最近，我正在浏览 tensorflow pix2pix tutorial 和我在玩我有点意外地玩了意识到，A nofollow noreferrer“> tf.keras.keras.model （在这种情况下，在教程中的Generator（））其中一个预测使用训练标志 true ，另一个to false 。
这是证明问题的代码：
# ...Tutorial steps where I load the model instead of creating a new one...
checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))
for example_input, example_target in test_dataset.take(1):
train_res1 = generate_images(generator, example_input, example_target) # Function definition as per tutorial expect that I return the 'Predicted Image'
train_res2 = generate_images(generator, example_input, example_target) # Now considering training is true (will alter the model), a small RGB difference is expected
notrain_res2 = generate_images2(generator, example_input, example_target) # Identical to 'generate_images' except that 'training=false', should be identical or similar to last one.
r_avg = np.average(train_res1[:,:, 0])
g_avg = np.average(train_res1[:,:, 1])
b_avg = np.average(train_res1[:,:, 2])
print(f"Training flag true iteration#1 = R average: {r_avg}, G average: {g_avg}, B average: {b_avg}")
r_avg = np.average(train_res2[:,:, 0])
g_avg = np.average(train_res2[:,:, 1])
b_avg = np.average(train_res2[:,:, 2])
print(f"Training flag true iteration#2 = R average: {r_avg}, G average: {g_avg}, B average: {b_avg}")
r_avg = np.average(notrain_res2[:,:, 0])
g_avg = np.average(notrain_res2[:,:, 1])
b_avg = np.average(notrain_res2[:,:, 2])
print(f"Training flag false = R average: {r_avg}, G average: {g_avg}, B average: {b_avg}")
为了避免任何混乱，以下是 generate_images2 的代码，与 generate_images 相同，除了“训练= false”和我返回预测：
def generate_images2(model, test_input, tar):
prediction = model(test_input, training=False)
plt.figure(figsize=(15, 15))
display_list = [test_input[0], tar[0], prediction[0]]
title = ['Input Image', 'Ground Truth', 'Predicted Image']
for i in range(3):
plt.subplot(1, 3, i+1)
plt.title(title[i], color = "w")
# Getting the pixel values in the [0, 1] range to plot.
plt.imshow(display_list[i] * 0.5 + 0.5)
plt.axis('off')
plt.show()
return display_list[2]
在这里您可以通过训练标志。
- 正如预期的那样，迭代＃1的RGB值与带有训练标志= true的迭代＃2之间存在较小的差异。这是由于训练模型的改变而预期的。
- 但是，当训练标志= false时，我希望RGB值与迭代＃2相似或相同，带有trinagragn flag = traire，但是如果您在黄色和红色圆圈中查看门，则RGB值明显不同。
- 训练= true 问题几乎总是更好的结果
：为什么tf.keras.model训练标志显着改变了预测结果？
I was recently going through the tensorflow pix2pix tutorial and after playing with it a bit I unexpectedly realized that there is a major difference between the predictions of a tf.keras.Model (In this case the Generator() from the tutorial) where one of the prediction use the training flag to true and the other to false.
Here is the code to demonstrate the issue:
# ...Tutorial steps where I load the model instead of creating a new one...
checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))
for example_input, example_target in test_dataset.take(1):
train_res1 = generate_images(generator, example_input, example_target) # Function definition as per tutorial expect that I return the 'Predicted Image'
train_res2 = generate_images(generator, example_input, example_target) # Now considering training is true (will alter the model), a small RGB difference is expected
notrain_res2 = generate_images2(generator, example_input, example_target) # Identical to 'generate_images' except that 'training=false', should be identical or similar to last one.
r_avg = np.average(train_res1[:,:, 0])
g_avg = np.average(train_res1[:,:, 1])
b_avg = np.average(train_res1[:,:, 2])
print(f"Training flag true iteration#1 = R average: {r_avg}, G average: {g_avg}, B average: {b_avg}")
r_avg = np.average(train_res2[:,:, 0])
g_avg = np.average(train_res2[:,:, 1])
b_avg = np.average(train_res2[:,:, 2])
print(f"Training flag true iteration#2 = R average: {r_avg}, G average: {g_avg}, B average: {b_avg}")
r_avg = np.average(notrain_res2[:,:, 0])
g_avg = np.average(notrain_res2[:,:, 1])
b_avg = np.average(notrain_res2[:,:, 2])
print(f"Training flag false = R average: {r_avg}, G average: {g_avg}, B average: {b_avg}")
Just to avoid any confusion, here is the code of generate_images2 which is identical to generate_images from tutorial except that 'training=False' and I return the prediction:
def generate_images2(model, test_input, tar):
prediction = model(test_input, training=False)
plt.figure(figsize=(15, 15))
display_list = [test_input[0], tar[0], prediction[0]]
title = ['Input Image', 'Ground Truth', 'Predicted Image']
for i in range(3):
plt.subplot(1, 3, i+1)
plt.title(title[i], color = "w")
# Getting the pixel values in the [0, 1] range to plot.
plt.imshow(display_list[i] * 0.5 + 0.5)
plt.axis('off')
plt.show()
return display_list[2]
Here you can vizualize my concerns with the training flag.
- As expected there are minor differences between the RGB values of iteration#1 and iteration#2 with training flag = True. This is expected due to training model alterations.
- However when training flag = False, I would expect the RGB values to be similar or identical to the iteration#2 with training flag = True but if you look at the door in the yellow and red circle s the RGB values are clearly different.
- The result is pretty much always better with training=True
Question: Why tf.keras.Model training flag significantly alters the prediction result?
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这是TensorFlow Repo的答案：
https：//githbithub.com/tensorflow/tensorflow/tensorflow/tensorflow/issues/ 36936
只有在培训期间发生一些事情，例如使用辍学。如果
训练= false，则忽略了下去层（请参阅 https://www.tensorflow.org/api_docs/python/tf/keras/layers/layers/dropout ）Here's an answer from the Tensorflow repo:
https://github.com/tensorflow/tensorflow/issues/36936
There are some things that only happen during training, for example dropout is used. If
training=Falsethen dropout layers are ignored (see https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dropout)