I am trying to solve a regression problem using pytorch. I have a pre-trained model to start with. When I was tuning hyperparameters, I found my batch size and train/validation loss have a weird correlation. Specifically:
batch size = 16 -\> train/val loss around 0.6 (for epoch 1)
batch size = 64 -\> train/val loss around 0.8 (for epoch 1)
batch size = 128 -\> train/val loss around 1 (for epoch 1)
I want to know if this is normal, or there is something wrong with my code.
optimizer: SGD with learning rate of 1e-3
Loss function:
def rmse(pred, real):
residuals = pred - real
square = torch.square(residuals)
sum_of_square = torch.sum(square)
mean = sum_of_square / pred.shape[0]
root = torch.sqrt(mean)
return root
train loop:
def train_loop(dataloader, model, optimizer, epoch):
num_of_batches = len(dataloader)
total_loss = 0
for batch, (X, y) in enumerate(dataloader):
optimizer.zero_grad()
pred = model(X)
loss = rmse(pred, y)
loss.backward()
optimizer.step()
total_loss += loss.item()
#lr_scheduler.step(epoch*num_of_batches+batch)
#last_lr = lr_scheduler.get_last_lr()[0]
train_loss = total_loss / num_of_batches
return train_loss
test loop:
def test_loop(dataloader, model):
size = len(dataloader.dataset)
num_of_batches = len(dataloader)
test_loss = 0
with torch.no_grad():
for X, y in dataloader:
pred = model(X)
test_loss += rmse(pred, y).item()
test_loss /= num_of_batches
return test_loss
I'll start with an a. analogy, b. dive into the math, and then c. end with a numerical experiment.
a.) What you are witnessing is roughly the same phenomenon as the difference between stochastic and batched gradient descent. In the analog case, the "true" gradient or direction in which the learned parameters should be shifted minimizes the loss over the entire training set of data. In stochastic gradient descent, the gradient shifts the learned parameters in the direction that minimizes the loss for a single example. As the size of the batch is increased from 1 towards the size of the overall dataset, the gradient estimated from the minibatch becomes closer to the gradient for the whole dataset.
Now, is stochastic gradient descent useful at all, given that it is imprecise wrt the whole dataset? Absolutely. In fact, the noise in this estimate can be useful for escaping local minima in the optimization. Analogously, any noise in your estimate of loss wrt the whole dataset is likely nothing to worry about.
b.) But let's next look at why this behavior occurs. RMSE is defined as:
where N is the total number of examples in your dataset. And if RMSE were calculated this way, we would expect the value to be roughly the same (and to approach exactly the same value as N becomes large). However, in your case, you are actually calculating the mean epoch loss as:
where B is the number of minibatches per epoch, and b is the number of examples per minibatch:
Thus, epoch loss is the average RMSE per minibatch. Rearranging, we can see:
when B is large (B = N) and the minibatch size is 1,
which clearly has quite different properties than RMSE defined above. However, as B becomes small B = 1, and minibatch size is N,
which is exactly equal to RMSE above. So as you increase the batch size, the expected value for the quantity you compute moves between these two expressions. This explains the (roughly square root) scaling of your loss with different minibatch sizes. Epoch loss is an estimate of RMSE (which can be thought of as the standard deviation of model prediction error). One training goal could be to drive this error standard deviation to zero, but your expression for epoch loss is also likely a good proxy for this. And both quantities are themselves proxies for whatever model performance you actually hope to obtain.
c. You can try this for yourself with a trivial toy problem. A normal distribution is used as a proxy for model error.
EXAMPLE 1: Compute RMSE for whole dataset ( of size 10000 x b)
import torch
for b in [1,2,3,5,9,10,100,1000,10000,100000]:
b_errors = []
for i in range (10000):
error = torch.normal(0,100,size = (1,b))
error = error **2
error = error.mean()
b_errors.append(error)
RMSE = torch.sqrt(sum(b_errors)/len(b_errors))
print("Average RMSE for b = {}: {}".format(N,RMSE))
Result:
Average RMSE for b = 1: 99.94982147216797
Average RMSE for b = 2: 100.38357543945312
Average RMSE for b = 3: 100.24600982666016
Average RMSE for b = 5: 100.97154998779297
Average RMSE for b = 9: 100.06820678710938
Average RMSE for b = 10: 100.12358856201172
Average RMSE for b = 100: 99.94219970703125
Average RMSE for b = 1000: 99.97941589355469
Average RMSE for b = 10000: 100.00338745117188
EXAMPLE 2: Compute Epoch Loss with B = 10000
import torch
for b in [1,2,3,5,9,10,100,1000,10000,100000]:
b_errors = []
for i in range (10000):
error = torch.normal(0,100,size = (1,b))
error = error **2
error = error.mean()
error = torch.sqrt(error)
b_errors.append(error)
avg = (sum(b_errors)/len(b_errors)
print("Average Epoch Loss for b = {}: {}".format(b,avg))
Result:
Average Epoch Loss for b = 1: 80.95650482177734
Average Epoch Loss for b = 2: 88.734375
Average Epoch Loss for b = 3: 92.08515930175781
Average Epoch Loss for b = 5: 95.56260681152344
Average Epoch Loss for b = 9: 97.49445343017578
Average Epoch Loss for b = 10: 97.20250701904297
Average Epoch Loss for b = 100: 99.6297607421875
Average Epoch Loss for b = 1000: 99.96969604492188
Average Epoch Loss for b = 10000: 99.99618530273438
Average Epoch Loss for b = 100000: 100.00079345703125
The first batch of the first epoch is always going to be pretty inconsistent between runs unless you setup a manual rng seed. Your loss is a result of how well your randomly initialized weights do with your randomly subsampled batch of training items. In other words, its meaningless (in this context) what your loss is on this first go-around, regardless of batch-size.
Related
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
I'm currently traininig a VAE model.
The images in question are microstructure rocks images (like these).
I defind a compount loss function having the sum 2 folds:
MSE as my images are grayscale but non binary.
KLL divergence.
I was having nan values for loss function, but figured out that a way around this is to use the weighted sum of the 2 losses. I've chosen the weight the MSE by the images size (256x256), so it becomes:
MSE = MSEx256x256
and the KLL divergence by 0.1 factor.
The nan problem was solved then, but my model when predicting just predicts one value for the whole image, so if I predict an output it will be an array of 256*256 values all the same at e.g. 0.502.
Model specs:
10 layers encoder / decoder
Latent vector space of dimension 5
SGD optimizer at lr=0.001
Loss values upon training goes from a billion number to 3000 from 2nd epoch and fluctuates around it
Accuracy upon training or valiudating is below 0.001, I've read this metric is irrelavnt anyway when it comes to VAE
Here is how I sample from the latent vector specs:
sample = Lambda(get_sample_from_dist, output_shape=(latent_dim, ), name='sample')([mu, log_sigma])
def get_sample_from_dist(args):
mean_vec, std_dev_vec = args
eta_vec = K.random_normal(shape=(K.shape(mean_vec)[0], K.int_shape(mean_vec)[1]), mean=0, stddev=1)
return mean_vec + K.exp(std_dev_vec) * eta_vec
and here is how the encoder generate mu and log_sigma:
x is the output of the last encoder layer
mu = Dense(latent_dim, name='latent_mu')(x)
log_sigma = Dense(latent_dim, name='latent_sigma')(x)
and here is my loss
def vae_loss_func(inputs, outputs, mu, log_sigma):
x1 = K.flatten(inputs)
x2 = K.flatten(outputs)
reconstruction_loss = losses.mse(x1, x2)*256**2
kl_loss = -0.5* 0.1*K.sum(1 + log_sigma - K.square(mu) - K.square(K.exp(log_sigma)), axis=-1)
vae_loss = K.mean(reconstruction_loss + kl_loss)
return vae_loss
Any thoughts where things are going wrong?
I tried different weighing factors in the loss function and using strides and dropouts layers, none of these worked. I'm expecting the generated image to be varying in pixel value and evenatually capturing the rock structure.
I'm trying to train a small CNN from scratch to classify images of 10 different animal species. The images have different dimensions, but I'd say around 300x300. Anyway, every image is resized to 224x224 before going into the model.
Here is the network I'm training:
# Convolution 1
self.cnn1 = nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3, stride=1, padding=0)
self.relu1 = nn.ReLU()
# Max pool 1
self.maxpool1 = nn.MaxPool2d(kernel_size=2)
# Convolution 2
self.cnn2 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, stride=1, padding=0)
self.relu2 = nn.ReLU()
# Max pool 2
self.maxpool2 = nn.MaxPool2d(kernel_size=2)
# Fully connected 1
self.fc1 = nn.Linear(32 * 54 * 54, 10)
I'm using a SGD optimizer with fixed learning rate = 0.005 and weight decay = 0.01. I'm using a cross entropy function.
The accuracy of the model is good (around 99% after the 43-th epoch). However:
in some epoch I get a 'nan' as training loss
in some other epoch the accuracy drops significantly (sometimes the two happen in the same epoch). However, in the next epoch the accuracy comes back to a normal level.
If I understood it correctly a nan in training loss most of the times is caused by gradient values getting too small (underflow) or too big (overflow). Could this be the case?
Should I try by increasing the weight decay to 0.05? Or should I do gradient clipping to avoid exploding gradients? If so which would be a reasonable bound?
Still I don't understand the second issue.
I am training a model in Keras with as follows:
model.fit(Xtrn, ytrn batch_size=16, epochs=50, verbose=1, shuffle=True,
callbacks=[model_checkpoint], validation_data=(Xval, yval))
The fitting output looks as follows:
As shown in the model.fit I have a batch size of 16 and a total of 8000 training samples as shown in the output. So from my understanding, training takes place every 16 batches. Which also means training is ran 500 times for a single epoch (i.e., 8000/16 =500)
So let's take the training accuracy printed in the output for Epoch 1/50, which in this case is 0.9381. I would like to know how is this training accuracy of 0.9381 derived.
Is it the:
Is the mean training accuracy, taken as the average from the 500 times training, performed for every batch?
OR,
Is it the best (or max) training accuracy from out of the 500 instances the training procedure is run?
Take a look at the BaseLogger in Keras where they're computing a running mean.
For each epoch the accuracy is the average of all the batches seen before in that epoch.
class BaseLogger(Callback):
"""Callback that accumulates epoch averages of metrics.
This callback is automatically applied to every Keras model.
"""
def on_epoch_begin(self, epoch, logs=None):
self.seen = 0
self.totals = {}
def on_batch_end(self, batch, logs=None):
logs = logs or {}
batch_size = logs.get('size', 0)
self.seen += batch_size
for k, v in logs.items():
if k in self.totals:
self.totals[k] += v * batch_size
else:
self.totals[k] = v * batch_size
def on_epoch_end(self, epoch, logs=None):
if logs is not None:
for k in self.params['metrics']:
if k in self.totals:
# Make value available to next callbacks.
logs[k] = self.totals[k] / self.seen
I am trying to understand a simple implementation of Softmax classifier from this link - CS231n - Convolutional Neural Networks for Visual Recognition. Here they implemented a simple softmax classifier. In the example of Softmax Classifier on the link, there are random 300 points on a 2D space and a label associated with them. The softmax classifier will learn which point belong to which class.
Here is the full code of the softmax classifier. Or you can see the link I have provided.
# initialize parameters randomly
W = 0.01 * np.random.randn(D,K)
b = np.zeros((1,K))
# some hyperparameters
step_size = 1e-0
reg = 1e-3 # regularization strength
# gradient descent loop
num_examples = X.shape[0]
for i in xrange(200):
# evaluate class scores, [N x K]
scores = np.dot(X, W) + b
# compute the class probabilities
exp_scores = np.exp(scores)
probs = exp_scores / np.sum(exp_scores, axis=1, keepdims=True) # [N x K]
# compute the loss: average cross-entropy loss and regularization
corect_logprobs = -np.log(probs[range(num_examples),y])
data_loss = np.sum(corect_logprobs)/num_examples
reg_loss = 0.5*reg*np.sum(W*W)
loss = data_loss + reg_loss
if i % 10 == 0:
print "iteration %d: loss %f" % (i, loss)
# compute the gradient on scores
dscores = probs
dscores[range(num_examples),y] -= 1
dscores /= num_examples
# backpropate the gradient to the parameters (W,b)
dW = np.dot(X.T, dscores)
db = np.sum(dscores, axis=0, keepdims=True)
dW += reg*W # regularization gradient
# perform a parameter update
W += -step_size * dW
b += -step_size * db
I cant understand how they computed the gradient here. I assume that they computed the gradient here -
dW = np.dot(X.T, dscores)
db = np.sum(dscores, axis=0, keepdims=True)
dW += reg*W # regularization gradient
But How? I mean Why gradient of dW is np.dot(X.T, dscores)? And Why the gradient of db is np.sum(dscores, axis=0, keepdims=True)?? So how they computed the gradient on weight and bias? Also why they computed the regularization gradient?
I am just starting to learn about convolutional neural networks and deep learning. And I heard that CS231n - Convolutional Neural Networks for Visual Recognition is a good starting place for that. I did not know where to place deep learning related post. So, i placed them on stackoverflow. If there is any place to post questions related to deep learning please let me know.
The gradients start being computed here:
# compute the gradient on scores
dscores = probs
dscores[range(num_examples),y] -= 1
dscores /= num_examples
First, this sets dscores equal to the probabilities computed by the softmax function. Then, it subtracts 1 from the probabilities computed for the correct classes in the second line, and then it divides by the number of training samples in the third line.
Why does it subtract 1? Because you want the probabilities of the correct labels to be 1, ideally. So it subtracts what it should predict from what it actually predicts: if it predicts something close to 1, the subtraction will be a large negative number (close to zero), so the gradient will be small, because you're close to a solution. Otherwise, it will be a small negative number (far from zero), so the gradient will be bigger, and you'll take larger steps towards the solution.
Your activation function is simply w*x + b. Its derivative with respect to w is x, which is why dW is the dot product between x and the gradient of the scores / output layer.
The derivative of w*x + b with respect to b is 1, which is why you simply sum dscores when backpropagating.
Gradient Descent
Backpropagation is to reduce the cost J of the entire system (softmax classifier here) and it is a problem to optimize the weight parameter W to minimize the cost. Providing the cost function J = f(W) is convex, the gradient descent W = W - α * f'(W) will result in the Wmin which minimizes J. The hyperparameter α is called learning rate which we need to optimize too, but not in this answer.
Y should be read as J in the diagram. Imagine you are on the surface of a place whose shape is defined as J = f(W) and you need to reach the point Wmin. There is no gravity so you do not know which way is toward the bottom but you know the function and your coordinate. How do you know which way you should go? You can find the direction from the derivative f'(W) and move to a new coordinate by W = W - α * f'(W). By repeating this, you can get closer and closer to the point Wmin.
Back propagation at Affin Layer
At the node where multiply or dot operation happens (affin), the function is J = f(W) = X * W. Suppose there are m number of fixed two dimensional coordinates represented as X. How can we find the hyper-plane which minimizes J = f(W) = X * W and its vector W?
We can get closer to the optimal W by repeating the gradient descent W += -α * X if α is appropriate.
Chain Rule
When there are layers after the Affine layer such as the softmax layer and the log loss layer in the softmax classifier, we can calculate the gradient with the chain rule. In the diagram, replace sigmoid with softmax.
As stated in Computing the Analytic Gradient with Backpropagation in the cs321 page, the gradient contribution from the softmax layer and the log loss layer is the dscore part. See the Note section below too.
By applying the gradient to that of the affine layer via the chain rule, the code is derived where α is replaced with step_size. In reality, the step_size needs to be learned as well.
dW = np.dot(X.T, dscores)
W += -step_size * dW
The bias gradient can be derived by applying the chain rule towards the bias b with the gradients (dscore) from the post layers.
db = np.sum(dscores, axis=0, keepdims=True)
Regularization
As stated in Regularization of the cs231 page, the cost function (objective) is adjusted by adding the regularization, which is reg_loss in the code. It is to reduce the over-fitting. The intuition is, in my understanding, if specific feature(s) cause overfitting, we can reduce it by inflating the cost with their weight parameters W, because the gradient descent will work to reduce the cost contributions from the weights. Since we do not know which ones, use all W. The reason of 0.5 * W*W is because it gives simple derivative W.
reg_loss = 0.5*reg*np.sum(W*W)
The gradient contribution reg*W is from the derivative of reg_loss. The reg is a hyper parameter to be learned in the real training.
reg_loss/dw -> 0.5 * reg * 2 * W
It is added to the gradient from the layers after the affin.
dW += reg*W # regularization gradient
The process to get the derivative from the cost including the regularization is omitted in the cs231 page referenced in the post, probably because it is a common practice to just put the gradient of the regularization, but confusing for those who are learning. See Coursera Machine Learning Week 3 Cost Function by Andrew Ng for the regularization.
Note
The bias parameter b is substituted with X0 as the bias can be omitted by shifting to the base.