In terms of support for environments, in particular public clouds, it is better for the farms promoted by the vendors of these clouds, so TensorFlow support is better in Google Cloud, MXNet in AWS, CNTK in Microsoft Azure, D4LJ in Android, Core ML in iOS. By languages, almost everyone has common support in Python, in particular, TensorFlow supports JavaScript, C ++, Java, Go, C # and Julia.
Many frameworks support TeserBodrd rendering. It is a complex Web interface for multi-level visualization of the state and the learning process and its debugging. To connect, you need to specify the path to the "tenserboard –logdir = $ PATH_MODEL" model and open localhost: 6006. Interface control is based on navigating through the graph of logical blocks and opening blocks of interest for subsequent repetition of the process.
For experiments, we need a programming language and a library. Often the language used is a simple language with a low entry threshold, such as Python. There may be other general-purpose languages like JavaScript or specialized languages like R. I'll take Python. In order not to install the language and libraries, we will use the free service colab.research.google.com/notebooks/intro.ipynb containing Jupiter Notebook. Notebook contains the ability not only to write code with comments in the console form, but to format it as a document. You can try Notebook features in the educational playbook https://colab.research.google.com/notebooks/welcome.ipynb, such as formatting text in the MD markup language with formulas in the TEX markup language, running scripts in Python, displaying the results of their work in text form and in the form of graphs using the standard Python library: NumPy (NamPay), matplotlib.pyplot. Colab itself provides a Tesla K80 graphics card for 12 hours at a time (per session) for free. It supports a variety of deep machine learning frameworks, including Keras, TenserFlow, and Pytorch. The price of a GPU instance in Google Cloud:
* Tesla T4: 1GPU 16GB GDDR6 0.35 $ / hour
* Tesla P4: 1GPU 8GB GDDR5 0.60 $ / hour
* Tesla V100: 1GPU 16GB HBM2 2.48 $ / hour
* Tesla P100: 1GPU 16GB HBM2 $ 1.46 / hour
Let's try. Let's follow the link colab.research.google.com and press the button "create a notepad". We will have a blank Notebook. You can enter an expression:
10 ** 3/2 + 3
and clicking on play – we get the result 503.0. You can display the graph of the parabola by clicking the "+ Code" button in the new cell in the code:
def F (x):
return x * x
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace (-5, 5, 100)
y = list (map (F, x))
plt.plot (x, y)
plt.ylabel ("Y")
plt.xlabel ("X")
Or displaying an image as well:
import os
! wget https://www.python.org/static/img/python-logo.png
import PIL
img = PIL.Image.open ("python-logo.png")
img
Popular frameworks:
* Caffe, Caffe2, CNTK, Kaldi, DL4J, Keras – a set of modules for design;
* TensorFlow, Theano, MXNet – graph programming;
* Torch and PyTorch – register the main parameters, and the graph will be built automatically.
Consider the PyTorch library (NumPy + CUDA + Autograd) because of its simplicity. Let's look at operations with tensors – multidimensional arrays. Let's connect the library and declare two tensors: press + Code, enter the code into the cell and press execute:
import torch
a = torch.FloatTensor ([[1, 2, 3], [5, 6, 7], [8, 9, 10]])
b = torch.FloatTensor ([[– 1, -2, -3], [-10, -20, -30], [-100, -200, -300]])
Element-wise operations such as "+", "-", "*", "/" on two matrices of the same dimensions perform operations with their corresponding elements:
a + b
tensor ([[0., 0., 0.],
[-5., -14., -23.],
[-92., -191., -290.]])
Another option for the elementwise operation is to apply one operation to all elements one by one, for example, multiply by -1 or apply a function:
a
tensor ([[1., 2., 3.],
[5., 6., 7.],
[8., 9., 10.]])
a * -1
tensor ([[-1., -2., -3.],
[-5., -6., -7.],
[-8., -9., -10.]])
a.abs ()
tensor ([[1., 2., 3.],
[5., 6., 7.],
[8., 9., 10.]])
There are also convolution operations, such as sum, min, max, which, as input, give the sum of all elements, the smallest or largest element of the matrix:
a.sum ()
tensor (51.)
a.min ()
tensor (1.)
a.max ()
tensor (10.)
But, we will be more interested in post-column operations (the operation will be performed on each column):
a.sum (0)
tensor ([14., 17., 20.])
a.min (0)
torch.return_types.min (values = tensor ([1., 2., 3.]), indices = tensor ([0, 0, 0]))
a.max (0)
torch.return_types.max (values = tensor ([8., 9., 10.]), indices = tensor ([2, 2, 2]))
As we remember, a neural network consists of three layers, a layer of neurons, and a neuron contains connections at the input with weights in the form of prime numbers. The weight is set by an ordinary number, then the incoming connections to the neuron can be described by a sequence of numbers – a vector (one-dimensional array or list), the length of which is the number of connections. Since the network is fully connected, all the neurons of this layer are connected to the previous one, and therefore the vectors demonstrating them also have the same length, creating a list of vectors of equal length – a matrix. It is a convenient and compact layer representation optimized for use on a computer. At the output of the neuron, there is an activation function (sigmoid or, ReLU for deep and ultra-deep networks), which determines whether the neuron outputs a value or not. To do this, it is necessary to apply it to each neuron, that is, to each column: we have already seen the operation on columns.
Accelerating
