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authornunzip <np.scarh@gmail.com>2019-02-27 23:49:55 +0000
committernunzip <np.scarh@gmail.com>2019-02-27 23:49:55 +0000
commit48a6fa973b65a696d087af35c1b410d73ecddde4 (patch)
tree94e47bacc1eeb125dc728c6fa9b7eb9718367ac2
parenta82ede1619aa57fe081805f10a799e1f8e3b53f9 (diff)
downloade4-gan-48a6fa973b65a696d087af35c1b410d73ecddde4.tar.gz
e4-gan-48a6fa973b65a696d087af35c1b410d73ecddde4.tar.bz2
e4-gan-48a6fa973b65a696d087af35c1b410d73ecddde4.zip
Fix accuracy plot
-rw-r--r--lenet.py2
1 files changed, 1 insertions, 1 deletions
diff --git a/lenet.py b/lenet.py
index fa27422..2372857 100644
--- a/lenet.py
+++ b/lenet.py
@@ -116,7 +116,7 @@ def train_classifier(x_train, y_train, x_val, y_val, batch_size=128, epochs=100,
model.compile(loss='categorical_crossentropy', metrics=metrics, optimizer=optimizer)
history = model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data = (x_val, y_val))
- plot_history(history, metric=metrics)
+ plot_history(history, metrics)
return model
def test_classifier(model, x_test, y_true):