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Fit DenseNet examples to Keras-2 (#53)

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Update DenseNet example to K2

* Fit DenseNet examples to keras-2
This commit is contained in:
Junwei Pan
2017-03-28 13:25:43 -07:00
committed by Fariz Rahman
parent 531c4dcab8
commit fcd054fe40
2 changed files with 37 additions and 33 deletions
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examples/cifar10_densenet.py
+4 -4
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@@ -26,7 +26,7 @@ img_rows, img_cols = 32, 32
img_channels = 3
# Parameters for the DenseNet model builder
img_dim = (img_channels, img_rows, img_cols) if K.imgae_data_format() == "channels_first" else (img_rows, img_cols, img_channels)
img_dim = (img_channels, img_rows, img_cols) if K.image_data_format() == "channels_first" else (img_rows, img_cols, img_channels)
depth = 40
nb_dense_block = 3
growth_rate = 12
@@ -71,11 +71,11 @@ model_checkpoint = ModelCheckpoint(weights_file, monitor="val_acc", save_best_on
callbacks = [lr_reducer, early_stopper, model_checkpoint]
model.fit_generator(generator.flow(trainX, Y_train, batch_size=batch_size), samples_per_epoch=len(trainX),
nb_epoch=nb_epoch,
model.fit_generator(generator.flow(trainX, Y_train, batch_size=batch_size), steps_per_epoch=len(trainX) // batch_size,
epochs=nb_epoch,
callbacks=callbacks,
validation_data=(testX, Y_test),
nb_val_samples=testX.shape[0], verbose=2)
verbose=2)
scores = model.evaluate(testX, Y_test, batch_size=batch_size)
print("Test loss : ", scores[0])