snapshot...

modules/deeplearning/cloud_fraction_fcn_abi_hkm_refl.py

@@ -118,7 +118,7 @@ def build_conv2d_block(conv, num_filters, activation, block_name, padding='SAME'
     with tf.name_scope(block_name):
         skip = conv
 
-        conv = tf.keras.layers.Conv2D(num_filters, kernel_size=5, strides=1, padding=padding, activation=activation)(conv)
+        conv = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
         conv = tf.keras.layers.MaxPool2D(padding=padding)(conv)
         conv = tf.keras.layers.BatchNormalization()(conv)
         print(conv.shape)