recurrent Layers: Fix doc strings on default activations
diff --git a/keras/layers/recurrent.py b/keras/layers/recurrent.py
index c3ddd5c9448..f31286db26b 100644
--- a/keras/layers/recurrent.py
+++ b/keras/layers/recurrent.py
@@ -783,7 +783,8 @@ class SimpleRNNCell(Layer):
         units: Positive integer, dimensionality of the output space.
         activation: Activation function to use
             (see [activations](../activations.md)).
-            If you pass None, no activation is applied
+            Default: hyperbolic tangent (`tanh`).
+            If you pass `None`, no activation is applied
             (ie. "linear" activation: `a(x) = x`).
         use_bias: Boolean, whether the layer uses a bias vector.
         kernel_initializer: Initializer for the `kernel` weights matrix,
@@ -941,7 +942,8 @@ class SimpleRNN(RNN):
         units: Positive integer, dimensionality of the output space.
         activation: Activation function to use
             (see [activations](../activations.md)).
-            If you pass None, no activation is applied
+            Default: hyperbolic tangent (`tanh`).
+            If you pass `None`, no activation is applied
             (ie. "linear" activation: `a(x) = x`).
         use_bias: Boolean, whether the layer uses a bias vector.
         kernel_initializer: Initializer for the `kernel` weights matrix,
@@ -1153,11 +1155,15 @@ class GRUCell(Layer):
         units: Positive integer, dimensionality of the output space.
         activation: Activation function to use
             (see [activations](../activations.md)).
-            If you pass None, no activation is applied
+            Default: hyperbolic tangent (`tanh`).
+            If you pass `None`, no activation is applied
             (ie. "linear" activation: `a(x) = x`).
         recurrent_activation: Activation function to use
             for the recurrent step
             (see [activations](../activations.md)).
+            Default: hard sigmoid (`hard_sigmoid`).
+            If you pass `None`, no activation is applied
+            (ie. "linear" activation: `a(x) = x`).
         use_bias: Boolean, whether the layer uses a bias vector.
         kernel_initializer: Initializer for the `kernel` weights matrix,
             used for the linear transformation of the inputs
@@ -1393,11 +1399,15 @@ class GRU(RNN):
         units: Positive integer, dimensionality of the output space.
         activation: Activation function to use
             (see [activations](../activations.md)).
-            If you pass None, no activation is applied
+            Default: hyperbolic tangent (`tanh`).
+            If you pass `None`, no activation is applied
             (ie. "linear" activation: `a(x) = x`).
         recurrent_activation: Activation function to use
             for the recurrent step
             (see [activations](../activations.md)).
+            Default: hard sigmoid (`hard_sigmoid`).
+            If you pass `None`, no activation is applied
+            (ie. "linear" activation: `a(x) = x`).
         use_bias: Boolean, whether the layer uses a bias vector.
         kernel_initializer: Initializer for the `kernel` weights matrix,
             used for the linear transformation of the inputs
@@ -1632,11 +1642,15 @@ class LSTMCell(Layer):
         units: Positive integer, dimensionality of the output space.
         activation: Activation function to use
             (see [activations](../activations.md)).
-            If you pass None, no activation is applied
+            Default: hyperbolic tangent (`tanh`).
+            If you pass `None`, no activation is applied
             (ie. "linear" activation: `a(x) = x`).
         recurrent_activation: Activation function to use
             for the recurrent step
             (see [activations](../activations.md)).
+            Default: hard sigmoid (`hard_sigmoid`).
+            If you pass `None`, no activation is applied
+            (ie. "linear" activation: `a(x) = x`).x
         use_bias: Boolean, whether the layer uses a bias vector.
         kernel_initializer: Initializer for the `kernel` weights matrix,
             used for the linear transformation of the inputs
@@ -1896,11 +1910,15 @@ class LSTM(RNN):
         units: Positive integer, dimensionality of the output space.
         activation: Activation function to use
             (see [activations](../activations.md)).
-            If you pass None, no activation is applied
+            Default: hyperbolic tangent (`tanh`).
+            If you pass `None`, no activation is applied
             (ie. "linear" activation: `a(x) = x`).
         recurrent_activation: Activation function to use
             for the recurrent step
             (see [activations](../activations.md)).
+            Default: hard sigmoid (`hard_sigmoid`).
+            If you pass `None`, no activation is applied
+            (ie. "linear" activation: `a(x) = x`).
         use_bias: Boolean, whether the layer uses a bias vector.
         kernel_initializer: Initializer for the `kernel` weights matrix,
             used for the linear transformation of the inputs.

* The default activation for input is `tanh`, not `linear`, as
  the previous doc string might mislead readers to think.
* Also clarify that the default activation for recurrent is
  `hard_sigmoid`.