title: Access outside functions and classes in Python
body: <p>How can i find the dimensions(.shape) of <strong>x</strong> outside the function without "global"</p>
  
  <pre><code>import numpy as np
  
  def f():
      x=np.array(([1.2],[3,4],[5,6]),dtype=float)
  </code></pre>
  
  <p>i would also like to know the same thing in this code.For example the dimensions of <strong>self.o_error</strong>.And generally anything inside a class</p>
  
  <pre><code>import numpy as np
  
  # X = (hours studying, hours sleeping), y = score on test, xPredicted = 4 hours studying &amp; 8 hours sleeping (input data for prediction)
  X = np.array(([2, 9], [1, 5], [3, 6]), dtype=float)
  y = np.array(([92], [86], [89]), dtype=float)
  xPredicted = np.array(([4,8]), dtype=float)
  
  # scale units
  X = X/np.amax(X, axis=0) # maximum of X array
  xPredicted = xPredicted/np.amax(xPredicted, axis=0) # maximum of xPredicted (our input data for the prediction)
  y = y/100 # max test score is 100
  
  class Neural_Network(object):
    def __init__(self):
    #parameters
      self.inputSize = 2
      self.outputSize = 1
      self.hiddenSize = 3
  
    #weights
      self.W1 = np.random.randn(self.inputSize, self.hiddenSize) # (3x2) weight matrix from input to hidden layer
      self.W2 = np.random.randn(self.hiddenSize, self.outputSize) # (3x1) weight matrix from hidden to output layer
  
    def forward(self, X):
      #forward propagation through our network
      self.z = np.dot(X, self.W1) # dot product of X (input) and first set of 3x2 weights
      self.z2 = self.sigmoid(self.z) # activation function
      self.z3 = np.dot(self.z2, self.W2) # dot product of hidden layer (z2) and second set of 3x1 weights
      o = self.sigmoid(self.z3) # final activation function
      return o
  
    def sigmoid(self, s):
      # activation function
      return 1/(1+np.exp(-s))
  
    def sigmoidPrime(self, s):
      #derivative of sigmoid
      return s * (1 - s)
  
    def backward(self, X, y, o):
      # backward propagate through the network
      self.o_error = y - o # error in output
      self.o_delta = self.o_error*self.sigmoidPrime(o) # applying derivative of sigmoid to error
  
      self.z2_error = self.o_delta.dot(self.W2.T) # z2 error: how much our hidden layer weights contributed to output error
      self.z2_delta = self.z2_error*self.sigmoidPrime(self.z2) # applying derivative of sigmoid to z2 error
  
      self.W1 += X.T.dot(self.z2_delta) # adjusting first set (input --&gt; hidden) weights
      self.W2 += self.z2.T.dot(self.o_delta) # adjusting second set (hidden --&gt; output) weights
  </code></pre>