further fix for tf2

Author: Fierralin

python/model.py

@@ -844,6 +844,8 @@ def build_model(self,config,placeholders):
     assert(self.version == 8 or self.version == 10)
 
     #Input layer---------------------------------------------------------------------------------
+    #tf.compat.v1.disable_eager_execution()   # Important, fix for tensorflow 2.4
+    tf.compat.v1.disable_v2_behavior()
     bin_inputs = (placeholders["bin_inputs"] if "bin_inputs" in placeholders else
                   tf.compat.v1.placeholder(tf.float32, [None] + self.bin_input_shape, name="bin_inputs"))
     global_inputs = (placeholders["global_inputs"] if "global_inputs" in placeholders else
@@ -1268,6 +1270,8 @@ def __init__(self,model,for_optimization,placeholders):
     shortterm_value_error_prediction = tf.math.softplus(moremiscvalues_output[:,0]) * 0.25
     shortterm_score_error_prediction = tf.math.softplus(moremiscvalues_output[:,1]) * 30.0
 
+    tf.compat.v1.disable_v2_behavior()
+
     #Loss function
     self.policy_target = (placeholders["policy_target"] if "policy_target" in placeholders else
                           tf.compat.v1.placeholder(tf.float32, [None] + model.policy_target_shape))
@@ -1545,6 +1549,7 @@ def __init__(self,model,for_optimization,placeholders):
 
     if for_optimization:
       #Prior/Regularization
+      tf.compat.v1.disable_v2_behavior()
       self.l2_reg_coeff = (placeholders["l2_reg_coeff"] if "l2_reg_coeff" in placeholders else
                            tf.compat.v1.placeholder(tf.float32))
       self.reg_loss_per_weight = self.l2_reg_coeff * (

python/tfrecordio.py

@@ -22,6 +22,7 @@ def make_raw_input_feature_placeholders(model_config,pos_len,batch_size):
   num_bin_input_features = Model.get_num_bin_input_features(model_config)
   num_global_input_features = Model.get_num_global_input_features(model_config)
 
+  tf.compat.v1.disable_v2_behavior()
   return {
     "binchwp": tf.compat.v1.placeholder(tf.uint8,[batch_size,num_bin_input_features,(pos_len*pos_len+7)//8]),
     "ginc": tf.compat.v1.placeholder(tf.float32,[batch_size,num_global_input_features]),

python/train.py

@@ -173,6 +173,7 @@ def trainlog(s):
     assign_ops = []
     for variable in itertools.chain(tf.compat.v1.model_variables(), tf.compat.v1.trainable_variables()):
       if variable.name.startswith("swa_model/"):
+        tf.compat.v1.disable_v2_behavior()
         placeholder = tf.compat.v1.placeholder(variable.dtype,variable.shape)
         assign_ops.append(tf.compat.v1.assign(variable,placeholder))
         swa_assign_placeholders[variable.name] = placeholder