snapshot...

modules/deeplearning/cnn_cld_frac.py

@@ -104,6 +104,32 @@ def build_residual_conv2d_block(conv, num_filters, block_name, activation=tf.nn.
     return conv
 
 
+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):
+        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=None)(conv)
+
+        conv = conv + skip
+        conv = tf.keras.layers.LeakyReLU()(conv)
+        print(conv.shape)
+
+    return conv
+
+
 class CNN:
     
     def __init__(self):
@@ -286,15 +312,15 @@ class CNN:
                     if np.sum(np.isnan(cell)) == 0:
                         cnt = np.sum(cell[t, :, ] == 1.0)
                         if cnt == 0:
-                            grd_down_2x[t, j, i] = 1
+                            grd_down_2x[t, j, i] = 0
                         elif cnt == 1:
-                            grd_down_2x[t, j, i] = 2
+                            grd_down_2x[t, j, i] = 1
                         elif cnt == 2:
-                            grd_down_2x[t, j, i] = 3
+                            grd_down_2x[t, j, i] = 2
                         elif cnt == 3:
-                            grd_down_2x[t, j, i] = 4
+                            grd_down_2x[t, j, i] = 3
                         elif cnt == 4:
-                            grd_down_2x[t, j, i] = 5
+                            grd_down_2x[t, j, i] = 4
                         pass
                     else:
                         grd_down_2x[t, j, i] = 0
@@ -407,7 +433,7 @@ class CNN:
         activation = tf.nn.relu
         momentum = 0.99
 
-        num_filters = 64
+        num_filters = 32
 
         input_2d = self.inputs[0]
         print('input: ', input_2d.shape)
@@ -415,7 +441,7 @@ class CNN:
         conv = input_2d
         print('input: ', conv.shape)
 
-        conv = conv_b = tf.keras.layers.Conv2D(num_filters, kernel_size=3, kernel_initializer='he_uniform', activation=activation, padding='SAME')(input_2d)
+        conv = conv_b = tf.keras.layers.Conv2D(num_filters, kernel_size=2, strides=1, kernel_initializer='he_uniform', activation=activation, padding='SAME')(input_2d)
         print(conv.shape)
 
         if NOISE_TRAINING:
@@ -423,22 +449,17 @@ class CNN:
 
         scale = 0.2
 
-        conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_1', scale=scale)
-
-        conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_2', scale=scale)
-
-        conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_3', scale=scale)
-
-        # conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_4', scale=scale)
+        conv_b = build_residual_block_1x1(conv_b, num_filters, 'Residual_Block_1')
 
-        # conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_5', scale=scale)
+        conv_b = build_residual_block_1x1(conv_b, num_filters, 'Residual_Block_2')
 
-        conv_b = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, kernel_initializer='he_uniform', padding=padding)(conv_b)
+        conv_b = build_residual_block_1x1(conv_b, num_filters, 'Residual_Block_3')
 
-        conv = conv + conv_b
+        # conv = conv + conv_b
+        conv = conv_b
         print(conv.shape)
 
-        self.logits = tf.keras.layers.Conv2D(1, kernel_size=3, strides=1, padding=padding, name='regression')(conv)
+        self.logits = tf.keras.layers.Conv2D(NumLogits, kernel_size=1, strides=1, padding=padding, name='regression')(conv)
 
         print(self.logits.shape)