Is it correct to add dropout to each layer and that it is done as in the below example? class MyHyperModel(kt.HyperModel): def build_model(self, hp): model = Sequential() for i in range(hp.Int('dense_layers',1,4)): model.add(Dense(hp.Choice('units', choice_units), hp.Choice("activation", ["elu", "exponential", "relu"]))) **model.add(layers.Dropout(hp.Choice('rate',[0.0,0.05,0.10,0.15,0.25])))** model.add(Dense(1, hp.Choice("activation", ["elu", "relu"]))) optimizer=tf.keras.optimizers.SGD(hp.Float('learning_rate',min_value=1e-6, max_value=1e-3,default=1e-5)) model.compile(loss='mse', optimizer=optimizer, metrics=['mse']) return model I.e. after each Dense layer, by adding model.add(layers.Dropout(hp.Choice('rate',[0.0,0.05,0.10,0.15,0.25]))) it will add dropout to each new Dense layer. Is this true? And if I wanted to vary the choice of dropout layer …
If one is training a basic FFNN (Feed-Forward Neural Network), one would apply regularizations like dropout, l1, l2 and gaussian noise, so that the model is robust and gives better results for unseen data. But my question is, once the model gives fairly good results, isn't it advisable to remove the reguarizations then train the model again for some time, so that its predictions are more accurate?
If you take the following sentence from an article on deep neural networks to regularize the classifier layer by estimating the marginalized effect of label-dropout during training. What does label-dropout mean
I have 100 samples having following data [1, 2, 3, 4] => [4, 8] [5, 6, 7, 8] => [12, 48] [9, 10, 11, 12] => [20, 120] ... [397, 398, 399, 400] => [796, 159200] Data on left of => is training data and output is 2 timestamp which is (0th element + 2nd element, 1st element*3rd element) Ex. Given:[1, 2, 3, 4] Solution: 1+3=4, 2*4=8. So output of [1, 2, 3, 4] is [4,8] And my model is …
For any layer in my neural net, should I apply dropout onto an entering vector, or on the pre-activated vector? In other words: $$\vec q=W\cdot \vec x$$ $$\vec h = activate(drop(\vec q))$$ or: $$\vec q=W\cdot (drop(\vec x)) $$ $$ \vec h = activate(\vec q)$$ I think the second variant is smoother (none of our current vector is fully dropped out, but is assembled from a mix of the dropped-out input) and is therefore softer.
I have a simple LSTM network developped using keras: model = Sequential() model.add(LSTM(rnn_size,input_shape=(2,w),dropout = 0.25 , recurrent_dropout=0.25)) model.add(Dense(2)) I would like to apply the MC dropout method. How can I enable dropout in the test phase in order to compute the uncertainty? Thanks.
I am a bit confused about the relation between terms "Dropout" and "BatchNorm". As I understand, Dropout is regularization technique, which is using only during training. BatchNorm is technique, which is using for accelerating training speed, improving accuracy and e.t.c. But I also saw some conflicting opinions about question: is BatchNorm regularization technique? So, can somebody,please, answer some questions: Is BatchNorm regularization technique? Why? Should we use BatchNorm only during training process? Why? Can we use Dropout and BatchNorm simultaneously? …
I am currently trying to build a CNN for around 100,000 images. There are 42 classes. I have used the default batch size of 32. This is how my model looks like: model = Sequential() model.add(Conv2D(filters = 32, kernel_size = (3, 3), activation = 'relu', input_shape = training_data.image_shape)) model.add(MaxPool2D(pool_size = (2, 2))) model.add(Dropout(rate = 0.3)) model.add(Conv2D(filters = 64, kernel_size = (3, 3), activation = 'relu')) model.add(MaxPool2D(pool_size = (2, 2))) model.add(Dropout(rate = 0.2)) model.add(Conv2D(filters = 126, kernel_size = (3, 3), activation …
I am playing a little with convnets. Specifically, I am using the kaggle cats-vs-dogs dataset which consists on 25000 images labeled as either cat or dog (12500 each). I've managed to achieve around 85% classification accuracy on my test set, however I set a goal of achieving 90% accuracy. My main problem is overfitting. Somehow it always ends up happening (normally after epoch 8-10). The architecture of my network is loosely inspired by VGG-16, more specifically my images are resized …
Say, one uses the MNIST dataset and splits the provided training data of size 60,000 into a training set (50,000) and a validation set (10,000). The provided test data of size 10,000 is used as the test set. The ML algorithm is a neural network. The training set is processed (in minibatches) by the code below. First, one sets the gradients to zero. Then, the model makes a prediction, and the loss is calculated. Next, the gradients are computed, and …
I am pretty new to Python and this board so I am not sure, if I am at the right place for my question since it doesn't include any code. If not so, please give my a hint for a better way/place to ask. I am struggling with using Monte Carlo Dropouts for determine Uncertainty for my image classificator using ResNet18. I have read several papers to this topic and I am still kinda confused about this topic. I know …
I am working on a problem of multi-class classification by MLP. I have set dropout to each middle layer. Now I observe the training accuracy is around 10% less than the testing accuracy. My guess is, dropout is active only during training but inactive during testing. So part of the neurons are reset at training (leading to low accuracy), but it is not happening for testing. My questions: Is my understanding correct? In other words, if I remove the dropout …
Dropout (paper, explanation) sets the output of some neurons to zero. So for a MLP, you could have the following architecture for the Iris flower dataset: 4 : 50 (tanh) : dropout (0.5) : 20 (tanh) : 3 (softmax) It would work like this: $$softmax(W_3 \cdot \tanh(W_2 \cdot \text{mask}(D, \tanh(W_1 \cdot input\_vector)))$$ with $input\_vector \in \mathbb{R}^{4 \times 1}$, $W_1 \in \mathbb{R}^{50 \times 4}$, $D \in \{0, 1\}^{50 \times 1}$, $W_2 \in \mathbb{R}^{20 \times 50}$, $W_3 \in \mathbb{R}^{20 \times 3}$ (ignoring …
I have read and seen that in CNN we apply DROPOUT layer between the FULLY CONNECTED layers to reduce overfitting. Can we also apply the dropout layer between the CONV layers and the POOL layers. I have not seen models with this method applied. Will it help in overfitting when applied between these layers or are there any disadvantages to it?
I understand how to use MC dropout from this answer, but I don't understand how MC dropout works, what its purpose is, and how it differs from normal dropout.
In financial markets, there is a simple problem of trading calendars varying across different countries. For example, Sweden observes Sweden National Day and Norway has Whit Monday. Typically, what happens then is that a time-series in the equity market that was closed for a holiday 'catches up' the next day when the market is open again. For example: +-----------------+------------+------------+------------+------------+ | Date | SEK 1 | SEK 2 | NOK 1 | NOK 2 | +-----------------+------------+------------+------------+------------+ | Date 1 | + …
In Keras, there are 2 methods to reduce over-fitting. L1,L2 regularization or dropout layer. What are some situations to use L1,L2 regularization instead of dropout layer? What are some situations when dropout layer is better?
I have some excessive amount of data for the size of NN I am able to teach in a reasonable time. If I feed all the data into the network it stops learning at some point and a resulting model shows all signs of being overfit. Intuitively if I increase dropout prob the model should learn less aggressively from data and gain from more data being fed into it. Is my logic sound?
If removing some neurons results in a better performing model, why not use a simpler neural network with fewer layers and fewer neurons in the first place? Why build a bigger, more complicated model in the beginning and suppress parts of it later?