models.py
def stack_encoder(self, input_tensor, output_num_feature_channels:int): # -> tensor
        """
        Performs one encoding stack of U-net but with batch norm
        """
        conv = tf.layers.conv2d(input_tensor, output_num_feature_channels, [3, 3], padding="SAME", activation=tf.nn.relu, kernel_initializer=self.weights_initializer)
        conv = tf.layers.conv2d(conv, output_num_feature_channels, [3, 3], padding="SAME", activation=tf.nn.relu, kernel_initializer=self.weights_initializer)
        conv = self.batch_normalization(conv)
        return conv

        Performs one encoding stack of U-net but with batch norm
        
Performs one encoding stack of U-net but with batch norm
Performs one encoding stack of U - net but with batch norm
def stack_encoder(self, input_tensor, output_num_feature_channels:int): 
        conv = tf.layers.conv2d(input_tensor, output_num_feature_channels, [3, 3], padding="SAME", activation=tf.nn.relu, kernel_initializer=self.weights_initializer)
        conv = tf.layers.conv2d(conv, output_num_feature_channels, [3, 3], padding="SAME", activation=tf.nn.relu, kernel_initializer=self.weights_initializer)
        conv = self.batch_normalization(conv)
        return conv
def stack_encoder ( self , input_tensor , output_num_feature_channels : int ) : conv = tf . layers . conv2d ( input_tensor , output_num_feature_channels , [ 3 , 3 ] , padding = "SAME" , activation = tf . nn . relu , kernel_initializer = self . weights_initializer ) conv = tf . layers . conv2d ( conv , output_num_feature_channels , [ 3 , 3 ] , padding = "SAME" , activation = tf . nn . relu , kernel_initializer = self . weights_initializer ) conv = self . batch_normalization ( conv ) return conv
Performs one encoding stack of U-net but with batch norm
Performs one encoding stack of U - net but with batch norm
# -> tensor """
        Performs one encoding stack of U-net but with batch norm
        """
self
input_tensor
output_num_feature_channels
int