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authornunzip <np.scarh@gmail.com>2019-03-06 13:13:25 +0000
committernunzip <np.scarh@gmail.com>2019-03-06 13:13:25 +0000
commitf2d09edb7fb511364347ae9df1915a6655f45a0a (patch)
tree6b719c2bdbe0047d7be4f746b23a2d8640a447b9
parent566e8c4c6fb643b3450365384331e9b4df863fdc (diff)
downloade4-gan-f2d09edb7fb511364347ae9df1915a6655f45a0a.tar.gz
e4-gan-f2d09edb7fb511364347ae9df1915a6655f45a0a.tar.bz2
e4-gan-f2d09edb7fb511364347ae9df1915a6655f45a0a.zip
Insert option to keep training the network with different splits
-rw-r--r--lenet.py6
1 files changed, 5 insertions, 1 deletions
diff --git a/lenet.py b/lenet.py
index c1c27b5..663c137 100644
--- a/lenet.py
+++ b/lenet.py
@@ -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)
@@ -114,8 +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('./model_gan.h5', by_name=False)
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)
return model