How to use the ModelCheckpoint callback with Keras and TensorFlow

Previously, we discussed how to save and serialize your models to disk after training is complete. We also learned how to spot underfitting and overfitting as they are happening, enabling you to kill off experiments that are not performing well while keeping the models that show promise while training.

A substantial dataset is useful when working with the ModelCheckpoint Callback in Keras. It allows us to see the Callback’s functionality in saving model weights during training, based on specific performance metrics.

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However, you might be wondering if it’s possible to combine both of these strategies. Can we serialize models whenever our loss/accuracy improves? Or is it possible to serialize only the best model (i.e., the one with the lowest loss or highest accuracy) during the training process? You bet. And luckily, we don’t have to build a custom callback either — this functionality is baked right into Keras.

To learn how to use the ModelCheckpoint callback with Keras and TensorFlow, just keep reading.

How to use the ModelCheckpoint callback with Keras and TensorFlow

A good application of checkpointing is to serialize your network to disk each time there is an improvement during training. We define an “improvement” to be either a decrease in loss or an increase in accuracy — we’ll set this parameter inside the actual Keras callback.

In this example, we’ll be training the MiniVGGNet architecture on the CIFAR-10 dataset and then serializing our network weights to disk each time model performance improves. To get started, open a new file, name it cifar10_checkpoint_improvements.py, and insert the following code:

# import the necessary packages
from sklearn.preprocessing import LabelBinarizer
from pyimagesearch.nn.conv import MiniVGGNet
from tensorflow.keras.callbacks import ModelCheckpoint
from tensorflow.keras.optimizers import SGD
from tensorflow.keras.datasets import cifar10
import argparse
import os

Lines 2-8 import our required Python packages. Take note of the ModelCheckpoint class imported on Line 4 — this class will enable us to checkpoint and serialize our networks to disk whenever we find an incremental improvement in model performance.

Next, let’s parse our command line arguments:

# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-w", "--weights", required=True,
	help="path to weights directory")
args = vars(ap.parse_args())

The only command line argument we need is --weights, the path to the output directory that will store our serialized models during the training process. We then perform our standard routine of loading the CIFAR-10 dataset from disk, scaling the pixel intensities to the range [0, 1], and then one-hot encoding the labels:

# load the training and testing data, then scale it into the
# range [0, 1]
print("[INFO] loading CIFAR-10 data...")
((trainX, trainY), (testX, testY)) = cifar10.load_data()
trainX = trainX.astype("float") / 255.0
testX = testX.astype("float") / 255.0

# convert the labels from integers to vectors
lb = LabelBinarizer()
trainY = lb.fit_transform(trainY)
testY = lb.transform(testY)

Given our data, we are now ready to initialize our SGD optimizer along with the MiniVGGNet architecture:

# initialize the optimizer and model
print("[INFO] compiling model...")
opt = SGD(lr=0.01, decay=0.01 / 40, momentum=0.9, nesterov=True)
model = MiniVGGNet.build(width=32, height=32, depth=3, classes=10)
model.compile(loss="categorical_crossentropy", optimizer=opt,
	metrics=["accuracy"])

We’ll use the SGD optimizer with an initial learning rate of α = 0.01 and then slowly decay it over the course of 40 epochs. We’ll also apply a momentum of γ = 0.9 and indicate that the Nesterov acceleration should also be used as well.

The MiniVGGNet architecture is instantiated to accept input images with a width of 32 pixels, a height of 32 pixels, and a depth of 3 (number of channels). We set classes=10 since the CIFAR-10 dataset has ten possible class labels.

The critical step to checkpointing our network can be found in the code block below:

# construct the callback to save only the *best* model to disk
# based on the validation loss
fname = os.path.sep.join([args["weights"],
	"weights-{epoch:03d}-{val_loss:.4f}.hdf5"])
checkpoint = ModelCheckpoint(fname, monitor="val_loss", mode="min",
	save_best_only=True, verbose=1)
callbacks = [checkpoint]

On Lines 37 and 38, we construct a special filename (fname) template string that Keras uses when writing our models to disk. The first variable in the template, {epoch:03d}, is our epoch number, written out to three digits.

The second variable is the metric we want to monitor for improvement, {val_loss:.4f}, the loss itself for validation set on the current epoch. Of course, if we wanted to monitor the validation accuracy we can replace val_loss with val_acc. If we instead wanted to monitor the training loss and accuracy the variable would become train_loss and train_acc, respectively (although I would recommend monitoring your validation metrics as they will give you a better sense on how your model will generalize).

Once the output filename template is defined, we then instantiate the ModelCheckpoint class on Lines 39 and 40. The first parameter to ModelCheckpoint is the string representing our filename template. We then pass in what we would like to monitor. In this case, we would like to monitor the validation loss (val_loss).

The mode parameter controls whether the ModelCheckpoint should be looking for values that minimize our metric or maximize it. Since we are working with loss, lower is better, so we set mode="min". If we were instead working with val_acc, we would set mode="max" (since higher accuracy is better).

Setting save_best_only=True ensures that the latest best model (according to the metric monitored) will not be overwritten. Finally, the verbose=1 setting simply logs a notification to our terminal when a model is being serialized to disk during training.

Line 41 then constructs a list of callbacks — the only callback we need is our checkpoint.

The last step is to simply train the network and allowing our checkpoint to take care of the rest:

# train the network
print("[INFO] training network...")
H = model.fit(trainX, trainY, validation_data=(testX, testY),
	batch_size=64, epochs=40, callbacks=callbacks, verbose=2)

To execute our script, simply open a terminal and execute the following command:

$ python cifar10_checkpoint_improvements.py --weights weights/improvements
[INFO] loading CIFAR-10 data...
[INFO] compiling model...
[INFO] training network...
Train on 50000 samples, validate on 10000 samples
Epoch 1/40
171s - loss: 1.6700 - acc: 0.4375 - val_loss: 1.2697 - val_acc: 0.5425
Epoch 2/40
Epoch 00001: val_loss improved from 1.26973 to 0.98481, saving model to test/
	weights-001-0.9848.hdf5
...
Epoch 40/40
Epoch 00039: val_loss did not improve
315s - loss: 0.2594 - acc: 0.9075 - val_loss: 0.5707 - val_acc: 0.8190

As we can see from my terminal output and Figure 1, every time the validation loss decreases we save a new serialized model to disk.

At the end of the training process, we have 18 separate files, one for each incremental improvement:

$ find ./  -printf "%f\n" | sort
./
weights-000-1.2697.hdf5
weights-001-0.9848.hdf5
weights-003-0.8176.hdf5
weights-004-0.7987.hdf5
weights-005-0.7722.hdf5
weights-006-0.6925.hdf5
weights-007-0.6846.hdf5
weights-008-0.6771.hdf5
weights-009-0.6212.hdf5
weights-012-0.6121.hdf5
weights-013-0.6101.hdf5
weights-014-0.5899.hdf5
weights-015-0.5811.hdf5
weights-017-0.5774.hdf5
weights-019-0.5740.hdf5
weights-022-0.5724.hdf5
weights-024-0.5628.hdf5
weights-033-0.5546.hdf5

As you can see, each filename has three components. The first is a static string, weights. We then have the epoch number. The final component of the filename is the metric we are measuring for improvement, which in this case is validation loss.

Our best validation loss was obtained on epoch 33 with a value of 0.5546. We could then take this model and load it from disk.

Keep in mind that your results will not match mine as networks are stochastic and initialized with random variables. Depending on the initial values, you might have dramatically different model checkpoints, but at the end of the training process, our networks should obtain similar accuracy (± a few percentage points).

Configuring your development environment

To follow this guide, you need to have the OpenCV library installed on your system.

Luckily, OpenCV is pip-installable:

$ pip install opencv-contrib-python

If you need help configuring your development environment for OpenCV, I highly recommend that you read my pip install OpenCV guide — it will have you up and running in a matter of minutes.

Having problems configuring your development environment?

All that said, are you:

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Checkpointing Best Neural Network Only

Perhaps the biggest downside with checkpointing incremental improvements is that we end up with a bunch of extra files that we are (unlikely) interested in, which is especially true if our validation loss moves up and down over training epochs — each of these incremental improvements will be captured and serialized to disk. In this case, it’s best to save only one model and simply overwrite it every time our metric improves during training.

Luckily, accomplishing this action is as simple as updating the ModelCheckpoint class to accept a simple string (i.e., a file path without any template variables). Then, whenever our metric improves, that file is simply overwritten. To understand the process, let’s create a second Python file named cifar10_checkpoint_best.py and review the differences.

First, we need to import our required Python packages:

# import the necessary packages
from sklearn.preprocessing import LabelBinarizer
from pyimagesearch.nn.conv import MiniVGGNet
from tensorflow.keras.callbacks import ModelCheckpoint
from tensorflow.keras.optimizers import SGD
from tensorflow.keras.datasets import cifar10
import argparse

Then parse our command line arguments:

# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-w", "--weights", required=True,
	help="path to best model weights file")
args = vars(ap.parse_args())

The name of the command line argument itself is the same (--weights), but the description of the switch is now different: “path to best model weights file.” Thus, this command line argument will be a simple string to an output path — there will be no template applied to this string.

From there we can load our CIFAR-10 dataset and prepare it for training:

# load the training and testing data, then scale it into the
# range [0, 1]
print("[INFO] loading CIFAR-10 data...")
((trainX, trainY), (testX, testY)) = cifar10.load_data()
trainX = trainX.astype("float") / 255.0
testX = testX.astype("float") / 255.0

# convert the labels from integers to vectors
lb = LabelBinarizer()
trainY = lb.fit_transform(trainY)
testY = lb.transform(testY)

As well as initialize our SGD optimizer and MiniVGGNet architecture:

# initialize the optimizer and model
print("[INFO] compiling model...")
opt = SGD(lr=0.01, decay=0.01 / 40, momentum=0.9, nesterov=True)
model = MiniVGGNet.build(width=32, height=32, depth=3, classes=10)
model.compile(loss="categorical_crossentropy", optimizer=opt,
	metrics=["accuracy"])

We are now ready to update the ModelCheckpoint code:

# construct the callback to save only the *best* model to disk
# based on the validation loss
checkpoint = ModelCheckpoint(args["weights"], monitor="val_loss",
	save_best_only=True, verbose=1)
callbacks = [checkpoint]

Notice how the fname template string is gone — all we are doing is supplying the value of --weights to ModelCheckpoint. Since there are no template values to fill in, Keras will simply overwrite the existing serialized weights file whenever our monitoring metric improves (in this case, validation loss).

Finally, we train on network in the code block below:

# train the network
print("[INFO] training network...")
H = model.fit(trainX, trainY, validation_data=(testX, testY),
	batch_size=64, epochs=40, callbacks=callbacks, verbose=2)

To execute our script, issue the following command:

$ python cifar10_checkpoint_best.py \
	--weights weights/best/cifar10_best_weights.hdf5
[INFO] loading CIFAR-10 data...
[INFO] compiling model...
[INFO] training network...
Train on 50000 samples, validate on 10000 samples
Epoch 1/40
Epoch 00000: val_loss improved from inf to 1.26677, saving model to
	test_best/cifar10_best_weights.hdf5
305s - loss: 1.6657 - acc: 0.4441 - val_loss: 1.2668 - val_acc: 0.5584
Epoch 2/40
Epoch 00001: val_loss improved from 1.26677 to 1.21923, saving model to
	test_best/cifar10_best_weights.hdf5
309s - loss: 1.1996 - acc: 0.5828 - val_loss: 1.2192 - val_acc: 0.5798
...
Epoch 40/40
Epoch 00039: val_loss did not improve
173s - loss: 0.2615 - acc: 0.9079 - val_loss: 0.5511 - val_acc: 0.8250

Here, you can see that we overwrite our cifar10_best_weights.hdf5 file with the updated network only if our validation loss decreases. This has two primary benefits:

1. There is only one serialized file at the end of the training process — the model epoch that obtained the lowest loss.
2. We are not capturing “incremental improvements” where loss fluctuates up and down. Instead, we only save and overwrite the existing best model if our metric obtains a loss lower than all previous epochs.

To confirm this, take a look at my weights/best directory where you can see there is only one output file:

$ ls -l weights/best/
total 17024
-rw-rw-r-- 1 adrian adrian 17431968 Apr 28 09:47 cifar10_best_weights.hdf5

You can then take this serialized MiniVGGNet and further evaluate it on the testing data or apply it to your own images.

Summary

In this tutorial, we reviewed how to monitor a given metric (e.g., validation loss, validation accuracy, etc.) during training and then save high-performing networks to disk. There are two methods to accomplish this inside Keras:

1. Checkpoint incremental improvements.
2. Checkpoint only the best model found during the process.

Personally, I prefer the latter over the former since it results in fewer files and a single output file that represents the best epoch found during the training process.

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