用Tensorflow选择性地优化Keras模型-Java 学习之路

我正在使用Tensorflow和Keras创建一个GAN（生成对手网络） . 出现的问题是当我尝试将我的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

然后将x_generate传递给鉴别器，该鉴别器仍然在Tensorflow中写入并且尚未转换为keras . 该部分正常工作，直到我传入优化参数 .

#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)

结果是一个错误陈述

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

在线上

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

1 回答

您正在使用图层的激活，而不是 var_list 中这些图层中的可训练参数 .

尝试以下内容：

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

回复于 2024-05-03T13:41:58+08:00

用Tensorflow选择性地优化Keras模型

1 回答

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