fix readme

README.cn.md

@@ -16,6 +16,8 @@ Jittor前端语言为Python。前端使用了模块化的设计，这是目前
 import jittor as jt
 from jittor import Module
 from jittor import nn
+import numpy as np
+
 class Model(Module):
     def __init__(self):
         self.layer1 = nn.Linear(1, 10)
@@ -33,13 +35,18 @@ def get_data(n): # generate random data for training test.
         y = x*x
         yield jt.float32(x), jt.float32(y)
 
-model = Model()
+
 learning_rate = 0.1
+batch_size = 50
+n = 1000
+
+model = Model()
 optim = nn.SGD(model.parameters(), learning_rate)
 
 for i,(x,y) in enumerate(get_data(n)):
     pred_y = model(x)
-    loss = ((pred_y - y)**2)
+    dy = pred_y - y
+    loss = dy * dy
     loss_mean = loss.mean()
     optim.step(loss_mean)
     print(f"step {i}, loss = {loss_mean.data.sum()}")

README.md

@@ -16,6 +16,8 @@ The following example shows how to model a two-layer neural network step by step
 import jittor as jt
 from jittor import Module
 from jittor import nn
+import numpy as np
+
 class Model(Module):
     def __init__(self):
         self.layer1 = nn.Linear(1, 10)
@@ -33,13 +35,18 @@ def get_data(n): # generate random data for training test.
         y = x*x
         yield jt.float32(x), jt.float32(y)
 
-model = Model()
+
 learning_rate = 0.1
+batch_size = 50
+n = 1000
+
+model = Model()
 optim = nn.SGD(model.parameters(), learning_rate)
 
 for i,(x,y) in enumerate(get_data(n)):
     pred_y = model(x)
-    loss = ((pred_y - y)**2)
+    dy = pred_y - y
+    loss = dy * dy
     loss_mean = loss.mean()
     optim.step(loss_mean)
     print(f"step {i}, loss = {loss_mean.data.sum()}")

README.src.md

@@ -21,6 +21,8 @@ The following example shows how to model a two-layer neural network step by step
 import jittor as jt
 from jittor import Module
 from jittor import nn
+import numpy as np
+
 class Model(Module):
     def __init__(self):
         self.layer1 = nn.Linear(1, 10)
@@ -38,13 +40,18 @@ def get_data(n): # generate random data for training test.
         y = x*x
         yield jt.float32(x), jt.float32(y)
 
-model = Model()
+
 learning_rate = 0.1
+batch_size = 50
+n = 1000
+
+model = Model()
 optim = nn.SGD(model.parameters(), learning_rate)
 
 for i,(x,y) in enumerate(get_data(n)):
     pred_y = model(x)
-    loss = ((pred_y - y)**2)
+    dy = pred_y - y
+    loss = dy * dy
     loss_mean = loss.mean()
     optim.step(loss_mean)
     print(f"step {i}, loss = {loss_mean.data.sum()}")