Feed Forward Neural Networks Added!

README.md

@@ -10,15 +10,11 @@ its extensive use of unittests assures a high level of reliability and correctne
 
 News
 ''''''''''''''''''''''''''''''''''''''''''''''''''''
-- Auto encoder module added including denoising, sparse, contractive, slowness AE's
-- Unittests added, examples
-- tutorials added
+- Feed Forward neural networks will be added
 
-- Upcoming (mid-term): Feed Forward neural networks will be added
-
-- Future: MDP integration
 - Future: Deep Boltzmann machines will be added
-- Future: RBM/DBM in tensorFlow
+- Future: RBM/DBM in tensorFlow / PyTorch
+
 
 Features index
 ''''''''''''''''''''''''''''''''''''''''''''''''''''

pydeep/base/basicstructure.py

@@ -303,8 +303,7 @@ def _add_hidden_units(self,
                 new_weights = numx.random.randn(self.input_dim, num_new_hiddens) * initial_weights
             else:
                 new_weights = initial_weights
-        self.w = numx.array(numx.insert(self.w, numx.ones(num_new_hiddens) * position, new_weights, axis=1),
-                            self.dtype)
+        self.w = numx.array(numx.insert(self.w, numx.array(numx.ones(num_new_hiddens) * position,dtype=int), new_weights, axis=1),self.dtype)
 
         # AUTO   -> Initialized to Hidden range mean
         # Scalar -> Initialized to given value
@@ -316,7 +315,7 @@ def _add_hidden_units(self,
                 new_oh = numx.zeros((1, num_new_hiddens)) + initial_offsets
             else:
                 new_oh = initial_offsets
-        self.oh = numx.array(numx.insert(self.oh, numx.ones(num_new_hiddens) * position, new_oh, axis=1), self.dtype)
+        self.oh = numx.array(numx.insert(self.oh, numx.array(numx.ones(num_new_hiddens) * position,dtype=int), new_oh, axis=1),self.dtype)
 
         # AUTO   -> Initialized to randn()*0.01
         # Scalar -> Initialized to given value + randn()*0.01
@@ -331,7 +330,7 @@ def _add_hidden_units(self,
                     new_bias = initial_bias + numx.zeros((1, num_new_hiddens))
                 else:
                     new_bias = numx.array(initial_bias, dtype=self.dtype)
-        self.bh = numx.array(numx.insert(self.bh, numx.ones(num_new_hiddens) * position, new_bias, axis=1), self.dtype)
+        self.bh = numx.array(numx.insert(self.bh, numx.array(numx.ones(num_new_hiddens) * position,dtype=int), new_bias, axis=1), self.dtype)
 
         self.output_dim = self.w.shape[1]
 
@@ -382,9 +381,9 @@ def _add_visible_units(self,
                 data = numx.concatenate(data)
             new_data_mean = data.mean(axis=0).reshape(1, num_new_visibles)
             new_data_std = data.std(axis=0).reshape(1, num_new_visibles)
-        self._data_mean = numx.array(numx.insert(self._data_mean, numx.ones(num_new_visibles) * position,
+        self._data_mean = numx.array(numx.insert(self._data_mean, numx.array(numx.ones(num_new_visibles)* position, dtype=int),
                                                  new_data_mean, axis=1), self.dtype)
-        self._data_std = numx.array(numx.insert(self._data_std, numx.ones(num_new_visibles) * position,
+        self._data_std = numx.array(numx.insert(self._data_std, numx.array(numx.ones(num_new_visibles) * position, dtype=int),
                                                 new_data_std, axis=1), self.dtype)
 
         # AUTO   -> Small random values out of 
@@ -400,7 +399,7 @@ def _add_visible_units(self,
                 new_weights = numx.random.randn(num_new_visibles, self.output_dim) * initial_weights
             else:
                 new_weights = initial_weights
-        self.w = numx.array(numx.insert(self.w, numx.ones(num_new_visibles) * position, new_weights, axis=0),
+        self.w = numx.array(numx.insert(self.w, numx.array(numx.ones(num_new_visibles) * position,dtype = int), new_weights, axis=0),
                             self.dtype)
 
         if initial_offsets is 'AUTO':
@@ -413,7 +412,7 @@ def _add_visible_units(self,
                 new_ov = numx.zeros((1, num_new_visibles)) + initial_offsets
             else:
                 new_ov = initial_offsets
-        self.ov = numx.array(numx.insert(self.ov, numx.ones(num_new_visibles) * position, new_ov, axis=1), self.dtype)
+        self.ov = numx.array(numx.insert(self.ov, numx.array(numx.ones(num_new_visibles) * position,dtype = int), new_ov, axis=1), self.dtype)
 
         # AUTO   -> data != None -> Initialized to the inverse sigmoid of 
         #           data mean
@@ -430,7 +429,7 @@ def _add_visible_units(self,
                 new_bias = numx.zeros((1, num_new_visibles)) + initial_bias
             else:
                 new_bias = initial_bias
-        self.bv = numx.array(numx.insert(self.bv, numx.ones(num_new_visibles) * position, new_bias, axis=1), self.dtype)
+        self.bv = numx.array(numx.insert(self.bv, numx.array(numx.ones(num_new_visibles) * position,dtype = int), new_bias, axis=1), self.dtype)
         self.input_dim = self.w.shape[0]
 
     def _remove_visible_units(self, indices):

pydeep/fnn/__init__.py

@@ -0,0 +1,33 @@
+''' Module initializer includes all sub-modules.
+        
+    :Version:
+        1.0
+        
+    :Date:
+        08.02.2016
+
+    :Author:
+        Jan Melchior
+
+    :Contact:
+        JanMelchior@gmx.de
+
+    :License:
+
+        Copyright (C) 2016 Jan Melchior
+
+        This program is free software: you can redistribute it and/or modify
+        it under the terms of the GNU General Public License as published by
+        the Free Software Foundation, either version 3 of the License, or
+        (at your option) any later version.
+
+        This program is distributed in the hope that it will be useful,
+        but WITHOUT ANY WARRANTY; without even the implied warranty of
+        MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+        GNU General Public License for more details.
+
+        You should have received a copy of the GNU General Public License
+        along with this program.  If not, see <http://www.gnu.org/licenses/>.
+
+'''
+__all__ = ["layer", "model", "trainer"]