lasagne.readthedocs.io

Welcome to Lasagne — Lasagne 0.2.dev1 documentation

Lasagne is a lightweight library to build and train neural networks in Theano. Lasagne is a work in progress, input is welcome. The available documentation is limited for now. The project is on GitHub. The Lasagne user guide explains how to install Lasagne, how to build and train neural networks using Lasagne, and how to contribute to the library as a developer. Run the MNIST example. Understand the MNIST example. Where to go from here. Propagating data through layers. A layer that changes the shape.

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Welcome to Lasagne — Lasagne 0.2.dev1 documentation | lasagne.readthedocs.io Reviews

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Lasagne is a lightweight library to build and train neural networks in Theano. Lasagne is a work in progress, input is welcome. The available documentation is limited for now. The project is on GitHub. The Lasagne user guide explains how to install Lasagne, how to build and train neural networks using Lasagne, and how to contribute to the library as a developer. Run the MNIST example. Understand the MNIST example. Where to go from here. Propagating data through layers. A layer that changes the shape.

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Welcome to Lasagne — Lasagne 0.2.dev1 documentation | lasagne.readthedocs.io Reviews

https://lasagne.readthedocs.io

Lasagne is a lightweight library to build and train neural networks in Theano. Lasagne is a work in progress, input is welcome. The available documentation is limited for now. The project is on GitHub. The Lasagne user guide explains how to install Lasagne, how to build and train neural networks using Lasagne, and how to contribute to the library as a developer. Run the MNIST example. Understand the MNIST example. Where to go from here. Propagating data through layers. A layer that changes the shape.

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lasagne.readthedocs.io

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lasagne.updates — Lasagne 0.2.dev1 documentation

http://lasagne.readthedocs.io/en/latest/modules/updates.html

Functions to generate Theano update dictionaries for training. The update functions implement different methods to control the learning rate for use with stochastic gradient descent. Update functions take a loss expression or a list of gradient expressions and a list of parameters as input and return an ordered dictionary of updates:. Stochastic Gradient Descent (SGD) updates. Stochastic Gradient Descent (SGD) updates with momentum. Stochastic Gradient Descent (SGD) updates with Nesterov momentum. A dict...

2

lasagne.regularization — Lasagne 0.2.dev1 documentation

http://lasagne.readthedocs.io/en/latest/modules/regularization.html

Functions to apply regularization to the weights in a network. We provide functions to calculate the L1 and L2 penalty. Penalty functions take a tensor as input and calculate the penalty contribution from that tensor:. Computes the L1 norm of a tensor. Computes the squared L2 norm of a tensor. A helper function can be used to apply a penalty function to a tensor or a list of tensors:. Computes the total cost for applying a specified penalty to a tensor or group of tensors. Regularize layer params weighted.

3

lasagne.utils — Lasagne 0.2.dev1 documentation

http://lasagne.readthedocs.io/en/latest/modules/utils.html

Converts data to a numpy array of dtype. The data to be converted. The input array in the. Dtype configured for Theano. If. Is an ndarray of correct dtype, it is returned as is. Creates empty Theano shared variable. Shortcut to create an empty Theano shared variable with the specified number of dimensions. The number of dimensions for the empty variable, defaults to 2. A numpy data-type, optional. The desired dtype for the variable. Defaults to the Theano. An empty Theano shared variable of dtype. The nu...

4

Installation — Lasagne 0.2.dev1 documentation

http://lasagne.readthedocs.io/en/latest/user/installation.html

Lasagne has a couple of prerequisites that need to be installed first, but it is not very picky about versions. The single exception is Theano: Due to its tight coupling to Theano, you will have to install a recent version of Theano (usually more recent than the latest official release! Fitting the version of Lasagne you choose to install. If you run into any trouble, please check the Theano installation instructions. Or into a virtual environment. On Linux, the default compiler is usually. Lasagne requi...

5

Tutorial — Lasagne 0.2.dev1 documentation

http://lasagne.readthedocs.io/en/latest/user/tutorial.html

Run the MNIST example. Understand the MNIST example. Convolutional Neural Network (CNN). Loss and update expressions. Where to go from here. This tutorial will walk you through building a handwritten digits classifier using the MNIST dataset, arguably the “Hello World” of neural networks. More tutorials and examples can be found in the Lasagne Recipes. For a more slow-paced introduction to artificial neural networks, we recommend Convolutional Neural Networks for Visual Recognition. Run the MNIST example.

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The simple example of Theano and Lasagne super power – Grzegorz Gwardys

https://grzegorzgwardys.wordpress.com/2016/05/15/the-simple-example-of-theano-and-lasagne-super-power

Experience with Machine/Deep Learning and more …. The simple example of Theano and Lasagne super power. May 15, 2016. May 16, 2016. I mentioned in my initial post “ Deep Learning Frameworks Overview. 8221; that my choice of Deep Learning library is (at least for now) Theano. Combination. However, I did not use, in all my post, some most important word: the experiment. Some extra “convolutional branch”, that is concatenated with last but one layer:. Input var) l conv1 =. Conv2DLayer( l in, num filters =.

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Combining CNN and RNN for spoken language identification · YerevaNN

http://yerevann.github.io/2016/06/26/combining-cnn-and-rnn-for-spoken-language-identification

Blog on neural networks. Combining CNN and RNN for spoken language identification. Last year Hrayr used convolutional networks to identify spoken language. From short audio recordings for a TopCoder contest. And got 95% accuracy. After the end of the contest we decided to try recurrent neural networks and their combinations with CNNs on the same task. The best combination allowed to reach 99.24% and an ensemble of 33 models reached 99.67%. This work became Hrayr’s bachelor’s thesis. We have tested severa...

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June | 2016 | N 2 Infinity and Beyond

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N 2 Infinity and Beyond. N 2 Infinity and Beyond. A physics PhD's adventures in computational neuroscience, machine learning, biophysics, and beyond. Follow Blog via Email. Enter your email address to follow this blog and receive notifications of new posts by email. Join 5 other followers. June 15, 2016. I3: International Institute for Intelligence. While I was previously discussing my opinion of Open AI. I mentioned that I would do something different if I was in charge. Here is my dream. Additionally, ...

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deep learning | N 2 Infinity and Beyond

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N 2 Infinity and Beyond. N 2 Infinity and Beyond. A physics PhD's adventures in computational neuroscience, machine learning, biophysics, and beyond. Follow Blog via Email. Enter your email address to follow this blog and receive notifications of new posts by email. Join 5 other followers. February 23, 2017. Deep Learning Seminar Course. This semester Terry Sejnowski. Pollack, JB, Book Review, Perceptrons. Minsky, M. and Papert, S., Perceptrons, expanded edition, Prologue and Epilogue, 1987. Hinton, G&#4...

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May 2016 – Grzegorz Gwardys

https://grzegorzgwardys.wordpress.com/2016/05

Experience with Machine/Deep Learning and more …. The simple example of Theano and Lasagne super power. May 15, 2016. May 16, 2016. I mentioned in my initial post “ Deep Learning Frameworks Overview. 8221; that my choice of Deep Learning library is (at least for now) Theano. Combination. However, I did not use, in all my post, some most important word: the experiment. Some extra “convolutional branch”, that is concatenated with last but one layer:. Input var) l conv1 =. Conv2DLayer( l in, num filters =.

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Experts | N 2 Infinity and Beyond

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N 2 Infinity and Beyond. N 2 Infinity and Beyond. A physics PhD's adventures in computational neuroscience, machine learning, biophysics, and beyond. Follow Blog via Email. Enter your email address to follow this blog and receive notifications of new posts by email. Join 5 other followers. February 23, 2017. Deep Learning Seminar Course. This semester Terry Sejnowski. Pollack, JB, Book Review, Perceptrons. Minsky, M. and Papert, S., Perceptrons, expanded edition, Prologue and Epilogue, 1987. Hinton, G&#4...

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Welcome to Lasagne — Lasagne 0.2.dev1 documentation

Lasagne is a lightweight library to build and train neural networks in Theano. Lasagne is a work in progress, input is welcome. The available documentation is limited for now. The project is on GitHub. The Lasagne user guide explains how to install Lasagne, how to build and train neural networks using Lasagne, and how to contribute to the library as a developer. Run the MNIST example. Understand the MNIST example. Where to go from here. Propagating data through layers. A layer that changes the shape.

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