added save_weights parameter to Scan

- Related with #343 it's now possible to avoid saving model weights in `scan_object`, which might be desirable for very long runs with very large networks, due to the memory cost of keeping the weights throughout the experiment.
- fixed a small bug in `AutoParams` where choosing `network=False` resulted in 'dense' to be split into characters
- `max_param_values` is now optional in `AutoScan` and instead created the issue to handle the underlying problem properly #367
- fixed all the tests accordingly to the change in `AutoScan` arguments

docs/Scan.md

@@ -38,7 +38,8 @@ Argument | Input | Description
 `minimize_loss` | bool | `reduction_metric` is a loss
 `disable_progress_bar` | bool | Disable live updating progress bar
 `print_params` | bool | Print each permutation hyperparameters
-`clear_tf_session` | bool | Clear backend session between permutations
+`clear_session` | bool | Clear backend session between permutations
+`save_weights` | bool | Save model weights (increases memory pressure for large models)
 
 NOTE: `boolean_limit` will only work if its the last argument in `Scan()` and the following bracket is on a newline:
 

talos/__init__.py

@@ -34,4 +34,4 @@
 del commands, scan, model, metrics, key
 del sub, keep_from_templates, template_sub, warnings
 
-__version__ = "0.6.2"
+__version__ = "0.6.3"

talos/autom8/autoparams.py

@@ -77,7 +77,7 @@ def _automated(self, shapes='fixed'):
         if self._network:
             self.networks()
         else:
-            self.params['network'] = 'dense'
+            self.params['network'] = ['dense']
         self.last_activations()
 
     def shapes(self, shapes='auto'):

talos/autom8/autoscan.py

@@ -2,8 +2,8 @@ class AutoScan:
 
     def __init__(self,
                  task,
-                 max_param_values,
-                 experiment_name):
+                 experiment_name,
+                 max_param_values=None):
 
         '''Configure the `AutoScan()` experiment and then use
         the property `start` in the returned class object to start
@@ -42,7 +42,9 @@ def start(self, x, y, **kwargs):
                                      **kwargs)
         except KeyError:
             p = talos.autom8.AutoParams(task=self.task)
-            p.resample_params(self.max_param_values)
+
+            if self.max_param_values is not None:
+                p.resample_params(self.max_param_values)
             params = p.params
             scan_object = talos.Scan(x=x,
                                      y=y,

test/commands/test_analyze.py

@@ -11,7 +11,7 @@ def test_analyze(scan_object):
     r = talos.Reporting(scan_object)
 
     # read from file
-    list_of_files = glob.glob('./test_latest/' + '/*.csv')
+    list_of_files = glob.glob('./testing_latest/' + '/*.csv')
 
     r = talos.Reporting(list_of_files[-1])
 

test/commands/test_autom8.py

@@ -37,25 +37,25 @@ def test_autom8():
 
     x, y = wrangle.utils.create_synth_data('binary', 50, 10, 1)
     p.losses(['binary_crossentropy'])
-    auto = talos.autom8.AutoScan('binary', 1, 'testinga')
+    auto = talos.autom8.AutoScan('binary', 'testinga', 1)
     scan_object = auto.start(x, y, params=p.params)
     talos.autom8.AutoPredict(scan_object, x, y, x, 'binary')
 
     x, y = wrangle.utils.create_synth_data('multi_label', 50, 10, 4)
     p.losses(['categorical_crossentropy'])
-    auto = talos.autom8.AutoScan('multi_label', 1, 'testingb')
+    auto = talos.autom8.AutoScan('multi_label', 'testingb', 1)
     auto.start(x, y, params=p.params)
     talos.autom8.AutoPredict(scan_object, x, y, x, 'multi_label')
 
     x, y = wrangle.utils.create_synth_data('multi_class', 50, 10, 3)
     p.losses(['sparse_categorical_crossentropy'])
-    auto = talos.autom8.AutoScan('multi_class', 1, 'testingc')
+    auto = talos.autom8.AutoScan('multi_class', 'testingc', 1)
     auto.start(x, y, params=p.params)
     talos.autom8.AutoPredict(scan_object, x, y, x, 'multi_class')
 
     x, y = wrangle.utils.create_synth_data('regression', 50, 10, 1)
     p.losses(['mae'])
-    auto = talos.autom8.AutoScan('continuous', 1, 'testingd')
+    auto = talos.autom8.AutoScan('continuous', 'testingd', 1)
     auto.start(x, y, params=p.params)
     talos.autom8.AutoPredict(scan_object, x, y, x, 'continuous')
 

test/commands/test_latest.py

@@ -33,7 +33,7 @@ def iris_model(x_train, y_train, x_val, y_val, params):
 
         out = model.fit(x_train,
                         y_train,
-                        callbacks=[talos.utils.ExperimentLogCallback('test_latest', params)],
+                        callbacks=[talos.utils.ExperimentLogCallback('testing_latest', params)],
                         batch_size=params['batch_size'],
                         epochs=params['epochs'],
                         validation_data=[x_val, y_val],
@@ -44,7 +44,7 @@ def iris_model(x_train, y_train, x_val, y_val, params):
     scan_object = talos.Scan(x, y,
                              model=iris_model,
                              params=p,
-                             experiment_name='test_latest',
+                             experiment_name='testing_latest',
                              round_limit=5,
                              reduction_method='gamify')
 

test/commands/test_scan.py

@@ -53,7 +53,7 @@ def iris_model(x_train, y_train, x_val, y_val, params):
                              y=y,
                              params=p,
                              model=iris_model,
-                             experiment_name='test',
+                             experiment_name='testingq',
                              val_split=0.3,
                              random_method='uniform_mersenne',
                              round_limit=15,
@@ -101,7 +101,7 @@ def iris_model(x_train, y_train, x_val, y_val, params):
                y=y,
                params=p,
                model=iris_model,
-               experiment_name="testint3",
+               experiment_name="testing3",
                x_val=None,
                y_val=None,
                val_split=0.3,