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author | Vasil Zlatanov <v@skozl.com> | 2019-02-27 18:32:26 +0000 |
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committer | Vasil Zlatanov <v@skozl.com> | 2019-02-27 18:32:26 +0000 |
commit | 9c26f3b6e6b317c910bf3bdafc9b070c151dff4a (patch) | |
tree | 6fa7bfd01fcb1cd82c7bc4ed31091e8ad9d5a963 /lenet.py | |
parent | 7465d4fdde046843cb8bca3b233c2cdd99c39722 (diff) | |
download | e4-gan-9c26f3b6e6b317c910bf3bdafc9b070c151dff4a.tar.gz e4-gan-9c26f3b6e6b317c910bf3bdafc9b070c151dff4a.tar.bz2 e4-gan-9c26f3b6e6b317c910bf3bdafc9b070c151dff4a.zip |
Add metric sna optimizer arguments
Diffstat (limited to 'lenet.py')
-rw-r--r-- | lenet.py | 8 |
1 files changed, 5 insertions, 3 deletions
@@ -106,12 +106,14 @@ 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): +def train_classifier(x_train, y_train, x_val, y_val, batch_size=128, epochs=100, metrics=['categorical_accuracy'], optimizer = None): shape = (32, 32, 1) model = get_lenet(shape) - sgd = optimizers.SGD(lr=0.001, decay=1e-6, momentum=0.9, nesterov=True) - model.compile(loss='categorical_crossentropy', optimizer=sgd) + if optimizer = None: + optimizer = optimizers.SGD(lr=0.001, decay=1e-6, momentum=0.9, nesterov=True) + + 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) |