snapshot...

modules/deeplearning/icing_fcn.py

@@ -75,43 +75,75 @@ zero_out_params = ['cld_reff_dcomp', 'cld_opd_dcomp', 'iwc_dcomp', 'lwc_dcomp']
 DO_ZERO_OUT = False
 
 
-def build_residual_block(input, drop_rate, num_neurons, activation, block_name, doDropout=True, doBatchNorm=True):
+# def build_residual_block(input, drop_rate, num_neurons, activation, block_name, doDropout=True, doBatchNorm=True):
+#     with tf.name_scope(block_name):
+#         if doDropout:
+#             fc = tf.keras.layers.Dropout(drop_rate)(input)
+#             fc = tf.keras.layers.Dense(num_neurons, activation=activation)(fc)
+#         else:
+#             fc = tf.keras.layers.Dense(num_neurons, activation=activation)(input)
+#         if doBatchNorm:
+#             fc = tf.keras.layers.BatchNormalization()(fc)
+#         print(fc.shape)
+#         fc_skip = fc
+#
+#         if doDropout:
+#             fc = tf.keras.layers.Dropout(drop_rate)(fc)
+#         fc = tf.keras.layers.Dense(num_neurons, activation=activation)(fc)
+#         if doBatchNorm:
+#             fc = tf.keras.layers.BatchNormalization()(fc)
+#         print(fc.shape)
+#
+#         if doDropout:
+#             fc = tf.keras.layers.Dropout(drop_rate)(fc)
+#         fc = tf.keras.layers.Dense(num_neurons, activation=activation)(fc)
+#         if doBatchNorm:
+#             fc = tf.keras.layers.BatchNormalization()(fc)
+#         print(fc.shape)
+#
+#         if doDropout:
+#             fc = tf.keras.layers.Dropout(drop_rate)(fc)
+#         fc = tf.keras.layers.Dense(num_neurons, activation=None)(fc)
+#         if doBatchNorm:
+#             fc = tf.keras.layers.BatchNormalization()(fc)
+#
+#         fc = fc + fc_skip
+#         fc = tf.keras.layers.LeakyReLU()(fc)
+#         print(fc.shape)
+#
+#     return fc
+
+
+def build_residual_block_1x1(input_layer, num_filters, activation, block_name, padding='SAME', drop_rate=0.5,
+                             do_drop_out=True, do_batch_norm=True):
+
     with tf.name_scope(block_name):
-        if doDropout:
-            fc = tf.keras.layers.Dropout(drop_rate)(input)
-            fc = tf.keras.layers.Dense(num_neurons, activation=activation)(fc)
-        else:
-            fc = tf.keras.layers.Dense(num_neurons, activation=activation)(input)
-        if doBatchNorm:
-            fc = tf.keras.layers.BatchNormalization()(fc)
-        print(fc.shape)
-        fc_skip = fc
-
-        if doDropout:
-            fc = tf.keras.layers.Dropout(drop_rate)(fc)
-        fc = tf.keras.layers.Dense(num_neurons, activation=activation)(fc)
-        if doBatchNorm:
-            fc = tf.keras.layers.BatchNormalization()(fc)
-        print(fc.shape)
-
-        if doDropout:
-            fc = tf.keras.layers.Dropout(drop_rate)(fc)
-        fc = tf.keras.layers.Dense(num_neurons, activation=activation)(fc)
-        if doBatchNorm:
-            fc = tf.keras.layers.BatchNormalization()(fc)
-        print(fc.shape)
-
-        if doDropout:
-            fc = tf.keras.layers.Dropout(drop_rate)(fc)
-        fc = tf.keras.layers.Dense(num_neurons, activation=None)(fc)
-        if doBatchNorm:
-            fc = tf.keras.layers.BatchNormalization()(fc)
-
-        fc = fc + fc_skip
-        fc = tf.keras.layers.LeakyReLU()(fc)
-        print(fc.shape)
-
-    return fc
+        skip = input_layer
+        if do_drop_out:
+            input_layer = tf.keras.layers.Dropout(drop_rate)(input_layer)
+        if do_batch_norm:
+            input_layer = tf.keras.layers.BatchNormalization()(input_layer)
+        conv = tf.keras.layers.Conv2D(num_filters, kernel_size=1, strides=1, padding=padding, activation=activation)(input_layer)
+        print(conv.shape)
+
+        if do_drop_out:
+            conv = tf.keras.layers.Dropout(drop_rate)(conv)
+        if do_batch_norm:
+            conv = tf.keras.layers.BatchNormalization()(conv)
+        conv = tf.keras.layers.Conv2D(num_filters, kernel_size=1, strides=1, padding=padding, activation=activation)(conv)
+        print(conv.shape)
+
+        if do_drop_out:
+            conv = tf.keras.layers.Dropout(drop_rate)(conv)
+        if do_batch_norm:
+            conv = tf.keras.layers.BatchNormalization()(conv)
+        conv = tf.keras.layers.Conv2D(num_filters, kernel_size=1, strides=1, padding=padding, activation=None)(conv)
+
+        conv = conv + skip
+        conv = tf.keras.layers.LeakyReLU()(conv)
+        print(conv.shape)
+
+    return conv
 
 
 class IcingIntensityFCN:
@@ -609,21 +641,20 @@ class IcingIntensityFCN:
         # activation = tf.nn.relu
         # activation = tf.nn.elu
         activation = tf.nn.leaky_relu
-        momentum = 0.99
         # padding = "VALID"
         padding = "SAME"
 
-        conv = tf.keras.layers.Conv2D(num_filters, kernel_size=1, strides=1, padding=padding, activation=activation)(input_layer)
+        conv = build_residual_block_1x1(input_layer, num_filters, activation, 'Residual_Block_1', padding=padding)
 
-        conv = tf.keras.layers.Conv2D(num_filters, kernel_size=1, strides=1, padding=padding, activation=activation)(conv)
+        conv = build_residual_block_1x1(conv, num_filters, activation, 'Residual_Block_2', padding=padding)
 
-        conv = tf.keras.layers.Conv2D(num_filters, kernel_size=1, strides=1, padding=padding, activation=activation)(conv)
         print(conv.shape)
 
         if NumClasses == 2:
             activation = tf.nn.sigmoid  # For binary
         else:
             activation = tf.nn.softmax  # For multi-class
+
         # Called logits, but these are actually probabilities, see activation
         logits = tf.keras.layers.Conv2D(1, kernel_size=1, strides=1, padding=padding, activation=activation)(conv)