Question

这是我收到的错误：文件＆＃34; mtrand.pyx＆＃34;，第1192行，mtrand.RandomState.randint（numpy / random / mtrand / mtrand.c：14128）

我对编码有些新意，但我真的想开始使用简单的ANN，所以我决定开始这个项目。

TypeError：randint（）至少需要1个位置参数（0给定）

＃ - - 编码：utf-8 - - ＆＃34;＆＃34;＆＃34; 创建于2016年9月18日星期日14:56:44

@author: Jamoonie
"""

##theano practice 

import numpy as np
import theano
import theano.tensor as T
from sklearn.datasets import load_digits


digits=load_digits()
print (digits.data.shape)

train_x = list(digits.data)
#print train_x.count

train_x = np.array(train_x)
#print train_x

train_y = list(digits.target)
#print train_y.count

train_y = np.array(train_y)
#print train_y

#q = T.matrix('q')     checking how matrix dot products work, and how the row,col of the W0 should be set up
#q = np.zeros([5,10])
#print q
#p = T.matrix('p')
#p = np.zeros([10,5])
# 
#print np.dot(q,p)

nn_input_dim = train_x.shape[1] ## if shape[0] it yields 1797, which is the number of rows

print nn_input_dim    ##shows 64; shape[1] yields 1 row thus 64 columns! which are the layers of data we want to apply

nn_hdim0 = 10

nn_output_dim = len(train_y)

#nn_hdim0 = np.transpose(np.zeros(digits.data.shape))

#print nn_hdim0

epsilon = 0.008

batch_size = 100 ## how much data input per iteration

X = T.matrix('X')
y = T.lvector('y')

## set weight shapeswith random values
#W0 = np.transpose(np.zeros(digits.data.shape))
W0 = theano.shared(np.random.randn(nn_input_dim,nn_hdim0),name='W0') ##the shape of W0 should be row=input_dim, col=# hidden nodes

b0 = theano.shared(np.zeros(nn_hdim0),name='b0')

W1 = theano.shared(np.random.randn(nn_hdim0,nn_output_dim),name='W1') ## shape of W1 should have row=#hidden nodes, col = output dimension

b1 = theano.shared(np.zeros(nn_output_dim),name='b1')

z0 = X.dot(W0)+b0

a0 = T.nnet.softmax(z0) ## first hidden layer result

z1 = a0.dot(W1)+b1

a1 = T.nnet.softmax(z1)  ## final result or prediction

loss = T.nnet.categorical_crossentropy(a1,y).mean() ## howmuch the prediction differrs from the real result

prediction = T.argmax(a1,axis=1) ## the maximum values of a1, presented in index posn 1

fwd_propagation = theano.function([X],a1) ## forward propagation function dpeneding on the array of X values and final prediction

calc_loss = theano.function([X,y],loss) 

predict= theano.function([X],prediction)

accuracy = theano.function([X],T.sum(T.eq(prediction,train_y))) ## T.eq is elementwise. so this does an elementwise sum of prediction and train_y

dW0 = T.grad(loss,W0)
dW1 = T.grad(loss,W1)
db0=T.grad(loss,b0)
db1=T.grad(loss,b1)

np.random.randint()
gradient_step = theano.function(
    [X,y], ##for each set of X,y values
    updates=((W1,W1-epsilon*dW1),       ##updates W1 by deltaW1(error)*learning rate and subtracting from original W1
             (W0,W0-epsilon*dW0),
            (b1,b1-epsilon*db1),
            (b0,b0-epsilon*db0)))

def build(iterations = 80000):
    W1.set_value(np.random.randn(nn_hdim0,nn_output_dim)/np.sqrt(nn_input_dim)) ## why dividing by the sqrt of nn_input_dim,i'm not sure, but they're meant to be random anyway.
    W0.set_value(np.random.randn(nn_input_dim,nn_hdim0)/np.sqrt(nn_input_dim))
    b1.set_value(np.zeros(nn_output_dim))
    b0.set_value(np.zeros(nn_hdim0))
    for i in range(0, iterations):
        batch_indicies=np.random.randint(0,17,size=100)
        batch_x,batch_y=train_x[batch_indicies],train_y[batch_indicies]
        gradient_step(batch_x,batch_y)
        ##so we're providing the values now for the weights, biases and input output values
        if i%2000==0:
            print("loss after iteration %r: %r" % (i, calc_loss(train_x,train_y)))
            print(accuracy(train_x))
        if i==80000:
            print (W0,b0,W1,b1)

build()

Answer 1

根据documentation，您至少需要指定从分布中提取的最小整数值。如果你想要一个小于213的随机数（准确地介于0到213之间）那么你会做r = np.random.randint(213)，如果你想要一个范围之间的随机数，让我们说213和537然后你会做，r = np.random.randint(213, 537)。此外，您试图从randint(..)获取一个随机数，甚至不将其存储到任何变量（或传递给任何函数），这是无用的。我建议您开始使用基本的Theano tutorials，从here开始。

Theano ANN＆＃34; TypeError：randint（）至少需要1个位置参数（0给定）＆＃34;

1 个答案: