over the training data:
fout.write('Epoch'+str(epoch)+'\n')
print('Epoch',epoch)
train_batches = 0
progbarcount = 0
progbar=Progbar(30)
# log the probabilities of prediction in each epoch
output_path = '../log/prediction_epoch_' + str(epoch) + '.out'
output_path_train = '../log/train_epoch_' + str(epoch) + '.out'
with file(output_path_train, 'wb') as outfile_train:
for batch in iterate_minibatches(X_train, Y_train, mini_batch, shuffle=True):
inputs, targets = batch
# reshape to fit the loss function calculation
targets = targets.reshape((targets.shape[0],1))
progbarcount = progbarcount + np.float(len(targets))/len(Y_train)*25
batch_pred_train = getpred_fn(inputs)
if train_batches == 0:
pred_train = batch_pred_train
y_label_train = targets
train_batches += 1
else:
pred_train = np.concatenate((pred_train,batch_pred_train))
y_label_train = np.concatenate((y_label_train,targets))
train_batches += 1
np.savetxt(outfile_train, batch_pred_train, fmt='%-7.2f')
err = train_fn(inputs, targets)
outfile_train.close()
progbar.update(progbarcount,values=[('train_batches',train_batches)])
# And a full pass over the validation data:
val_batches = 0
with file(output_path, 'wb') as outfile:
pred_val = []
pred = []
for batch in iterate_minibatches(X_val, Y_val, np.min([mini_batch,len(Y_val)]), shuffle=False):
inputs, targets = batch
batch_pred_val = getpred_test_fn(inputs)
pdd = getpred_test_fn(inputs)
if val_batches == 0:
pred_val = batch_pred_val
y_label_val = targets
else:
pred_val = np.concatenate((pred_val,batch_pred_val))
y_label_val = np.concatenate((y_label_val,targets))
np.savetxt(outfile, batch_pred_val, fmt='%-7.2f')
val_batches += 1
pred.append(pdd)
np.save(modelsavedir+str(epoch) +'.npy',pred)
outfile.close()
progbar.update(30,values=[('val_batches',val_batches)])
## For softmax
# train_acc = get_acc(y_label_train, pred_train[:,1], threshold)
# val_acc = get_acc(y_label_val, pred_val[:,1], threshold)
# For sigmoid
train_acc = get_acc(y_label_train, pred_train, threshold)
val_acc = get_acc(y_label_val, pred_val, threshold)
# img_save_path = '../log/curve_epoch_' + str(epoch) + '.png'
# plot_recall_curve(y_label_val,pred_val,img_save_path)
# all_param_values = lasagne.layers.get_all_param_values(networkout)
# print ('Accuracy : {:.2f} %'.format(100*train_acc[0][0]))
# print ('Precision : {:.2f} %'.format(100*train_acc[0][3]))
# print ('Positive Recall: {:.2f} %'.format(100*train_acc[0][1]))
# print ('Negtive Recall : {:.2f} %'.format(100*train_acc[0][2]))
#
# print ('Val Accuracy : {:.2f} %'.format(100*val_acc[0]))
# print ('Val Precision : {:.2f} %'.format(100*val_acc[3]))
# print ('Val Positive Recall: {:.2f} %'.format(100*val_acc[1]))
# print ('Val Negtive Recall : {:.2f} %'.format(100*val_acc[2]))
print ('Accuracy : {:.2f} % {:.2f} %'.format(100*train_acc[0][0], 100*val_acc[0]))
print ('Precision : {:.2f} % {:.2f} %'.format(100*train_acc[0][3], 100*val_acc[3]))
if 0.9 <= val_acc[1] and val_acc[1]<0.98 and val_acc[2]>0.25:
print('----------------------------------------------------------------------------')
print ('Positive Recall: {:.2f} % {:.2f} %'.format(100*train_acc[0][1], 100*val_acc[1]))
if 0.9 <= val_acc[1] and val_acc[1]<0.98 and val_acc[2]>0.25:
print('----------------------------------------------------------------------------')
print ('Negtive Recall : {:.2f} % {:.2f} %'.format(100*train_acc[0][2], 100*val_acc[2]))
if 1 == len(list_sort) and 0.9 <= val_acc[1] and val_acc[1]<0.98 and val_acc[2]>=0.25:
list_sort.insert(0, [val_acc[0],val_acc[3],val_acc[1],val_acc[2],epoch])
saveflag = True
i = 0
while i< min(len(list_sort),50) and 1 <> len(list_sort) and 0.9 <= val_acc[1] and val_acc[1]<0.98 and val_acc[2]>=0.25:
if val_acc[1] > list_sort[i][2] and abs(train_acc[0][1]-val_acc[1])< 0.1 and train_acc[0][0] < 0.98:
list_sort.insert(i, [val_acc[0],val_acc[3],val_acc[1],val_acc[2],epoch])
break
elif val_acc[1] == list_sort[i][2] and val_acc[2] > list_sort[i][3] and \
abs(train_acc[0][1]-val_acc[1])< 0.1 and train_acc[0][0] < 0.98:
list_sort.insert(i, [val_acc[0],val_acc[3],val_acc[1],val_acc[2],epoch])
break
else:
i +=1
if 1==len(list_sort) or 0.9 > val_acc[1] or val_acc[1]>=0.98 or val_acc[2]<0.25:
saveflag = False
fout.write('Accuracy : {:.2f} %'.format(100*train_acc[0][0]))
fout.write('Precision : {:.2f} %'.format(100*train_acc[0][3]))
fout.write('Positive Recall: {:.2f} %'.format(100*train_acc[0][1]))
fout.write('Negtive Recall : {:.2f} %'.format(100*train_acc[0][2]))
fout.write('Val Accuracy : {:.2f} %'.format(100*val_acc[0]))
fout.write('Val Precision : {:.2f} %'.format(100*val_acc[3]))
fout.write('Val Positive Recall: {:.2f} %'.format(100*val_acc[1]))
fout.write('Val Negtive Recall : {:.2f} %'.format(100*val_acc[2]))
if True == saveflag:
saveto = ('1w_3k_7k_SavedModels_Epoch_'+str(round(val_acc[1],2)*10)+'_'+str(round(val_acc[2],2)*10)+'_' + str(epoch) +
'_dro_' + str(dro) +'_lr_'+ str(lr) +'_ts_'+ str(threshold)+ '.params')
write_model_param(networkout,modelsavedir,saveto)
else:
time.sleep(30)
for one in list_sort[:50]:
print(one)
# After training, we compute and print the test error:
fout.close()
print ("Training Completed!")
return 0
def dl_progress(count, block_size, total_size):
global progbar
if progbar is None:
progbar = Progbar(total_size)
else:
progbar.update(count * block_size)
def dl_progress(count, block_size, total_size, progbar=None):
if progbar is None:
progbar = Progbar(total_size)
else:
progbar.update(count * block_size)
def mainmodel(model=modeltype, mini_batch=20,num_epochs=30, dro=0.7,lr=0.0001, preload=preload,saveto=saveto):
print("model:%s minibatch:%d num_epochs:%d dropout:%f learingrate:%f\n" % (model,mini_batch,num_epochs,dro,lr))
# Load the dataset
print("Loading data...")
X_train, y_train, X_val, y_val, X_test, y_test = load_data()
# Prepare Theano variables for inputs and targets
input_var = T.tensor4('input')
target_var = T.ivector('targets')
# Create neural network model (depending on first command line parameter)
print("Building model and compiling functions...")
## add input_var when needed in transfer learning model
network = build_model(input_var=input_var,dro=dro)
if preload:
# with open(preload, 'r') as f:
# data = pickle.load(f)
# print(data)
# lasagne.layers.set_all_param_values(model, data)
if model=='vgg':
read_model_param(network['fc7'],modelloaddir, preload)
else:
read_model_param(network['pool5/7x7_s1'],modelloaddir, preload)
print('pretrained model loaded')
start_time = time.time()
networkout=network['prob']
#networkout=network
prediction = lasagne.layers.get_output(networkout)
loss = lasagne.objectives.categorical_crossentropy(prediction, target_var)
loss = loss.mean()
acc = T.mean(T.eq(T.argmax(prediction, axis=1), target_var),
dtype=theano.config.floatX)
params = lasagne.layers.get_all_params(networkout, trainable=True)
updates = lasagne.updates.adagrad(
loss, params, learning_rate=lr, epsilon=1e-06)
test_prediction = lasagne.layers.get_output(networkout, deterministic=True)
test_loss = lasagne.objectives.categorical_crossentropy(test_prediction,
target_var)
test_loss = test_loss.mean()
test_acc = T.mean(T.eq(T.argmax(test_prediction, axis=1), target_var),
dtype=theano.config.floatX)
train_fn = theano.function([input_var, target_var], [loss,acc], updates=updates)
# Compile a second function computing the validation loss and accuracy:
val_fn = theano.function([input_var, target_var], [test_loss, test_acc])
# Finally, launch the training loop.
print("Starting training...")
time.time()-start_time
# We iterate over epochs:
train_accuracy=[]
train_losses=[]
val_accuracy=[]
val_losses=[]
time_id=str(int(time.time()))
#outfilename='./benchmark/'+model+'_'+str(mini_batch)+'_'+str(num_epochs)+'_'+str(dro)+'_'+str(lr)+'_'+time_id+'.txt'
outfilename='./benchmark/'+model+'_'+str(mini_batch)+'_'+str(num_epochs)+'_'+str(dro)+'_'+str(lr)+'.txt'
print (outfilename)
outfile=open(outfilename,'w')
#outfile.write("model:%s mini_batch:%s num_epochs:%s dro:%s learningrate:%s\n" % (model,mini_batch,num_epochs,dro,lr))
for epoch in range(num_epochs):
# In each epoch, we do a full pass over the training data:
print('Epoch',epoch)
train_err = 0
train_acc = 0
train_batches = 0
progbarcount = 0
progbar=Progbar(30)
for batch in iterate_minibatches(X_train, y_train, mini_batch, shuffle=True):
inputs, targets = batch
progbarcount = progbarcount + np.float(len(targets))/len(y_train)*25
err, acc = train_fn(inputs, targets)
train_err += err
train_acc += acc
train_batches += 1
#print(train_batches)
progbar.update(progbarcount,values=[('acc',round(train_acc/train_batches,3)),
('loss',round(train_err/train_batches,3))])
train_accuracy.append(round(train_acc/train_batches,3))
train_losses.append(round(train_err/train_batches,3))
# And a full pass over the validation data:
val_err = 0
val_acc = 0
val_batches = 0
for batch in iterate_minibatches(X_val, y_val, np.min([mini_batch,len(y_val)]), shuffle=False):
inputs, targets = batch
err, acc = val_fn(inputs, targets)
val_err += err
val_acc += acc
val_batches += 1
#print(val_acc,val_err,val_batches)
progbar.update(30,values=[('val_acc',round(val_acc/val_batches,3)),
('val_loss',round(val_err/val_batches,3))])
val_accuracy.append(round(val_acc/val_batches,3))
val_losses.append(round(val_err/val_batches,3))
# After training, we compute and print the test error:
print (val_losses)
'''
outfile.write('\n')
outfile.write('train_acc: ')
for it in train_accuracy:
outfile.write('%s,' % it)
outfile.write('\n')
outfile.write('train_loss: ')
for it in train_losses:
outfile.write('%s,' % it)
outfile.write('\n')
outfile.write('val_acc: ')
for it in val_accuracy:
outfile.write('%s,' % it)
outfile.write('\n')
outfile.write('vall_loss: ')
for it in val_losses:
outfile.write('%s,' % it)
outfile.write('\n')
'''
test_err = 0
test_acc = 0
test_batches = 0
for batch in iterate_minibatches(X_val, y_val, np.min([mini_batch,len(y_val)]), shuffle=False):
inputs, targets = batch
err, acc = val_fn(inputs, targets)
test_err += err
test_acc += acc
test_batches += 1
print("")
print("Final results:")
print(" test loss:\t\t\t{:.6f}".format(test_err / test_batches))
print(" test accuracy:\t\t{:.2f} %".format(
test_acc / test_batches * 100))
#outfile.write('\n')
#outfile.write("test accuracy:\t\t{:.2f} %".format(test_acc / test_batches * 100))
outfile.write("test accuracy:\t{:.2f}".format(test_acc / test_batches))
outfile.close()
write_model_param(networkout,modelsavedir,saveto)
return train_err/train_batches, train_acc/train_batches * 100,
val_err/val_batches, val_acc/val_batches * 100