logsoftmax works

tensor.py

@@ -14,7 +14,11 @@ class Context:
     self.saved_tensors.extend(x)
 
 class Tensor:
-  def __init__(self, data, _children=()):
+  def __init__(self, data):
+    #print(type(data), data)
+    if type(data) != np.ndarray:
+      print("error constructing tensor with %r" % data)
+      assert(False)
     self.data = data
     self.grad = None
 
@@ -22,7 +26,7 @@ class Tensor:
     self._ctx = None
 
   def __str__(self):
-    return "Tensor of shape %r with grad %r" % (self.data.shape, self.grad)
+    return "Tensor %r with grad %r" % (self.data, self.grad)
 
   def backward(self, allow_fill=True):
     #print("running backward on", self)
@@ -37,7 +41,12 @@ class Tensor:
     assert(self.grad is not None)
 
     grads = self._ctx.arg.backward(self._ctx, self.grad)
+    if len(self._ctx.parents) == 1:
+      grads = [grads]
     for t,g in zip(self._ctx.parents, grads):
+      if g.shape != t.data.shape:
+        print("grad shape must match tensor shape in %r, %r != %r" % (self._ctx.arg, g.shape, t.data.shape))
+        assert(False)
       t.grad = g
       t.backward(False)
 
@@ -64,7 +73,7 @@ class ReLU(Function):
     input, = ctx.saved_tensors
     grad_input = grad_output.copy()
     grad_input[input < 0] = 0
-    return grad_input,
+    return grad_input
 register('relu', ReLU)
 
 class Dot(Function):
@@ -85,11 +94,27 @@ class Sum(Function):
   @staticmethod
   def forward(ctx, input):
     ctx.save_for_backward(input)
-    return input.sum()
+    return np.array(input.sum())
 
   @staticmethod
   def backward(ctx, grad_output):
-    input = ctx.saved_tensors
+    input, = ctx.saved_tensors
     return grad_output * np.ones_like(input)
 register('sum', Sum)
 
+class LogSoftmax(Function):
+  @staticmethod
+  def forward(ctx, input):
+    def logsumexp(x):
+      c = x.max(axis=1)
+      return c + np.log(np.exp(x-c.reshape((-1, 1))).sum(axis=1))
+    output = input - logsumexp(input)
+    ctx.save_for_backward(output)
+    return output
+
+  @staticmethod
+  def backward(ctx, grad_output):
+    output, = ctx.saved_tensors
+    return grad_output - np.exp(output)*grad_output.sum(axis=1).reshape((-1, 1))
+register('logsoftmax', LogSoftmax)
+