Fix ImageDataGenerator docs
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docs/templates/preprocessing/image.md
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16
docs/templates/preprocessing/image.md
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@ -14,7 +14,7 @@ keras.preprocessing.image.ImageDataGenerator(featurewise_center=True,
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vertical_flip=False)
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vertical_flip=False)
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```
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```
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Generate batches of tensor image data with real-time data augmentation.
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Generate batches of tensor image data with real-time data augmentation. The data will be looped over (in batches) indefinitely.
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- __Arguments__:
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- __Arguments__:
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- __featurewise_center__: Boolean. Set input mean to 0 over the dataset.
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- __featurewise_center__: Boolean. Set input mean to 0 over the dataset.
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@ -62,9 +62,19 @@ datagen = ImageDataGenerator(
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# (std, mean, and principal components if ZCA whitening is applied)
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# (std, mean, and principal components if ZCA whitening is applied)
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datagen.fit(X_train)
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datagen.fit(X_train)
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# fits the model on batches with real-time data augmentation:
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model.fit_generator(datagen.flow(X_train, Y_train, batch_size=32),
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samples_per_epoch=len(X_train), nb_epoch=nb_epoch)
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# here's a more "manual" example
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for e in range(nb_epoch):
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for e in range(nb_epoch):
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print 'Epoch', e
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print 'Epoch', e
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# batch train with realtime data augmentation
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batches = 0
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for X_batch, Y_batch in datagen.flow(X_train, Y_train):
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for X_batch, Y_batch in datagen.flow(X_train, Y_train, batch_size=32):
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loss = model.train(X_batch, Y_batch)
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loss = model.train(X_batch, Y_batch)
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batches += 1
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if batches >= len(X_train) / 32:
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# we need to break the loop by hand because
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# the generator loops indefinitely
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break
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```
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```
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