kandi X-RAY | convnetjs Summary
kandi X-RAY | convnetjs Summary
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
Trending Discussions on convnetjs
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
- Array elements (via
instanceof Arraycombined with ^ in iterated elements)
- ES6 Class instances (via
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
This is what you'll see if you inspect the
nn var shown above.
Here's what I've tried:
nn instanceof Object=== true
Object.getPrototypeOf(nn)- interestingly, this exposes the functions assigned to
Net.prototypein the link above, line 12 onwards. Seemed like a lead.
Object.getPrototypeOf(nn) instanceof Object=== true. Makes sense, since it's an object containing custom functions attached to the prototype.
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....
ANSWERAnswered 2019-Aug-13 at 09:04
If resorted to using this to verify it this is a plain object or one with custom prototypes:
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?...
ANSWERAnswered 2019-Mar-27 at 20:16
From 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):
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?
ANSWERAnswered 2018-Jul-20 at 18:12
The 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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