Simplify code

[handwriting-recognition.git] / train.py

diff --git a/train.py b/train.py
index 5ff76669709f1d2a9549dd25b1f16a7b4c97f632..8510624665ba1ddc66300e00030624b40d7025ac 100755 (executable)
--- a/train.py
+++ b/train.py
@@ -14,9 +14,9 @@ nnet_batch = 10000
 #                   (input)--> [Linear->Sigmoid] -> [Linear->Sigmoid] -->(output)
 # handle 10,000 vectors at a time
 Z1 = nnet.LinearLayer(input_shape=(rows * cols, nnet_batch), n_out=80)
-A1 = nnet.SigmoidLayer(Z1.Z.shape)
-ZO = nnet.LinearLayer(input_shape=A1.A.shape, n_out=10)
-AO = nnet.SigmoidLayer(ZO.Z.shape)
+A1 = nnet.SigmoidLayer(Z1.shape)
+ZO = nnet.LinearLayer(input_shape=A1.shape, n_out=10)
+AO = nnet.SigmoidLayer(ZO.shape)
 net = (Z1, A1, ZO, AO)
 
 res = nnet.forward(net, test_images[:, 0:10000])