CODEWITHSUNDEEP
pythonnumpymatrixdeep-learningsoftmax
kapooraae489
kapooraae489

Reputation: 47

Numpy function type error: only size-1 arrays can be converted to Python scalars

class GCN:
  def __init__(self,alpha,adj,feature,hiddenlayer_neurons,output_layer_neurons):
    self.alpha=alpha
    self.adj=adj
    self.feature=feature
    self.hiddenlayer_neurons=hiddenlayer_neurons
    self.output_layer_neurons=output_layer_neurons
  
  def weightlayers(self):
    self.weights1= np.random.normal(loc=0,scale=0.5,size=(features.shape[1],self.hiddenlayer_neurons))
    print(features.shape)
    print(adj.shape)
    self.weights2= np.random.normal(loc=0,scale=0.5,size=(self.hiddenlayer_neurons,self.output_layer_neurons))
    self.bias1= np.random.normal(loc=0, scale=0.05, size=self.hiddenlayer_neurons)
    self.bias2=np.random.normal(loc=0, scale=0.05, size= self.output_layer_neurons)
    return self.weights1,self.weights2,self.bias1,self.bias2

  def sigmoid(self,x):
    sigma=1/(1+np.exp(-x))
    return sigma
  
  def softmax(self,inputs):
    inputs=inputs.astype(np.float)
    inputs=np.vectorize(inputs)
    f=np.exp(inputs) / float(sum(np.exp(inputs)))
    #f2 = np.vectorize(f)
    return f

  def forwardpropagation(self):
    self.weights1,self.weights2,self.bias1,self.bias2=self.weightlayers()

    self.bias1=(np.reshape(self.bias1,(-1,1))).T
    self.bias2=(np.reshape(self.bias2,(-1,1))).T
    print(self.bias1.ndim)
    #self.sigmoid=self.sigmoid()
    self.adj=self.adj.T
    self.input= self.adj.dot(self.feature).dot(self.weights1) + (self.bias1)
    print(self.input.shape)
    self.sigmaactivation= self.sigmoid(self.input)
    self.hiddeninput=(self.sigmaactivation @ self.weights2 ) + (self.bias2)
    self.output=self.softmax(self.hiddeninput)
    return self.output

For the softmax function it is throwing the above mentioned error. Following previous answers for somewhat similar question I tried to vectorize and convert it to float.But that does't seen to work.

When I vectorize it, I get this error :

TypeError: loop of ufunc does not support argument 0 of type vectorize which has no callable exp method.

Upvotes: 1

Views: 190

Answers (1)

hpaulj
hpaulj

Reputation: 231395

For inputs as 2d numeric array, you don't need all that vectorize or float conversion.

Consider a small 2d array (integer dtype, but doesn't matter):

In [110]: arr = np.arange(6).reshape(2,3)
In [111]: np.exp(arr)
Out[111]: 
array([[  1.        ,   2.71828183,   7.3890561 ],
       [ 20.08553692,  54.59815003, 148.4131591 ]])

sum is a python function, that does 1d summation - note the result is (3,) shape array. Trying to do a scalar float conversion on that produces your error:

In [112]: sum(np.exp(arr))
Out[112]: array([ 21.08553692,  57.31643186, 155.8022152 ])
In [113]: float(sum(np.exp(arr)))
Traceback (most recent call last):
  File "<ipython-input-113-0972ef0e1a76>", line 1, in <module>
    float(sum(np.exp(arr)))
TypeError: only size-1 arrays can be converted to Python scalars

np.sum does the sum on all values, returning one value. That's float, but that isn't important.

In [114]: np.sum(np.exp(arr))
Out[114]: 234.2041839862982

That can be used to scale the individual values:

In [115]: f=np.exp(arr)
     ...: f/np.sum(f)
Out[115]: 
array([[0.00426978, 0.01160646, 0.03154963],
       [0.08576079, 0.23312201, 0.63369132]])

Upvotes: 2

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