fix image description

recognize_digits/README.en.md

@@ -42,7 +42,7 @@ In such a classification problem, we usually use the cross entropy loss function
 
 $$  crossentropy(label, y) = -\sum_i label_ilog(y_i) $$
 
-Fig. 2 shows a softmax regression network, with weights in black, and bias in red. +1 indicates bias is 1.
+Fig. 2 shows a softmax regression network, with weights in blue, and bias in red. +1 indicates bias is 1.
 
 <p align="center">
 <img src="image/softmax_regression_en.png" width=400><br/>
@@ -57,7 +57,7 @@ The Softmax regression model described above uses the simplest two-layer neural
 2.  After the second hidden layer, we get $ H_2 = \phi(W_2H_1 + b_2) $.
 3.  Finally, after output layer, we get $Y=softmax(W_3H_2 + b_3)$, the final classification result vector.
 
-Fig. 3. is Multilayer Perceptron network, with weights in black, and bias in red. +1 indicates bias is 1.
+Fig. 3. is Multilayer Perceptron network, with weights in blue, and bias in red. +1 indicates bias is 1.
 
 <p align="center">
 <img src="image/mlp_en.png" width=500><br/>

recognize_digits/index.en.html

@@ -83,7 +83,7 @@ In such a classification problem, we usually use the cross entropy loss function
 
 $$  crossentropy(label, y) = -\sum_i label_ilog(y_i) $$
 
-Fig. 2 shows a softmax regression network, with weights in black, and bias in red. +1 indicates bias is 1.
+Fig. 2 shows a softmax regression network, with weights in blue, and bias in red. +1 indicates bias is 1.
 
 <p align="center">
 <img src="image/softmax_regression_en.png" width=400><br/>
@@ -98,7 +98,7 @@ The Softmax regression model described above uses the simplest two-layer neural
 2.  After the second hidden layer, we get $ H_2 = \phi(W_2H_1 + b_2) $.
 3.  Finally, after output layer, we get $Y=softmax(W_3H_2 + b_3)$, the final classification result vector.
 
-Fig. 3. is Multilayer Perceptron network, with weights in black, and bias in red. +1 indicates bias is 1.
+Fig. 3. is Multilayer Perceptron network, with weights in blue, and bias in red. +1 indicates bias is 1.
 
 <p align="center">
 <img src="image/mlp_en.png" width=500><br/>

recognize_digits/index.html

@@ -83,7 +83,7 @@ $$ y_i = softmax(\sum_j W_{i,j}x_j + b_i) $$
 
 $$  crossentropy(label, y) = -\sum_i label_ilog(y_i) $$
 
-图2为softmax回归的网络图，图中权重用黑线表示、偏置用红线表示、+1代表偏置参数的系数为1。
+图2为softmax回归的网络图，图中权重用蓝线表示、偏置用红线表示、+1代表偏置参数的系数为1。
 
 <p align="center">
 <img src="image/softmax_regression.png" width=400><br/>
@@ -99,7 +99,7 @@ Softmax回归模型采用了最简单的两层神经网络，即只有输入层
 3.  最后，再经过输出层，得到的$Y=softmax(W_3H_2 + b_3)$，即为最后的分类结果向量。
       
 
-图3为多层感知器的网络结构图，图中权重用黑线表示、偏置用红线表示、+1代表偏置参数的系数为1。
+图3为多层感知器的网络结构图，图中权重用蓝线表示、偏置用红线表示、+1代表偏置参数的系数为1。
 
 <p align="center">
 <img src="image/mlp.png" width=500><br/>