Perform missing aet.nnet.sigmoid to aet.sigmoid replacements

doc/library/d3viz/index.ipynb

@@ -100,7 +100,7 @@
     "x = aet.dmatrix('x')\n",
     "wh = th.shared(rng.normal(0, 1, (nfeatures, nhiddens)), borrow=True)\n",
     "bh = th.shared(np.zeros(nhiddens), borrow=True)\n",
-    "h = aet.nnet.sigmoid(aet.dot(x, wh) + bh)\n",
+    "h = aet.sigmoid(aet.dot(x, wh) + bh)\n",
     "\n",
     "wy = th.shared(rng.normal(0, 1, (nhiddens, noutputs)))\n",
     "by = th.shared(np.zeros(noutputs), borrow=True)\n",
@@ -390,7 +390,7 @@
    "outputs": [],
    "source": [
     "x, y, z = aet.scalars('xyz')\n",
-    "e = aet.nnet.sigmoid((x + y + z)**2)\n",
+    "e = aet.sigmoid((x + y + z)**2)\n",
     "op = th.compile.builders.OpFromGraph([x, y, z], [e])\n",
     "\n",
     "e2 = op(x, y, z) + op(z, y, x)\n",

doc/library/d3viz/index.rst

@@ -63,7 +63,7 @@ hidden layer and a softmax output layer.
     x = aet.dmatrix('x')
     wh = th.shared(rng.normal(0, 1, (nfeatures, nhiddens)), borrow=True)
     bh = th.shared(np.zeros(nhiddens), borrow=True)
-    h = aet.nnet.sigmoid(aet.dot(x, wh) + bh)
+    h = aet.sigmoid(aet.dot(x, wh) + bh)
 
     wy = th.shared(rng.normal(0, 1, (nhiddens, noutputs)))
     by = th.shared(np.zeros(noutputs), borrow=True)
@@ -211,7 +211,7 @@ node defines a nested graph, which will be visualized accordingly by ``d3viz``.
 .. code:: python
 
     x, y, z = aet.scalars('xyz')
-    e = aet.nnet.sigmoid((x + y + z)**2)
+    e = aet.sigmoid((x + y + z)**2)
     op = th.compile.builders.OpFromGraph([x, y, z], [e])
 
     e2 = op(x, y, z) + op(z, y, x)

doc/library/scan.rst

@@ -276,9 +276,9 @@ the following:
    trng = aesara.tensor.random.utils.RandomStream(1234)
 
    def OneStep(vsample) :
-       hmean = aet.nnet.sigmoid(aesara.dot(vsample, W) + bhid)
+       hmean = aet.sigmoid(aesara.dot(vsample, W) + bhid)
        hsample = trng.binomial(size=hmean.shape, n=1, p=hmean)
-       vmean = aet.nnet.sigmoid(aesara.dot(hsample, W.T) + bvis)
+       vmean = aet.sigmoid(aesara.dot(hsample, W.T) + bvis)
        return trng.binomial(size=vsample.shape, n=1, p=vmean,
                             dtype=aesara.config.floatX)
 
@@ -358,9 +358,9 @@ updated:
 
     # OneStep, with explicit use of the shared variables (W, bvis, bhid)
     def OneStep(vsample, W, bvis, bhid):
-        hmean = aet.nnet.sigmoid(aesara.dot(vsample, W) + bhid)
+        hmean = aet.sigmoid(aesara.dot(vsample, W) + bhid)
         hsample = trng.binomial(size=hmean.shape, n=1, p=hmean)
-        vmean = aet.nnet.sigmoid(aesara.dot(hsample, W.T) + bvis)
+        vmean = aet.sigmoid(aesara.dot(hsample, W.T) + bvis)
         return trng.binomial(size=vsample.shape, n=1, p=vmean,
                          dtype=aesara.config.floatX)
 
@@ -394,9 +394,9 @@ Using the original Gibbs sampling example, with ``strict=True`` added to the
 
     # Same OneStep as in original example.
     def OneStep(vsample) :
-        hmean = aet.nnet.sigmoid(aesara.dot(vsample, W) + bhid)
+        hmean = aet.sigmoid(aesara.dot(vsample, W) + bhid)
         hsample = trng.binomial(size=hmean.shape, n=1, p=hmean)
-        vmean = aet.nnet.sigmoid(aesara.dot(hsample, W.T) + bvis)
+        vmean = aet.sigmoid(aesara.dot(hsample, W.T) + bvis)
         return trng.binomial(size=vsample.shape, n=1, p=vmean,
                              dtype=aesara.config.floatX)
 
@@ -423,9 +423,9 @@ variables passed explicitly to ``OneStep`` and to scan:
 
     # OneStep, with explicit use of the shared variables (W, bvis, bhid)
     def OneStep(vsample, W, bvis, bhid) :
-        hmean = aet.nnet.sigmoid(aesara.dot(vsample, W) + bhid)
+        hmean = aet.sigmoid(aesara.dot(vsample, W) + bhid)
         hsample = trng.binomial(size=hmean.shape, n=1, p=hmean)
-        vmean = aet.nnet.sigmoid(aesara.dot(hsample, W.T) + bvis)
+        vmean = aet.sigmoid(aesara.dot(hsample, W.T) + bvis)
         return trng.binomial(size=vsample.shape, n=1, p=vmean,
                              dtype=aesara.config.floatX)
 

doc/library/tensor/nnet/basic.rst

@@ -54,7 +54,7 @@
 
        x, y, b = aet.dvectors('x', 'y', 'b')
        W = aet.dmatrix('W')
-       y = aet.nnet.sigmoid(aet.dot(W, x) + b)
+       y = aet.sigmoid(aet.dot(W, x) + b)
 
    .. note:: The underlying code will return an exact 0 or 1 if an
       element of x is too small or too big.
@@ -174,8 +174,8 @@
        x, y, b, c = aet.dvectors('x', 'y', 'b', 'c')
        W = aet.dmatrix('W')
        V = aet.dmatrix('V')
-       h = aet.nnet.sigmoid(aet.dot(W, x) + b)
-       x_recons = aet.nnet.sigmoid(aet.dot(V, h) + c)
+       h = aet.sigmoid(aet.dot(W, x) + b)
+       x_recons = aet.sigmoid(aet.dot(V, h) + c)
        recon_cost = aet.nnet.binary_crossentropy(x_recons, x).mean()
 
 .. function:: sigmoid_binary_crossentropy(output,target)
@@ -203,11 +203,11 @@
        x, y, b, c = aet.dvectors('x', 'y', 'b', 'c')
        W = aet.dmatrix('W')
        V = aet.dmatrix('V')
-       h = aet.nnet.sigmoid(aet.dot(W, x) + b)
+       h = aet.sigmoid(aet.dot(W, x) + b)
        x_precons = aet.dot(V, h) + c
        # final reconstructions are given by sigmoid(x_precons), but we leave
        # them unnormalized as sigmoid_binary_crossentropy applies sigmoid
-       recon_cost = aet.nnet.sigmoid_binary_crossentropy(x_precons, x).mean()
+       recon_cost = aet.sigmoid_binary_crossentropy(x_precons, x).mean()
 
 .. function:: categorical_crossentropy(coding_dist,true_dist)
 

tests/d3viz/models.py

@@ -20,7 +20,7 @@ class Mlp:
         x = dmatrix("x")
         wh = shared(self.rng.normal(0, 1, (nfeatures, nhiddens)), borrow=True)
         bh = shared(np.zeros(nhiddens), borrow=True)
-        h = aet.nnet.sigmoid(aet.dot(x, wh) + bh)
+        h = aet.sigmoid(aet.dot(x, wh) + bh)
 
         wy = shared(self.rng.normal(0, 1, (nhiddens, noutputs)))
         by = shared(np.zeros(noutputs), borrow=True)
@@ -46,7 +46,7 @@ class OfgNested:
 class Ofg:
     def __init__(self):
         x, y, z = scalars("xyz")
-        e = aet.nnet.sigmoid((x + y + z) ** 2)
+        e = aet.sigmoid((x + y + z) ** 2)
         op = OpFromGraph([x, y, z], [e])
         e2 = op(x, y, z) + op(z, y, x)
 
@@ -57,7 +57,7 @@ class Ofg:
 class OfgSimple:
     def __init__(self):
         x, y, z = scalars("xyz")
-        e = aet.nnet.sigmoid((x + y + z) ** 2)
+        e = aet.sigmoid((x + y + z) ** 2)
         op = OpFromGraph([x, y, z], [e])
         e2 = op(x, y, z)