IDIFU Womens Glitter Sequins High Chunky Heel Slide Sandals Peep Toe Platform Mules Black bMXAaZId

B01H68FA80
IDIFU Womens Glitter Sequins High Chunky Heel Slide Sandals Peep Toe Platform Mules Black bMXAaZId
  • fabric
  • Rubber sole
  • Platform measures approximately 1.25
  • Heel measurement: about 4.3 inches; Platform measurement: around 1.25 inches; Comfortable and soft
  • Glitter sequins decorated; High chunky heel, peep toe, slip on and platform design
  • Durable, breathable and anti-skid; Healthy and safe for your feet; Well protect your feet
  • Suitable for summer, beach and holiday; A good gift for your better half and good friends
  • Fitted with any other charming outfits; Add more charming details to your appearance
IDIFU Womens Glitter Sequins High Chunky Heel Slide Sandals Peep Toe Platform Mules Black bMXAaZId IDIFU Womens Glitter Sequins High Chunky Heel Slide Sandals Peep Toe Platform Mules Black bMXAaZId

ENG

Search

Menú
Company Women CTAS Gemma Hi Color White/Mouse/White Size 75 2lLEtUq94s

May 21, 2015

There’s something magical about Recurrent Neural Networks (RNNs). I still remember when I trained my first recurrent network for Image Captioning . Within a few dozen minutes of training my first baby model (with rather arbitrarily-chosen hyperparameters) started to generate very nice looking descriptions of images that were on the edge of making sense. Sometimes the ratio of how simple your model is to the quality of the results you get out of it blows past your expectations, and this was one of those times. What made this result so shocking at the time was that the common wisdom was that RNNs were supposed to be difficult to train (with more experience I’ve in fact reached the opposite conclusion). Fast forward about a year: I’m training RNNs all the time and I’ve witnessed their power and robustness many times, and yet their magical outputs still find ways of amusing me. This post is about sharing some of that magic with you.

We’ll train RNNs to generate text character by character and ponder the question “how is that even possible?”

By the way, together with this post I am also releasing code on Github that allows you to train character-level language models based on multi-layer LSTMs. You give it a large chunk of text and it will learn to generate text like it one character at a time. You can also use it to reproduce my experiments below. But we’re getting ahead of ourselves; What are RNNs anyway?

Recurrent Neural Networks

Sequences . Depending on your background you might be wondering: What makes Recurrent Networks so special ? A glaring limitation of Vanilla Neural Networks (and also Convolutional Networks) is that their API is too constrained: they accept a fixed-sized vector as input (e.g. an image) and produce a fixed-sized vector as output (e.g. probabilities of different classes). Not only that: These models perform this mapping using a fixed amount of computational steps (e.g. the number of layers in the model). The core reason that recurrent nets are more exciting is that they allow us to operate over sequences of vectors: Sequences in the input, the output, or in the most general case both. A few examples may make this more concrete:

Sequences
Each rectangle is a vector and arrows represent functions (e.g. matrix multiply). Input vectors are in red, output vectors are in blue and green vectors hold the RNN's state (more on this soon). From left to right: Vanilla mode of processing without RNN, from fixed-sized input to fixed-sized output (e.g. image classification). Sequence output (e.g. image captioning takes an image and outputs a sentence of words). Sequence input (e.g. sentiment analysis where a given sentence is classified as expressing positive or negative sentiment). Sequence input and sequence output (e.g. Machine Translation: an RNN reads a sentence in English and then outputs a sentence in French). Synced sequence input and output (e.g. video classification where we wish to label each frame of the video). Notice that in every case are no pre-specified constraints on the lengths sequences because the recurrent transformation (green) is fixed and can be applied as many times as we like.

As you might expect, the sequence regime of operation is much more powerful compared to fixed networks that are doomed from the get-go by a fixed number of computational steps, and hence also much more appealing for those of us who aspire to build more intelligent systems. Moreover, as we’ll see in a bit, RNNs combine the input vector with their state vector with a fixed (but learned) function to produce a new state vector. This can in programming terms be interpreted as running a fixed program with certain inputs and some internal variables. Viewed this way, RNNs essentially describe programs. In fact, it is known that RNNs are Turing-Complete in the sense that they can to simulate arbitrary programs (with proper weights). But similar to universal approximation theorems for neural nets you shouldn’t read too much into this. In fact, forget I said anything.

You are using an outdated browser. Please KEESKY Quick Drying Water Shoes Barefoot Mesh Aqua Shoes For Men and Women Rose Red qTqWy0
to improve your experience.

MEANINGFUL PUBLIC PARTICIPATION

A cloud platform for authentic citizen engagement on legislation. Legislation Lab is supported by GovRight's expertise and support in the field of online participation.

Vans Unisex Sk8 Hi Slim Shoes Leopard Herringbone Skate Shoes US Mens Size 95/Womens 11 ReNIBaHU0O

HUGS IDEA Animals Pattern Fashion Womens Lightweight Running Sneakers Purple Leopard VJa5oM5

Comprehensive technology and expertise engaging your citizen audience Your own, free legal reform initiative online

A New Citizen Voice

Methodology and technology designed to give citizens a voice in the legislative process.

Participatory

Citizens voice their opinions and ideas on specific parts of legislation.

International

Available in many languages, and easily translated to many more.

Analytical

Data analysis for understanding the opinions and demographics of participants.

Authentic

A transparent participation model, proving authenticity through transparency.

Legislation Lab's methodology is based on GovRight's experience with constitutional crowdsourcing.

Bills of NY City Council

New Yorkers are invited to join the New York online community to co-draft the laws of the City Council.

Constitution of Chile

La Constitution De Todos is a citizen initiative that aims to raise awareness and promote public dialogue about the Chilean constitution.

Constitution of Kurdistan

Dastoori Kurdistan engages Kurdish citizens on the constitutional drafting process. The findings will be submitted to the Kurdish parliament as a contribution on the drafting process.

GovRight is a group of specialists in technology, law, and policy working to improve the relationship between governments, organizations and the public.

Legislation Lab is a product of DStory Custom Shoes Grunge USA Flag High Top Retro Womens Sneaker Multicoloured23 mvratM
and © GovRight 2015

Display footer
Copyright Illustrated Light 2018