3.23. Utilities¶

This notebook demonstrates the Conx utility functions.

First, we import the conx library:

In [1]:

import conx as cx
net = cx.Network("XOR Network", 2, 4, 1, activation="sigmoid")

Using Theano backend.
Conx, version 3.6.0

3.23.1. Images¶

array_to_image(array, scale=1.0, shape=None)

In [2]:

r, g, b = 0, 0, 1
array = [[[r,g,b] for col in range(400)] for row in range(200)]
image = cx.array_to_image(array)
image

Out[2]:

image_to_array(image)

In [3]:

cx.shape(cx.image_to_array(image))

Out[3]:

(200, 400, 3)

In [4]:

cx.image_to_array(image)[0][0]

Out[4]:

[0.0, 0.0, 1.0]

svg_to_image(svg, background=(255, 255, 255, 255))

In [7]:

svg = net._repr_svg_()
image = cx.svg_to_image(svg)
image

Out[7]:

crop_image(image, x1, y1, x2, y2)

In [8]:

cx.crop_image(image, 10, 10, 200, 200)

Out[8]:

heatmap()

In [15]:

import math
def function(x, y):
    return math.sqrt(x ** 2 + y ** 2)
hm = cx.heatmap(function)
hm

plot3D()

In [28]:

cx.plot3D([["Data", [(0,1,0), (1,2,3), (1,3,2), (1,7,3)]]], mode="scatter")

plot()

In [19]:

symbols = {"Apples": "x", "Bananas": "o-"}

In [24]:

cx.plot([["Apples", [3, 5, 7]],
         ["Bananas",[4, 2, 8]]],
        symbols=symbols)

Out[24]:

plot_f()

In [30]:

cx.plot_f(lambda x: x ** 2)

scatter()

In [23]:

cx.scatter([["Apples", [(3,3), (1,5), (7,4)]],
            ["Bananas",[(3,4), (5,4), (8,2)]]],
          symbols=symbols)

Out[23]:

show(item, title, background=(255, 255, 255, 255))

In [10]:

cx.view(net)

In [11]:

cx.view(net, title="My Title", background=(0, 128, 255, 255))

view_image(image, title)

In [12]:

cx.view_image(image)

show_svg(svg, title, background=(255, 255, 255, 255))

In [13]:

cx.view_svg(net._repr_svg_())

movie(…)

In [ ]:

cx.movie()

reshape(matrix, new_shape)

In [42]:

m = [[1, 2, 3], [4, 5, 6]]
print(cx.shape(m))
m = cx.reshape(m, (3, 2))
print(cx.shape(m))
m

(2, 3)
(3, 2)

Out[42]:

[[1, 2], [3, 4], [5, 6]]

onehot(i, width)

In [43]:

cx.onehot(5, 10)

Out[43]:

[0, 0, 0, 0, 0, 1, 0, 0, 0, 0]

shape(item)

In [45]:

cx.shape([[3], [4]])

Out[45]:

(2, 1)

argmax(seq)

In [50]:

cx.argmax([1, 2, 3, 4, 8, 4, 3, 2, 1])

Out[50]:

argmin(seq)

In [49]:

cx.argmin([7, 3, 2, 5, 1, 7])

Out[49]:

maximum(seq)

In [52]:

cx.maximum([8, 4, 2, 6, 8])

Out[52]:

minimum(seq)

In [53]:

cx.minimum([8, 4, 2, 6, 8])

Out[53]:

binary(i, width)

In [56]:

cx.binary(23, 10)

Out[56]:

[0, 0, 0, 0, 0, 1, 0, 1, 1, 1]

choice(seq=None, p=None)

In [57]:

cx.choice("abcdefg", p=[0, .1, .2, .4, .1, .2, 0])

Out[57]:

'f'

download(url, directory=”./”, force=False, unzip=True)

In [61]:

from IPython.display import Image
cx.download("https://github.com/Calysto/conx-notebooks/raw/master/calysto.gif")
Image(data="calysto.gif")

Using cached https://github.com/Calysto/conx-notebooks/raw/master/calysto.gif as './calysto.gif'.

Out[61]:

<IPython.core.display.Image object>

frange(start, stop=None, step=1.0)

In [62]:

cx.frange(-1, 1, .25)

Out[62]:

[-1.0, -0.75, -0.5, -0.25, 0.0, 0.25, 0.5, 0.75]

get_device()

In [39]:

cx.get_device()

Out[39]:

'cpu'

get_colormap()

In [31]:

cx.get_colormap()

Out[31]:

'seismic_r'

get_error_colormap()

In [32]:

cx.get_error_colormap()

Out[32]:

'seismic_r'

In [35]:

cx.AVAILABLE_COLORMAPS[:10]

Out[35]:

['Accent',
 'Accent_r',
 'Blues',
 'Blues_r',
 'BrBG',
 'BrBG_r',
 'BuGn',
 'BuGn_r',
 'BuPu',
 'BuPu_r']

set_colormap()

In [37]:

cx.set_colormap('Accent')

set_error_colormap(s)

In [36]:

cx.set_error_colormap("gray")

import_keras_model(model, network_name)