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| author | nunzip <np.scarh@gmail.com> | 2019-03-06 13:56:06 +0000 |
|---|---|---|
| committer | nunzip <np.scarh@gmail.com> | 2019-03-06 13:56:06 +0000 |
| commit | dc5c65eed1f3ca96324d2e057cd7d815cd4a2df5 (patch) | |
| tree | 40f94797f49ea5b0a43ef1107b18dcf4503df15a | |
| parent | 2a720c237259baa2d968286244f9e43794c7e4d9 (diff) | |
| download | e4-gan-dc5c65eed1f3ca96324d2e057cd7d815cd4a2df5.tar.gz e4-gan-dc5c65eed1f3ca96324d2e057cd7d815cd4a2df5.tar.bz2 e4-gan-dc5c65eed1f3ca96324d2e057cd7d815cd4a2df5.zip | |
Update on old commit for training the classifier in steps
| -rw-r--r-- | lenet.py | 6 |
1 files changed, 4 insertions, 2 deletions
@@ -101,7 +101,7 @@ def plot_history(history, metric = None): plt.ylabel('Loss') plt.xlabel('Epoch') -def train_classifier(x_train, y_train, x_val, y_val, batch_size=128, epochs=100, metrics=[categorical_accuracy], optimizer = None): +def train_classifier(x_train, y_train, x_val, y_val, batch_size=128, epochs=100, metrics=[categorical_accuracy], optimizer = None, keep_training = False): shape = (32, 32, 1) # Pad data to 32x32 (MNIST is 28x28) @@ -114,10 +114,12 @@ def train_classifier(x_train, y_train, x_val, y_val, batch_size=128, epochs=100, optimizer = optimizers.SGD(lr=0.001, decay=1e-6, momentum=0.9, nesterov=True) model.compile(loss='categorical_crossentropy', metrics=metrics, optimizer=optimizer) - + if keep_training: + model.load_weights('./weights.h5') history = model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data = (x_val, y_val)) plot_history(history, 'categorical_accuracy') plot_history(history) + model.save_weights('./weights.h5') return model def test_classifier(model, x_test, y_true): |