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I have a piece of code like the following. I have to implement image2vector() that takes an input of shape (length, height, 3) and returns a vector of shape (length*height*3). It doesn't give me a result of what I expect. Actually, I don't understand the difference between the result which I got and the expected one.
def image2vector(image):
v = None
v = image.reshape(1, 9, image.shape[0] * image.shape[1] * image.shape[2])
return v
image = np.array([[[ 0.67826139, 0.29380381],
[ 0.90714982, 0.52835647],
[ 0.4215251 , 0.45017551]],
[[ 0.92814219, 0.96677647],
[ 0.85304703, 0.52351845],
[ 0.19981397, 0.27417313]],
[[ 0.60659855, 0.00533165],
[ 0.10820313, 0.49978937],
[ 0.34144279, 0.94630077]]])
print ("image2vector(image) = " + str(image2vector(image)))
I got te following result:
image2vector(image) = [[ 0.67826139 0.29380381 0.90714982 0.52835647 0.4215251 0.45017551
0.92814219 0.96677647 0.85304703 0.52351845 0.19981397 0.27417313
0.60659855 0.00533165 0.10820313 0.49978937 0.34144279 0.94630077]]
But I want to get the following one:
[[ 0.67826139] [ 0.29380381] [ 0.90714982] [ 0.52835647] [ 0.4215251 ] [ 0.45017551] [ 0.92814219] [ 0.96677647] [ 0.85304703] [ 0.52351845] [ 0.19981397] [ 0.27417313] [ 0.60659855] [ 0.00533165] [ 0.10820313] [ 0.49978937] [ 0.34144279] [ 0.94630077]]
What is the difference between them? How I get the second matrix from the first one?
Your image does not have the shape (length, height, 3)
In [1]: image = np.array([[[ 0.67826139, 0.29380381],
...: [ 0.90714982, 0.52835647],
...: [ 0.4215251 , 0.45017551]],
...:
...: [[ 0.92814219, 0.96677647],
...: [ 0.85304703, 0.52351845],
...: [ 0.19981397, 0.27417313]],
...:
...: [[ 0.60659855, 0.00533165],
...: [ 0.10820313, 0.49978937],
...: [ 0.34144279, 0.94630077]]])
In [2]: image.shape
Out[2]: (3, 3, 2)
and you can't do the reshape you try:
In [3]: image.reshape(1, 9, image.shape[0] * image.shape[1] * image.shape[2])
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-3-aac5649a99ea> in <module>
----> 1 image.reshape(1, 9, image.shape[0] * image.shape[1] * image.shape[2])
ValueError: cannot reshape array of size 18 into shape (1,9,18)
It has only 18 elements; you can't increase the number of elements with reshape.
In [4]: image.reshape(1, image.shape[0] * image.shape[1] * image.shape[2])
Out[4]:
array([[0.67826139, 0.29380381, 0.90714982, 0.52835647, 0.4215251 ,
0.45017551, 0.92814219, 0.96677647, 0.85304703, 0.52351845,
0.19981397, 0.27417313, 0.60659855, 0.00533165, 0.10820313,
0.49978937, 0.34144279, 0.94630077]])
In [5]: _.shape
Out[5]: (1, 18)
The apparently desired shape is:
In [6]: image.reshape(image.shape[0] * image.shape[1] * image.shape[2],1)
Out[6]:
array([[0.67826139],
[0.29380381],
[0.90714982],
[0.52835647],
...
[0.94630077]])
In [7]: _.shape
Out[7]: (18, 1)
The difference if you want just a vector array, or you want a row or column vector.
usually column vector "vertical vector" has the shape(n,1) and row vector "horizontal" has the shape (1,n)
import numpy as np
image = np.array([[[ 0.67826139, 0.29380381],
[ 0.90714982, 0.52835647],
[ 0.4215251 , 0.45017551]],
[[ 0.92814219, 0.96677647],
[ 0.85304703, 0.52351845],
[ 0.19981397, 0.27417313]],
[[ 0.60659855, 0.00533165],
[ 0.10820313, 0.49978937],
[ 0.34144279, 0.94630077]]])
reshapedImage = image.reshape(18,1)
reshapedImage
array([[0.67826139],
[0.29380381],
[0.90714982],
[0.52835647],
[0.4215251],
[0.45017551],
[0.92814219],
[0.96677647],
[0.85304703],
[0.52351845],
[0.19981397],
[0.27417313],
[0.60659855],
[0.00533165],
[0.10820313],
[0.49978937],
[0.34144279],
[0.94630077]], dtype=object)
Below is my input for the contour plot. Even if the values are different in z, it returns a plot with flat contours. I am using python3.
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import matplotlib.cm as cmx
import matplotlib as mpl
x = [8,10,12,14,16,18,20]
y = [ 0.4,0.45,0.5, 0.55, 0.6,0.65,0.7, 0.75,0.8,0.85,0.9,0.95,1.,1.05,1.1 ,1.15, 1.2 ]
z= [[ 1.78963157e-05, 1.61644497e-05, 1.82095363e-05, 1.68437063e-05, 1.72978900e-05, 1.32008829e-05, 1.34279038e-05],
[ -6.88334562e-07, -1.36603740e-06, -4.87142628e-05, -3.72777265e-04, -1.13885082e-03, -2.21202558e-03, -3.04815629e-03],
[ -2.75334480e-06, -1.69193652e-04, -3.32760366e-03, -1.41131639e-02, -3.22716385e-02, -5.08755872e-02, -6.43446161e-02] ,
[ -1.99621453e-05, -6.62870148e-03, -5.51200368e-02, -1.46310900e-01, -2.50479130e-01, -3.44686045e-01, -4.14010369e-01],
[ -8.05638699e-04, -6.86469566e-02, -3.09807186e-01, -5.95417818e-01, -8.70429863e-01, -1.10896694e+00, -1.28863817e+00],
[ -1.09028691e-02, -2.86631096e-01, -8.72346699e-01, -1.38425321e+00, -1.83269423e+00, -2.21171217e+00, -2.49614674e+00],
[ -5.44918164e-02, -7.04700599e-01, -1.61944488e+00, -2.23166225e+00, -2.70610186e+00, -3.05736645e+00, -3.31027451e+00],
[ -1.46581196e-01, -1.27933729e+00, -2.34440457e+00, -2.90545526e+00, -3.30654522e+00, -3.61596075e+00, -3.81523854e+00],
[ -2.83847825e-01, -1.94187773e+00, -2.97264260e+00, -3.53393575e+00, -3.91886829e+00, -4.39978913e+00, -4.40556012e+00],
[ -4.52972035e-01, -2.60277547e+00, -3.69173458e+00, -6.28054855e+00, -6.28057405e+00, -6.28058156e+00, -6.28059977e+00],
[ -6.52260330e-01, -3.24424411e+00, -6.28054559e+00, -4.23917726e+00, -3.53008773e+00, -3.57473901e+00, -3.51794282e+00],
[ -8.83887486e-01, -4.43537561e+00, -3.81221898e+00, -2.95280246e+00, -2.74353298e+00, -2.75130073e+00, -2.76171281e+00],
[ -1.14292285e+00, -6.28045385e+00, -2.91582775e+00, -2.43497586e+00, -2.34172630e+00, -2.35585837e+00, -2.33020785e+00],
[ -1.48005357e+00, -3.32913613e+00, -2.51281694e+00, -2.22591860e+00, -2.20713439e+00, -2.18431855e+00, -2.15543300e+00],
[ -1.95083193e+00, -2.84478852e+00, -2.30980115e+00, -2.17543461e+00, -2.18077741e+00, -2.15239738e+00, -2.12150557e+00],
[ -2.67504634e+00, -2.47989403e+00, -2.17826984e+00, -2.13298637e+00, -2.14346365e+00, -2.13308098e+00, -2.11940317e+00],
[ -6.27708545e+00, -2.24516401e+00, -2.07349772e+00, -2.04499101e+00, -2.07452620e+00, -2.07811441e+00, -2.08486007e+00]]
fig,ax = plt.subplots()
cmap=plt.cm.bwr
ax.pcolormesh(x, y, z, cmap=cmap, rasterized=True, norm=mpl.colors.Normalize(vmin=-6,vmax=0))
This is what I got:
enter image description here
Found it! I was creating the x list and it wasn't sorted. 8 was at the end of the list. So I was getting a flat plot. Now I sort x and y parameters and it's working. I hope this answer help other save some time:)
I want to visualize weights of the layer of a neural network. I'm using pytorch.
import torch
import torchvision.models as models
from matplotlib import pyplot as plt
def plot_kernels(tensor, num_cols=6):
if not tensor.ndim==4:
raise Exception("assumes a 4D tensor")
if not tensor.shape[-1]==3:
raise Exception("last dim needs to be 3 to plot")
num_kernels = tensor.shape[0]
num_rows = 1+ num_kernels // num_cols
fig = plt.figure(figsize=(num_cols,num_rows))
for i in range(tensor.shape[0]):
ax1 = fig.add_subplot(num_rows,num_cols,i+1)
ax1.imshow(tensor[i])
ax1.axis('off')
ax1.set_xticklabels([])
ax1.set_yticklabels([])
plt.subplots_adjust(wspace=0.1, hspace=0.1)
plt.show()
vgg = models.vgg16(pretrained=True)
mm = vgg.double()
filters = mm.modules
body_model = [i for i in mm.children()][0]
layer1 = body_model[0]
tensor = layer1.weight.data.numpy()
plot_kernels(tensor)
The above gives this error ValueError: Floating point image RGB values must be in the 0..1 range.
My question is should I normalize and take absolute value of the weights to overcome this error or is there anyother way ?
If I normalize and use absolute value I think the meaning of the graphs change.
[[[[ 0.02240197 -1.22057354 -0.55051649]
[-0.50310904 0.00891289 0.15427093]
[ 0.42360783 -0.23392732 -0.56789106]]
[[ 1.12248898 0.99013627 1.6526649 ]
[ 1.09936976 2.39608836 1.83921957]
[ 1.64557672 1.4093554 0.76332706]]
[[ 0.26969245 -1.2997849 -0.64577204]
[-1.88377869 -2.0100112 -1.43068039]
[-0.44531786 -1.67845118 -1.33723605]]]
[[[ 0.71286005 1.45265901 0.64986968]
[ 0.75984162 1.8061738 1.06934202]
[-0.08650422 0.83452386 -0.04468433]]
[[-1.36591709 -2.01630116 -1.54488969]
[-1.46221244 -2.5365622 -1.91758668]
[-0.88827479 -1.59151018 -1.47308767]]
[[ 0.93600738 0.98174071 1.12213969]
[ 1.03908169 0.83749604 1.09565806]
[ 0.71188802 0.85773659 0.86840987]]]
[[[-0.48592842 0.2971966 1.3365227 ]
[ 0.47920835 -0.18186836 0.59673625]
[-0.81358945 1.23862112 0.13635623]]
[[-0.75361633 -1.074965 0.70477796]
[ 1.24439156 -1.53563368 -1.03012812]
[ 0.97597247 0.83084011 -1.81764793]]
[[-0.80762428 -0.62829626 1.37428832]
[ 1.01448071 -0.81775147 -0.41943246]
[ 1.02848887 1.39178836 -1.36779451]]]
...,
[[[ 1.28134537 -0.00482408 0.71610934]
[ 0.95264435 -0.09291686 -0.28001019]
[ 1.34494913 0.64477581 0.96984017]]
[[-0.34442815 -1.40002513 1.66856039]
[-2.21281362 -3.24513769 -1.17751861]
[-0.93520379 -1.99811196 0.72937071]]
[[ 0.63388056 -0.17022935 2.06905985]
[-0.7285465 -1.24722099 0.30488953]
[ 0.24900314 -0.19559766 1.45432627]]]
[[[-0.80684513 2.1764245 -0.73765725]
[-1.35886598 1.71875226 -1.73327696]
[-0.75233924 2.14700699 -0.71064663]]
[[-0.79627383 2.21598244 -0.57396138]
[-1.81044972 1.88310981 -1.63758397]
[-0.6589964 2.013237 -0.48532376]]
[[-0.3710472 1.4949851 -0.30245575]
[-1.25448656 1.20453358 -1.29454732]
[-0.56755757 1.30994892 -0.39370224]]]
[[[-0.67361742 -3.69201088 -1.23768616]
[ 3.12674141 1.70414758 -1.76272404]
[-0.22565465 1.66484773 1.38172317]]
[[ 0.28095332 -2.03035069 0.69989491]
[ 1.97936332 1.76992691 -1.09842575]
[-2.22433758 0.52577412 0.18292744]]
[[ 0.48471382 -1.1984663 1.57565165]
[ 1.09911084 1.31910467 -0.51982772]
[-2.76202297 -0.47073677 0.03936549]]]]
It sounds as if you already know your values are not in that range. Yes, you must re-scale them to the range 0.0 - 1.0. I suggest that you want to retain visibility of negative vs positive, but that you let 0.5 be your new "neutral" point. Scale such that current 0.0 values map to 0.5, and your most extreme value (largest magnitude) scale to 0.0 (if negative) or 1.0 (if positive).
Thanks for the vectors. It looks like your values are in the range -2.25 to +2.0. I suggest a rescaling new = (1/(2*2.25)) * old + 0.5
Problem:
Plotting a closed 2D polygon works fine.
When I try plotting it 1D (by replacing the second dimension with a constant), it fails in some special cases: Then the polygon (which becomes a line) is not drawn completely.
What I tried:
Different plotting-styles like . , o -- of which only the latter one reproduces the problem while non-line-like-styles work.
Shifted/rolled the polygon-array which did not work when shifting by 1 or 2, but for 3 and 4 that solved the problem.
Asked a friend who is clueless ^^
Helped myself so far by directly plotting a line between x.min() and x.max().
For simplicity I removed the z-Data. When plotting x in combination with z, it also works - as long as z is far away from being constant. But then I get a wobbly graph instead of a straight line.
Still How can it be, that whether the data is fully plotted or not depends on its order? What did I do wrong?
I tried reducing the data for one of the special cases, but did not get very far.
Here's the working mini-example to produce the image, I'm sorry for the long dataset but could not figure out which values to remove while keeping the problem reproducable.
import numpy as np
from matplotlib import pyplot as plt
s = np.array([
[-0.08527125, 0.08810856],
[-0.08967261, -0.06748633],
[-0.08772675, -0.08396971],
[-0.08766724, -0.08440267],
[-0.08748191, -0.08521958],
[-0.08438602, -0.09529874],
[-0.08385044, -0.09684308],
[-0.08202714, -0.10180355],
[-0.07874478, -0.1066276 ],
[-0.07857811, -0.10686354],
[-0.07789635, -0.10778213],
[-0.07781094, -0.10789337],
[-0.07710836, -0.10880646],
[-0.07007289, -0.11655674],
[-0.06962708, -0.11703841],
[-0.06727917, -0.11933749],
[-0.06584873, -0.12070607],
[-0.06552574, -0.12100522],
[-0.06527846, -0.12121293],
[-0.06401669, -0.12214381],
[-0.06349801, -0.12245992],
[-0.06328962, -0.12258028],
[-0.0627093 , -0.12290901],
[-0.06225359, -0.12314782],
[-0.06116991, -0.12368258],
[-0.06041895, -0.12403051],
[-0.06017477, -0.12413955],
[-0.05992829, -0.12424804],
[-0.04659197, -0.13006904],
[-0.04634663, -0.13017319],
[-0.04628731, -0.1301966 ],
[-0.04567821, -0.13041967],
[-0.04552972, -0.13047215],
[-0.04521702, -0.13058213],
[-0.00325617, -0.14513102],
[ 0.00180445, -0.14683444],
[ 0.00950078, -0.14923653],
[ 0.01392647, -0.15030046],
[ 0.01518804, -0.15045976],
[ 0.02734024, -0.15177574],
[ 0.02813995, -0.15177997],
[ 0.02882764, -0.15176844],
[ 0.02947446, -0.15171012],
[ 0.03001744, -0.15165147],
[ 0.0309922 , -0.15149313],
[ 0.03121784, -0.15145567],
[ 0.031327 , -0.15143527],
[ 0.03142507, -0.15141573],
[ 0.03150791, -0.15139799],
[ 0.03222764, -0.15124224],
[ 0.03798838, -0.14988557],
[ 0.038991 , -0.1496481 ],
[ 0.0391266 , -0.14961472],
[ 0.03920601, -0.14959498],
[ 0.03938681, -0.14954736],
[ 0.03991586, -0.14940614],
[ 0.05465674, -0.14538513],
[ 0.05500815, -0.14528457],
[ 0.05512499, -0.14524203],
[ 0.05522656, -0.14520209],
[ 0.05558739, -0.14504381],
[ 0.05580602, -0.1449474 ],
[ 0.05609256, -0.14478439],
[ 0.05713959, -0.14412759],
[ 0.05743526, -0.14392656],
[ 0.05889567, -0.14277855],
[ 0.06059875, -0.14143159],
[ 0.06530732, -0.13766523],
[ 0.06933674, -0.13424303],
[ 0.0702827 , -0.13335664],
[ 0.07036637, -0.13327235],
[ 0.07405533, -0.12951652],
[ 0.07810863, -0.125367 ],
[ 0.08087981, -0.12226141],
[ 0.08176499, -0.12123622],
[ 0.08381519, -0.1150717 ],
[ 0.08734418, -0.0927558 ],
[ 0.08230393, 0.09131774],
[ 0.08183173, 0.097691 ],
[ 0.08169106, 0.09931977],
[ 0.08130204, 0.10177584],
[ 0.07914312, 0.11230088],
[ 0.07895476, 0.11316267],
[ 0.07514703, 0.12076318],
[ 0.07464758, 0.12166996],
[ 0.0733333 , 0.12382546],
[ 0.07297538, 0.12425848],
[ 0.06620658, 0.13199037],
[ 0.05294461, 0.14660768],
[ 0.05262742, 0.14694802],
[ 0.05097036, 0.14849098],
[ 0.04972845, 0.14937964],
[ 0.04815287, 0.15002114],
[ 0.04783434, 0.15013311],
[ 0.04757239, 0.15022502],
[ 0.02928349, 0.15634586],
[ 0.02842644, 0.15662035],
[ 0.02776939, 0.15677726],
[ 0.0270936 , 0.15690821],
[ 0.02666639, 0.15698384],
[ 0.02610376, 0.15707915],
[ 0.02601474, 0.15709266],
[ 0.025126 , 0.15722434],
[ 0.02474755, 0.15727606],
[ 0.02296123, 0.15742128],
[ 0.02202472, 0.15744494],
[ 0.02086636, 0.15744469],
[ 0.01967163, 0.15742308],
[ 0.01872141, 0.15737815],
[ 0.01568162, 0.15718458],
[-0.00722516, 0.15361993],
[-0.00785781, 0.15350418],
[-0.02865655, 0.14966376],
[-0.02928172, 0.1495445 ],
[-0.02970399, 0.14942282],
[-0.03124785, 0.14896281],
[-0.03160079, 0.14884731],
[-0.03311488, 0.14832349],
[-0.04643741, 0.14369722],
[-0.04831254, 0.14301643],
[-0.04846789, 0.14294296],
[-0.04902343, 0.14264339],
[-0.04980039, 0.14218488],
[-0.05127431, 0.14122099],
[-0.05343824, 0.13978482],
[-0.06641953, 0.1306598 ],
[-0.06719992, 0.13009146],
[-0.06732267, 0.12997761],
[-0.06842394, 0.12872382],
[-0.06895024, 0.12808815],
[-0.07854825, 0.11574519],
[-0.08365013, 0.10504 ],
[-0.0837306 , 0.10486896],
[-0.08384365, 0.10417084],
[-0.08444646, 0.09768441]])
# make first and last point the same, to close the circle
s = np.concatenate([s,s[:1]])
y = s[:,0]
x = s[:,1]
# plot the polygon xy
plt.plot(x, y, 'b--', label='Polygon with x/y')
# plot only x values of the polygon (does not work)
plt.plot(x, np.zeros(len(x)), 'g-', linewidth=10, label='does not stretch over red dots')
# do the same, but with dots to show x values (does work)
plt.plot(x, np.zeros(len(x)), 'r.')
# do the same, a little bit lower, but with shifted/rolled x values.
# rolled by 1 or 2 does not help, but by 3 or 4 does.
plt.plot(np.roll(x,3), np.ones(len(x))-1.02, '-', color="lightgreen", linewidth=10, label="stretches over red dots")
# do the same again with dots to show x values
plt.plot(np.roll(x,3), np.ones(len(x))-1.02, 'r.')
plt.legend(loc='lower center').get_frame().set_alpha(1)
plt.show()
Python 3.5 x64 via Anaconda in Windows10. Matplotlib is v2.0.0
How long is a swimming pool that is 0 mm wide? In theory, it has a length of course, but in practice you couldn't measure it. The same happens here, a polygon without extention in one dimension could potentially be rendered to any length. And you would see the same effect happening to both polygons depending on the figure size, zoom level, axis range.
I think you already found a solution, by plotting a line instead of a polygon. And that would also be my recommendation.
I have list as following and need to be tranposed to a numpy array
samplelist= [ [ ['Name-1','Name-2','Name-3'] , ['Age-1','Age-2','Age-3'] ],
[ ['new_Name_1','new_Name_2','new_Name_3'], ['new_Age_1','new_Age_2','new_Age_3'] ]
]
Expected Result:
samplearray = [ [ ['Name-1','Age-1'], ['Name-2','Age-2'], ['Name-3','Age-3'] ],
[ ['new_Name_1','new_Age_1], ['new_Name_2','new_Age_2'], ['new_Name_3','new_Age_3'] ]
]
np.transpose results:
np.transpose(a)
array([[['Name-1', 'new_Name_1'],
['Age-1', 'new_Age_1']],
[['Name-2', 'new_Name_2'],
['Age-2', 'new_Age_2']],
[['Name-3', 'new_Name_3'],
['Age-3', 'new_Age_3']]],
dtype='|S10')
samplelist is a 3-D array.
In [58]: samplelist.shape
Out[58]: (2, 2, 3)
Using transpose swaps the first and last axes (0 and 2):
In [55]: samplelist.T
Out[55]:
array([[['Name-1', 'new_Name_1'],
['Age-1', 'new_Age_1']],
[['Name-2', 'new_Name_2'],
['Age-2', 'new_Age_2']],
[['Name-3', 'new_Name_3'],
['Age-3', 'new_Age_3']]],
dtype='|S10')
In [57]: samplelist.swapaxes(0,2)
Out[57]:
array([[['Name-1', 'new_Name_1'],
['Age-1', 'new_Age_1']],
[['Name-2', 'new_Name_2'],
['Age-2', 'new_Age_2']],
[['Name-3', 'new_Name_3'],
['Age-3', 'new_Age_3']]],
dtype='|S10')
To get the desired array, swap axes 1 and 2:
import numpy as np
samplelist = np.array([
[ ['Name-1','Name-2','Name-3'] , ['Age-1','Age-2','Age-3'] ],
[ ['new_Name_1','new_Name_2','new_Name_3'], ['new_Age_1','new_Age_2','new_Age_3'] ]
])
print(samplelist.swapaxes(1,2))
# [[['Name-1' 'Age-1']
# ['Name-2' 'Age-2']
# ['Name-3' 'Age-3']]
# [['new_Name_1' 'new_Age_1']
# ['new_Name_2' 'new_Age_2']
# ['new_Name_3' 'new_Age_3']]]