conv_autoencoder.py 文件源码

def build_model(nb_filters=32, nb_pool=2, nb_conv=3):
    model = models.Sequential()
    d = Dense(30)
    c = Convolution2D(nb_filters, nb_conv, nb_conv, border_mode='same', input_shape=(1, 28, 28))
    mp =MaxPooling2D(pool_size=(nb_pool, nb_pool))
    # =========      ENCODER     ========================
    model.add(c)
    model.add(Activation('tanh'))
    model.add(mp)
    model.add(Dropout(0.25))
    # =========      BOTTLENECK     ======================
    model.add(Flatten())
    model.add(d)
    model.add(Activation('tanh'))
    # =========      BOTTLENECK^-1   =====================
    model.add(DependentDense(nb_filters * 14 * 14, d))
    model.add(Activation('tanh'))
    model.add(Reshape((nb_filters, 14, 14)))
    # =========      DECODER     =========================
    model.add(DePool2D(mp, size=(nb_pool, nb_pool)))
    model.add(Deconvolution2D(c, border_mode='same'))
    model.add(Activation('tanh'))

    return model