Refactoring: Simplified some code by using the to_list function.

keras/engine/training.py

@@ -25,6 +25,7 @@
 from .. import losses
 from .. import metrics as metrics_module
 from ..utils.generic_utils import slice_arrays
+from ..utils.generic_utils import to_list
 from ..utils.generic_utils import unpack_singleton
 from ..legacy import interfaces
 
@@ -155,8 +156,7 @@ def compile(self, optimizer,
         masks = self.compute_mask(self.inputs, mask=None)
         if masks is None:
             masks = [None for _ in self.outputs]
-        if not isinstance(masks, list):
-            masks = [masks]
+        masks = to_list(masks)
 
         # Prepare loss weights.
         if loss_weights is None:

keras/engine/training_arrays.py

@@ -14,6 +14,7 @@
 from .. import callbacks as cbks
 from ..utils.generic_utils import Progbar
 from ..utils.generic_utils import slice_arrays
+from ..utils.generic_utils import to_list
 from ..utils.generic_utils import unpack_singleton
 
 
@@ -152,8 +153,7 @@ def fit_loop(model, f, ins,
                 callbacks.on_batch_begin(step_index, batch_logs)
                 outs = f(ins)
 
-                if not isinstance(outs, list):
-                    outs = [outs]
+                outs = to_list(outs)
                 for l, o in zip(out_labels, outs):
                     batch_logs[l] = o
 
@@ -165,8 +165,7 @@ def fit_loop(model, f, ins,
                 val_outs = test_loop(model, val_f, val_ins,
                                      steps=validation_steps,
                                      verbose=0)
-                if not isinstance(val_outs, list):
-                    val_outs = [val_outs]
+                val_outs = to_list(val_outs)
                 # Same labels assumed.
                 for l, o in zip(out_labels, val_outs):
                     epoch_logs['val_' + l] = o
@@ -198,8 +197,7 @@ def fit_loop(model, f, ins,
                     ins_batch[i] = ins_batch[i].toarray()
 
                 outs = f(ins_batch)
-                if not isinstance(outs, list):
-                    outs = [outs]
+                outs = to_list(outs)
                 for l, o in zip(out_labels, outs):
                     batch_logs[l] = o
 
@@ -212,8 +210,7 @@ def fit_loop(model, f, ins,
                         val_outs = test_loop(model, val_f, val_ins,
                                              batch_size=batch_size,
                                              verbose=0)
-                        if not isinstance(val_outs, list):
-                            val_outs = [val_outs]
+                        val_outs = to_list(val_outs)
                         # Same labels assumed.
                         for l, o in zip(out_labels, val_outs):
                             epoch_logs['val_' + l] = o
@@ -267,8 +264,7 @@ def predict_loop(model, f, ins, batch_size=32, verbose=0, steps=None):
         unconcatenated_outs = []
         for step in range(steps):
             batch_outs = f(ins)
-            if not isinstance(batch_outs, list):
-                batch_outs = [batch_outs]
+            batch_outs = to_list(batch_outs)
             if step == 0:
                 for batch_out in batch_outs:
                     unconcatenated_outs.append([])
@@ -296,8 +292,7 @@ def predict_loop(model, f, ins, batch_size=32, verbose=0, steps=None):
                 ins_batch[i] = ins_batch[i].toarray()
 
             batch_outs = f(ins_batch)
-            if not isinstance(batch_outs, list):
-                batch_outs = [batch_outs]
+            batch_outs = to_list(batch_outs)
             if batch_index == 0:
                 # Pre-allocate the results arrays.
                 for batch_out in batch_outs:

keras/engine/training_generator.py

@@ -12,6 +12,7 @@
 from ..utils.data_utils import GeneratorEnqueuer
 from ..utils.data_utils import OrderedEnqueuer
 from ..utils.generic_utils import Progbar
+from ..utils.generic_utils import to_list
 from ..utils.generic_utils import unpack_singleton
 from .. import callbacks as cbks
 
@@ -211,8 +212,7 @@ def fit_generator(model,
                                             sample_weight=sample_weight,
                                             class_weight=class_weight)
 
-                if not isinstance(outs, list):
-                    outs = [outs]
+                outs = to_list(outs)
                 for l, o in zip(out_labels, outs):
                     batch_logs[l] = o
 
@@ -236,8 +236,7 @@ def fit_generator(model,
                             batch_size=batch_size,
                             sample_weight=val_sample_weights,
                             verbose=0)
-                    if not isinstance(val_outs, list):
-                        val_outs = [val_outs]
+                    val_outs = to_list(val_outs)
                     # Same labels assumed.
                     for l, o in zip(out_labels, val_outs):
                         epoch_logs['val_' + l] = o
@@ -342,8 +341,7 @@ def evaluate_generator(model, generator,
                                  'or (x, y). Found: ' +
                                  str(generator_output))
             outs = model.test_on_batch(x, y, sample_weight=sample_weight)
-            if not isinstance(outs, list):
-                outs = [outs]
+            outs = to_list(outs)
             outs_per_batch.append(outs)
 
             if x is None or len(x) == 0:
@@ -450,8 +448,7 @@ def predict_generator(model, generator,
                 x = generator_output
 
             outs = model.predict_on_batch(x)
-            if not isinstance(outs, list):
-                outs = [outs]
+            outs = to_list(outs)
 
             if not all_outs:
                 for out in outs:

keras/engine/training_utils.py

@@ -9,6 +9,7 @@
 
 from .. import backend as K
 from .. import losses
+from ..utils.generic_utils import to_list
 
 
 def standardize_single_array(x):
@@ -321,8 +322,7 @@ def collect_metrics(metrics, output_names):
         nested_metrics = []
         for name in output_names:
             output_metrics = metrics.get(name, [])
-            if not isinstance(output_metrics, list):
-                output_metrics = [output_metrics]
+            output_metrics = to_list(output_metrics)
             nested_metrics.append(output_metrics)
         return nested_metrics
     else:

keras/legacy/layers.py

@@ -8,6 +8,7 @@
 from ..engine import Layer, InputSpec
 from .. import backend as K
 from ..utils import conv_utils
+from ..utils.generic_utils import to_list
 from .. import regularizers
 from .. import constraints
 from .. import activations
@@ -521,10 +522,8 @@ def __call__(self, inputs, initial_state=None, **kwargs):
             # Compute the full input spec, including state
             input_spec = self.input_spec
             state_spec = self.state_spec
-            if not isinstance(input_spec, list):
-                input_spec = [input_spec]
-            if not isinstance(state_spec, list):
-                state_spec = [state_spec]
+            input_spec = to_list(input_spec)
+            state_spec = to_list(state_spec)
             self.input_spec = input_spec + state_spec
 
             # Compute the full inputs, including state

keras/utils/multi_gpu_utils.py

@@ -224,8 +224,7 @@ def get_slice(data, i, parts):
                 # Apply model on slice
                 # (creating a model replica on the target device).
                 outputs = model(inputs)
-                if not isinstance(outputs, list):
-                    outputs = [outputs]
+                outputs = to_list(outputs)
 
                 # Save the outputs for merging back together later.
                 for o in range(len(outputs)):

keras/wrappers/scikit_learn.py

@@ -11,6 +11,7 @@
 
 from ..utils.np_utils import to_categorical
 from ..utils.generic_utils import has_arg
+from ..utils.generic_utils import to_list
 from ..models import Sequential
 
 
@@ -291,8 +292,7 @@ def score(self, x, y, **kwargs):
             y = to_categorical(y)
 
         outputs = self.model.evaluate(x, y, **kwargs)
-        if not isinstance(outputs, list):
-            outputs = [outputs]
+        outputs = to_list(outputs)
         for name, output in zip(self.model.metrics_names, outputs):
             if name == 'acc':
                 return output