diff --git a/nnlib/nnlib.py b/nnlib/nnlib.py
index 04551f9..6843457 100644
--- a/nnlib/nnlib.py
+++ b/nnlib/nnlib.py
@@ -514,7 +514,10 @@ NLayerDiscriminator = nnlib.NLayerDiscriminator
                     
             def Conv2D (filters, kernel_size, strides=(1, 1), padding='valid', data_format=None, dilation_rate=(1, 1), activation=None, use_bias=True, kernel_initializer=RandomNormal(0, 0.02), bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None):
                 return keras.layers.convolutional.Conv2D( filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, data_format=data_format, dilation_rate=dilation_rate, activation=activation, use_bias=use_bias, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, kernel_constraint=kernel_constraint, bias_constraint=bias_constraint )
-
+        
+            def Conv2DTranspose(filters, kernel_size, strides=(1, 1), padding='valid', output_padding=None, data_format=None, dilation_rate=(1, 1), activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None):
+                return keras.layers.Conv2DTranspose(filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, output_padding=output_padding, data_format=data_format, dilation_rate=dilation_rate, activation=activation, use_bias=use_bias, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, kernel_constraint=kernel_constraint, bias_constraint=bias_constraint)
+            
             def func(input):
 
                
@@ -548,8 +551,8 @@ NLayerDiscriminator = nnlib.NLayerDiscriminator
                 for i in range(n_blocks):
                     x = ResnetBlock(ngf*4)(x)
                     
-                x = ReLU()(XNormalization(PixelShuffler()(Conv2D(ngf*2 *4, 3, 1, 'same')(x))))
-                x = ReLU()(XNormalization(PixelShuffler()(Conv2D(ngf   *4, 3, 1, 'same')(x))))
+                x = ReLU()(XNormalization(Conv2DTranspose(ngf*2, 3, 2, 'same')(x)))
+                x = ReLU()(XNormalization(Conv2DTranspose(ngf  , 3, 2, 'same')(x)))
                 
                 x = ReflectionPadding2D((3,3))(x)
                 x = Conv2D(output_nc, 7, 1, 'valid')(x)