updated codebase

mobula/layers/BatchNorm.py

@@ -2,11 +2,12 @@
 
 class BatchNorm(Layer):
     def __init__(self, model, *args, **kwargs):
-        Layer.__init__(self, model, *args, **kwargs)
+        super().__init__(model, *args, **kwargs)
         self.momentum = kwargs.get('momentum', 0.9)
         self.eps = kwargs.get('eps', 1e-5)
         self.use_global_stats = kwargs.get('use_global_stats', False)
         self.axis = kwargs.get('axis', 1)
+
     def reshape(self):
         assert 0 <= self.axis < self.X.ndim
         self.cshape = [1] * self.X.ndim
@@ -21,6 +22,7 @@ def reshape(self):
         # Current Mean
         self.moving_mean = np.zeros(self.cshape)
         self.moving_var = np.ones(self.cshape)
+
     def forward(self):
         if self.is_training() and not self.use_global_stats:
             # The mean and var of this batch
@@ -38,6 +40,7 @@ def forward(self):
         self.nx = (self.X - self.batch_mean) * self.nd
         # Scale and Shift
         self.Y = np.multiply(self.nx, self.W) + self.b
+
     def backward(self):
         # Compute self.dX, self.dW, self.db
         dnx = np.multiply(self.dY, self.W)
@@ -48,9 +51,11 @@ def backward(self):
         self.dX = dnx * self.nd + dvar * xsm * (2.0 / m) + dmean * (1.0 / m)
         self.dW = np.sum(self.dY * self.nx, self.valid_axes, keepdims = True)
         self.db = np.sum(self.dY, self.valid_axes, keepdims = True)
+
     @property
     def params(self):
         return [self.W, self.b, self.moving_mean, self.moving_var]
+
     @property
     def grads(self):
         return [self.dW, self.db]

mobula/layers/ContrastiveLoss.py

@@ -1,10 +1,11 @@
 from .LossLayer import *
+import numpy as np
 
 class ContrastiveLoss(LossLayer):
     def __init__(self, models, *args, **kwargs):
-        # models = [X1, X2, sim]
-        LossLayer.__init__(self, models, *args, **kwargs)
+        super().__init__(models, *args, **kwargs)
         self.margin = kwargs.get("margin", 1.0)
+
     def forward(self):
         self.sim = (self.X[2] == 1).ravel()
         n = self.sim.shape[0]
@@ -14,6 +15,7 @@ def forward(self):
         df = (self.margin - self.dist).ravel()
         self.bdf = ((~self.sim) & (df > 0))
         self.Y = (np.sum(dist_sq[self.sim]) + np.sum(np.square(df[self.bdf]))) / (2.0 * n)
+
     def backward(self):
         n = self.sim.shape[0]
         dX = np.zeros(self.X[0].shape)

mobula/layers/CrossEntropy.py

@@ -1,12 +1,16 @@
 from .LossLayer import *
+import numpy as np
 
 class CrossEntropy(LossLayer):
     def __init__(self, model, *args, **kwargs):
-        LossLayer.__init__(self, model, *args, **kwargs)
+        super().__init__(model, *args, **kwargs)
+
     def reshape(self):
         self.Y = 0.0 
+
     def forward(self):
-        self.Y = np.mean(- np.multiply(self.label, np.log(self.X)) - \
-               np.multiply(1.0 - self.label, np.log(1.0 - self.X)))
+        self.Y = np.mean(- np.multiply(self.label, np.log(self.X + 1e-15)) - \
+               np.multiply(1.0 - self.label, np.log(1.0 - self.X + 1e-15)))
+
     def backward(self):
-        self.dX = (-self.label / self.X + (1.0 - self.label) / (1.0 - self.X)) * self.dY
+        self.dX = (-self.label / (self.X + 1e-15) + (1.0 - self.label) / (1.0 - self.X + 1e-15)) * self.dY

mobula/layers/MSE.py

@@ -1,12 +1,16 @@
 from .LossLayer import *
+import numpy as np
 
 class MSE(LossLayer):
     def __init__(self, model, *args, **kwargs):
-        LossLayer.__init__(self, model, *args, **kwargs)
+        super().__init__(model, *args, **kwargs)
+
     def reshape(self):
         self.Y = 0.0 
+
     def forward(self):
         self.d = (self.X - self.label)
         self.Y = np.mean(np.square(self.d))
+
     def backward(self):
         self.dX = (2 * self.d) * self.dY

mobula/layers/Tanh.py

@@ -1,11 +1,15 @@
 from .Layer import *
+import numpy as np
 
 class Tanh(Layer):
     def __init__(self, model, *args, **kwargs):
-        Layer.__init__(self, model, *args, **kwargs)
+        super().__init__(model, *args, **kwargs)
+
     def reshape(self):
         self.Y = np.zeros(self.X.shape)
+
     def forward(self):
         self.Y = 2.0 / (1.0 + np.exp(-2.0 * self.X)) - 1.0
+
     def backward(self):
         self.dX = np.multiply(self.dY, 1.0 - np.square(self.Y))

requirements.txt

@@ -0,0 +1,2 @@
+numpy
+numpy_groupies

setup.py

@@ -1,29 +1,29 @@
 from setuptools import setup, find_packages
 
 setup(
-        name = 'mobula',
-        version = '1.0.1',
-        description = 'A Lightweight & Flexible Deep Learning (Neural Network) Framework in Python',
-        author = 'wkcn',
-        author_email = '[email protected]',
-        url = 'https://github.com/wkcn/mobula',
-        packages = find_packages(),
-        package_data = {
-            '' : ['*.md'],
-            'docs' : ['docs/*.md'],
-            'examples' : ['examples/*.py']
-        },
-        keywords = 'Deep Learning Framework in Python',
-        license = 'MIT',
-        classifiers = [
-            'Programming Language :: Python',
-            'Programming Language :: Python :: 2',
-            'Programming Language :: Python :: 3',
-            'Topic :: Scientific/Engineering :: Mathematics',
-            'License :: OSI Approved :: MIT License'
-        ],
-        install_requires = [
-            'numpy',
-            'numpy_groupies'
-        ]
+    name='mobula',
+    version='1.0.2',
+    description='A Lightweight & Flexible Deep Learning (Neural Network) Framework in Python',
+    author='wkcn',
+    author_email='[email protected]',
+    url='https://github.com/wkcn/mobula',
+    packages=find_packages(),
+    package_data={
+        '': ['*.md'],
+        'docs': ['docs/*.md'],
+        'examples': ['examples/*.py']
+    },
+    keywords='Deep Learning Framework in Python',
+    license='MIT',
+    classifiers=[
+        'Programming Language :: Python',
+        'Programming Language :: Python :: 3',
+        'Topic :: Scientific/Engineering :: Mathematics',
+        'License :: OSI Approved :: MIT License'
+    ],
+    install_requires=[
+        'numpy',
+        'numpy_groupies'
+    ],
+    python_requires='>=3.6',
 )