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| author | nunzip <np.scarh@gmail.com> | 2019-02-27 23:16:08 +0000 |
|---|---|---|
| committer | nunzip <np.scarh@gmail.com> | 2019-02-27 23:16:08 +0000 |
| commit | d0f97c01830b1018b1327461c1503bc1cf316eae (patch) | |
| tree | e81ebee869255582aba6bf7d2845369c0aab6937 | |
| parent | 723ac9bb7094b482a2efc6bc97139409d2bde9c4 (diff) | |
| download | e4-gan-d0f97c01830b1018b1327461c1503bc1cf316eae.tar.gz e4-gan-d0f97c01830b1018b1327461c1503bc1cf316eae.tar.bz2 e4-gan-d0f97c01830b1018b1327461c1503bc1cf316eae.zip | |
Plot accuracy instead of loss
| -rw-r--r-- | lenet.py | 2 |
1 files changed, 1 insertions, 1 deletions
@@ -115,7 +115,7 @@ 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) + plot_history(history, metric=metrics) return model def test_classifier(model, x_test, y_true): |