Updated version number to 0.0.1 for release. Some final changes.

MANIFEST.in

@@ -0,0 +1,4 @@
+include LICENSE
+include README.md
+include tox.ini
+graft tests

README.md

@@ -3,8 +3,8 @@
 ## Introduction
 Keras-surgeon provides simple methods for modifying trained 
 [Keras][] models. The following functionality is currently implemented:
-* Delete neurons/channels from layers
-* Delete layers
+* delete neurons/channels from layers
+* delete layers
 * insert layers
 * replace layers
 
@@ -17,21 +17,28 @@ inspired the name of this package.
 The `operations` module contains simple methods to perform network surgery on a 
 single layer within a model.\
 Example usage:
-
+```python
+from kerassurgeon.operations import delete_layer, insert_layer, delete_channels
+# delete layer_1 from a model
+model = delete_layer(model, layer_1)
+# insert new_layer_1 before layer_2 in a model
+model = insert_layer(model, layer_2, new_layer_3)
+# delete channels 0, 4 and 67 from layer_2 in model
+model = delete_channels(model, layer_2, [0,4,67])
+```
 
 The `Surgeon` class enables many modifications to be performed in a single operation.\
 Example usage:
 ```python
-# model is a Keras model
-# layer_1 and layer_2 are Keras layers from model
-# channels is a list of integers; indices of the channels to be deleted
+# delete channels 2, 6 and 8 from layer_1 and insert new_layer_1 before 
+# layer_2 in a model
 from kerassurgeon import Surgeon
 surgeon = Surgeon(model)
-surgeon.add_job('delete_channels', model, layer_1, channels=channels)
-surgeon.add_job('insert_layer', model, layer_2, new_layer=layer_3)
+surgeon.add_job('delete_channels', model, layer_1, channels=[2, 6, 8])
+surgeon.add_job('insert_layer', model, layer_2, new_layer=new_layer_1)
 new_model = surgeon.operate()
 ```
-The `identify` module contains method to identify which channels to prune.
+The `identify` module contains methods to identify which channels to prune.
 
 
 ## Documentation

setup.cfg

@@ -0,0 +1,2 @@
+[metadata]
+description-file = README.md

setup.py

@@ -5,17 +5,15 @@
 
 setup(
     name='kerassurgeon',
-    version="0.0.0a1",
-    url='',
+    version="0.0.1",
+    url='https://github.com/BenWhetton/keras-surgeon',
     license='MIT',
     description='A library for performing network surgery on trained Keras models.'
                 'Useful for deep neural network pruning.',
     author='Ben Whetton',
     author_email='Ben.Whetton@gmail.com',
     install_requires=['keras'],
-
     extras_require={'pd': ['pandas'], },
-
     tests_require=['pytest'],
     packages=find_packages('src'),
     package_dir={'': 'src'}

src/kerassurgeon/__init__.py

@@ -1 +1,3 @@
 from kerassurgeon.surgeon import Surgeon
+
+__version__ = '0.0.1'

src/kerassurgeon/operations.py

@@ -1,27 +1,6 @@
 from kerassurgeon.surgeon import Surgeon
 
 
-def rebuild_sequential(layers):
-    """Rebuild a sequential model from a list of layers.
-
-    Arguments:
-        layers: List of Keras layers
-
-    Returns:
-        A Keras Sequential model
-    """
-    from keras.models import Sequential
-
-    weights = []
-    for layer in layers:
-        weights.append(layer.get_weights())
-
-    new_model = Sequential(layers=layers)
-    for i, layer in enumerate(new_model.layers):
-        layer.set_weights(weights[i])
-    return new_model
-
-
 def delete_layer(model, layer, *, node_indices=None, copy=True):
     """Delete instances of a layer from a Keras model.
 

tests/test_surgeon.py

@@ -56,11 +56,6 @@ def model_2():
     return Model(model.inputs, model.outputs)
 
 
-def test_rebuild_sequential(model_1):
-    model_2 = operations.rebuild_sequential(model_1.layers)
-    assert compare_models_seq(model_1, model_2)
-
-
 def test_rebuild_submodel(model_2):
     output_nodes = [model_2.output_layers[i].inbound_nodes[node_index]
                     for i, node_index in