Questions tagged [backpropagation]

For questions about the back-propagation (aka "backprop", and often abbreviated as "BP") algorithm, which is used to compute the gradient of the objective function (e.g. the mean squared error) with respect to the parameters (or weights) of the neural network, when trained with gradient descent.

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27
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5answers
26k views

What is the time complexity for training a neural network using back-propagation?

Suppose that a NN contains $n$ hidden layers, $m$ training examples, $x$ features, and $n_i$ nodes in each layer. What is the time complexity to train this NN using back-propagation? I have a basic ...
3
votes
1answer
1k views

Is back-propagation applied for each data point or for a batch of data points?

I am new to deep learning and trying to understand the concept of back-propagation. I have a doubt about when the back-propagation is applied. Assume that I have a training data set of 1000 images ...
4
votes
2answers
428 views

What exactly is averaged when doing batch gradient descent?

I have a question about how the averaging works when doing mini-batch gradient descent. I think I now understood the general gradient descent algorithm, but only for online learning. When doing mini-...
5
votes
3answers
977 views

What is the actual learning algorithm: back-propagation or gradient descent?

What is the actual learning algorithm: back-propagation or gradient descent (or, in general, the optimization algorithm)? I am reading through chapter 8 of Parallel Distributed Processing hand book ...
0
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1answer
80 views

Is my backpropagation code correct? [closed]

I am trying to implement the back-propagation algorithm for the following neural network. ...
7
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1answer
7k views

How is the gradient calculated for the middle layer's weights?

I am trying to understand backpropagation. I used a simple neural network with one input $x$, one hidden layer $h$ and one output layer $y$, with weight $w_1$ connecting $x$ to $h$, and $w_2$ ...
10
votes
2answers
589 views

What are the learning limitations of neural networks trained with backpropagation?

In 1969, Seymour Papert and Marvin Minsky showed that Perceptrons could not learn the XOR function. This was solved by the backpropagation network with at least one hidden layer. This type of network ...
6
votes
1answer
3k views

How to avoid falling into the “local minima” trap?

How do I avoid my gradient descent algorithm into falling into the "local minima" trap while backpropogating on my neural network? Are there any methods which help me avoid it?
5
votes
2answers
691 views

In deep learning, is it possible to use discontinuous activation functions?

In deep learning, is it possible to use discontinuous activation functions (e.g. one with jump discontinuity)? (My guess: for example, ReLU is non-differentiable at a single point, but it still has a ...
3
votes
1answer
328 views

Hand computing feed forward and back propagation of neural network

I used to treat back propagation as a black box but lately I want to understand more about it. I have used mattmuzr's and DuttA's explanaiton as a guide to hand compute a simple neural network. I have ...
1
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1answer
168 views

Do you need to store prevous values of weights and layers on recurrent layer while BPTT?

The Back propagation through time on recurrent layer is defined similar to normal one, means somethin like ...
7
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3answers
827 views

How do I know if my backpropagation is implemented correctly?

I'm working on implementation of the backpropagation algorithm for a simple neural network which predicts a probability of survival (1 or 0) and I can't get it above 80% no matter how much I try to ...
5
votes
1answer
184 views

What, exactly, does the REINFORCE update equation mean?

I understand that this is the update for the parameters of a policy in REINFORCE: $$ \Delta \theta_{t}=\alpha \nabla_{\theta} \log \pi_{\theta}\left(a_{t} \mid s_{t}\right) v_{t} $$ Where 𝑣𝑡 is ...
4
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1answer
114 views

Why is my derivation of the back-propagation equations inconsistent with Andrew Ng's slides from Coursera?

I am using the cross-entropy cost function to calculate its derivatives using different variables $Z, W$ and $b$ at different instances. Please refer image below for calculation. As per my knowledge, ...
3
votes
1answer
388 views

How does backpropagation work on a custom loss function whose components have magnitudes of different orders?

I want to use a custom loss function which is a weighted combination of l1 and DSSIM losses. The DSSIM loss is limited between 0 and 0.5 where as the l1 loss can be orders of magnitude greater and is ...
3
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1answer
2k views

How do I calculate the gradient of the hinge loss function?

With reference to the research paper entitled Sentiment Embeddings with Applications to Sentiment Analysis, I am trying to implement its sentiment ranking model in Python, for which I am required to ...
3
votes
2answers
116 views

Are filters fixed or learned?

No matter what I google or what paper I read, I can't find an answer to my question. In a deep convolutional neural network, let's say AlexNet (Krizhevsky, 2012), filters' weights are learned by means ...
2
votes
1answer
42 views

How to improve a trained model over time (i.e. with more predictions)?

I built a model using the tutorial on the TensorFlow site. It was a simple image classification neural network. I trained it and saved the model and weights together on a ...
2
votes
3answers
125 views

Why does a neuron in a multi-layer network need several input connections?

For example, if I have the following architecture: Each neuron in the hidden layer has a connection from each one in the input layer. 3 x 1 Input Matrix and a 4 x 3 weight matrix (for the ...
1
vote
1answer
638 views

How should I implement the backward pass through a flatten layer of a CNN?

I am making a NN library without any other external NN library, so I am implementing all layers, including the flatten layer, and algorithms (forward and backward pass) from scratch. I know the ...