Add support for creating tensors from lists

pygrad/tensor.py

@@ -7,6 +7,8 @@ class Tensor:
   # TODO Implement 'requires_grad' functionality.
   def __init__(self, value):
     # TODO Add support for scalar values.
+    if isinstance(value, list):
+      value = np.array(value)
     if not isinstance(value, np.ndarray):
       print(f"{type(value)} is not compatible with {np.ndarray}")
       exit(-1)
@@ -36,6 +38,16 @@ class Tensor:
     tensor._back = back
     return tensor
 
+  def sub(self, other):
+    tensor = Tensor(np.add(self.value, other.value))
+    tensor._save(self, other)
+
+    def back(upstream):
+      return np.dot(np.ones_like(self.value).T, upstream), -np.dot(np.ones_like(self.value).T, upstream)
+
+    tensor._back = back
+    return tensor
+
   def mul(self, other):
     tensor = Tensor(np.dot(self.value, other.value))
     tensor._save(self, other)
@@ -58,6 +70,16 @@ class Tensor:
     tensor._back = back
     return tensor
 
+  def neg(self):
+    tensor = Tensor(-self.value)
+    tensor._save(self)
+
+    def back(upstream):
+      return [np.dot(-np.ones_like(self.value), upstream)]
+
+    tensor._back = back
+    return tensor
+
   def expt(self, exponent):
     tensor = Tensor(self.value ** exponent)
     tensor._save(self)
@@ -91,6 +113,17 @@ class Tensor:
     tensor._back = back
     return tensor
 
+  def log(self):
+    tensor = Tensor(np.log(self.value))
+    tensor._save(self)
+
+    def back(upstream):
+      a, = tensor._parents
+      return [np.dot(1 / a.value, upstream)]
+
+    tensor._back = back
+    return tensor
+
   def tanh(self):
     tensor = Tensor(np.tanh(self.value))
     tensor._save(self)