Training large models take a lot of time and it is a good idea to save the trained models to files to avoid training them again and again. There are a number of reasons to do this. For example, you might want to do inference on a machine that is different from the one where the model was trained. Sometimes model’s performance on validation set decreases towards the end of the training because of overfitting. If you saved your model parameters after every epoch, at the end you can decide to use the model that performs best on the validation set. Another reason would be to train your model using one language (like Python that has a lot of tools for training) and run inference using a different language (like Scala probably because your application is built on Scala).

In this tutorial, we will learn ways to save and load Gluon models. There are two ways to save/load Gluon models:

Parameters of any Gluon model can be saved using the save_parameters and load_parameters method. This does not save model architecture. This method is used to save parameters of dynamic (non-hybrid) models. Model architecture cannot be saved for dynamic models because model architecture changes during execution.

2. Save/load model parameters AND architecture

The Model architecture of Hybrid models stays static and don’t change during execution. Therefore both model parameters AND architecture can be saved and loaded using export, imports methods.

Let’s look at the above methods in more detail. Let’s start by importing the modules we’ll need.

from __future__ import print_function

import mxnet as mx
import mxnet.ndarray as nd
from mxnet import nd, autograd, gluon
from mxnet.gluon.data.vision import transforms

import numpy as np


## Setup: build and train a simple model¶

We need a trained model before we can save it to a file. So let’s go ahead and build a very simple convolutional network and train it on MNIST data.

Let’s define a helper function to build a LeNet model and another helper to train LeNet with MNIST.

# Use GPU if one exists, else use CPU
ctx = mx.gpu() if mx.test_utils.list_gpus() else mx.cpu()

# MNIST images are 28x28. Total pixels in input layer is 28x28 = 784
num_inputs = 784
# Clasify the images into one of the 10 digits
num_outputs = 10
# 64 images in a batch
batch_size = 64

batch_size, shuffle=True)

# Build a simple convolutional network
def build_lenet(net):
with net.name_scope():
# First convolution
# Second convolution
# Flatten the output before the fully connected layers
# First fully connected layers with 512 neurons
# Second fully connected layer with as many neurons as the number of classes

return net

# Train a given model using MNIST data
def train_model(model):
# Initialize the parameters with Xavier initializer
model.collect_params().initialize(mx.init.Xavier(), ctx=ctx)
# Use cross entropy loss
softmax_cross_entropy = gluon.loss.SoftmaxCrossEntropyLoss()
trainer = gluon.Trainer(model.collect_params(), 'adam', {'learning_rate': .001})

# Train for one epoch
for epoch in range(1):
# Iterate through the images and labels in the training data
for batch_num, (data, label) in enumerate(train_data):
# get the images and labels
data = data.as_in_context(ctx)
label = label.as_in_context(ctx)
# Run the forward pass
output = model(data)
# Compute the loss
loss = softmax_cross_entropy(output, label)
loss.backward()
# Update parameters
trainer.step(data.shape[0])

# Print loss once in a while
if batch_num % 50 == 0:
curr_loss = nd.mean(loss).asscalar()
print("Epoch: %d; Batch %d; Loss %f" % (epoch, batch_num, curr_loss))


Let’s build a model and train it. After training, we will save and restore this model from a file.

net = build_lenet(gluon.nn.Sequential())
train_model(net)

Epoch: 0; Batch 0; Loss 2.288904
Epoch: 0; Batch 50; Loss 0.269372
Epoch: 0; Batch 100; Loss 0.238990
Epoch: 0; Batch 150; Loss 0.320592
Epoch: 0; Batch 200; Loss 0.048619
Epoch: 0; Batch 250; Loss 0.121555
Epoch: 0; Batch 300; Loss 0.083645
Epoch: 0; Batch 350; Loss 0.040627
Epoch: 0; Batch 400; Loss 0.195946
Epoch: 0; Batch 450; Loss 0.155514
Epoch: 0; Batch 500; Loss 0.031762
Epoch: 0; Batch 550; Loss 0.056516
Epoch: 0; Batch 600; Loss 0.095174
Epoch: 0; Batch 650; Loss 0.054901
Epoch: 0; Batch 700; Loss 0.030067
Epoch: 0; Batch 750; Loss 0.102611
Epoch: 0; Batch 800; Loss 0.010036
Epoch: 0; Batch 850; Loss 0.051853
Epoch: 0; Batch 900; Loss 0.008402


## Saving model parameters to file¶

Okay, we now have a model (net) that we can save to a file. Let’s save the parameters of this model to a file using the save_parameters function.

file_name = "net.params"
net.save_parameters(file_name)


We have successfully saved the parameters of the model into a file.

Note: Block.collect_params().save() is not a recommended way to save parameters of a Gluon network if you plan to load the parameters back into a Gluon network using Block.load_parameters().

Let’s now create a network with the parameters we saved into the file. We build the network again using the helper first and then load the weights from the file we saved using the load_parameters function.

new_net = build_lenet(gluon.nn.Sequential())


Note that to do this, we need the definition of the network as Python code. If we want to recreate this network on a different machine using the saved weights, we need the same Python code (build_lenet) that created the network to create the new_net object shown above. This means Python code needs to be copied over to any machine where we want to run this network.

If our network is Hybrid, we can even save the network architecture into files and we won’t need the network definition in a Python file to load the network. We’ll see how to do it in the next section.

Let’s test the model we just loaded from file.

import matplotlib.pyplot as plt

"""Run inference using ten random images.
Print both input and output of the model"""

def transform(data, label):
return data.astype(np.float32)/255, label.astype(np.float32)

# Load ten random images from the test dataset
10, shuffle=True)

for data, label in sample_data:

# Display the images
img = nd.transpose(data, (1,0,2,3))
img = nd.reshape(img, (28,10*28,1))
imtiles = nd.tile(img, (1,1,3))
plt.imshow(imtiles.asnumpy())
plt.show()

# Display the predictions
data = nd.transpose(data, (0, 3, 1, 2))
out = net(data.as_in_context(ctx))
predictions = nd.argmax(out, axis=1)
print('Model predictions: ', predictions.asnumpy())

break



Model predictions: [1. 1. 4. 5. 0. 5. 7. 0. 3. 6.]

## Saving model parameters AND architecture to file¶

Hybrid models can be serialized as JSON files using the export function. Once serialized, these models can be loaded from other language bindings like C++ or Scala for faster inference or inference in different environments.

Note that the network we created above is not a Hybrid network and therefore cannot be serialized into a JSON file. So, let’s create a Hybrid version of the same network and train it.

net = build_lenet(gluon.nn.HybridSequential())
net.hybridize()
train_model(net)

Epoch: 0; Batch 0; Loss 2.323284
Epoch: 0; Batch 50; Loss 0.444733
Epoch: 0; Batch 100; Loss 0.103407
Epoch: 0; Batch 150; Loss 0.166772
Epoch: 0; Batch 200; Loss 0.227569
Epoch: 0; Batch 250; Loss 0.069515
Epoch: 0; Batch 300; Loss 0.074086
Epoch: 0; Batch 350; Loss 0.074382
Epoch: 0; Batch 400; Loss 0.026569
Epoch: 0; Batch 450; Loss 0.097248
Epoch: 0; Batch 500; Loss 0.059895
Epoch: 0; Batch 550; Loss 0.053194
Epoch: 0; Batch 600; Loss 0.076294
Epoch: 0; Batch 650; Loss 0.047274
Epoch: 0; Batch 700; Loss 0.007898
Epoch: 0; Batch 750; Loss 0.039478
Epoch: 0; Batch 800; Loss 0.031342
Epoch: 0; Batch 850; Loss 0.059289
Epoch: 0; Batch 900; Loss 0.037809


We now have a trained hybrid network. This can be exported into files using the export function. The export function will export the model architecture into a .json file and model parameters into a .params file.

net.export("lenet", epoch=1)


export in this case creates lenet-symbol.json and lenet-0001.params in the current directory.

### From a different frontend¶

One of the main reasons to serialize model architecture into a JSON file is to load it from a different frontend like C, C++ or Scala. Here is a couple of examples:

### From Python¶

Serialized Hybrid networks (saved as .JSON and .params file) can be loaded and used inside Python frontend using gluon.nn.SymbolBlock. To demonstrate that, let’s load the network we serialized above.

deserialized_net = gluon.nn.SymbolBlock.imports("lenet-symbol.json", ['data'], "lenet-0001.params")


deserialized_net now contains the network we deserialized from files. Let’s test the deserialized network to make sure it works.

verify_loaded_model(deserialized_net)


Model predictions: [4. 8. 0. 1. 5. 5. 8. 8. 1. 9.]

That’s all! We learned how to save and load Gluon networks from files. Parameters of any Gluon network can be persisted into files. For hybrid networks, both the architecture of the network and the parameters can be saved to and loaded from files.