modify description for dtype, batch_size, and checkpoint_freq

XYM-1 · Dec 10, 2021 · 1325d7b · 1325d7b
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commit 1325d7b
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diff --git a/paddlescience/network/network_fc.py b/paddlescience/network/network_fc.py
@@ -26,7 +26,7 @@ class FCNet(NetworkBase):
         num_outs(int): Number of outputs.
         num_layers(int): Number of layers.
         hidden_size(int): Hiden size in each layer.
-        dtype(string): Optional, default 'float32'. The data type of the weights and bias, could be 'float32' or 'float64'.
+        dtype(string): Optional, default 'float32'. The data type of the weights and bias, we only support 'float32' now.
         activation(string): Optional, default 'tanh'. The type of activation function used in each layer, could be 'tanh' or 'sigmoid'.
 
     Example:

diff --git a/paddlescience/solver/solver.py b/paddlescience/solver/solver.py
@@ -41,7 +41,9 @@ def solve(self, num_epoch=1000, batch_size=None, checkpoint_freq=1000):
         Train the network with respect to num_epoch.
  
         Parameters:
-            num_epoch(int): Optional, default 1000. Number epochs.
+            num_epoch(int): Optional, default 1000. Number of epochs.
+            batch_size(int|None): Under develop. Optional, default None. How many sample points are used as a batch during training.
+            checkpoint_freq(int): Under develop. Optional, default 1000. How many epochs to store the training status once.
 
         Return:
             solution(Callable): A python func functhion that takes a GeometryDiscrete as input and a numpy array as outputs.