pygrad/tensor.py

@@ -1,13 +1,8 @@
 import numpy as np
 
-def tensor(*args, **kwargs):
-  return Tensor(*args, **kwargs)
-
 class Tensor:
   # TODO Implement 'requires_grad' functionality.
   def __init__(self, value):
-    # NOTE We technically could support both numpy arrays and scalar values,
-    # but it is too much work.
     if not isinstance(value, np.ndarray):
       print(f"{type(value)} is not compatible with {np.ndarray}")
       exit(-1)
@@ -48,17 +43,6 @@ class Tensor:
     tensor._back = back
     return tensor
 
-  def div(self, other):
-    tensor = Tensor(self.value / other.value)
-    tensor._save(self, other)
-
-    def back(upstream):
-      a, b = tensor._parents
-      return 1 / np.dot(b.value, upstream), -a.value / np.dot(b.value ** 2, upstream)
-
-    tensor._back = back
-    return tensor
-
   def expt(self, exponent):
     tensor = Tensor(self.value ** exponent)
     tensor._save(self)
@@ -92,18 +76,6 @@ class Tensor:
     tensor._back = back
     return tensor
 
-  def tanh(self):
-    tensor = Tensor(np.tanh(self.value))
-    tensor._save(self)
-
-    def back(upstream):
-      # dtanh(x)/dx = 1 - tanh2(x)
-      a, = tensor._parents
-      return [1 - np.dot(np.tanh(a.value) ** 2, upstream)]
-
-    tensor._back = back
-    return tensor
-
   # TODO Compute gradients only for tensors that need it.
   def _backprop(self, upstream):
     # Backprop through the tensor iff it has any parents.