TensorFlow in 5 Minutes (tutorial)
Why Stack Overflow recommends it
Stack Overflow users link it as a quick starter example when someone wants to try TensorFlow or machine-learning character recognition, and also cite it when asking about errors from running its demo code.
Recommended by 1 people across 1 different months, 2015 to 2018.
What it is
A short 2016 tutorial by Siraj Raval that builds a handwritten digit (MNIST-style) image classifier in Python using TensorFlow in under 40 lines of code. It links to the source code on GitHub plus setup guides and other TensorFlow introductions.
What people said
Recommends bvdb · 2015-04 · Stack Overflow · on “Recognize a number from an image”
I would recommend to combine 2 basic neural network components: Perceptron Self Organized Map (SOM) A perceptron is a very simple neural network component. It takes multiple inputs and produces 1 output. You need to train it by feeding it both inputs and outputs. It's a self learning component. Internally it has a collection of weight factors, which are used to calculate the output. These weight factors are perfected during training. The beautiful thing about a perceptron is that, (with a proper training) it can handle data that it has never seen before. You can make a perceptron more powerful by arranging it in a multi-layer network , meaning that the output of one perceptron acts as the in
Cites DJ Howarth · 2017-03 · Stack Overflow · on “Tensorflow TimeoutError connection attempt failed”
Source Code: [LINK] Video Tutorial: [this video] C:\Users\AppData\Local\Programs\Python\Python35\python.exe C:/Users/AppData/Local/Programs/Python/Python35/Scripts/tensorflow_demo-master/tensorflow_demo-master/board.py Traceback (most recent call last): File "C:\Users\AppData\Local\Programs\Python\Python35\lib\urllib\request.py", line 1254, in do_open h.request(req.get_method(), req.selector, req.data, headers) File "C:\Users\AppData\Local\Programs\Python\Python35\lib\http\client.py", line 1106, in request self._send_request(method, url, body, headers) File "C:\Users\AppData\Local\Programs\Python\Python35\lib\http\client.py", line 1151, in _send_request self.endheaders(body) File "C:\Users\AppDat
Cites Victor Ocampo · 2017-06 · Stack Overflow · on “Can not convert a function into a Tensor or Operation. Tensorflow Error”
So I am getting this Error in tensorflow (1.2) (python 3): WARNING:tensorflow:Passing a `GraphDef` to the SummaryWriter is deprecated. Pass a `Graph` object instead, such as `sess.graph`. Traceback (most recent call last): File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 267, in __init__ fetch, allow_tensor=True, allow_operation=True)) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 2584, in as_graph_element return self._as_graph_element_locked(obj, allow_tensor, allow_operation) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 2673, in _as_graph_element_locked % (type(obj).__n
Cites James Poag · 2018-06 · Stack Overflow · on “Character recognition from an image C++”
My next thought is that with visual recognition, character placement, size, scale, rotation, font-family, serifs, etc... you won't get data that will always conform to normal. You could take a training set and start accumulating characters by interpolating known samples taken from real world examples. Perhaps use the Photoshopped chars as seeds, but compute error values and/or interpolate. Once you've setup a training phase, you're on your way to ML techniques [this video] .