- April 17, 2018
- Vasilis Vryniotis
- . 31 Feedback
UPDATE: Sadly my Pull-Request to Keras that modified the behaviour of the Batch Normalization layer was not accepted. You’ll be able to learn the small print right here. For these of you who’re courageous sufficient to mess with customized implementations, you could find the code in my department. I would keep it and merge it with the most recent steady model of Keras (2.1.6, 2.2.2 and 2.2.4) for so long as I take advantage of it however no guarantees.
Most individuals who work in Deep Studying have both used or heard of Keras. For these of you who haven’t, it’s a terrific library that abstracts the underlying Deep Studying frameworks akin to TensorFlow, Theano and CNTK and gives a high-level API for coaching ANNs. It’s straightforward to make use of, permits quick prototyping and has a pleasant energetic group. I’ve been utilizing it closely and contributing to the challenge periodically for fairly a while and I undoubtedly advocate it to anybody who needs to work on Deep Studying.
Although Keras made my life simpler, fairly many occasions I’ve been bitten by the odd conduct of the Batch Normalization layer. Its default conduct has modified over time, however it nonetheless causes issues to many customers and in consequence there are a number of associated open points on Github. On this weblog submit, I’ll attempt to construct a case for why Keras’ BatchNormalization layer doesn’t play good with Switch Studying, I’ll present the code that fixes the issue and I’ll give examples with the outcomes of the patch.
On the subsections under, I present an introduction on how Switch Studying is utilized in Deep Studying, what’s the Batch Normalization layer, how learnining_phase works and the way Keras modified the BN conduct over time. For those who already know these, you possibly can safely bounce on to part 2.
1.1 Utilizing Switch Studying is essential for Deep Studying
One of many explanation why Deep Studying was criticized up to now is that it requires an excessive amount of information. This isn’t all the time true; there are a number of strategies to handle this limitation, considered one of which is Switch Studying.
Assume that you’re engaged on a Laptop Imaginative and prescient software and also you need to construct a classifier that distinguishes Cats from Canine. You don’t really need tens of millions of cat/canine photographs to coach the mannequin. As a substitute you need to use a pre-trained classifier and fine-tune the highest convolutions with much less information. The thought behind it’s that because the pre-trained mannequin was match on photographs, the underside convolutions can acknowledge options like strains, edges and different helpful patterns that means you need to use its weights both pretty much as good initialization values or partially retrain the community along with your information.
Keras comes with a number of pre-trained fashions and easy-to-use examples on learn how to fine-tune fashions. You’ll be able to learn extra on the documentation.
1.2 What’s the Batch Normalization layer?
The Batch Normalization layer was launched in 2014 by Ioffe and Szegedy. It addresses the vanishing gradient downside by standardizing the output of the earlier layer, it quickens the coaching by lowering the variety of required iterations and it permits the coaching of deeper neural networks. Explaining precisely the way it works is past the scope of this submit however I strongly encourage you to learn the unique paper. An oversimplified clarification is that it rescales the enter by subtracting its imply and by dividing with its customary deviation; it might probably additionally study to undo the transformation if mandatory.
1.3 What’s the learning_phase in Keras?
Some layers function in another way throughout coaching and inference mode. Probably the most notable examples are the Batch Normalization and the Dropout layers. Within the case of BN, throughout coaching we use the imply and variance of the mini-batch to rescale the enter. Then again, throughout inference we use the shifting common and variance that was estimated throughout coaching.
Keras is aware of by which mode to run as a result of it has a built-in mechanism known as learning_phase. The educational section controls whether or not the community is on practice or take a look at mode. If it isn’t manually set by the person, throughout match() the community runs with learning_phase=1 (practice mode). Whereas producing predictions (for instance once we name the predict() & consider() strategies or on the validation step of the match()) the community runs with learning_phase=0 (take a look at mode). Although it isn’t beneficial, the person can also be in a position to statically change the learning_phase to a selected worth however this must occur earlier than any mannequin or tensor is added within the graph. If the learning_phase is ready statically, Keras might be locked to whichever mode the person chosen.
1.4 How did Keras implement Batch Normalization over time?
Keras has modified the conduct of Batch Normalization a number of occasions however the latest vital replace occurred in Keras 2.1.3. Earlier than v2.1.3 when the BN layer was frozen (trainable = False) it saved updating its batch statistics, one thing that brought about epic complications to its customers.
This was not only a bizarre coverage, it was really incorrect. Think about {that a} BN layer exists between convolutions; if the layer is frozen no modifications ought to occur to it. If we do replace partially its weights and the following layers are additionally frozen, they may by no means get the possibility to regulate to the updates of the mini-batch statistics resulting in increased error. Fortunately ranging from model 2.1.3, when a BN layer is frozen it now not updates its statistics. However is that sufficient? Not if you’re utilizing Switch Studying.
Under I describe precisely what’s the downside and I sketch out the technical implementation for fixing it. I additionally present a number of examples to point out the consequences on mannequin’s accuracy earlier than and after the patch is utilized.
2.1 Technical description of the issue
The issue with the present implementation of Keras is that when a BN layer is frozen, it continues to make use of the mini-batch statistics throughout coaching. I consider a greater method when the BN is frozen is to make use of the shifting imply and variance that it realized throughout coaching. Why? For a similar explanation why the mini-batch statistics shouldn’t be up to date when the layer is frozen: it might probably result in poor outcomes as a result of the following layers should not educated correctly.
Assume you’re constructing a Laptop Imaginative and prescient mannequin however you don’t have sufficient information, so that you determine to make use of one of many pre-trained CNNs of Keras and fine-tune it. Sadly, by doing so that you get no ensures that the imply and variance of your new dataset contained in the BN layers might be just like those of the unique dataset. Do not forget that in the intervening time, throughout coaching your community will all the time use the mini-batch statistics both the BN layer is frozen or not; additionally throughout inference you’ll use the beforehand realized statistics of the frozen BN layers. Because of this, should you fine-tune the highest layers, their weights might be adjusted to the imply/variance of the new dataset. However, throughout inference they may obtain information that are scaled in another way as a result of the imply/variance of the unique dataset might be used.
Above I present a simplistic (and unrealistic) structure for demonstration functions. Let’s assume that we fine-tune the mannequin from Convolution ok+1 up till the highest of the community (proper facet) and we hold frozen the underside (left facet). Throughout coaching all BN layers from 1 to ok will use the imply/variance of your coaching information. It will have detrimental results on the frozen ReLUs if the imply and variance on every BN should not near those realized throughout pre-training. It’s going to additionally trigger the remainder of the community (from CONV ok+1 and later) to be educated with inputs which have completely different scales evaluating to what is going to obtain throughout inference. Throughout coaching your community can adapt to those modifications, however the second you turn to prediction-mode, Keras will use completely different standardization statistics, one thing that may swift the distribution of the inputs of the following layers resulting in poor outcomes.
2.2 How will you detect if you’re affected?
One method to detect it’s to set statically the training section of Keras to 1 (practice mode) and to 0 (take a look at mode) and consider your mannequin in every case. If there’s vital distinction in accuracy on the identical dataset, you’re being affected by the issue. It’s value stating that, because of the means the learning_phase mechanism is applied in Keras, it’s sometimes not suggested to mess with it. Modifications on the learning_phase can have no impact on fashions which are already compiled and used; as you possibly can see on the examples on the following subsections, the easiest way to do that is to begin with a clear session and alter the learning_phase earlier than any tensor is outlined within the graph.
One other method to detect the issue whereas working with binary classifiers is to examine the accuracy and the AUC. If the accuracy is near 50% however the AUC is near 1 (and in addition you observe variations between practice/take a look at mode on the identical dataset), it could possibly be that the possibilities are out-of-scale due the BN statistics. Equally, for regression you need to use MSE and Spearman’s correlation to detect it.
2.3 How can we repair it?
I consider that the issue might be mounted if the frozen BN layers are literally simply that: completely locked in take a look at mode. Implementation-wise, the trainable flag must be a part of the computational graph and the conduct of the BN must rely not solely on the learning_phase but additionally on the worth of the trainable property. You’ll find the small print of my implementation on Github.
By making use of the above repair, when a BN layer is frozen it’ll now not use the mini-batch statistics however as a substitute use those realized throughout coaching. Because of this, there might be no discrepancy between coaching and take a look at modes which ends up in elevated accuracy. Clearly when the BN layer shouldn’t be frozen, it’ll proceed utilizing the mini-batch statistics throughout coaching.
2.4 Assessing the consequences of the patch
Although I wrote the above implementation not too long ago, the concept behind it’s closely examined on real-world issues utilizing varied workarounds which have the identical impact. For instance, the discrepancy between coaching and testing modes and might be prevented by splitting the community in two components (frozen and unfrozen) and performing cached coaching (passing information by means of the frozen mannequin as soon as after which utilizing them to coach the unfrozen community). However, as a result of the “belief me I’ve accomplished this earlier than” sometimes bears no weight, under I present a number of examples that present the consequences of the brand new implementation in follow.
Listed here are a number of vital factors concerning the experiment:
- I’ll use a tiny quantity of information to deliberately overfit the mannequin and I’ll practice & validate the mannequin on the identical dataset. By doing so, I count on close to excellent accuracy and equivalent efficiency on the practice/validation dataset.
- If throughout validation I get considerably decrease accuracy on the identical dataset, I’ll have a transparent indication that the present BN coverage impacts negatively the efficiency of the mannequin throughout inference.
- Any preprocessing will happen outdoors of Turbines. That is accomplished to work round a bug that was launched in v2.1.5 (at the moment mounted on upcoming v2.1.6 and newest grasp).
- We are going to power Keras to make use of completely different studying phases throughout analysis. If we spot variations between the reported accuracy we are going to know we’re affected by the present BN coverage.
The code for the experiment is proven under:
import numpy as np
from keras.datasets import cifar10
from scipy.misc import imresize
from keras.preprocessing.picture import ImageDataGenerator
from keras.functions.resnet50 import ResNet50, preprocess_input
from keras.fashions import Mannequin, load_model
from keras.layers import Dense, Flatten
from keras import backend as Ok
seed = 42
epochs = 10
records_per_class = 100
# We take solely 2 lessons from CIFAR10 and a really small pattern to deliberately overfit the mannequin.
# We can even use the identical information for practice/take a look at and count on that Keras will give the identical accuracy.
(x, y), _ = cifar10.load_data()
def filter_resize(class):
# We do the preprocessing right here as a substitute within the Generator to get round a bug on Keras 2.1.5.
return [preprocess_input(imresize(img, (224,224)).astype('float')) for img in x[y.flatten()==category][:records_per_class]]
x = np.stack(filter_resize(3)+filter_resize(5))
records_per_class = x.form[0] // 2
y = np.array([[1,0]]*records_per_class + [[0,1]]*records_per_class)
# We are going to use a pre-trained mannequin and finetune the highest layers.
np.random.seed(seed)
base_model = ResNet50(weights="imagenet", include_top=False, input_shape=(224, 224, 3))
l = Flatten()(base_model.output)
predictions = Dense(2, activation='softmax')(l)
mannequin = Mannequin(inputs=base_model.enter, outputs=predictions)
for layer in mannequin.layers[:140]:
layer.trainable = False
for layer in mannequin.layers[140:]:
layer.trainable = True
mannequin.compile(optimizer="sgd", loss="categorical_crossentropy", metrics=['accuracy'])
mannequin.fit_generator(ImageDataGenerator().move(x, y, seed=42), epochs=epochs, validation_data=ImageDataGenerator().move(x, y, seed=42))
# Retailer the mannequin on disk
mannequin.save('tmp.h5')
# In each take a look at we are going to clear the session and reload the mannequin to power Learning_Phase values to vary.
print('DYNAMIC LEARNING_PHASE')
Ok.clear_session()
mannequin = load_model('tmp.h5')
# This accuracy ought to match precisely the one of many validation set on the final iteration.
print(mannequin.evaluate_generator(ImageDataGenerator().move(x, y, seed=42)))
print('STATIC LEARNING_PHASE = 0')
Ok.clear_session()
Ok.set_learning_phase(0)
mannequin = load_model('tmp.h5')
# Once more the accuracy ought to match the above.
print(mannequin.evaluate_generator(ImageDataGenerator().move(x, y, seed=42)))
print('STATIC LEARNING_PHASE = 1')
Ok.clear_session()
Ok.set_learning_phase(1)
mannequin = load_model('tmp.h5')
# The accuracy might be near the one of many coaching set on the final iteration.
print(mannequin.evaluate_generator(ImageDataGenerator().move(x, y, seed=42)))
Let’s examine the outcomes on Keras v2.1.5:
Epoch 1/10 1/7 [===>..........................] - ETA: 25s - loss: 0.8751 - acc: 0.5312 2/7 [=======>......................] - ETA: 11s - loss: 0.8594 - acc: 0.4531 3/7 [===========>..................] - ETA: 7s - loss: 0.8398 - acc: 0.4688 4/7 [================>.............] - ETA: 4s - loss: 0.8467 - acc: 0.4844 5/7 [====================>.........] - ETA: 2s - loss: 0.7904 - acc: 0.5437 6/7 [========================>.....] - ETA: 1s - loss: 0.7593 - acc: 0.5625 7/7 [==============================] - 12s 2s/step - loss: 0.7536 - acc: 0.5744 - val_loss: 0.6526 - val_acc: 0.6650 Epoch 2/10 1/7 [===>..........................] - ETA: 4s - loss: 0.3881 - acc: 0.8125 2/7 [=======>......................] - ETA: 3s - loss: 0.3945 - acc: 0.7812 3/7 [===========>..................] - ETA: 2s - loss: 0.3956 - acc: 0.8229 4/7 [================>.............] - ETA: 1s - loss: 0.4223 - acc: 0.8047 5/7 [====================>.........] - ETA: 1s - loss: 0.4483 - acc: 0.7812 6/7 [========================>.....] - ETA: 0s - loss: 0.4325 - acc: 0.7917 7/7 [==============================] - 8s 1s/step - loss: 0.4095 - acc: 0.8089 - val_loss: 0.4722 - val_acc: 0.7700 Epoch 3/10 1/7 [===>..........................] - ETA: 4s - loss: 0.2246 - acc: 0.9375 2/7 [=======>......................] - ETA: 3s - loss: 0.2167 - acc: 0.9375 3/7 [===========>..................] - ETA: 2s - loss: 0.2260 - acc: 0.9479 4/7 [================>.............] - ETA: 2s - loss: 0.2179 - acc: 0.9375 5/7 [====================>.........] - ETA: 1s - loss: 0.2356 - acc: 0.9313 6/7 [========================>.....] - ETA: 0s - loss: 0.2392 - acc: 0.9427 7/7 [==============================] - 8s 1s/step - loss: 0.2288 - acc: 0.9456 - val_loss: 0.4282 - val_acc: 0.7800 Epoch 4/10 1/7 [===>..........................] - ETA: 4s - loss: 0.2183 - acc: 0.9688 2/7 [=======>......................] - ETA: 3s - loss: 0.1899 - acc: 0.9844 3/7 [===========>..................] - ETA: 2s - loss: 0.1887 - acc: 0.9792 4/7 [================>.............] - ETA: 1s - loss: 0.1995 - acc: 0.9531 5/7 [====================>.........] - ETA: 1s - loss: 0.1932 - acc: 0.9625 6/7 [========================>.....] - ETA: 0s - loss: 0.1819 - acc: 0.9688 7/7 [==============================] - 8s 1s/step - loss: 0.1743 - acc: 0.9747 - val_loss: 0.3778 - val_acc: 0.8400 Epoch 5/10 1/7 [===>..........................] - ETA: 3s - loss: 0.0973 - acc: 1.0000 2/7 [=======>......................] - ETA: 3s - loss: 0.0828 - acc: 1.0000 3/7 [===========>..................] - ETA: 2s - loss: 0.0851 - acc: 1.0000 4/7 [================>.............] - ETA: 1s - loss: 0.0897 - acc: 1.0000 5/7 [====================>.........] - ETA: 1s - loss: 0.0928 - acc: 1.0000 6/7 [========================>.....] - ETA: 0s - loss: 0.0936 - acc: 1.0000 7/7 [==============================] - 8s 1s/step - loss: 0.1337 - acc: 0.9838 - val_loss: 0.3916 - val_acc: 0.8100 Epoch 6/10 1/7 [===>..........................] - ETA: 4s - loss: 0.0747 - acc: 1.0000 2/7 [=======>......................] - ETA: 3s - loss: 0.0852 - acc: 1.0000 3/7 [===========>..................] - ETA: 2s - loss: 0.0812 - acc: 1.0000 4/7 [================>.............] - ETA: 1s - loss: 0.0831 - acc: 1.0000 5/7 [====================>.........] - ETA: 1s - loss: 0.0779 - acc: 1.0000 6/7 [========================>.....] - ETA: 0s - loss: 0.0766 - acc: 1.0000 7/7 [==============================] - 8s 1s/step - loss: 0.0813 - acc: 1.0000 - val_loss: 0.3637 - val_acc: 0.8550 Epoch 7/10 1/7 [===>..........................] - ETA: 1s - loss: 0.2478 - acc: 0.8750 2/7 [=======>......................] - ETA: 2s - loss: 0.1966 - acc: 0.9375 3/7 [===========>..................] - ETA: 2s - loss: 0.1528 - acc: 0.9583 4/7 [================>.............] - ETA: 1s - loss: 0.1300 - acc: 0.9688 5/7 [====================>.........] - ETA: 1s - loss: 0.1193 - acc: 0.9750 6/7 [========================>.....] - ETA: 0s - loss: 0.1196 - acc: 0.9792 7/7 [==============================] - 8s 1s/step - loss: 0.1084 - acc: 0.9838 - val_loss: 0.3546 - val_acc: 0.8600 Epoch 8/10 1/7 [===>..........................] - ETA: 4s - loss: 0.0539 - acc: 1.0000 2/7 [=======>......................] - ETA: 2s - loss: 0.0900 - acc: 1.0000 3/7 [===========>..................] - ETA: 2s - loss: 0.0815 - acc: 1.0000 4/7 [================>.............] - ETA: 1s - loss: 0.0740 - acc: 1.0000 5/7 [====================>.........] - ETA: 1s - loss: 0.0700 - acc: 1.0000 6/7 [========================>.....] - ETA: 0s - loss: 0.0701 - acc: 1.0000 7/7 [==============================] - 8s 1s/step - loss: 0.0695 - acc: 1.0000 - val_loss: 0.3269 - val_acc: 0.8600 Epoch 9/10 1/7 [===>..........................] - ETA: 4s - loss: 0.0306 - acc: 1.0000 2/7 [=======>......................] - ETA: 3s - loss: 0.0377 - acc: 1.0000 3/7 [===========>..................] - ETA: 2s - loss: 0.0898 - acc: 0.9583 4/7 [================>.............] - ETA: 1s - loss: 0.0773 - acc: 0.9688 5/7 [====================>.........] - ETA: 1s - loss: 0.0742 - acc: 0.9750 6/7 [========================>.....] - ETA: 0s - loss: 0.0708 - acc: 0.9792 7/7 [==============================] - 8s 1s/step - loss: 0.0659 - acc: 0.9838 - val_loss: 0.3604 - val_acc: 0.8600 Epoch 10/10 1/7 [===>..........................] - ETA: 3s - loss: 0.0354 - acc: 1.0000 2/7 [=======>......................] - ETA: 3s - loss: 0.0381 - acc: 1.0000 3/7 [===========>..................] - ETA: 2s - loss: 0.0354 - acc: 1.0000 4/7 [================>.............] - ETA: 1s - loss: 0.0828 - acc: 0.9688 5/7 [====================>.........] - ETA: 1s - loss: 0.0791 - acc: 0.9750 6/7 [========================>.....] - ETA: 0s - loss: 0.0794 - acc: 0.9792 7/7 [==============================] - 8s 1s/step - loss: 0.0704 - acc: 0.9838 - val_loss: 0.3615 - val_acc: 0.8600 DYNAMIC LEARNING_PHASE [0.3614931714534759, 0.86] STATIC LEARNING_PHASE = 0 [0.3614931714534759, 0.86] STATIC LEARNING_PHASE = 1 [0.025861846953630446, 1.0]
As we are able to see above, throughout coaching the mannequin learns very properly the info and achieves on the coaching set near-perfect accuracy. Nonetheless on the finish of every iteration, whereas evaluating the mannequin on the identical dataset, we get vital variations in loss and accuracy. Word that we shouldn’t be getting this; we’ve overfitted deliberately the mannequin on the particular dataset and the coaching/validation datasets are equivalent.
After the coaching is accomplished we consider the mannequin utilizing 3 completely different learning_phase configurations: Dynamic, Static = 0 (take a look at mode) and Static = 1 (coaching mode). As we are able to see the primary two configurations will present equivalent outcomes when it comes to loss and accuracy and their worth matches the reported accuracy of the mannequin on the validation set within the final iteration. However, as soon as we swap to coaching mode, we observe a large discrepancy (enchancment). Why it that? As we stated earlier, the weights of the community are tuned anticipating to obtain information scaled with the imply/variance of the coaching information. Sadly, these statistics are completely different from those saved within the BN layers. For the reason that BN layers have been frozen, these statistics have been by no means up to date. This discrepancy between the values of the BN statistics results in the deterioration of the accuracy throughout inference.
Let’s see what occurs as soon as we apply the patch:
Epoch 1/10 1/7 [===>..........................] - ETA: 26s - loss: 0.9992 - acc: 0.4375 2/7 [=======>......................] - ETA: 12s - loss: 1.0534 - acc: 0.4375 3/7 [===========>..................] - ETA: 7s - loss: 1.0592 - acc: 0.4479 4/7 [================>.............] - ETA: 4s - loss: 0.9618 - acc: 0.5000 5/7 [====================>.........] - ETA: 2s - loss: 0.8933 - acc: 0.5250 6/7 [========================>.....] - ETA: 1s - loss: 0.8638 - acc: 0.5417 7/7 [==============================] - 13s 2s/step - loss: 0.8357 - acc: 0.5570 - val_loss: 0.2414 - val_acc: 0.9450 Epoch 2/10 1/7 [===>..........................] - ETA: 4s - loss: 0.2331 - acc: 0.9688 2/7 [=======>......................] - ETA: 2s - loss: 0.3308 - acc: 0.8594 3/7 [===========>..................] - ETA: 2s - loss: 0.3986 - acc: 0.8125 4/7 [================>.............] - ETA: 1s - loss: 0.3721 - acc: 0.8281 5/7 [====================>.........] - ETA: 1s - loss: 0.3449 - acc: 0.8438 6/7 [========================>.....] - ETA: 0s - loss: 0.3168 - acc: 0.8646 7/7 [==============================] - 9s 1s/step - loss: 0.3165 - acc: 0.8633 - val_loss: 0.1167 - val_acc: 0.9950 Epoch 3/10 1/7 [===>..........................] - ETA: 1s - loss: 0.2457 - acc: 1.0000 2/7 [=======>......................] - ETA: 2s - loss: 0.2592 - acc: 0.9688 3/7 [===========>..................] - ETA: 2s - loss: 0.2173 - acc: 0.9688 4/7 [================>.............] - ETA: 1s - loss: 0.2122 - acc: 0.9688 5/7 [====================>.........] - ETA: 1s - loss: 0.2003 - acc: 0.9688 6/7 [========================>.....] - ETA: 0s - loss: 0.1896 - acc: 0.9740 7/7 [==============================] - 9s 1s/step - loss: 0.1835 - acc: 0.9773 - val_loss: 0.0678 - val_acc: 1.0000 Epoch 4/10 1/7 [===>..........................] - ETA: 1s - loss: 0.2051 - acc: 1.0000 2/7 [=======>......................] - ETA: 2s - loss: 0.1652 - acc: 0.9844 3/7 [===========>..................] - ETA: 2s - loss: 0.1423 - acc: 0.9896 4/7 [================>.............] - ETA: 1s - loss: 0.1289 - acc: 0.9922 5/7 [====================>.........] - ETA: 1s - loss: 0.1225 - acc: 0.9938 6/7 [========================>.....] - ETA: 0s - loss: 0.1149 - acc: 0.9948 7/7 [==============================] - 9s 1s/step - loss: 0.1060 - acc: 0.9955 - val_loss: 0.0455 - val_acc: 1.0000 Epoch 5/10 1/7 [===>..........................] - ETA: 4s - loss: 0.0769 - acc: 1.0000 2/7 [=======>......................] - ETA: 2s - loss: 0.0846 - acc: 1.0000 3/7 [===========>..................] - ETA: 2s - loss: 0.0797 - acc: 1.0000 4/7 [================>.............] - ETA: 1s - loss: 0.0736 - acc: 1.0000 5/7 [====================>.........] - ETA: 1s - loss: 0.0914 - acc: 1.0000 6/7 [========================>.....] - ETA: 0s - loss: 0.0858 - acc: 1.0000 7/7 [==============================] - 9s 1s/step - loss: 0.0808 - acc: 1.0000 - val_loss: 0.0346 - val_acc: 1.0000 Epoch 6/10 1/7 [===>..........................] - ETA: 1s - loss: 0.1267 - acc: 1.0000 2/7 [=======>......................] - ETA: 2s - loss: 0.1039 - acc: 1.0000 3/7 [===========>..................] - ETA: 2s - loss: 0.0893 - acc: 1.0000 4/7 [================>.............] - ETA: 1s - loss: 0.0780 - acc: 1.0000 5/7 [====================>.........] - ETA: 1s - loss: 0.0758 - acc: 1.0000 6/7 [========================>.....] - ETA: 0s - loss: 0.0789 - acc: 1.0000 7/7 [==============================] - 9s 1s/step - loss: 0.0738 - acc: 1.0000 - val_loss: 0.0248 - val_acc: 1.0000 Epoch 7/10 1/7 [===>..........................] - ETA: 4s - loss: 0.0344 - acc: 1.0000 2/7 [=======>......................] - ETA: 3s - loss: 0.0385 - acc: 1.0000 3/7 [===========>..................] - ETA: 3s - loss: 0.0467 - acc: 1.0000 4/7 [================>.............] - ETA: 1s - loss: 0.0445 - acc: 1.0000 5/7 [====================>.........] - ETA: 1s - loss: 0.0446 - acc: 1.0000 6/7 [========================>.....] - ETA: 0s - loss: 0.0429 - acc: 1.0000 7/7 [==============================] - 9s 1s/step - loss: 0.0421 - acc: 1.0000 - val_loss: 0.0202 - val_acc: 1.0000 Epoch 8/10 1/7 [===>..........................] - ETA: 4s - loss: 0.0319 - acc: 1.0000 2/7 [=======>......................] - ETA: 3s - loss: 0.0300 - acc: 1.0000 3/7 [===========>..................] - ETA: 3s - loss: 0.0320 - acc: 1.0000 4/7 [================>.............] - ETA: 2s - loss: 0.0307 - acc: 1.0000 5/7 [====================>.........] - ETA: 1s - loss: 0.0303 - acc: 1.0000 6/7 [========================>.....] - ETA: 0s - loss: 0.0291 - acc: 1.0000 7/7 [==============================] - 9s 1s/step - loss: 0.0358 - acc: 1.0000 - val_loss: 0.0167 - val_acc: 1.0000 Epoch 9/10 1/7 [===>..........................] - ETA: 4s - loss: 0.0246 - acc: 1.0000 2/7 [=======>......................] - ETA: 3s - loss: 0.0255 - acc: 1.0000 3/7 [===========>..................] - ETA: 3s - loss: 0.0258 - acc: 1.0000 4/7 [================>.............] - ETA: 2s - loss: 0.0250 - acc: 1.0000 5/7 [====================>.........] - ETA: 1s - loss: 0.0252 - acc: 1.0000 6/7 [========================>.....] - ETA: 0s - loss: 0.0260 - acc: 1.0000 7/7 [==============================] - 9s 1s/step - loss: 0.0327 - acc: 1.0000 - val_loss: 0.0143 - val_acc: 1.0000 Epoch 10/10 1/7 [===>..........................] - ETA: 4s - loss: 0.0251 - acc: 1.0000 2/7 [=======>......................] - ETA: 2s - loss: 0.0228 - acc: 1.0000 3/7 [===========>..................] - ETA: 2s - loss: 0.0217 - acc: 1.0000 4/7 [================>.............] - ETA: 1s - loss: 0.0249 - acc: 1.0000 5/7 [====================>.........] - ETA: 1s - loss: 0.0244 - acc: 1.0000 6/7 [========================>.....] - ETA: 0s - loss: 0.0239 - acc: 1.0000 7/7 [==============================] - 9s 1s/step - loss: 0.0290 - acc: 1.0000 - val_loss: 0.0127 - val_acc: 1.0000 DYNAMIC LEARNING_PHASE [0.012697912137955427, 1.0] STATIC LEARNING_PHASE = 0 [0.012697912137955427, 1.0] STATIC LEARNING_PHASE = 1 [0.01744014158844948, 1.0]
Initially, we observe that the community converges considerably quicker and achieves excellent accuracy. We additionally see that there isn’t any longer a discrepancy when it comes to accuracy once we swap between completely different learning_phase values.
2.5 How does the patch carry out on an actual dataset?
So how does the patch carry out on a extra practical experiment? Let’s use Keras’ pre-trained ResNet50 (initially match on imagenet), take away the highest classification layer and fine-tune it with and with out the patch and evaluate the outcomes. For information, we are going to use CIFAR10 (the usual practice/take a look at break up supplied by Keras) and we are going to resize the pictures to 224×224 to make them appropriate with the ResNet50’s enter measurement.
We are going to do 10 epochs to coach the highest classification layer utilizing RSMprop after which we are going to do one other 5 to fine-tune every little thing after the 139th layer utilizing SGD(lr=1e-4, momentum=0.9). With out the patch our mannequin achieves an accuracy of 87.44%. Utilizing the patch, we get an accuracy of 92.36%, virtually 5 factors increased.
2.6 Ought to we apply the identical repair to different layers akin to Dropout?
Batch Normalization shouldn’t be the one layer that operates in another way between practice and take a look at modes. Dropout and its variants even have the identical impact. Ought to we apply the identical coverage to all these layers? I consider not (despite the fact that I’d love to listen to your ideas on this). The reason being that Dropout is used to keep away from overfitting, thus locking it completely to prediction mode throughout coaching would defeat its goal. What do you suppose?
I strongly consider that this discrepancy have to be solved in Keras. I’ve seen much more profound results (from 100% all the way down to 50% accuracy) in real-world functions attributable to this downside. I plan to ship already despatched a PR to Keras with the repair and hopefully it is going to be accepted.
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