Merge 8eb245c into d221c57

sknn/.ropeproject/config.py

@@ -0,0 +1,86 @@
+# The default ``config.py``
+
+
+def set_prefs(prefs):
+    """This function is called before opening the project"""
+
+    # Specify which files and folders to ignore in the project.
+    # Changes to ignored resources are not added to the history and
+    # VCSs.  Also they are not returned in `Project.get_files()`.
+    # Note that ``?`` and ``*`` match all characters but slashes.
+    # '*.pyc': matches 'test.pyc' and 'pkg/test.pyc'
+    # 'mod*.pyc': matches 'test/mod1.pyc' but not 'mod/1.pyc'
+    # '.svn': matches 'pkg/.svn' and all of its children
+    # 'build/*.o': matches 'build/lib.o' but not 'build/sub/lib.o'
+    # 'build//*.o': matches 'build/lib.o' and 'build/sub/lib.o'
+    prefs['ignored_resources'] = ['*.pyc', '*~', '.ropeproject',
+                                  '.hg', '.svn', '_svn', '.git', 
+                                  '__pycache__']
+
+    # Specifies which files should be considered python files.  It is
+    # useful when you have scripts inside your project.  Only files
+    # ending with ``.py`` are considered to be python files by
+    # default.
+    #prefs['python_files'] = ['*.py']
+
+    # Custom source folders:  By default rope searches the project
+    # for finding source folders (folders that should be searched
+    # for finding modules).  You can add paths to that list.  Note
+    # that rope guesses project source folders correctly most of the
+    # time; use this if you have any problems.
+    # The folders should be relative to project root and use '/' for
+    # separating folders regardless of the platform rope is running on.
+    # 'src/my_source_folder' for instance.
+    #prefs.add('source_folders', 'src')
+
+    # You can extend python path for looking up modules
+    #prefs.add('python_path', '~/python/')
+
+    # Should rope save object information or not.
+    prefs['save_objectdb'] = True
+    prefs['compress_objectdb'] = False
+
+    # If `True`, rope analyzes each module when it is being saved.
+    prefs['automatic_soa'] = True
+    # The depth of calls to follow in static object analysis
+    prefs['soa_followed_calls'] = 0
+
+    # If `False` when running modules or unit tests "dynamic object
+    # analysis" is turned off.  This makes them much faster.
+    prefs['perform_doa'] = True
+
+    # Rope can check the validity of its object DB when running.
+    prefs['validate_objectdb'] = True
+
+    # How many undos to hold?
+    prefs['max_history_items'] = 32
+
+    # Shows whether to save history across sessions.
+    prefs['save_history'] = True
+    prefs['compress_history'] = False
+
+    # Set the number spaces used for indenting.  According to
+    # :PEP:`8`, it is best to use 4 spaces.  Since most of rope's
+    # unit-tests use 4 spaces it is more reliable, too.
+    prefs['indent_size'] = 4
+
+    # Builtin and c-extension modules that are allowed to be imported
+    # and inspected by rope.
+    prefs['extension_modules'] = []
+
+    # Add all standard c-extensions to extension_modules list.
+    prefs['import_dynload_stdmods'] = True
+
+    # If `True` modules with syntax errors are considered to be empty.
+    # The default value is `False`; When `False` syntax errors raise
+    # `rope.base.exceptions.ModuleSyntaxError` exception.
+    prefs['ignore_syntax_errors'] = False
+
+    # If `True`, rope ignores unresolvable imports.  Otherwise, they
+    # appear in the importing namespace.
+    prefs['ignore_bad_imports'] = False
+
+
+def project_opened(project):
+    """This function is called after opening the project"""
+    # Do whatever you like here!

sknn/mlp.py

@@ -186,7 +186,11 @@ def _create_layer(self, name, layer, irange):
             return mlp.Softmax(
                 layer_name=layer.name,
                 n_classes=layer.units,
-                irange=irange)
+                irange=irange,
+                init_bias_target_marginals=layer.init_bias_target_marginals)
+
+        if layer.type == "Pretrained":
+            return mlp.PretrainedLayer(layer_name=layer.name,layer_content=layer.content)
 
     def _create_mlp(self):
         mlp.logger.setLevel(logging.WARNING)
@@ -205,6 +209,10 @@ def _create_mlp(self):
                 lim *= numpy.sqrt(2)
             elif layer.type == 'Sigmoid':
                 lim *= 4
+            if layer.init_bias_target_marginals is not None:
+                X, y = layer.init_bias_target_marginals
+                layer.init_bias_target_marginals = self._create_matrix_input(X=X, y=y)[0]
+                layer.init_bias_target_marginals.y_labels = y
 
             mlp_layer = self._create_layer(layer.name, layer, irange=lim)
             mlp_layers.append(mlp_layer)
@@ -221,7 +229,7 @@ def _create_mlp(self):
 
         for l, p, count in zip(self.layers, self.mlp.layers, self.unit_counts[1:]):
             space = p.get_output_space()
-            if isinstance(l, Convolution):                
+            if isinstance(l, Convolution):
                 log.debug("  - Convl: {}{: <10}{} Output: {}{: <10}{} Channels: {}{}{}".format(
                     ansi.BOLD, l.type, ansi.ENDC,
                     ansi.BOLD, repr(space.shape), ansi.ENDC,
@@ -254,7 +262,7 @@ def _create_specs(self, X, y=None):
 
         # Then compute the number of units in each layer for initialization.
         self.unit_counts = [numpy.product(X.shape[1:]) if self.is_convolution else X.shape[1]]
-        res = X.shape[1:3] if self.is_convolution else None 
+        res = X.shape[1:3] if self.is_convolution else None
         for l in self.layers:
             if isinstance(l, Convolution):
                 if l.border_mode == 'valid':
@@ -296,6 +304,7 @@ def _initialize(self, X, y):
         self.trainer = self._create_mlp_trainer(self.vs)
         self.trainer.setup(self.mlp, self.ds)
 
+
     @property
     def is_initialized(self):
         """Check if the neural network was setup already.

sknn/nn.py

@@ -85,6 +85,7 @@ def __init__(
             units=None,
             pieces=None,
             weight_decay=None,
+            init_bias_target_marginals=None,
             dropout=None):
 
         assert warning is None,\
@@ -100,6 +101,9 @@ def __init__(
         self.pieces = pieces
         self.weight_decay = weight_decay
         self.dropout = dropout
+        if init_bias_target_marginals is not None:
+            assert type == 'Softmax', 'The init_bias_target_marginals is only defined for softmax layer.'
+        self.init_bias_target_marginals = init_bias_target_marginals
 
     def set_params(self, **params):
         """Setter for internal variables that's compatible with ``scikit-learn``.