snapshot...

modules/deeplearning/cnn_cld_frac.py

@@ -61,25 +61,34 @@ label_idx = params.index(label_param)
 print('data_params: ', data_params)
 print('label_param: ', label_param)
 
-x_134 = np.arange(134)
-y_134 = np.arange(134)
-x_64 = np.arange(64)
-y_64 = np.arange(64)
-x_134_2 = x_134[3:131:2]
-y_134_2 = y_134[3:131:2]
-t = np.arange(0, 64, 0.5)
-s = np.arange(0, 64, 0.5)
-
-x_128_2 = x_134[3:131:2]
-y_128_2 = y_134[3:131:2]
-x_128 = x_134[3:131]
-y_128 = y_134[3:131]
+# x_134 = np.arange(134)
+# y_134 = np.arange(134)
+# x_64 = np.arange(64)
+# y_64 = np.arange(64)
+# x_134_2 = x_134[3:131:2]
+# y_134_2 = y_134[3:131:2]
+# t = np.arange(0, 64, 0.5)
+# s = np.arange(0, 64, 0.5)
+#
+# x_128_2 = x_134[3:131:2]
+# y_128_2 = y_134[3:131:2]
+# x_128 = x_134[3:131]
+# y_128 = y_134[3:131]
 
 #----------- New
 # x_134_2 = x_134[1:134:2]
 # t = np.arange(1, 66, 0.5)
 #--------------------------
 
+x_64 = np.arange(64)
+y_64 = np.arange(64)
+t = np.arange(0, 64, 0.5)
+s = np.arange(0, 64, 0.5)
+x_128_2 = slice(3, 131, 2)
+y_128_2 = slice(3, 131, 2)
+x_128 = slice(3, 131)
+y_128 = slice(3, 131)
+
 
 def build_residual_conv2d_block(conv, num_filters, block_name, activation=tf.nn.relu, padding='SAME',
                                 kernel_initializer='he_uniform', scale=None,