我正在用Keras训练一个生成对抗网络(GAN(。
我的日志报告两个网络(鉴别器和组合模型(都达到了 100% 的准确性。这表明出了问题。
我尝试运行推理，发现鉴别器确实是 100% 准确的，但生成器只产生噪声，根本没有愚弄鉴别器。
我的问题：为什么 Keras 报告我的组合模型的准确性为 100%？
法典：
generator = create_generator(input_shape=(374,))
in_vector = Input(shape=(374,))
fake_images = generator(in_vector)
discriminator = create_discriminator()
disc_optimizer = keras.optimizers.SGD(lr=1e-4)
discriminator.compile(optimizer=disc_optimizer, loss='binary_crossentropy', metrics=['accuracy'])
discriminator.trainable = False
for l in discriminator.layers:
l.trainable = False
gan_output = discriminator(fake_images)
gan = Model(in_vector, gan_output)
gan_optimizer = keras.optimizers.RMSprop(lr=1e-5)
gan.compile(optimizer=gan_optimizer, loss='binary_crossentropy', metrics=['accuracy'])
start_time = datetime.datetime.now()
tensorboard = TensorBoard(log_dir=f'data/logs/gawwn/{start_time}')
tensorboard.set_model(gan)
d_train_logs = ['train_discriminator_loss',
'train_discriminator_accuracy']
g_train_logs = ['train_generator_loss',
'train_generator_accuracy']
val_logs = ['val_discriminator_loss',
'val_discriminator_accuracy',
'val_generator_loss',
'val_generator_accuracy']
d_train_step, g_train_step, val_step = 0, 0, 0
valid = np.ones((batch_size, 1))
fake = np.zeros((batch_size, 1))
noise_sigma = 0.00
noise_decay = 0.95
for epoch in range(1, 1 + epochs):
d_loss = [1]
while d_loss[0] > d_loss_thres:
for i, (x_vectors, x_images, y) in enumerate(train_loader.load_batch(batch_size)):
# ---------------------
# Train Discriminator
# ---------------------
# Generate a batch of new images
gen_imgs = generator.predict(x_vectors)
# Train the discriminator
data = np.concatenate([y, gen_imgs], axis=0)
labels = np.concatenate([valid[:len(y)], fake[:len(y)]])
train_batch = list(zip(data, labels))
np.random.shuffle(train_batch)
data, labels = zip(*train_batch)
data, labels = np.array(data), np.array(labels)
d_loss = discriminator.train_on_batch(data, labels)
# d_loss_real = discriminator.train_on_batch(y, valid[:len(y)])
# d_loss_fake = discriminator.train_on_batch(gen_imgs, fake[:len(y)])
# d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
write_log(tensorboard, d_train_logs, d_loss, d_train_step)
d_train_step += 1
time_elaped = datetime.datetime.now() - start_time
print(f'D step {d_train_step}: loss={d_loss[0]}; acc={d_loss[1]}; time={time_elaped}')
g_loss = [1]
while g_loss[0] > g_loss_thres:
for i, (x_vectors, x_images, y) in enumerate(train_loader.load_batch(batch_size)):
# ---------------------
# Train Generator
# ---------------------
# Train the generator (to have the discriminator label samples as valid)
g_loss = gan.train_on_batch(x_vectors, valid[:len(y)])
# Plot the progress
write_log(tensorboard, g_train_logs, g_loss, g_train_step)
g_train_step += 1
time_elaped = datetime.datetime.now() - start_time
print(f'G step {g_train_step}: loss={g_loss[0]}; acc={g_loss[1]}; time={time_elaped}')
# If at save interval => save generated image samples
if epoch % sample_interval == 0:
d_losses = []
g_losses = []
for x_vectors, x_images, y in val_loader.load_batch(batch_size):
gen_imgs = generator.predict(x_vectors)
d_loss_real = discriminator.test_on_batch(y, valid[:len(y)])
d_loss_fake = discriminator.test_on_batch(gen_imgs, fake[:len(y)])
d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
d_losses.append(d_loss)
g_loss = gan.test_on_batch(x_vectors, valid[:len(y)])
g_losses.append(g_loss)
d_loss = np.average(d_losses, axis=0)
g_loss = np.average(g_losses, axis=0)
write_log(tensorboard, val_logs, [d_loss[0], d_loss[1], g_loss[0], g_loss[1]], val_step)
val_step += 1
sample_images(val_loader, generator, epoch)
save_model(generator, epoch, 'generator')
save_model(discriminator, epoch, 'discriminator')
最后几个步骤的结果：
D step 349: loss=0.09932675957679749; acc=1.0; time=0:05:58.468997
D step 350: loss=0.10563915222883224; acc=0.9900000095367432; time=0:05:59.088657
D step 351: loss=0.09658461064100266; acc=1.0; time=0:05:59.533442
G step 214: loss=0.167491614818573; acc=0.9800000190734863; time=0:06:00.196747
G step 215: loss=0.13409791886806488; acc=1.0; time=0:06:00.891946
G step 216: loss=0.1523411124944687; acc=0.9722222089767456; time=0:06:01.402974
D step 352: loss=0.10553492605686188; acc=0.9900000095367432; time=0:06:02.015083
D step 353: loss=0.10318870842456818; acc=0.9900000095367432; time=0:06:02.654599
D step 354: loss=0.07871382683515549; acc=1.0; time=0:06:03.131933
G step 217: loss=0.1493617743253708; acc=0.9800000190734863; time=0:06:03.827815
G step 218: loss=0.12147567421197891; acc=0.9599999785423279; time=0:06:04.537494
G step 219: loss=0.17327196896076202; acc=1.0; time=0:06:05.099841
D step 355: loss=0.10441411286592484; acc=0.9900000095367432; time=0:06:05.768096
D step 356: loss=0.09612423181533813; acc=1.0; time=0:06:06.451947
D step 357: loss=0.1072489321231842; acc=0.9861111044883728; time=0:06:06.937882
推理：
>>> np.reshape(discriminator.predict(ground_truth), (5, 10))
array([[0.5296475 , 0.52787906, 0.5270807 , 0.5260455 , 0.528732 ,
0.52820367, 0.53157693, 0.52730876, 0.5244186 , 0.52673554],
[0.5229454 , 0.5239704 , 0.53051734, 0.52862865, 0.52718925,
0.52680767, 0.52621156, 0.5308223 , 0.52489233, 0.5297055 ],
[0.53033316, 0.5260847 , 0.5300899 , 0.52788675, 0.529595 ,
0.52183014, 0.5321261 , 0.5251559 , 0.52876014, 0.52384466],
[0.528658 , 0.52737784, 0.53003156, 0.52685475, 0.53047454,
0.52759105, 0.52710444, 0.52546424, 0.52709824, 0.52520245],
[0.5283209 , 0.52810913, 0.52451426, 0.5196351 , 0.5299184 ,
0.5274567 , 0.52686375, 0.5269972 , 0.5248108 , 0.5263274 ]],
dtype=float32)
>>> np.reshape(gan.predict(input_vector), (5, 10))
array([[0.4719111 , 0.47217596, 0.47209665, 0.47233126, 0.4741753 ,
0.4712048 , 0.4721919 , 0.47193947, 0.47010162, 0.47092766],
[0.47291884, 0.47334394, 0.4714141 , 0.46976995, 0.47092718,
0.47233835, 0.47164065, 0.47276756, 0.47107005, 0.47187868],
[0.47153524, 0.47157907, 0.4706026 , 0.47128928, 0.47320494,
0.47089615, 0.47108623, 0.47432283, 0.47186196, 0.47404772],
[0.47164053, 0.47348404, 0.4701542 , 0.4741918 , 0.4702833 ,
0.47303212, 0.4726331 , 0.47118646, 0.47191456, 0.47318774],
[0.47043982, 0.47027725, 0.47308347, 0.47376725, 0.4733549 ,
0.47157207, 0.47205287, 0.47177386, 0.47119975, 0.4707804 ]],
dtype=float32)
注意gan = Model(in_vector, gan_output)，因此您的模型被定义为从输入向量到鉴别器输出的所有层，包括中间的生成器。所以当你打电话
gan.compile(optimizer=gan_optimizer, loss='binary_crossentropy', metrics=['accuracy'])，它会自动使用鉴别器的输出来确定准确性。因此，为了获得生成器的准确性，您可以使用回调并手动计算"准确性"，但是这可能为您的生成器定义(另外，当您考虑生成器时，生成器没有典型的准确性指标，您将与什么进行比较？此外，如果您的生成器产生随机噪声，这并不意味着精度应该为 0，并且由于您只使用鉴别器的精度并且它成功地将输出识别为不属于基础分布，因此精度保持 100%(这很容易，因为生成器的输出是随机噪声(。简而言之，鉴别器的高精度并不意味着生成器是否成功愚弄了鉴别器。事实上，当鉴别器的准确率接近50%时，这意味着生成器确实对输入数据进行了很好的建模，而鉴别器无法区分两者，并且正在进行随机猜测。因此，您所看到的是预期的行为