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| author | nunzip <np.scarh@gmail.com> | 2019-03-06 20:41:43 +0000 |
|---|---|---|
| committer | nunzip <np.scarh@gmail.com> | 2019-03-06 20:41:43 +0000 |
| commit | c626433a54bead146083596d08c2ed05c2aed5ee (patch) | |
| tree | eb14f5d5ca3b6c7ac84730491ab13fd74697a252 /lenet.py | |
| parent | f2d09edb7fb511364347ae9df1915a6655f45a0a (diff) | |
| parent | 8842630c10bc302c5961ed7a763fcbd6282449cb (diff) | |
| download | e4-gan-c626433a54bead146083596d08c2ed05c2aed5ee.tar.gz e4-gan-c626433a54bead146083596d08c2ed05c2aed5ee.tar.bz2 e4-gan-c626433a54bead146083596d08c2ed05c2aed5ee.zip | |
Merge branch 'new_branch'
Diffstat (limited to 'lenet.py')
| -rw-r--r-- | lenet.py | 5 |
1 files changed, 2 insertions, 3 deletions
@@ -114,14 +114,13 @@ 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('./model_gan.h5', by_name=False) - + 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)) model.save_weights('./model_gan.h5') plot_history(history, 'categorical_accuracy') plot_history(history) + model.save_weights('./weights.h5') return model def test_classifier(model, x_test, y_true): |