zoo/autoencoder/dc_autoencoder_c.py
def example():
    ''' Example for constructing/training an AutoEncoder  model on CIFAR-10
    '''
    # Example of constructing an AutoEncoder
    ae = AutoEncoder(input_shape=(32, 32, 3))
    ae.model.summary()

    from tensorflow.keras.datasets import cifar10
    import numpy as np
    (x_train, y_train), (x_test, y_test) = cifar10.load_data()
    x_train = (x_train / 255.0).astype(np.float32)
    x_test  = (x_test  / 255.0).astype(np.float32)

    ae.compile()
    ae.model.fit(x_train, x_train, epochs=10, batch_size=32, validation_split=0.1, verbose=1)
    ae.model.evaluate(x_test, x_test)
 Example for constructing/training an AutoEncoder  model on CIFAR-10
    
Example for constructing/training an AutoEncoder  model on CIFAR-10
Example for constructing / training an AutoEncoder model on CIFAR - 10
def example():
    ae = AutoEncoder(input_shape=(32, 32, 3))
    ae.model.summary()
    from tensorflow.keras.datasets import cifar10
    import numpy as np
    (x_train, y_train), (x_test, y_test) = cifar10.load_data()
    x_train = (x_train / 255.0).astype(np.float32)
    x_test  = (x_test  / 255.0).astype(np.float32)
    ae.compile()
    ae.model.fit(x_train, x_train, epochs=10, batch_size=32, validation_split=0.1, verbose=1)
    ae.model.evaluate(x_test, x_test)
def example ( ) : ae = AutoEncoder ( input_shape = ( 32 , 32 , 3 ) ) ae . model . summary ( ) from tensorflow . keras . datasets import cifar10 import numpy as np ( x_train , y_train ) , ( x_test , y_test ) = cifar10 . load_data ( ) x_train = ( x_train / 255.0 ) . astype ( np . float32 ) x_test = ( x_test / 255.0 ) . astype ( np . float32 ) ae . compile ( ) ae . model . fit ( x_train , x_train , epochs = 10 , batch_size = 32 , validation_split = 0.1 , verbose = 1 ) ae . model . evaluate ( x_test , x_test )
Example for constructing/training an AutoEncoder  model on CIFAR-10
Example for constructing / training an AutoEncoder model on CIFAR - 10
''' Example for constructing/training an AutoEncoder  model on CIFAR-10
    ''' # Example of constructing an AutoEncoder