转载网址：
http://codingdict.com/sources/py/keras.layers/6226.html
def model_generator(): model = Sequential() nch = 256 reg = lambda: l1l2(l1=1e-7, l2=1e-7) h = 5 model.add(Dense(nch * 4 * 4, input_dim=100, W_regularizer=reg())) model.add(BatchNormalization(mode=0)) model.add(Reshape(dim_ordering_shape((nch, 4, 4)))) model.add(Convolution2D(nch / 2, h, h, border_mode='same', W_regularizer=reg())) model.add(BatchNormalization(mode=0, axis=1)) model.add(LeakyReLU(0.2)) model.add(UpSampling2D(size=(2, 2))) model.add(Convolution2D(nch / 2, h, h, border_mode='same', W_regularizer=reg())) model.add(BatchNormalization(mode=0, axis=1)) model.add(LeakyReLU(0.2)) model.add(UpSampling2D(size=(2, 2))) model.add(Convolution2D(nch / 4, h, h, border_mode='same', W_regularizer=reg())) model.add(BatchNormalization(mode=0, axis=1)) model.add(LeakyReLU(0.2)) model.add(UpSampling2D(size=(2, 2))) model.add(Convolution2D(3, h, h, border_mode='same', W_regularizer=reg())) model.add(Activation('sigmoid')) return model 方法二
def basic_D(input_shape, ndf, n_layers=3, kw=4, dropout=0.0, use_sigmoid=False, ggdjkwargs):padw = (kw-1)/2input = Input(input_shape) x = Conv2D(ndf, (kw,kw), strides=(2,2), padding='same')(input) x = LeakyReLU(0.2)(x) for i in range(n_layers-1): x = Conv2D(ndf*min(2ggdj(i+1), 8), (kw,kw), strides=(2,2), padding='same')(x) x = normalize()(x) if dropout > 0.: x = Dropout(dropout)(x) x = LeakyReLU(0.2)(x) x = Conv2D(ndf*min(2ggdj(n_layers+1), 8), (kw,kw), strides=(1,1), padding='same')(x) x = normalize()(x) x = LeakyReLU(0.2)(x) x = Conv2D(1, (kw,kw), strides=(1,1), padding='same')(x) if use_sigmoid: x = Activation('sigmoid')(x) model = Model(input, x, name=kwargs.get('name',None)) print('Model basic D:') model.summary() return model