def test_sigmoid():
 def ref_sigmoid(x):
  if (x >= 0):
   return (1 / (1 + np.exp((- x))))
  else:
   z = np.exp(x)
   return (z / (1 + z))
 sigmoid = np.vectorize(ref_sigmoid)
 x = K.placeholder(ndim=2)
 f = K.function([x], [activations.sigmoid(x)])
 test_values = get_standard_values()
 result = f([test_values])[0]
 expected = sigmoid(test_values)
 assert_allclose(result, expected, rtol=1e-05)

def test_sigmoid ( ) : def ref_sigmoid ( x ) : if ( x >= 0 ) : return ( 1 / ( 1 + np . exp ( ( - x ) ) ) ) else : z = np . exp ( x ) return ( z / ( 1 + z ) ) sigmoid = np . vectorize ( ref_sigmoid ) x = K . placeholder ( ndim = 2 ) f = K . function ( [ x ] , [ activations . sigmoid ( x ) ] ) test_values = get_standard_values ( ) result = f ( [ test_values ] ) [ 0 ] expected = sigmoid ( test_values ) assert_allclose ( result , expected , rtol = 1e-05 )
test using a numerically stable reference sigmoid implementation .