GAN：Gradient Tape 计算判别器的零梯度答案

【问题标题】：GAN: Gradient Tape computes zero gradients for discriminatorGAN：Gradient Tape 计算判别器的零梯度
【发布时间】：2021-06-29 11:45:42
【问题描述】：

我正在使用梯度带计算鉴别器的梯度。鉴别器模型具有自定义层和过滤器，但梯度为零。生成器的梯度计算得很好。该模型正在处理音频和自定义过滤器，这些过滤器初始化为 tf 变量，可训练集为 true

这是鉴别器

def discriminator(images_from_before, filters):
    #Discriminator
    #3x1 Convolution
    conv_3 = tf.nn.conv1d(input = images_from_before,
                filters = filters.filters[0][0],
                stride = 1,
                padding = 'SAME',
                data_format = 'NWC')
    #9x1 Convolution
    conv_9 = tf.nn.conv1d(input = images_from_before,
                filters = filters.filters[0][1],
                stride = 1,
                padding = 'SAME',
                data_format = 'NWC')
    #27x1 Convolution
    conv_27 = tf.nn.conv1d(input = images_from_before,
                filters = filters.filters[0][2], #some of the whole tfVariable
                stride = 1,
                padding = 'SAME',
                data_format = 'NWC')
    #81x1 Convolution
    conv_81 = tf.nn.conv1d(input = images_from_before,
                filters = filters.filters[0][3], #some of the whole tfVariable
                stride = 1,
                padding = 'SAME',
                data_format = 'NWC')
            
    out = tf.concat([conv_3,conv_9,conv_27,conv_81], 2)

    out = leaky_relu(out,0.2)

    #7x Discriminator block
    for i in range(7):
        out = discriminator_block(out,filters.filters[i+1],filters.BN_val[i])

    #Flatten width of the out tensor    
    mul_FC = out.shape[1] * out.shape[2]

    #FC - Dropout - LeakyReLU
    #Flatten out
    out = tf.reshape(out, shape = [out.shape[0],mul_FC])
    out = FClayer(out,filters.FC1, filters.bias1)
    out = Dropout(out)
    out = leaky_relu(out,0.2)

    #FC - Sigmoid
    out = FClayer(out, filters.FC2, filters.bias2)

    out = tf.math.sigmoid(out)

    #implicit mean over the minibatch samples
    out = tf.math.reduce_mean(out)

    out = tf.clip_by_value(out,0.1, 0.9)

    return out

梯度计算如下

with tf.GradientTape() as gen_tape, tf.GradientTape() as dis_tape:
        gen_out = generator.generator(downsampled, num_of_blocks, gen_var)
        
        gen_dis = discriminator.discriminator(gen_out,dis_var)     
        
        #compute losses
        gen_loss = losses.generator_loss(
                            losses.L2_loss(upsampled, gen_out),
                            losses.Lf_loss(upsampled, gen_out,auto_var,4),
                            losses.Ladv_loss(gen_dis,dis_var), #updated
                            lamda_f,
                            lamda_adv)

        dis_loss = losses.discriminator_loss(0.1, 
                                             gen_dis)
        
    print('Gen loss: {}'.format(gen_loss.numpy()))
    print('Dis loss: {}'.format(dis_loss.numpy()))
    


    gen_grads = gen_tape.gradient(gen_loss, [gen_var.dfilters,
                                             gen_var.ufilters,
                                             gen_var.finalfilter,
                                             gen_var.prelu])
    
    disc_grads = dis_tape.gradient(dis_loss, [dis_var.filters,
                                              dis_var.BN_val,
                                              dis_var.FC1,
                                              dis_var.bias1,
                                              dis_var.FC2,
                                              dis_var.bias2])

【问题讨论】：

标签： python tensorflow machine-learning deep-learning

【解决方案1】：

渐变胶带需要有参数persistent=True

【讨论】：