tensorflow - 张量流图构建期间的ValueError

Question

我正在尝试使用 tensorflow 训练 GAN，但在图形构建过程中出现此错误：

ValueError: Input 0 of layer conv1_1 is incompatible with the layer: its rank is undefined, but the layer requires a defined rank.

我猜这是因为我的鉴别器和生成器在我调用的不同函数中。理想情况下，我希望避免将我的整个架构从方法中推出，因为这会导致一个冗长的 python 文件。

在通过 sess.run() 调用推送第一个训练示例之前，我尝试使用占位符的默认值，但这导致在训练的每个阶段都在图表中运行相同的示例（可能是因为这就是 tensorflow构建了图表）

我的训练循环代码如下。请让我知道查看生成器和鉴别器功能本身是否会有所帮助。

    img1 = tf.placeholder(dtype = tf.float32)
    img2 = tf.placeholder(dtype = tf.float32)
    lr = tf.placeholder(dtype = tf.float32)

    synthetic_imgs, synthetic_logits, mse, _ = self.gen(img1)
    fake_result, fake_logits_hr, fake_feature_2 = self.disc(img1, synthetic_imgs)
    ground_truth_result, ground_truth_logits_hr, truth_feature_2 = self.disc(img1, img2)

    _, fake_logits_lr = self.disc_two(img1, synthetic_imgs)
    _, ground_truth_logits_lr = self.disc_two(img1, img2)

    a = tf.nn.sigmoid_cross_entropy_with_logits

    dis_labels = tf.random.uniform((self.batch_size, 1), minval = -0.2, maxval = 0.3)
    gen_labels = tf.random.uniform((self.batch_size, 1), minval = 0.75, maxval = 1.2)
    dis_loss = #Discriminator Loss

    gen_loss = #Generator Loss

    #May want to change to -log(MSE)
    d_vars = [var for var in tf.trainable_variables() if 'disc' in var.name]
    g_vars = [var for var in tf.trainable_variables() if 'g_' in var.name]

    dis_loss = tf.reduce_mean(dis_loss)
    gen_loss = tf.reduce_mean(gen_loss)

    with tf.variable_scope('optimizers', reuse = tf.AUTO_REUSE) as scope:           
        gen_opt = tf.train.AdamOptimizer(learning_rate = lr, name = 'gen_opt')
        disc_opt = tf.train.AdamOptimizer(learning_rate = lr, name = 'dis_opt')

    gen1 = gen_opt.minimize(gen_loss, var_list = g_vars)
    disc1 = disc_opt.minimize(dis_loss, var_list = d_vars)

    #Tensorboard code for visualizing gradients
    #global_step = variable_scope.get_variable("global_step", [], trainable=False, dtype=dtypes.int64, initializer=init_ops.constant_initializer(0, dtype=dtypes.int64))

    # gen_training = tf.contrib.layers.optimize_loss(gen_loss, global_step, learning_rate=lr, optimizer=gen_opt, summaries=["gradients"], variables = g_vars)
    # disc_training = tf.contrib.layers.optimize_loss(dis_loss, global_step, learning_rate=lr, optimizer=disc_opt, summaries=["gradients"], variables = d_vars)

    #summary = tf.summary.merge_all()


    with tf.Session() as sess:  
        print('start session')      

        sess.run(tf.global_variables_initializer())
        #print(tf.trainable_variables()) #Find variable corresponding to conv filter weights, which you can use for tensorboard visualization
        #Code to load each training example

        gen = self.pairs()

        for i in range(self.num_epochs):
            print(str(i+ 1) + 'th epoch')
            for j in range(self.num_batches):

                i_1 = None
                i_2 = None
                #Crektes batch
                for k in range(self.batch_size):
                    p = next(gen)
                    try:
                        i_1 = np.concatenate((i1, self.load_img(p[0])), axis = 0)
                        i_2 = np.concatenate((i2, self.load_img(p[1])), axis = 0)
                    except Exception:
                        i_1 = self.load_img(p[0])
                        i_2 = self.load_img(p[1])

                l_r = 8e-4 * (0.5)**(i//100) #Play around with this value
                test, gLoss, _ = sess.run([img1, gen_loss, gen1], feed_dict = {i1 : i_1, i2 : i_2, learn_rate : l_r})
                dLoss, _ = sess.run([dis_loss, disc1], feed_dict = {i1 : i_1, i2 : i_2, learn_rate : l_r})

                print(test.shape)
                cv2.imwrite('./saved_imgs/gan_test'+str(j)+'.png', np.squeeze(test, axis = 3)[0])
                #Code to display gradients and other relevant stats on tensorboard
                #Will be under histogram tab, labelled OptimizeLoss
                # if j%500 == 0:
                #   writer = tf.summary.FileWriter(sess.graph, logdir = './tensorboard/1') #Change logdir for each run
                #   summary_str = sess.run(summary, feed_dict = {i1 : i_1, i2 : i_2, learn_rate : l_r})
                #   writer.add_summmary(summary_str, str(i)+': '+str(j))#Can change to one epoch only if necessary 
                #   writer.flush()

                if j % 12 == 0: #Prints loss statistics every 12th batch                    
                    #print('Epoch: '+str(i))
                    print('Generator Loss: '+str(gLoss))
                    print('Discriminator Loss: '+str(dLoss))

            self.save_model(sess, i)

标签： tensorflowmachine-learning