Я новичок в NumPy.Сегодня, когда я использую его для работы с линейной регрессией, он показывает следующее:
KeyError Traceback (most recent call
last)
~/anaconda3/lib/python3.6/site-packages/autograd/numpy/numpy_extra.py
in new_array_node(value, tapes)
84 try:
---> 85 return array_dtype_mappings[value.dtype](value, tapes)
86 except KeyError:
KeyError: dtype('int64')
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call
last)
<ipython-input-4-aebe8f7987b0> in <module>()
24 return cost/float(np.size(y))
25
---> 26 weight_h, cost_h = gradient_descent(least_squares, alpha,
max_its, w)
27
28 # a)
<ipython-input-2-1b74c4f818f4> in gradient_descent(g, alpha, max_its,
w)
12 for k in range(max_its):
13 # evaluate the gradient
---> 14 grad_eval = gradient(w)
15
16 # take gradient descent step
~/anaconda3/lib/python3.6/site-packages/autograd/core.py in
gradfun(*args, **kwargs)
19 @attach_name_and_doc(fun, argnum, 'Gradient')
20 def gradfun(*args,**kwargs):
---> 21 return
backward_pass(*forward_pass(fun,args,kwargs,argnum))
22 return gradfun
23
~/anaconda3/lib/python3.6/site-packages/autograd/core.py in
forward_pass(fun, args, kwargs, argnum)
57 tape = CalculationTape()
58 arg_wrt = args[argnum]
---> 59 start_node = new_node(safe_type(getval(arg_wrt)),
[tape])
60 args = list(args)
61 args[argnum] = merge_tapes(start_node, arg_wrt)
~/anaconda3/lib/python3.6/site-packages/autograd/core.py in
new_node(value, tapes)
185 def new_node(value, tapes=[]):
186 try:
--> 187 return Node.type_mappings[type(value)](value, tapes)
188 except KeyError:
189 return NoDerivativeNode(value, tapes)
~/anaconda3/lib/python3.6/site-packages/autograd/numpy/numpy_extra.py
in new_array_node(value, tapes)
85 return array_dtype_mappings[value.dtype](value, tapes)
86 except KeyError:
---> 87 raise TypeError("Can't differentiate wrt numpy arrays
of dtype {0}".format(value.dtype))
88 Node.type_mappings[anp.ndarray] = new_array_node
89
TypeError: Can't differentiate wrt numpy arrays of dtype int64
Я действительно понятия не имею о том, что произошло.Я думаю, это может быть связано со структурой массива в NumPy.Или я забыл скачать какие-нибудь пакеты?Ниже приведены мои оригинальные коды.
# import statements
datapath = 'datasets/'
from autograd import numpy as np
# import automatic differentiator to compute gradient module
from autograd import grad
# gradient descent function
def gradient_descent(g,alpha,max_its,w):
# compute gradient module using autograd
gradient = grad(g)
# run the gradient descent loop
weight_history = [w] # weight history container
cost_history = [g(w)] # cost function history container
for k in range(max_its):
# evaluate the gradient
grad_eval = gradient(w)
# take gradient descent step
w = w - alpha*grad_eval
# record weight and cost
weight_history.append(w)
cost_history.append(g(w))
return weight_history,cost_history
# load in dataset
csvname = datapath + 'kleibers_law_data.csv'
data = np.loadtxt(csvname,delimiter=',')
# get input and output of dataset
x = data[:-1,:]
y = data[-1:,:]
x = np.log(x)
y = np.log(y)
#Data Initiation
alpha = 0.01
max_its = 1000
w = np.array([0,0])
#linear model
def model(x, w):
a = w[0] + np.dot(x.T, w[1:])
return a.T
def least_squares(w):
cost = np.sum((model(x,w)-y)**2)
return cost/float(np.size(y))
weight_h, cost_h = gradient_descent(least_squares, alpha, max_its, w)
# a)
k = np.linspace(-5.5, 7.5, 250)
y = weight_h[max_its][0] + k*weight_h[max_its][1]
plt.figure()
plt.plot(x, y, label='Linear Line', color='g')
plt.xlabel('log of mass')
plt.ylabel('log of metabolic rate')
plt.title("Answer Of a")
plt.legend()
plt.show()
# b)
w0 = weight_h[max_its][0]
w1 = weight_h[max_its][1]
print("Nonlinear relationship between the body mass x and the metabolic
rate y is " /
+ str(w0) + " + " + "log(xp)" + str(w1) + " = " + "log(yp)")
# c)
x2 = np.log(10)
Kj = np.exp(w0 + w1*x2)*1000/4.18
print("It needs " + str(Kj) + " calories")
Может кто-нибудь помочь мне разобраться?Большое спасибо.