I try to multiply two matrices in a python program using Keras.
import keras.backend as K
import numpy as np
A = np.random.rand(10,500)
B = np.random.rand(500,6000)
x = K.placeholder(shape=A.shape)
y = K.placeholder(shape=B.shape)
xy = K.dot(x, y)
xy.eval(A,B)
I know this cannot work, but I also don't know how I can make it work.
You need to use a variable instead of a place holder.
import keras.backend as K
import numpy as np
A = np.random.rand(10,500)
B = np.random.rand(500,6000)
x = K.variable(value=A)
y = K.variable(value=B)
z = K.dot(x,y)
# Here you need to use K.eval() instead of z.eval() because this uses the backend session
K.eval(z)
Related
import numpy as np
x = np.array([[1,1,1],[2,2,2],[3,3,3]])
xt = np.array([1,2,3])
L = len(xt)
for i in range(0,L):
s = x-xt[i]
is there another way to get the same results without the use of a for loop, thanks.
How to execute this code:
import numpy as np
import math
x = np.arange(1,9, 0.5)
k = math.cos(x)
print(x)
I got an error like this:
TypeError: only size-1 arrays can be converted to Python scalars
Thank you in advance.
So this is happening because math.cos doesn't accept numpy arrays larger than size 1. That's why if you had a np array of size 1, your approach would still work.
A simpler way you can achieve the result is to use np.cos(x) directly:
import numpy as np
x = np.arange(1,9, 0.5)
k = np.cos(x)
print(x)
print(k)
If you have to use the math module, you can try iterating through the array and applying math.cos to each member of the array:
import numpy as np
import math
x = np.arange(1,9,0.5)
for item in x:
k = math.cos(item)
print(k) # or add to a new array/list
You're looking for something like this?
import numpy as np
import math
x = np.arange(1,9, 0.5)
for ang in x:
k = math.cos(ang)
print(k)
You are trying to pass ndarray (returned by arange) to a function, which expects just real number. Use np.cos instead.
If you want pure-Python:
You can use math.fun in map like below:
import math
x = range(1,9)
print(list(map(math.cos, x)))
Output:
[0.5403023058681398, -0.4161468365471424, -0.9899924966004454, -0.6536436208636119, 0.2836621854632263, 0.9601702866503661, 0.7539022543433046, -0.14550003380861354]
I am trying to implement STFT with Pytorch. But the output from the Pytorch implementation is slightly off, when compared with the implementation from Librosa.
Librosa version
import numpy as np
from librosa.core import stft
import matplotlib.pyplot as plt
np.random.seed(3)
y = np.sin(2*np.pi*50*np.linspace(0,10,2048))+np.sin(2*np.pi*20*np.linspace(0,10,2048)) + np.random.normal(scale=1,size=2048)
S_stft = np.abs(stft(y, hop_length=512, n_fft=2048,center=False))
plt.plot(S_stft)
Pytorch version
import torch
from torch.autograd import Variable
from torch.nn.functional import conv1d
from scipy.signal.windows import hann
stride = 512
def create_filters(d,k,low=50,high=6000):
x = np.arange(0, d, 1)
wsin = np.empty((k,1,d), dtype=np.float32)
wcos = np.empty((k,1,d), dtype=np.float32)
start_freq = low
end_freq = high
# num_cycles = start_freq*d/44000.
# scaling_ind = np.log(end_freq/start_freq)/k
window_mask = hann(2048, sym=False) # same as 0.5-0.5*np.cos(2*np.pi*x/(k))
for ind in range(k):
wsin[ind,0,:] = window_mask*np.sin(2*np.pi*ind/k*x)
wcos[ind,0,:] = window_mask*np.cos(2*np.pi*ind/k*x)
return wsin,wcos
wsin, wcos = create_filters(2048,2048)
wsin_var = Variable(torch.from_numpy(wsin), requires_grad=False)
wcos_var = Variable(torch.from_numpy(wcos),requires_grad=False)
network_input = torch.from_numpy(y).float()
network_input = network_input.reshape(1,-1)
zx = np.sqrt(conv1d(network_input[:,None,:], wsin_var, stride=stride).pow(2)+conv1d(network_input[:,None,:], wcos_var, stride=stride).pow(2))
pytorch_Xs = zx.cpu().numpy()
plt.plot(pytorch_Xs[0,:1025,0])
My Question
The two graphs might look the same, but if I check the two outputs with np.allclose, we can see that they are slightly different.
np.allclose(S_stft, pytorch_Xs[0,:1025,0].reshape(1025,1))
output >>> False
Only when I tune up the tolerance to 1e-5, it gives me True result
np.allclose(S_stft, pytorch_Xs[0,:1025,0].reshape(1025,1),atol=1e-5)
output >>> True
What causes the difference in values? Is it because of the data conversion by using torch.from_numpy(y).float()?
I would like to have a difference in value less than 1e-7, 1e-8 is even better.
The difference is from the difference between their default bit.
NumPy's float is 64bit by default.
PyTorch's float is 32bit by default.
This feels like it should be a simple problem but I am newer to python, in R i would use a foreach loop that gave me an option to combine.
I have tried a for loop that lets me print out all the values i need but i want them collected into a vector of values that i can use later.
from scipy.stats import gamma
import scipy.stats as stats
import numpy as np
import random
data2 = np.random.gamma(1,2, size = 500)
gammT = np.log(data2 + 1)
mean = np.mean(gammT)
sd = np.std(gammT)
a = (mean/ sd)**2
b = (sd**2)/ mean
for i in range(1,100):
gammT = random.sample(list(gammT), 500)
gamm = np.random.gamma(a,b, size = len(gammT))
s = stats.anderson_ksamp([gammT,gamm])
s = s[2]
print(s)
So i am able to print all the values i want but i want them all to be gathered together in a vector of values. I have tried to append and make lists but am not able to get them together.
from scipy.stats import gamma
import scipy.stats as stats
import numpy as np
import random
gammT = np.log(data2.iScore + 1)
mean = np.mean(gammT)
sd = np.std(gammT)
a = (mean/ sd)**2
b = (sd**2)/ mean
#initialize empty list
result=[]
for i in range(100):
# removed (1,100) you only need range(100) for 100 elements
gammT = random.sample(list(gammT), 500)
gamm = np.random.gamma(a,b, size = len(gammT))
s = stats.anderson_ksamp([gammT,gamm])
s = s[2]
#append calculation to list
result.append(s)
print(s)
print(result)
I want to integrate a function that has no closed form solution with an unknown variable and then plot vs the unknown variable. To try a simpler test, I tried to use the integral of f(x,c) = (x^2+c), integrated with respect to x and plot with different values of c. However, the code below gets the error
only size-1 arrays can be converted to Python scalars
even though the integral of a number, e.g. integral(5), seems to return the correct scalar value.
import numpy as np
import matplotlib.pyplot as plt
from scipy import integrate
def f(x,c):
return x**2+c
def integral(c):
return integrate.quad(f,0,10, args = (c,))[0]
y = np.linspace(0,20,200)
plt.plot(y, integral(y))
You pass a numpy array as the argument c while you wanted to integrate over x for all the items of c. Therefore you can use this:
def f(x,c):
return x**2+c
def integrate_f(c):
result = np.zeros(len(c))
counter = 0
for item in c:
result[counter] = integrate.quad(f,0,10, args = (item))[0]
counter +=1
return result
c_array = np.linspace(0,1,200)
plt.plot(c_array, integrate_f(c_array))
onno was a bit faster. But here is my similar solution. You need to loop over all the different c:
import numpy as np
import matplotlib.pyplot as plt
from scipy import integrate
def f(x,c):
return x**2+c
def getIntegral(c_list):
result = []
for c in c_list:
integral = integrate.quad(f,0,10,args = c)[0]
result.append(integral)
return result
if __name__ == "__main__":
c_list = np.linspace(0,20,200)
plt.plot(c_list, getIntegral(c_list))
plt.show()