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pixelcnn.py
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pixelcnn.py
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#!/usr/bin/env python
# coding: utf-8
# In[31]:
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import numpy as np
# In[32]:
from keras.utils.generic_utils import get_custom_objects
from keras.layers import Activation
from keras.backend import sigmoid
# In[33]:
def activation_funtion(x): #creating a gated activation function for use
return np.dot(tanh(x),sigmoid(x))
get_custom_objects().update({'gated': Activation(activation_funtion)})
# In[35]:
#PixelCNN Layer
class ConvLayer(layers.Layer,):
def __init__(self):
super(PixelConvLayer, self).__init__()
self.mask_type = mask_type
self.convolution = layers.Conv2D(**kwargs)
def Create(self,input_shape):
self.convolution.Create(input_shape)
kernel_shape = self.convolution.kernel.get_shape()
self.maks = np.zeros(shape=kernel_shape)
self.mask[: kernel_shape[0] // 2, ...] = 1.0
self.mask[kernel_shape[0] // 2, : kernel_shape[1] // 2, ...] = 1.0
if self.mask_type == "B":
self.mask[kernel_shape[0] // 2, kernel_shape[1] // 2, ...] = 1.0
def Call(self):
self.convolution.kernel.assign(self.convolution.kernel * self.mask)
return self.convolution(inputs)
# In[36]:
#Residual Skip Connection layer
class ResidualSkipConnect(layers.Layer):
def __init__(self,filters,**kwargs):
super(ResidualSkipConnect,self).__init__(**kwargs)
self.convolution1 = keras.layers.Conv2D(filters=filters,kernel_size=1,activation='gated' )
self.pixel_convolution = ConvLayer(mask_type='B',filters=filters//2,kernel_size=2,activation='gated',padding='same')
self.convolution2 = keras.layers.Conv2D(filters=filters,kernel_size=1,activation='gated')
def Call(self,input):
pass
# In[28]:
#Parameterized Skip Connection Layer
class ParamSkipConnect():
def __init__(self):
pass
# In[ ]: