Added version/get_() to dataset; allow Experiment to have single valu…

…e option; WIP: net.evaluate(verbose=-1)

conx/dataset.py

@@ -642,7 +642,8 @@ def __init__(self,
                  name=None,
                  description=None,
                  input_shapes=None,
-                 target_shapes=None):
+                 target_shapes=None,
+                 version="1.0.0"):
         """
         Dataset constructor.
 
@@ -660,6 +661,7 @@ def __init__(self,
         self.warn_categories = {}
         self.network = network
         self.name = name
+        self.version = version
         self.description = description
         self.DATASETS = {name: function for (name, function) in
                          inspect.getmembers(conx.datasets, inspect.isfunction)}
@@ -669,6 +671,21 @@ def __init__(self,
         if target_shapes is not None:
             self._target_shapes = target_shapes
 
+    def get_id(self):
+        """
+        Get the version number of the dataset.
+
+        >>> ds = Dataset(name="Test", version="1.0.0")
+        >>> ds.get_id()
+        'Test-1.0.0'
+
+        >>> ds.version = "2.0.1"
+        >>> ds.get_id()
+        'Test-2.0.1'
+
+        """
+        return "%s-%s" % (self.name, self.version)
+
     def __getattr__(self, item):
         """
         Construct a virtual Vector for easy access to internal

conx/network.py

@@ -862,7 +862,7 @@ def reset(self, clear=False, **overrides):
 
     def evaluate(self, batch_size=None, show=False, show_inputs=True, show_targets=True,
                  kverbose=0, sample_weight=None, steps=None, tolerance=None, force=False,
-                 max_col_width=15, select=None):
+                 max_col_width=15, select=None, verbose=0):
         """
         Evaluate the train and/or test sets.
 
@@ -917,6 +917,9 @@ def evaluate(self, batch_size=None, show=False, show_inputs=True, show_targets=T
             for i in range(len(self.model.metrics_names)):
                 print("    %15s: %10s" % (self.model.metrics_names[i], self.pf(results[i])))
             print("    %15s: %10s" % ("Total", num_test))
+            if verbose == -1:
+                ## FIXME: combine results from train and test
+                return results
         else: # all (or select data):
             if select is None:
                 print("All Data Results:")
@@ -937,6 +940,8 @@ def evaluate(self, batch_size=None, show=False, show_inputs=True, show_targets=T
             for i in range(len(self.model.metrics_names)):
                 print("    %15s: %10s" % (self.model.metrics_names[i], self.pf(results[i])))
             print("    %15s: %10s" % ("Total", len(self.dataset)))
+            if verbose == -1:
+                return results
 
     def _evaluate_range(self, slice, show_inputs, show_targets, batch_size,
                         kverbose, sample_weight, steps, force, max_col_width):

conx/utils.py

@@ -2221,9 +2221,9 @@ class Experiment():
     ...     return category, net
     >>> exp = Experiment("XOR")
     >>> exp.run(function,
-    ...         epochs=[5],
+    ...         epochs=5,
     ...         accuracy=[0.8],
-    ...         tolerance=[0.2],
+    ...         tolerance=0.2,
     ...         optimizer=["adam", "sgd"],
     ...         activation=["sigmoid", "relu"],
     ...         dir="/tmp/")
@@ -2287,7 +2287,8 @@ def run(self, function, trials=1, dir="./", save=True, cache=False, **options):
         """
         options = sorted(options.items())
         keys = [option[0] for option in options]
-        values = [option[1] for option in options]
+        values = [option[1] if isinstance(option[1], (tuple, list)) else [option[1]]
+                  for option in options]
         for trial in range(1, trials + 1):
             count = 1
             for combination in itertools.product(*values):