RNN

pchavanne · Jan 31, 2017 · 27c87e2 · 27c87e2
1 parent 981804b
commit 27c87e2
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Showing 2 changed files with 18 additions and 19 deletions.
diff --git a/yadll/activations.py b/yadll/activations.py
@@ -7,7 +7,7 @@
 
 
 def get_activation(activator):
-    """
+    r"""
     Call an activation function from an activator object
 
     Parameters
@@ -27,8 +27,8 @@ def get_activation(activator):
 
 
 def linear(x):
-    """Linear activation function
-    :math:`\\varphi(x) = x`
+    r"""Linear activation function
+    :math:`\varphi{x} = x`
 
     Parameters
     ----------
@@ -45,8 +45,8 @@ def linear(x):
 
 
 def sigmoid(x):
-    """Sigmoid function
-    :math:`\\varphi(x) = \\frac{1}{1 + e^{-x}}`
+    r"""Sigmoid function
+    :math:`\varphi(x) = \frac{1}{1 + e^{-x}}`
 
     Parameters
     ----------
@@ -63,8 +63,8 @@ def sigmoid(x):
 
 
 def ultra_fast_sigmoid(x):
-    """Ultra fast Sigmoid function return an approximated standard sigmoid
-    :math:`\\varphi(x) = \\frac{1}{1 + e^{-x}}`
+    r"""Ultra fast Sigmoid function return an approximated standard sigmoid
+    :math:`\varphi(x) = \frac{1}{1 + e^{-x}}`
 
     Parameters
     ----------
@@ -87,7 +87,7 @@ def ultra_fast_sigmoid(x):
 
 def tanh(x):
     """Tanh activation function
-    :math:`\\varphi(x) = \\tanh(x)`
+    :math:`\varphi(x) = \tanh(x)`
 
     Parameters
     ----------
@@ -104,9 +104,8 @@ def tanh(x):
 
 
 def softmax(x):
-    """Softmax activation function
-    :math:`\\varphi(\\mathbf{x})_j =
-    \\frac{e^{\mathbf{x}_j}}{\sum_{k=1}^K e^{\mathbf{x}_k}}`
+    r"""Softmax activation function
+    :math:`\varphi(x)_j = \frac{\exp{x_j}}{\sum_{k=1}^K \exp{x_k}}`
     where :math:`K` is the total number of neurons in the layer. This
     activation function gets applied row-wise.
 
@@ -125,7 +124,7 @@ def softmax(x):
 
 
 def softplus(x):
-    """Softplus activation function :math:`\\varphi(x) = \\log(1 + e^x)`
+    r"""Softplus activation function :math:`\varphi{x} = \log{1 + \exp{x}}`
 
     Parameters
     ----------
@@ -142,8 +141,8 @@ def softplus(x):
 
 
 def relu(x, alpha=0):
-    """Rectified linear unit activation function
-    :math:`\\varphi(x) = \\max(alpha * x, x)`
+    r"""Rectified linear unit activation function
+    :math:`\varphi{x} = \max{x, \alpha * x}`
 
     Parameters
     ----------

diff --git a/yadll/objectives.py b/yadll/objectives.py
@@ -41,7 +41,7 @@ def mean_absolute_error(prediction, target):
 def binary_hinge_error(prediction, target):
     r"""
     Binary Hinge Error: BHE
-    .. math:: hinge_i = \frac{1}{n} \sum_{j}{\max(1. - target_{i,j} * prediction_{i,j}, 0.)}
+    .. math:: BHE_i = \frac{1}{n} \sum_{j}{\max(1. - target_{i,j} * prediction_{i,j}, 0.)}
 
     """
     return T.mean(T.maximum(1. - target * prediction, 0.), axis=-1)
@@ -50,7 +50,7 @@ def binary_hinge_error(prediction, target):
 def categorical_hinge_error(prediction, target):
     r"""
     Categorical Hinge Error: CHE
-    .. math:: hinge_i = \frac{1}{n} \sum_{j}{\max(1. - target_{i,j} * prediction_{i,j}, 0.)}
+    .. math:: CHE_i = \frac{1}{n} \sum_{j}{\max(1. - target_{i,j} * prediction_{i,j}, 0.)}
 
     """
     return T.mean(T.maximum(1. - target * prediction, 0.), axis=-1)
@@ -60,8 +60,8 @@ def binary_crossentropy_error(prediction, target):
     r"""
     Binary Cross-entropy Error: BCE
 
-    .. math:: BCE_i = \frac{1}{n} \sum_{j}{-(target_{i,j} * \log(prediction_{i,j})
-        + (1 - target_{i,j}) * \log(1 - prediction_{i,j}))}
+    .. math:: BCE_i = - \frac{1}{n} \sum_{j}{target_{i,j} * \log(prediction_{i,j}
+        - (1 - target_{i,j}) * \log(1 - prediction_{i,j}))}
 
     """
     clip_pred = T.clip(prediction, EPSILON, 1 - EPSILON)
@@ -72,7 +72,7 @@ def categorical_crossentropy_error(prediction, target):
     r"""
     Categorical Cross-entropy Error: CCE
 
-    .. math:: MAE_i = \frac{1}{n} \sum_{j}{\big|{target_{i,j} - prediction_{i,j}\big|}
+    .. math:: CCE_i = - \frac{1}{n} \sum_{j}{target_{i,j} * \log(prediction_{i,j})}
 
     """
     prediction /= prediction.sum(axis=-1, keepdims=True)