I am training a conditional GAN that generates image time series (similar to video prediction). I built a conditional GAN based on this paper. However, several probelms happened when I was training the cGAN.
Problems of training cGAN:
The discriminator's loss stucks at one.
It seems like the generator's loss is not effected by discriminator no matter how I adjust the hyper parameters related to the discriminator.
Training loss of discriminator
D_loss = (fake_D_loss + true_D_loss) / 2
fake_D_loss = Hinge_loss(D(G(x, z)))
true_D_loss = Hinge_loss(D(x, y))
The margin of hinge loss = 1
Training loss of generator
D_loss = -torch.mean(D(G(x,z))
G_loss = weighted MAE
Gradient flow of discriminator
Gradient flow of generator
Several settings of the cGAN:
The output layer of discriminator is linear sum.
The discriminator is trained twice per epoch while the generator is only trained once.
The number of neurons of the generator and discriminator are exactly the same as the paper.
I replaced the ReLU (original setting) to LeakyReLU to avoid nan.
I added gradient norm to avoid gradient vanishing problem.
Other hyper parameters are listed as follows:
Hyper parameters
Paper
Mine
number of input images
4
4
number of predicted images
18
10
batch size
16
16
opt_g, opt_d
Adam
Adam
lr_g
5e-5
5e-5
lr_d
2e-4
2e-4
The loss function I use for discriminator.
def HingeLoss(pred, validity, margin=1.):
if validity:
loss = F.relu(margin - pred)
else:
loss = F.relu(margin + pred)
return loss.mean()
The loss function for examining the validity of predicted image from generator.
def HingeLossG(pred):
return -torch.mean(pred)
I use the trainer of pytorch_lightning to train the model. The training codes I wrote are as follows.
def training_step(self, batch, batch_idx, optimizer_idx):
x, y = batch
x.requires_grad = True
if self.n_sample > 1:
pred = [self(x) for _ in range(self.n_sample)]
pred = torch.mean(torch.stack(pred, dim=0), dim=0)
else:
pred = self(x)
##### TRAIN DISCRIMINATOR #####
if optimizer_idx == 1:
true_D_loss = self.discriminator_loss(self.discriminator(x, y), True)
fake_D_loss = self.discriminator_loss(self.discriminator(x, pred.detach()), False)
D_loss = (fake_D_loss + true_D_loss) / 2
return D_loss
##### TRAIN GENERATOR #####
if optimizer_idx == 0:
G_loss = self.generator_loss(pred, y)
GD_loss = self.generator_d_loss(self.discriminator(x, pred.detach()))
train_G_loss = G_loss + GD_loss
return train_G_loss
I have several guesses of why these problems may occur:
Since the original model predicts 18 frames rather than 10 frames (my version), maybe the number of neurons in the original generator is too much for my case (predicting 10 frames), leading an exceedingly powerful generator that breaks the balance of training. However, I've tried to lower the learning rate of generator to 1e-5 (original 5e-5) or increase the training times of discriminator to 3 to 5 times. It seems that the loss curve of generator didn't much changed.
Various results of training cGAN
I have also adjust the weights of generator's loss, but the same problems still occurred.
The architecture codes of this model: https://github.com/hyungting/DGMR-pytorch
what is the standard way to detect if a model has converged? I was going to record 5 losses with 95 confidence intervals each loss and if they all agreed then I’d halt the script. I assume training until convergence must be implemented already in PyTorch or PyTorch Lightning somewhere. I don’t need a perfect solution, just the standard way to do this automatically - i.e. halt when converged.
My solution is easy to implement. Once create a criterion and changes the reduction to none. Then it will output a tensor of size [B]. Every you log you record that and it's 95 confidence interval (or std if you prefer, but that is much less accuracy). Then every time you add a new loss with it's confidence interval make sure it remains of size 5 (or 10) and that the 5 losses are within a 95 CI of each other. Then if that is true halt.
You can compute the CI with this:
def torch_compute_confidence_interval(data: Tensor,
confidence: float = 0.95
) -> Tensor:
"""
Computes the confidence interval for a given survey of a data set.
"""
n = len(data)
mean: Tensor = data.mean()
# se: Tensor = scipy.stats.sem(data) # compute standard error
# se, mean: Tensor = torch.std_mean(data, unbiased=True) # compute standard error
se: Tensor = data.std(unbiased=True) / (n**0.5)
t_p: float = float(scipy.stats.t.ppf((1 + confidence) / 2., n - 1))
ci = t_p * se
return mean, ci
and you can create the criterion as follow:
loss: nn.Module = nn.CrossEntropyLoss(reduction='none')
so the train loss is now of size [B].
note that I know how to train with a fixed number of epochs, so I am not really looking for that - just the halting criterion for when to stop when models looks converged, what a person would sort of do when they look at their learning curve but automatically.
ref:
https://forums.pytorchlightning.ai/t/what-is-the-standard-way-to-halt-a-script-when-it-has-converged/1415
Set an EarlyStopping (https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.callbacks.EarlyStopping.html#pytorch_lightning.callbacks.EarlyStopping) callback in your trainer by
checkpoint_callbacks = [
EarlyStopping(
monitor="val_f1_score",
min_delta=0.01,
patience=10, # NOTE no. val epochs, not train epochs
verbose=False,
mode="min",
),
]
trainer = pl.Trainer(callbacks=callbacks)
This will monitor changes in val_f1_score during training (notice that you have to log this value with self.log("val_f1_score", val_f1) in your pl.LightningModule). And it will stop the training if the minimum change to quantity to qualify as an improvement (min_delta) for more than the number of epoch specified as patience
I have a multiclassification problem that depends on historical data. I am trying LSTM using loss='sparse_categorical_crossentropy'. The train accuracy and loss increase and decrease respectively. But, my test accuracy starts to fluctuate wildly.
What I am doing wrong?
Input data:
X = np.reshape(X, (X.shape[0], X.shape[1], 1))
X.shape
(200146, 13, 1)
My model
# fix random seed for reproducibility
seed = 7
np.random.seed(seed)
# define 10-fold cross validation test harness
kfold = StratifiedKFold(n_splits=10, shuffle=False, random_state=seed)
cvscores = []
for train, test in kfold.split(X, y):
regressor = Sequential()
# Units = the number of LSTM that we want to have in this first layer -> we want very high dimentionality, we need high number
# return_sequences = True because we are adding another layer after this
# input shape = the last two dimensions and the indicator
regressor.add(LSTM(units=50, return_sequences=True, input_shape=(X[train].shape[1], 1)))
regressor.add(Dropout(0.2))
# Extra LSTM layer
regressor.add(LSTM(units=50, return_sequences=True))
regressor.add(Dropout(0.2))
# 3rd
regressor.add(LSTM(units=50, return_sequences=True))
regressor.add(Dropout(0.2))
#4th
regressor.add(LSTM(units=50))
regressor.add(Dropout(0.2))
# output layer
regressor.add(Dense(4, activation='softmax', kernel_regularizer=regularizers.l2(0.001)))
# Compile the RNN
regressor.compile(optimizer='adam', loss='sparse_categorical_crossentropy',metrics=['accuracy'])
# Set callback functions to early stop training and save the best model so far
callbacks = [EarlyStopping(monitor='val_loss', patience=9),
ModelCheckpoint(filepath='best_model.h5', monitor='val_loss', save_best_only=True)]
history = regressor.fit(X[train], y[train], epochs=250, callbacks=callbacks,
validation_data=(X[test], y[test]))
# plot train and validation loss
pyplot.plot(history.history['loss'])
pyplot.plot(history.history['val_loss'])
pyplot.title('model train vs validation loss')
pyplot.ylabel('loss')
pyplot.xlabel('epoch')
pyplot.legend(['train', 'validation'], loc='upper right')
pyplot.show()
# evaluate the model
scores = regressor.evaluate(X[test], y[test], verbose=0)
print("%s: %.2f%%" % (regressor.metrics_names[1], scores[1]*100))
cvscores.append(scores[1] * 100)
print("%.2f%% (+/- %.2f%%)" % (np.mean(cvscores), np.std(cvscores)))
Results:
trainingmodel
Plot
What you are describing here is overfitting. This means your model keeps learning about your training data and doesn't generalize, or other said it is learning the exact features of your training set. This is the main problem you can deal with in deep learning. There is no solution per se. You have to try out different architectures, different hyperparameters and so on.
You can try with a small model that underfits (that is the train acc and validation are at low percentage) and keep increasing your model until it overfits. Then you can play around with the optimizer and other hyperparameters.
By smaller model I mean one with fewer hidden units or fewer layers.
you seem to have too many LSTM layers stacked over and over again which eventually leads to overfitting. Probably should decrease the num of layers.
Your model seems to be overfitting, since the training error keeps on reducing while validation error fails to. Overall, it fails to generalize.
You should try reducing the model complexity by removing some of the LSTM layers. Also, try varying the batch sizes, it will reduce the number of fluctuations in the loss.
You can also consider varying the learning rate.
I am new in Keras and I learned fitting and evaluating the model.
After evaluating the model one can see the actual predictions made by model.
I am wondering Is it also possible to see the predictions during fitting in Keras? Till now I cant find any code doing this.
Since this question doesn't specify "epochs", and since using callbacks may represent extra computation, I don't think it's exactly a duplication.
With tensorflow, you can use a custom training loop with eager execution turned on. A simple tutorial for creating a custom training loop: https://www.tensorflow.org/tutorials/eager/custom_training_walkthrough
Basically you will:
#transform your data in to a Dataset:
dataset = tf.data.Dataset.from_tensor_slices(
(x_train, y_train)).shuffle(some_buffer).batch(batchSize)
#the above is buggy in some versions regarding shuffling, you may need to shuffle
#again between each epoch
#create an optimizer
optimizer = tf.keras.optimizers.Adam()
#create an epoch loop:
for e in range(epochs):
#create a batch loop
for i, (x, y_true) in enumerate(dataset):
#create a tape to record actions
with tf.GradientTape() as tape:
#take the model's predictions
y_pred = model(x)
#calculate loss
loss = tf.keras.losses.binary_crossentropy(y_true, y_pred)
#calculate gradients
gradients = tape.gradient(loss, model.trainable_weights)
#apply gradients
optimizer.apply_gradients(zip(gradients, model.trainable_weights)
You can use the y_pred var for doing anything, including getting its numpy_pred = y_pred.numpy() value.
The tutorial gives some more details about metrics and validation loop.
I am having a lot of trouble with a neural network model using R neuralnet() function. When I train a network on all of the data as expected the predictions are very accurate. However, when I split the data into training and test sets, the test predictions are terrible. I cannot figure what all I am doing wrong. I would appreciate any advice or help troubleshooting as I don't think I'll be able to figure this out on my own. Thanks in advance.
I have included the R code and some plots and an example of the data below the full data is 3600 observations.
Best Regards-Pat
UPDATE 05/12/18: BASED ON FEEDBACK THAT THIS LOOKS LIKE OVERFITTING, I TRIED STOPPING THE TRAINING EARLIER AND FOUND THAT THE MSE OF THE TEST PREDICTION NEVER GETS VERY LOW AND IS LOWEST APPROACHING 0 TRAINING EPOCHS AND RISES FROM THERE (PLOT INCLUDED AND CODE APPENDED)
###########
#ANN Models
###########
#Load libraries
library(plyr)
library(ggplot2)
library(gridExtra)
library(neuralnet)
#Retain only numerically coded data from data1 in (data2) for ANN fitting
data2 = data1[,c(3:7)]
#Calculate Min and Max for Scaling
max_data = apply(data2,2,max)
min_data = apply(data2,2,min)
#Scale data 0-1
data2_scaled = scale(data2,center=min_data,scale=max_data-min_data)
#Check data structure
data2_scaled
#Fit neural net model
model_nn1 = neuralnet(formula=time~instructions+nodes+machine_num+app_num,data=data2_scaled,hidden=c(8,8),stepmax=1000000,threshold=0.01)
#Calculate Min and Max Response for rescaling
max_time = max(data2$time)
min_time = min(data2$time)
#Rescale neural net response predictions
pred_nn1 = model_nn1$net.result[[1]][,1]*(max_time-min_time)+min_time
#Compare model predictions to actual values
a03 = cbind.data.frame(data1$time,pred_nn1,data1$machine,data1$app)
colnames(a03) = c("actual","prediction","machine","app")
attach(a03)
p01 = ggplot(a03,aes(x=actual,y=prediction))+
geom_point(aes(color=machine),size=1)+
scale_y_continuous("Predicted Execution Time [s]",breaks=seq(0,1000,100),limits=c(0,1000))+
scale_x_continuous("Actual Execution Time [s]",breaks=seq(0,1000,100),limits=c(0,1000))+
ggtitle("Neural Net Fit (ALL DATA):\nActual vs. Predicted Execution Time")+
geom_abline(intercept=0,slope=1)+
theme_light()
p02 = ggplot(a03,aes(x=actual,y=prediction))+
geom_point(aes(color=app),size=1)+
scale_y_continuous("Predicted Execution Time [s]",breaks=seq(0,1000,100),limits=c(0,1000))+
scale_x_continuous("Actual Execution Time [s]",breaks=seq(0,1000,100),limits=c(0,1000))+
ggtitle("Neural Net Fit (ALL DATA):\nActual vs. Predicted Execution Time")+
geom_abline(intercept=0,slope=1)+
theme_light()
grid.arrange(p01,p02,nrow=1)
#Visualize ANN
plot(model_nn1)
#Epochs taken to train "steps"
model_nn1$result.matrix[3,]
#########################
#Testing and Training ANN
#########################>
#Split the data into a test and training set
index = sample(1:nrow(data2_scaled),round(0.80*nrow(data2_scaled)))
train_data = as.data.frame(data2_scaled[index,])
test_data = as.data.frame(data2_scaled[-index,])
model_nn2 = neuralnet(formula=time~instructions+nodes+machine_num+app_num,data=train_data,hidden=c(3,2),stepmax=1000000,threshold=0.01)
pred_nn2_scaled = compute(model_nn2,test_data[,c(1,2,4,5)])
pred_nn2 = pred_nn2_scaled$net.result*(max_time-min_time)+min_time
test_data_time = test_data$time*(max_time-min_time)+min_time
a04 = cbind.data.frame(test_data_time,pred_nn2,data1[-index,2],data1[-index,1])
colnames(a04) = c("actual","prediction","machine","app")
attach(a04)
p01 = ggplot(a04,aes(x=actual,y=prediction))+
geom_point(aes(color=machine),size=1)+
scale_y_continuous("Predicted Execution Time [s]",breaks=seq(0,1000,100),limits=c(0,1000))+
scale_x_continuous("Actual Execution Time [s]",breaks=seq(0,1000,100),limits=c(0,1000))+
ggtitle("Neural Net Fit (TEST DATA):\nActual vs. Predicted Execution Time")+
geom_abline(intercept=0,slope=1)+
theme_light()
p02 = ggplot(a04,aes(x=actual,y=prediction))+
geom_point(aes(color=app),size=1)+
scale_y_continuous("Predicted Execution Time [s]",breaks=seq(0,1000,100),limits=c(0,1000))+
scale_x_continuous("Actual Execution Time [s]",breaks=seq(0,1000,100),limits=c(0,1000))+
ggtitle("Neural Net Fit (TEST DATA):\nActual vs. Predicted Execution Time")+
geom_abline(intercept=0,slope=1)+
theme_light()
grid.arrange(p01,p02,nrow=1)
#EARLY STOPPING TEST
i = 1000
summary_data = data.frame(matrix(rep(0,4*i),ncol=4))
colnames(summary_data) = c("treshold","epochs","train_mse","test_mse")
for (j in 1:i){
a = runif(1,min=0.01,max=10)
#Train the model
model_nn2 = neuralnet(formula=time~instructions+nodes+machine_num+app_num,data=train_data,hidden=3,stepmax=1000000,threshold=a)
#Calculate Min and Max Response for rescaling
max_time = max(data2$time)
min_time = min(data2$time)
#Predict test data from trained nn
pred_nn2_scaled = compute(model_nn2,test_data[,c(1,2,4,5)])
#Rescale test prediction
pred_test_data_time = pred_nn2_scaled$net.result*(max_time-min_time)+min_time
#Rescale test actual
test_data_time = test_data$time*(max_time-min_time)+min_time
#Rescale train prediction
pred_train_data_time = model_nn2$net.result[[1]][,1]*(max_time-min_time)+min_time
#Rescale train actual
train_data_time = train_data$time*(max_time-min_time)+min_time
#Calculate mse
test_mse = mean((pred_test_data_time-test_data_time)^2)
train_mse = mean((pred_train_data_time-train_data_time)^2)
#Summarize
summary_data[j,1] = a
summary_data[j,2] = model_nn2$result.matrix[3,]
summary_data[j,3] = round(train_mse,3)
summary_data[j,4] = round(test_mse,3)
print(summary_data[j,])
}
plot(summary_data$epochs,summary_data$test_mse,pch=19,xlim=c(0,2000),ylim=c(0,300000),cex=0.5,xlab="Training Steps",ylab="MSE",main="Early Stopping Test: Comparing MSE : TEST=BLACK TRAIN=RED")
points(summary_data$epochs,summary_data$train_mse,pch=19,col=2,cex=0.5)
I would guess that it is overfitting. The network is learning to reproduce the data like a dictionary instead of learning the underlying function in the data. There are various things which can cause this and ways to address them.
Things which cause overfitting are:
The network could be training for too long.
The network could having far more weights than training examples.
Ways to reduce overfitting are:
Create a validation dataset and stop training the network as soon as the
validation set's loss starts increasing. This is a necessity.
Reducing the network size. (Less weights)
Using a regularization technique like weight decay or dropout.
Also, it may be possible that the problem is too difficult for a neural network to solve based on the data it is given. Reproducing training data does not prove that the network can solve the problem, it only proves that the network can remember things like a dictionary.