Adds end-to-end LPC training

Making LPC computation and prediction differentiable

dnn/training_tf2/diffembed.py

@@ -0,0 +1,49 @@
+"""
+Modification of Tensorflow's Embedding Layer:
+    1. Not restricted to be the first layer of a model
+    2. Differentiable (allows non-integer lookups)
+        - For non integer lookup, this layer linearly interpolates between the adjacent embeddings in the following way to preserver gradient flow
+            - E = (1 - frac(x))*embed(floor(x)) + frac(x)*embed(ceil(x)) 
+"""
+
+import tensorflow as tf
+from tensorflow.keras.layers import Layer
+
+class diff_Embed(Layer):
+    """
+    Parameters:
+        - units: int
+            Dimension of the Embedding
+        - dict_size: int
+            Number of Embeddings to lookup
+        - pcm_init: boolean
+            Initialized for the embedding matrix
+    """
+    def __init__(self, units=128, dict_size = 256, pcm_init = True, initializer = None, **kwargs):
+        super(diff_Embed, self).__init__(**kwargs)
+        self.units = units
+        self.dict_size = dict_size
+        self.pcm_init = pcm_init
+        self.initializer = initializer
+
+    def build(self, input_shape):  
+        w_init = tf.random_normal_initializer()
+        if self.pcm_init:  
+            w_init = self.initializer
+        self.w = tf.Variable(initial_value=w_init(shape=(self.dict_size, self.units),dtype='float32'),trainable=True)
+
+    def call(self, inputs):  
+        alpha = inputs - tf.math.floor(inputs)
+        alpha = tf.expand_dims(alpha,axis = -1)
+        alpha = tf.tile(alpha,[1,1,1,self.units])
+        inputs = tf.cast(inputs,'int32')
+        M = (1 - alpha)*tf.gather(self.w,inputs) + alpha*tf.gather(self.w,tf.clip_by_value(inputs + 1, 0, 255))
+        return M
+
+    def get_config(self):
+        config = super(diff_Embed, self).get_config()
+        config.update({"units": self.units})
+        config.update({"dict_size" : self.dict_size})
+        config.update({"pcm_init" : self.pcm_init})
+        config.update({"initializer" : self.initializer})
+        return config