snapshot...

modules/deeplearning/cloud_fraction_fcn_abi_hkm_refl.py

@@ -114,6 +114,26 @@ def build_residual_conv2d_block(conv, num_filters, block_name, activation=tf.nn.
     return conv
 
 
+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.MaxPool2D(padding=padding)(conv)
+        conv = tf.keras.layers.BatchNormalization()(conv)
+        print(conv.shape)
+
+        skip = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=None)(skip)
+        skip = tf.keras.layers.MaxPool2D(padding=padding)(skip)
+        skip = tf.keras.layers.BatchNormalization()(skip)
+
+        conv = conv + skip
+        conv = tf.keras.layers.LeakyReLU()(conv)
+        print(conv.shape)
+
+    return conv
+
+
 def upsample_mean(grd):
     bsize, ylen, xlen = grd.shape
     up = np.zeros((bsize, ylen*2, xlen*2))