minor

modules/deeplearning/icing_fcn.py

@@ -530,41 +530,6 @@ class IcingIntensityFCN:
 
         self.get_evaluate_dataset(idxs)
 
-    def build_1d_cnn(self):
-        print('build_1d_cnn')
-        # padding = 'VALID'
-        padding = 'SAME'
-
-        # activation = tf.nn.relu
-        # activation = tf.nn.elu
-        activation = tf.nn.leaky_relu
-
-        num_filters = 6
-
-        conv = tf.keras.layers.Conv1D(num_filters, 5, strides=1, padding=padding)(self.inputs[1])
-        conv = tf.keras.layers.MaxPool1D(padding=padding)(conv)
-        print(conv)
-
-        num_filters *= 2
-        conv = tf.keras.layers.Conv1D(num_filters, 3, strides=1, padding=padding)(conv)
-        conv = tf.keras.layers.MaxPool1D(padding=padding)(conv)
-        print(conv)
-
-        num_filters *= 2
-        conv = tf.keras.layers.Conv1D(num_filters, 3, strides=1, padding=padding)(conv)
-        conv = tf.keras.layers.MaxPool1D(padding=padding)(conv)
-        print(conv)
-
-        num_filters *= 2
-        conv = tf.keras.layers.Conv1D(num_filters, 3, strides=1, padding=padding)(conv)
-        conv = tf.keras.layers.MaxPool1D(padding=padding)(conv)
-        print(conv)
-
-        flat = tf.keras.layers.Flatten()(conv)
-        print(flat)
-
-        return flat
-
     def build_cnn(self):
         print('build_cnn')
         # padding = "VALID"