How can I do the following in tensorflow?
mat = [4,2,6,2,3] #
mat[2] = 0 # simple zero the 3rd element
I can't use the [] brackets because it only works on constants and not on
variables. I cant use the slice function either because that returns a tensor and you can't assign to a tensor.
import tensorflow as tf
sess = tf.Session()
var1 = tf.Variable(initial_value=[2, 5, -4, 0])
assignZerosOP = (var1[2] = 0) # < ------ This is what I want to do
sess.run(tf.initialize_all_variables())
print sess.run(var1)
sess.run(assignZerosOP)
print sess.run(var1)
Will print
[2, 5, -4, 0]
[2, 5, 0, 0])
You can't change a tensor - but, as you noted, you can change a variable.
There are three patterns you could use to accomplish what you want:
(a) Use tf.scatter_update to directly poke to the part of the variable you want to change.
import tensorflow as tf
a = tf.Variable(initial_value=[2, 5, -4, 0])
b = tf.scatter_update(a, [1], [9])
init = tf.initialize_all_variables()
with tf.Session() as s:
s.run(init)
print s.run(a)
print s.run(b)
print s.run(a)
[ 2 5 -4 0]
[ 2 9 -4 0]
[ 2 9 -4 0]
(b) Create two tf.slice()s of the tensor, excluding the item you want to change, and then tf.concat(0, [a, 0, b]) them back together.
(c) Create b = tf.zeros_like(a), and then use tf.select() to choose which items from a you want, and which zeros from b that you want.
I've included (b) and (c) because they work with normal tensors, not just variables.
Related
I have 2 tensors. The first tensor is 1D (e.g. a tensor of 3 values). The second tensor is 2D, with the first dim as the IDs to first tensor in a one-many relationship (e.g. a tensor with a shape of 6, 2)
# e.g. simple example of dot product
import torch
a = torch.tensor([2, 4, 3])
b = torch.tensor([[0, 2], [0, 3], [0, 1], [1, 4], [2, 3], [2, 1]]) # 1st column is the index to tensor a, 2nd column is the value
output = [(2*2)+(2*3)+(2*1),(4*4),(3*3)+(3*1)]
output = [12, 16, 12]
Current what I have is to find the size of each id in b (e.g. [3,1,2]) then using torch.split to group them into a list of tensors and running a for loop through the groups. It is fine for a small tensor, but when the size of the tensors are in millions, with tens of thousands of arbitrary-sized groups, it became very slow.
Any better solutions?
You can use numpy.bincount or torch.bincount to sum the elements of b by key:
import numpy as np
a = np.array([2,4,3])
b = np.array([[0,2], [0,3], [0,1], [1,4], [2,3], [2,1]])
print( np.bincount(b[:,0], b[:,1]) )
# [6. 4. 4.]
print( a * np.bincount(b[:,0], b[:,1]) )
# [12. 16. 12.]
import torch
a = torch.tensor([2,4,3])
b = torch.tensor([[0,2], [0,3], [0,1], [1,4], [2,3], [2,1]])
torch.bincount(b[:,0], b[:,1])
# tensor([6., 4., 4.], dtype=torch.float64)
a * torch.bincount(b[:,0], b[:,1])
# tensor([12., 16., 12.], dtype=torch.float64)
References:
numpy.bincount official documentation;
torch.bincount official documentation;
How can I reduce a numpy array based on a key rather than an axis?
Another alternative in pytorch if gradient is needed.
import torch
a = torch.tensor([2,4,3])
b = torch.tensor([[0,2], [0,3], [0,1], [1,4], [2,3], [2,1]])
output = torch.zeros(a.shape[0], dtype=torch.long).index_add_(0, b[:, 0], b[:, 1]) * a
alternatively, torch.tensor.scatter_add also works.
I want to have multiple types of seaborn plots using the same y axis but with different x coordinates (see image below).
I've tried doing this multiple different ways with specifying the X-axis coordinates differently but can't seem to get it to work.
Here is an example of almost working code
x=[1,2,3,3,3,4,4,5,5,6] # first violin
y=[4,4,5,5,5,5,6] # second violin
z=[5,5,6] # swarmplot over second violin
for data,label in [(x,'x'),(y,'y'),(z,'z')]:
for i in data:
c2v['value'].append(i)
c2v['key'].append(label)
data=pd.DataFrame(c2v)
data.head()
print(data.loc[data.key=='z'])
fig,ax=plt.subplots(1,figsize=(5,5),dpi=200)
ax = sns.violinplot(data=data.loc[data.key.isin(['x','y'])], x='key', y='value',palette=['honeydew','lightgreen'])
sns.swarmplot(x=['swarmplot']*len(data), y=data['value'], order=ax.get_xticklabels() + ['swarmplot'], ax=ax) #.loc[data.key=='z',:]
ax.set_xlabel('')
It produces the following image:
However, it is plotting all values associated with x/y/z instead of just z. When I slice the dataframe to only 'z' in the swarmplot as below, I get an error:
sns.swarmplot(x=['swarmplot']*len(data), y=data.loc[data.key=='z',:]['value'], order=ax.get_xticklabels() + ['swarmplot'], ax=ax)
KeyError: 'swarmplot'
Any suggestions?
To draw a second plot onto the same x-axis, you can use order= giving a list of existing tick labels, appending the new labels.
Here is an example:
import seaborn as sns
tips = sns.load_dataset('tips')
ax = sns.swarmplot(data=tips, x='day', y='total_bill')
sns.violinplot(x=['violin']*len(tips), y=tips['total_bill'], order=ax.get_xticklabels() + ['violin'], ax=ax)
ax.set_xlabel('')
The problem with the code in the new question, is that the x= and y= of the swarmplot need the same number of elements. It also seems the swarmplot resets the y limits, so I added some code to readjust those:
from matplotlib import pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
x = [1, 2, 3, 3, 3, 4, 4, 5, 5, 6] # first violin
y = [4, 4, 5, 5, 5, 5, 6] # second violin
z = [5, 5, 6] # swarmplot over second violin
data = pd.DataFrame({'value': np.concatenate([x, y, z]),
'key': ['x'] * len(x) + ['y'] * len(y) + ['z'] * len(z)})
fig, ax = plt.subplots(1, figsize=(5, 5))
ax = sns.violinplot(data=data.loc[data.key.isin(['x', 'y'])], x='key', y='value', palette=['honeydew', 'lightgreen'])
ymin1, ymax1 = ax.get_ylim()
swarm_data = data.loc[data.key == 'z', :]['value']
sns.swarmplot(x=['swarmplot'] * len(swarm_data), y=swarm_data, order=ax.get_xticklabels() + ['swarmplot'], ax=ax)
ymin2, ymax2 = ax.get_ylim()
ax.set_ylim(min(ymin1, ymin2), max(ymax1, ymax2))
ax.set_xlabel('')
ax.set_xticks(np.arange(3))
ax.set_xticklabels(['x', 'y', 'swarmplot'])
plt.show()
You can simplify things by directly using the data without creating a dataframe:
x = [1, 2, 3, 3, 3, 4, 4, 5, 5, 6] # first violin
y = [4, 4, 5, 5, 5, 5, 6] # second violin
z = [5, 5, 6] # swarmplot over second violin
fig, ax = plt.subplots(1, figsize=(5, 5))
ax = sns.violinplot(x=['x']*len(x) + ['y']*len(y), y=x + y, palette=['honeydew', 'lightgreen'])
ymin1, ymax1 = ax.get_ylim()
sns.swarmplot(x=['swarmplot'] * len(z), y=z, order=ax.get_xticklabels() + ['swarmplot'], ax=ax)
ymin2, ymax2 = ax.get_ylim()
ax.set_ylim(min(ymin1, ymin2), max(ymax1, ymax2))
ax.set_xticks(np.arange(3))
ax.set_xticklabels(['x', 'y', 'swarmplot'])
plt.show()
I have a matrix
A = [[ 1. 2. 3.]
[ 4. 5. 6.]]
and a vector
b = [ 5. 10. 15.]
I want to add each column of A (A[:,i]) to b[i], i.e.
[[ 6. 12. 18.]
[ 9. 15. 21.]]
an easy way to do it would be
A = tf.constant([[1., 2, 3],[1, 2, 3]])
b = tf.constant([[5, 10, 15.]])
e = tf.ones((2,1))
a + tf.matmul( e, b ) # outer product "repmat"
But it seems terribly wasteful to do this and have to construct an entire auxiliary matrix which we will eventually throw out. Is there a more idomatic way of doing this without writing my own op?
As mentioned you can do A + b:
import tensorflow as tf
tf.InteractiveSession()
A = tf.constant([[1., 2, 3], [4, 5, 6]])
b = tf.constant([[5, 10, 15.]])
(A + b).eval()
returns:
array([[ 6., 12., 18.],
[ 9., 15., 21.]], dtype=float32)
The reason this works is because of array broadcasting. The Numpy broadcasting page has great info and tensorflow broadcasting works the same way. Basically for each dimension (moving from trailing dimension to leading dimension) tensorflow/numpy attempts checks to see if the dimensions are compatible (either they have the same number of elements or one of them only has 1 element).
In your case A is of shape [2, 3] and b is of shape [1, 3]. The second dimensions match, but because b has a first dimension with only a single element that element of b is "broadcast" along the first dimension of A (two elements).
I am trying to slice a four-dimensional tensor using the tf.slice() operator, as follows:
x_image = tf.reshape(x, [-1,28,28,1], name='Images_2D')
slice_im = tf.slice(x_image,[0,2,2],[1, 24, 24])
However, when I try to run this code, I get the following exception:
raise ValueError("Shape %s must have rank %d" % (self, rank))
ValueError: Shape TensorShape([Dimension(None), Dimension(28), Dimension(28), Dimension(1)]) must have rank 3
How can I slice this tensor?
The tf.slice(input, begin, size) operator requires that the begin and size vectors—which define the subtensor to be sliced—have the same length as the number of dimensions in input. Therefore, to slice a 4-D tensor, you must pass a vector (or list) of four numbers as the second and third arguments of tf.slice().
For example:
x_image = tf.reshape(x, [-1, 28, 28, 1], name='Images_2D')
slice_im = tf.slice(x_image, [0, 2, 2, 0], [1, 24, 24, 1])
# Or, using the indexing operator:
slice_im = x_image[0:1, 2:26, 2:26, :]
The indexing operator is slightly more powerful, as it can also reduce the rank of the output, if for a dimension you specify a single integer, rather than a range:
slice_im = x_image[0:1, 2:26, 2:26, :]
print slice_im_2d.get_shape() # ==> [1, 24, 24, 1]
slice_im_2d = x_image[0, 2:26, 2:26, 0]
print slice_im_2d.get_shape() # ==> [24, 24]
I have the following code
a_alpha_beta = zeros((2, len( atime ) ))
for i in range( len( atime ) ):
alpha_beta = transf.clarke(signal[0][i], signal[1][i], signal[2][i])
a_alpha_beta[0][i] = alpha_beta[0][0]
a_alpha_beta[1][i] = alpha_beta[1][0]
How can I optimize the code above, for example how can I copy alpha_beta to a_alpha_beta?
I don't exactly know what the function transf.clarke does, but copy operations that you desire can be done as follows:
import numpy as np
# generate a test input
x = np.arange(0, 10).reshape((2, 5))
print x
# simply copy 2d array
y = x.copy()
print y
# some new data (i.e., alpha_beta in the original code)
z = np.array([[10, 11, 12],
[13, 14, 15]])
print z
# replace the iteration by numpy slicing to obtain the same result
x[0, :] = z[0, 0]
x[1, :] = z[1, 0]
print x