convnetjs | Deep Learning in Javascript | Machine Learning library
kandi X-RAY | convnetjs Summary
kandi X-RAY | convnetjs Summary
Deep Learning in Javascript. Train Convolutional Neural Networks (or ordinary ones) in your browser.
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Top functions reviewed by kandi - BETA
- Draw the grid .
- Creates a new filter window .
- resize image
- helper function to convert data to array
- Convenience convolution logic .
- Convenience convolution logic
- Step 1 .
- Shows an effect .
- resize canvas
- Resizes the image .
convnetjs Key Features
convnetjs Examples and Code Snippets
convnet = fully_connected(convnet, 1, activation='sigmoid')
convnet = regression(convnet, optimizer='adam', learning_rate=learningRate, loss='binary_crossentropy', name='targets')
convnet = dropout(convnet, 0.7)
import tensorflow as tf
import tflearn
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.estimator import regression
from tflearn.layers.merge_ops
Community Discussions
Trending Discussions on convnetjs
QUESTION
I'm working on logic that detects any stateful variables that can be safely saved and restored via JSON as the storage vector.
Part of this means detecting types that are "safe" to dump and restore, which is easy for:
- numbers, strings and booleans (via
typeof
) - Array elements (via
instanceof Array
combined with ^ in iterated elements) - ES6 Class instances (via
typeof
object andvalue.__proto__.constructor.name
)
There is one type I'm struggling with though. It's the one created from calling:
var nn = new convnetjs.Net();
Which comes from this: https://github.com/karpathy/convnetjs/blob/master/src/convnet_net.js#L8
What's interesting is that Chrome dev tools detects this as a "Net" object:
This is what you'll see if you inspect the nn
var shown above.
Here's what I've tried:
nn instanceof Object
=== trueObject.getPrototypeOf(nn)
- interestingly, this exposes the functions assigned toNet.prototype
in the link above, line 12 onwards. Seemed like a lead.Object.getPrototypeOf(nn).toString()
==[object Object]
Object.getPrototypeOf(nn) instanceof Object
=== true. Makes sense, since it's an object containing custom functions attached to the prototype.Object.getPrototypeOf(nn).constructor.name
===Object
Would anyone know or have ideas how I could detect this type of object safely? Plain objects are fine, but I don't want to overwrite objects with modified prototypes.
...ANSWER
Answered 2019-Aug-13 at 09:04If resorted to using this to verify it this is a plain object or one with custom prototypes:
QUESTION
I have a neural network model that is created in convnet.js that I have to define using Keras. Does anyone have an idea how can I do that?
...ANSWER
Answered 2019-Mar-27 at 20:16From the Convnet.js doc : "your last layer must be a loss layer ('softmax' or 'svm' for classification, or 'regression' for regression)." Also : "Create a regression layer which takes a list of targets (arbitrary numbers, not necessarily a single discrete class label as in softmax/svm) and backprops the L2 Loss."
It's unclear. I suspect "regression" layer is just another layer of Dense (Fully connected) neurons. The 'regression' word probably refers to linear activity. So, no 'relu' this time ?
Anyway, it would probably look something like (no sequential mode):
QUESTION
I have been playing around with the MIT DeepTraffic Challenge Also watching the lecture and reading the slides
After getting a General understanding of the architecture I was wondering what exactly the reward function given by the Environment is.
- Is it the same as the Input of the gridcell (max. drivable Speed)?
- And are they using Reward Clipping, or not?
I also found this javascript Codebase, which does not really help my understanding either.
...ANSWER
Answered 2018-Jul-20 at 18:12The reward is scaled average speed within the interval: [-3, 3].
The implementation of the deeptraffic environment locates in this file: https://selfdrivingcars.mit.edu/deeptraffic/gameopt.js
I'm trying to make it readable. Here's the WIP one: https://github.com/mljack/deeptraffic/blob/master/gameopt.js
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