Questions tagged [neural-network]

Artificial neural networks (ANN), are composed of 'neurons' - programming constructs that mimic the properties of biological neurons. A set of weighted connections between the neurons allows information to propagate through the network to solve artificial intelligence problems without the network designer having had a model of a real system.

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265
votes
11answers
244k views

What are deconvolutional layers?

I recently read Fully Convolutional Networks for Semantic Segmentation by Jonathan Long, Evan Shelhamer, Trevor Darrell. I don't understand what "deconvolutional layers" do / how they work. The ...
170
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5answers
115k views

What is the "dying ReLU" problem in neural networks?

Referring to the Stanford course notes on Convolutional Neural Networks for Visual Recognition, a paragraph says: "Unfortunately, ReLU units can be fragile during training and can "die". For ...
165
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6answers
254k views

How to draw Deep learning network architecture diagrams?

I have built my model. Now I want to draw the network architecture diagram for my research paper. Example is shown below:
160
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6answers
147k views

When to use GRU over LSTM?

The key difference between a GRU and an LSTM is that a GRU has two gates (reset and update gates) whereas an LSTM has three gates (namely input, output and forget gates). Why do we make use of GRU ...
149
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17answers
124k views

Best python library for neural networks

I'm using Neural Networks to solve different Machine learning problems. I'm using Python and pybrain but this library is almost discontinued. Are there other good alternatives in Python?
141
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18answers
158k views

How do you visualize neural network architectures?

When writing a paper / making a presentation about a topic which is about neural networks, one usually visualizes the networks architecture. What are good / simple ways to visualize common ...
110
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10answers
111k views

Choosing a learning rate

I'm currently working on implementing Stochastic Gradient Descent, SGD, for neural nets using back-propagation, and while I understand its purpose I have some ...
96
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4answers
59k views

Backprop Through Max-Pooling Layers?

This is a small conceptual question that's been nagging me for a while: How can we back-propagate through a max-pooling layer in a neural network? I came across max-pooling layers while going through ...
82
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4answers
38k views

How are 1x1 convolutions the same as a fully connected layer?

I've recently read Yan LeCuns comment on 1x1 convolutions: In Convolutional Nets, there is no such thing as "fully-connected layers". There are only convolution layers with 1x1 convolution kernels ...
79
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1answer
73k views

When to use (He or Glorot) normal initialization over uniform init? And what are its effects with Batch Normalization?

I knew that Residual Network (ResNet) made He normal initialization popular. In ResNet, He normal initialization is used , while the first layer uses He uniform initialization. I've looked through ...
68
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7answers
130k views

Cross-entropy loss explanation

Suppose I build a neural network for classification. The last layer is a dense layer with Softmax activation. I have five different classes to classify. Suppose for a single training example, the <...
66
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4answers
26k views

What is the difference between "equivariant to translation" and "invariant to translation"

I'm having trouble understanding the difference between equivariant to translation and invariant to translation. In the book Deep Learning. MIT Press, 2016 (I. Goodfellow, A. Courville, and Y. Bengio)...
64
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5answers
40k views

Adding Features To Time Series Model LSTM

have been reading up a bit on LSTM's and their use for time series and its been interesting but difficult at the same time. One thing I have had difficulties with understanding is the approach to ...
61
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5answers
73k views

RNN vs CNN at a high level

I've been thinking about the Recurrent Neural Networks (RNN) and their varieties and Convolutional Neural Networks (CNN) and their varieties. Would these two points be fair to say: Use CNNs to break ...
60
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5answers
96k views

What is the difference between Gradient Descent and Stochastic Gradient Descent?

What is the difference between Gradient Descent and Stochastic Gradient Descent? I am not very familiar with these, can you describe the difference with a short example?
58
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5answers
52k views

Neural networks: which cost function to use?

I am using TensorFlow for experiments mainly with neural networks. Although I have done quite some experiments (XOR-Problem, MNIST, some Regression stuff, ...) now, I struggle with choosing the "...
56
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2answers
55k views

What is the difference between LeakyReLU and PReLU?

I thought both, PReLU and Leaky ReLU are $$f(x) = \max(x, \alpha x) \qquad \text{ with } \alpha \in (0, 1)$$ Keras, however, has both functions in the docs. Leaky ReLU Source of LeakyReLU: ...
56
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3answers
23k views

How to fight underfitting in a deep neural net

When I started with artificial neural networks (NN) I thought I'd have to fight overfitting as the main problem. But in practice I can't even get my NN to pass the 20% error rate barrier. I can't even ...
52
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2answers
43k views

Sparse_categorical_crossentropy vs categorical_crossentropy (keras, accuracy)

Which is better for accuracy or are they the same? Of course, if you use categorical_crossentropy you use one hot encoding, and if you use ...
51
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9answers
69k views

Why should the data be shuffled for machine learning tasks

In machine learning tasks it is common to shuffle data and normalize it. The purpose of normalization is clear (for having same range of feature values). But, after struggling a lot, I did not find ...
49
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5answers
123k views

How to get accuracy, F1, precision and recall, for a keras model?

I want to compute the precision, recall and F1-score for my binary KerasClassifier model, but don't find any solution. Here's my actual code: ...
48
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5answers
26k views

How do subsequent convolution layers work?

This question boils down to "how do convolution layers exactly work. Suppose I have an $n \times m$ greyscale image. So the image has one channel. In the first layer, I apply a $3\times 3$ ...
43
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2answers
62k views

Why not always use the ADAM optimization technique?

It seems the Adaptive Moment Estimation (Adam) optimizer nearly always works better (faster and more reliably reaching a global minimum) when minimising the cost function in training neural nets. Why ...
43
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3answers
85k views

What is Ground Truth

In the context of Machine Learning, I have seen the term Ground Truth used a lot. I have searched a lot and found the following definition in Wikipedia: In machine learning, the term "ground ...
42
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13answers
23k views

Data science related funny quotes [closed]

It has been customary for the users of different communities to quote funny things about their fields. It may be fun to share your funny things about Machine Learning, Deep Learning, Data Science and ...
42
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2answers
41k views

How to prepare/augment images for neural network?

I would like to use a neural network for image classification. I'll start with pre-trained CaffeNet and train it for my application. How should I prepare the input images? In this case, all the ...
40
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5answers
49k views

Does gradient descent always converge to an optimum?

I am wondering whether there is any scenario in which gradient descent does not converge to a minimum. I am aware that gradient descent is not always guaranteed to converge to a global optimum. I am ...
40
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1answer
27k views

What loss function to use for imbalanced classes (using PyTorch)?

I have a dataset with 3 classes with the following items: Class 1: 900 elements Class 2: 15000 elements Class 3: 800 elements I need to predict class 1 and class 3, which signal important ...
39
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1answer
19k views

The difference between `Dense` and `TimeDistributedDense` of `Keras`

I am still confused about the difference between Dense and TimeDistributedDense of Keras ...
39
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3answers
45k views

Choosing between CPU and GPU for training a neural network

I've seen discussions about the 'overhead' of a GPU, and that for 'small' networks, it may actually be faster to train on a CPU (or network of CPUs) than a GPU. What is meant by 'small'? For ...
38
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3answers
24k views

Why is ReLU used as an activation function?

Activation functions are used to introduce non-linearities in the linear output of the type w * x + b in a neural network. Which I am able to understand ...
37
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6answers
47k views

Are there free cloud services to train machine learning models?

I want to train a deep model with a large amount of training data, but my desktop does not have that power to train such a deep model with these abundant data. I'd like to know whether there are any ...
37
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1answer
55k views

How does Keras calculate accuracy?

How does Keras calculate accuracy from the classwise probabilities? Say, for example we have 100 samples in the test set which can belong to one of two classes. We also have a list of the classwise ...
36
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6answers
8k views

How to set the number of neurons and layers in neural networks

I am a beginner to neural networks and have had trouble grasping two concepts: How does one decide the number of middle layers a given neural network have? 1 vs. 10 or whatever. How does one decide ...
34
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2answers
46k views

How to calculate mAP for detection task for the PASCAL VOC Challenge?

How to calculate the mAP (mean Average Precision) for the detection task for the Pascal VOC leaderboards? There said - at page 11: Average Precision (AP). For the VOC2007 challenge, the interpolated ...
34
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3answers
16k views

Early stopping on validation loss or on accuracy?

I am currently training a neural network and I cannot decide which to use to implement my Early Stopping criteria: validation loss or a metrics like accuracy/f1score/auc/whatever calculated on the ...
33
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2answers
45k views

Should we apply normalization to test data as well?

I am doing a project on author identification problem. I had applied the tf-idf normalization to train data and then trained a svm on that data. Now when using the classifier should I normalize test ...
33
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1answer
25k views

How to decide neural network architecture?

I was wondering how do we have to decide how many nodes in hidden layers, and how many hidden layers to put when we build a neural network architecture. I understand the input and output layer ...
32
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6answers
13k views

Why do convolutional neural networks work?

I have often heard people saying that why convolutional neural networks are still poorly understood. Is it known why convolutional neural networks always end up learning increasingly sophisticated ...
32
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1answer
66k views

What is the best Keras model for multi-class classification?

I am working on research, where need to classify one of three event WINNER=(win, draw, lose) ...
32
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4answers
33k views

What is the meaning of "The number of units in the LSTM cell"?

From Tensorflow code: Tensorflow. RnnCell. num_units: int, The number of units in the LSTM cell. I can't understand what this means. What are the units of LSTM ...
31
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2answers
30k views

Why use both validation set and test set?

Consider a neural network: For a given set of data, we divide it into training, validation and test set. Suppose we do it in the classic 60:20:20 ratio, then we prevent overfitting by validating the ...
31
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1answer
32k views

RNN's with multiple features

I have a bit of self taught knowledge working with Machine Learning algorithms (the basic Random Forest and Linear Regression type stuff). I decided to branch out and begin learning RNN's with Keras. ...
31
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4answers
24k views

Neural Network parse string data?

So, I'm just starting to learn how a neural network can operate to recognize patterns and categorize inputs, and I've seen how an artificial neural network can parse image data and categorize the ...
31
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4answers
36k views

Guidelines for selecting an optimizer for training neural networks

I have been using neural networks for a while now. However, one thing that I constantly struggle with is the selection of an optimizer for training the network (using backprop). What I usually do is ...
30
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2answers
32k views

Are there any rules for choosing the size of a mini-batch?

When training neural networks, one hyperparameter is the size of a minibatch. Common choices are 32, 64, and 128 elements per mini batch. Are there any rules/guidelines on how big a mini-batch should ...
29
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8answers
7k views

Why is it wrong to train and test a model on the same dataset?

What are the pitfalls of doing so and why is it a bad practice? Is it possible that the model starts to learn the images by heart instead of understanding the underlying logic?
28
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4answers
11k views

Role derivative of sigmoid function in neural networks

I try to understand role of derivative of sigmoid function in neural networks. First I plot sigmoid function, and derivative of all points from definition using python. What is the role of this ...
28
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3answers
47k views

Neural Network for Multiple Output Regression

I have a dataset containing 34 input columns and 8 output columns. One way to solve the problem is to take the 34 inputs and build individual regression model for each output column. I am wondering ...
26
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3answers
2k views

Why are NLP and Machine Learning communities interested in deep learning?

I hope you can help me, as I have some questions on this topic. I'm new in the field of deep learning, and while I did some tutorials, I can't relate or distinguish concepts from one another.

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