使用 TensorFlow 有选择地优化 Keras 模型

Question

我正在使用 Tensorflow 和 Keras 创建一个 GAN（生成对抗网络）。出现的问题是当我尝试将生成器训练参数列表传递到训练步骤的 vars_list 时。

我的发电机看起来像

def create_generator(z_noise):
    #build layer one
    l1 = Dense(h1_size)(z_noise)
    L1 = LeakyReLU(0.1)(l1)
    #layer 2 
    l2 = Dense(h2_size)(L1)
    L2 = LeakyReLU(0.1)(l2)
    #layer 3
    l3 = Dense(h3_size)(l2)
    #generated data
    x_generate = sigmoid(l3)
    #params
    g_params = [l1, L1, l2, L2, l3]

    return x_generate, g_params

然后 x_generate 被传递给仍然用 Tensorflow 编写但尚未转换为 keras 的鉴别器。在我传入优化参数之前，该部分工作正常。

#generate the nets

x_generated, g_params = create_generator(z_prior)
y_data, y_generated, d_params = create_discriminator(x_data, x_generated, keep_prob)

#declare loss functions
d_loss = - (tf.log(y_data) + tf.log(1 - y_generated)) # inverted due to inability to do normal maximization
g_loss = - tf.log(y_generated)

#optimizer
optimizer = tf.train.AdamOptimizer(learning_rate=0.0001)
d_trainer = optimizer.minimize(d_loss, var_list=d_params)
g_trainer = optimizer.minimize(g_loss, var_list=g_params)

结果是一个错误说明

NotImplementedError: ('Trying to update a Tensor ', <tf.Tensor 'dense_4/BiasAdd:0' shape=(256, 20) dtype=float32>)

在线

g_trainer = optimizer.minimize(loss, var_list=g_params)

Answer 1

您使用的是层的激活，而不是 var_list 中那些层中的可训练参数。

尝试如下操作：

def create_generator(z_noise):
    with tf.variable_scope('generator', reuse=tf.AUTO_REUSE):
        #build layer one
        l1 = Dense(h1_size)(z_noise)
        L1 = LeakyReLU(0.1)(l1)
        #layer 2 
        l2 = Dense(h2_size)(L1)
        L2 = LeakyReLU(0.1)(l2)
        #layer 3
        l3 = Dense(h3_size)(l2)
        #generated data
        x_generate = sigmoid(l3)

    g_params = tf.get_collection(
            tf.GraphKeys.GLOBAL_VARIABLES, scope='generator')

    return x_generate, g_params

使用 TensorFlow 有选择地优化 Keras 模型

Selectively Optimizing a Keras Model with Tensorflow

python

keras

tensorflow

generative-adversarial-network