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- C:UsersjohndAppDataLocalProgramsPythonPython35libsite-packageskeraspreprocessingimage.py:653: UserWarning: Expected input to be images (as Numpy array) following the data format convention "channels_first" (channels on axis 1), i.e. expected either 1, 3 or 4 channels on axis 1. However, it was passed an array with shape (60000, 1, 28, 28) (1 channels).
- ' (' + str(x.shape[self.channel_axis]) + ' channels).')
- from keras.datasets import mnist
- from keras.preprocessing.image import ImageDataGenerator
- from matplotlib import pyplot
- from keras import backend as K
- K.set_image_dim_ordering('th')
- # load data
- (X_train, y_train), (X_test, y_test) = mnist.load_data()
- # reshape to be [samples][pixels][width][height]
- X_train = X_train.reshape(X_train.shape[0], 1, 28, 28)
- X_test = X_test.reshape(X_test.shape[0], 1, 28, 28)
- # convert from int to float
- X_train = X_train.astype('float32')
- X_test = X_test.astype('float32')
- # define data preparation
- datagen = ImageDataGenerator(zca_whitening=True)
- # fit parameters from data
- datagen.fit(X_train)
- # configure batch size and retrieve one batch of images
- for X_batch, y_batch in datagen.flow(X_train, y_train, batch_size=9):
- # create a grid of 3x3 images
- for i in range(0, 9):
- pyplot.subplot(330 + 1 + i)
- pyplot.imshow(X_batch[i].reshape(28, 28), cmap=pyplot.get_cmap('gray'))
- # show the plot
- pyplot.show()
- break
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