Add LeakyReLU support (#9)

* Update lock

* Some updates for LeakyReLU

* Revert "Update lock"

This reverts commit 8c30750.

* support custom alpha values for leakyrelu

* support custom alpha values for leakyrelu

* clean up

* clean up

* clean up, bump version

* bump

* bump

* bump

* bump

Author: sshane

README.md

@@ -19,6 +19,7 @@ The goal of this tool is to provide a quick and easy way to execute Keras models
     - Works with all supported layers
 - Activations:
   - ReLU
+  - LeakyReLU (supports custom alphas)
   - Sigmoid
   - Softmax
   - Tanh
@@ -84,7 +85,7 @@ predict([np.random.rand(3).astype(np.float32)])
 
 ## Dependencies
 Thanks to [@apiad](https://github.com/apiad) you can now use [Poetry](https://github.com/python-poetry/poetry) to install all the needed dependencies for this tool! However the requirements are a pretty short list:
-- It seems most versions of TensorFlow that include Keras work perfectly fine. Tested from 1.14 to 2.1.0 using Actions and no issues have occurred. **(Make sure you use implementation 2/v3 with GRU layers if not on TF 2.x)**
+- It seems most versions of TensorFlow that include Keras work perfectly fine. Tested from 1.14 to 2.2 using Actions and no issues have occurred. **(Make sure you use implementation 2/v3 with GRU layers if not on TF 2.x)**
   - **Important**: You must create your models with tf.keras currently (not keras)
 - Python >= 3.6 (for the glorious f-strings!)
 

konverter/__init__.py

@@ -76,7 +76,11 @@ def build_konverted_model(self):
           if layer.info.activation.needs_function:
             lyr_w_act = f'l{idx} = {layer.info.activation.alias.lower()}(l{idx})'
           else:  # eg. tanh or relu
-            lyr_w_act = layer.info.activation.string.lower().format(f'l{idx}')
+            if layer.info.activation.alpha is None:
+              lyr_w_act = layer.info.activation.string.lower().format(f'l{idx}')
+            else:  # custom alpha for leakyrelu
+              lyr_w_act = layer.info.activation.string.lower().format(f'l{idx}', layer.info.activation.alpha)
+
             lyr_w_act = f'l{idx} = {lyr_w_act}'
           model_builder['model'].append(lyr_w_act)
 
@@ -212,8 +216,11 @@ def load_model(self):
     if isinstance(self.input_model, str):
       self.input_model = self.input_model.replace('\\', '/')
       if os.path.exists(self.input_model):
-        models = importlib.import_module('tensorflow.keras.models')
-        self.model = models.load_model(self.input_model)
+        load_model = importlib.import_module('tensorflow.keras.models').load_model  # only import when needed
+
+        # FIXME: for some reason tf 2 can't load models with LeakyReLU without custom_objects
+        custom_leakyrelu = importlib.import_module('tensorflow.keras.layers').LeakyReLU
+        self.model = load_model(self.input_model, custom_objects={'LeakyReLU': custom_leakyrelu})
       else:
         raise Exception(error('The supplied model file path doesn\'t exist!', ret=True))
     else:

konverter/__main__.py

@@ -3,9 +3,10 @@
 import konverter
 from konverter.utils.general import success, info, warning, error, COLORS, color_logo, blue_grad
 
-KONVERTER_VERSION = "v0.2.1"  # fixme: unify this
+KONVERTER_VERSION = "v0.2.2"  # fixme: unify this
 KONVERTER_LOGO_COLORED = color_logo(KONVERTER_VERSION)
 
+
 class KonverterCLI:
   def __init__(self, args):
     self.args = args

konverter/utils/konverter_support.py

@@ -23,6 +23,10 @@ def get_class_from_name(self, name, search_in):
     for attr_class in attrs:
       if name == attr_class.name:
         return attr_class()  # new instance of class
+    if search_in == 'activations':  # not found
+      base = Activations.Unsupported()
+      base.name = name
+      return base
     return False
 
   def in_models(self, name):
@@ -94,6 +98,9 @@ def get_layer_info(self, layer):
         raise Exception('None or multiple activations?')
 
     if layer_class.info.has_activation:
+      if layer_class.info.activation.name == 'keras.layers.LeakyReLU':  # set alpha
+        layer_class.info.activation.alpha = round(float(layer.activation.alpha), 5)
+
       # check layer activation against this layer's supported activations
       if layer_class.info.activation.name in self.attr_map(layer_class.supported_activations, 'name'):
         layer_class.info.supported = True

konverter/utils/model_attributes.py

@@ -21,45 +21,58 @@ class Activations:
       ex. activation in string format
     To add new activations, use the code_converter function and add them here!
   """
-  class _BaseAcivation:
+
+  class _BaseActivation:
     name = None
     alias = None
     string = None
+    alpha = None
     needs_function = True
 
-  class ReLU(_BaseAcivation):
+  class ReLU(_BaseActivation):
     name = 'keras.activations.relu'
     alias = 'relu'
     string = 'np.maximum(0, {})'
     needs_function = False
 
-  class Sigmoid(_BaseAcivation):
+  class LeakyReLU(_BaseActivation):
+    name = 'keras.layers.LeakyReLU'
+    alias = 'LeakyReLU'
+    string = 'np.where({0} > 0, {0}, {0} * {1})'
+    alpha = 0.3  # default from tensorflow
+    needs_function = False
+
+  class Sigmoid(_BaseActivation):
     name = 'keras.activations.sigmoid'
     alias = 'sigmoid'
     string = 'def sigmoid(x):\n\treturn 1 / (1 + np.exp(-x))'
 
-  class Softmax(_BaseAcivation):
+  class Softmax(_BaseActivation):
     name = 'keras.activations.softmax'
     alias = 'softmax'
     string = 'def softmax(x):\n\treturn np.exp(x) / np.sum(np.exp(x), axis=0)'
 
-  class Tanh(_BaseAcivation):
+  class Tanh(_BaseActivation):
     name = 'keras.activations.tanh'
     alias = 'tanh'
     string = 'np.tanh({})'  # don't define a function if you don't want your string added to file as a function
     needs_function = False
 
-  class Linear(_BaseAcivation):
+  class Linear(_BaseActivation):
     name = 'keras.activations.linear'
     alias = 'linear'
 
+  class Unsupported(_BaseActivation):  # propogated with act info and returned to Konverter if act is unsupported
+    pass
+
 
 class Layers:
   """
     The class that contains the supported layers and any information we will need to generate models
       ex. function in string format
     To add new layers, use the code_converter function and add them here!
   """
+
   class _BaseLayer:
     name = None
     alias = None
@@ -70,7 +83,7 @@ class _BaseLayer:
   class Dense(_BaseLayer):
     name = 'keras.layers.Dense'
     alias = 'dense'
-    supported_activations = [Activations.ReLU, Activations.Sigmoid, Activations.Softmax, Activations.Tanh, Activations.Linear]
+    supported_activations = [Activations.ReLU, Activations.Sigmoid, Activations.Softmax, Activations.Tanh, Activations.Linear, Activations.LeakyReLU]
     string = 'np.dot({}, w[{}]) + b[{}]'  # n0 is the previous layer, n1 is weight, n2 is bias
 
   class Dropout(_BaseLayer):
@@ -128,7 +141,7 @@ class BaseLayerInfo:
   weights = None
   biases = None
 
-  gamma = None
+  gamma = None  # for BN
   beta = None
   mean = None
   std = None

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "keras-konverter"
-version = "0.2.1"
+version = "0.2.2"
 description = "A tool to convert simple Keras models to pure Python + NumPy"
 readme = "README.md"
 repository = "https://github.com/ShaneSmiskol/Konverter"