Merge branch 'master' of github.com:fchollet/keras

docs/mkdocs.yml

@@ -1,6 +1,6 @@
 site_name: Keras Documentation
 theme: readthedocs
-theme_dir: theme
+#theme_dir: theme
 docs_dir: sources
 repo_url: http://github.com/fchollet/keras
 site_url: http://keras.io/

keras/activations.py

@@ -4,18 +4,28 @@ from . import backend as K
 from .utils.generic_utils import deserialize_keras_object
 
 
-def softmax(x):
+def softmax(x, axis=-1):
+    """Softmax activation function.
+
+    # Arguments
+        x : Tensor.
+        axis: Integer, axis along which the softmax normalization is applied.
+
+    # Returns
+        Tensor, output of softmax transformation.
+
+    # Raises
+        ValueError: In case `dim(x) == 1`.
+    """
     ndim = K.ndim(x)
     if ndim == 2:
         return K.softmax(x)
-    elif ndim == 3:
-        e = K.exp(x - K.max(x, axis=-1, keepdims=True))
-        s = K.sum(e, axis=-1, keepdims=True)
+    elif ndim > 2:
+        e = K.exp(x - K.max(x, axis=axis, keepdims=True))
+        s = K.sum(e, axis=axis, keepdims=True)
         return e / s
     else:
-        raise ValueError('Cannot apply softmax to a tensor '
-                         'that is not 2D or 3D. '
-                         'Here, ndim=' + str(ndim))
+        raise ValueError('Cannot apply softmax to a tensor that is 1D')
 
 
 def elu(x, alpha=1.0):

keras/backend/tensorflow_backend.py

@@ -1136,8 +1136,7 @@ def any(x, axis=None, keepdims=False):
     """
     axis = _normalize_axis(axis, ndim(x))
     x = tf.cast(x, tf.bool)
-    x = tf.reduce_any(x, reduction_indices=axis, keep_dims=keepdims)
-    return tf.cast(x, tf.uint8)
+    return tf.reduce_any(x, reduction_indices=axis, keep_dims=keepdims)
 
 
 def all(x, axis=None, keepdims=False):
@@ -1153,8 +1152,7 @@ def all(x, axis=None, keepdims=False):
     """
     axis = _normalize_axis(axis, ndim(x))
     x = tf.cast(x, tf.bool)
-    x = tf.reduce_all(x, reduction_indices=axis, keep_dims=keepdims)
-    return tf.cast(x, tf.uint8)
+    return tf.reduce_all(x, reduction_indices=axis, keep_dims=keepdims)
 
 
 def argmax(x, axis=-1):

keras/layers/merge.py

@@ -197,8 +197,8 @@ class Concatenate(_Merge):
         for input_i, mask_i in zip(inputs, mask):
             if mask_i is None:
                 # Input is unmasked. Append all 1s to masks,
-                # but cast it to uint8 first
-                masks.append(K.cast(K.ones_like(input_i), 'uint8'))
+                # but cast it to bool first
+                masks.append(K.cast(K.ones_like(input_i), 'bool'))
             elif K.ndim(mask_i) < K.ndim(input_i):
                 # Mask is smaller than the input, expand it
                 masks.append(K.expand_dims(mask_i))

keras/legacy/layers.py

@@ -297,8 +297,8 @@ class Merge(Layer):
             for input_i, mask_i in zip(inputs, mask):
                 if mask_i is None:
                     # Input is unmasked. Append all 1s to masks,
-                    # but cast it to uint8 first
-                    masks.append(K.cast(K.ones_like(input_i), 'uint8'))
+                    # but cast it to bool first
+                    masks.append(K.cast(K.ones_like(input_i), 'bool'))
                 elif K.ndim(mask_i) < K.ndim(input_i):
                     # Mask is smaller than the input, expand it
                     masks.append(K.expand_dims(mask_i))