First, let us import the necessary libraries:
# Import necessary libraries
import jax # Importing the jax library for array manipulation and automatic differentiation
from flax import linen as nn # Importing the flax linen module for building neural network models
from flax.training import train_state # Importing the train_state module for managing training state
from flax.training.checkpoints import restore_checkpoint # Importing the restore_checkpoint module for restoring model checkpoints
from flax.training.common_utils import convert_to_jaxpr, get_variables # Importing the convert_to_jaxpr and get_variables modules for converting models to jaxpr format and getting model variables
from flax.training.train_loop import train # Importing the train module for training models
Next, let us define our model architecture using Flax’s Linear module:
# Define a class for our model
class MyModel(nn.Module):
# Define a setup function to initialize the model
def setup(self):
# Define a dense layer with 1024 outputs and ReLU activation function
self.dense = nn.Dense(
num_outputs=1024, activation="relu")
# Add a dropout layer with a dropout rate of 0.5 to prevent overfitting
self.dropout = nn.Dropout(rate=0.5)
# Define a second dense layer with 10 outputs and softmax activation function (output layer)
self.dense2 = nn.Dense(num_outputs=10, activation="softmax")
In this example, we have defined a simple neural network with two dense layers: one with 1024 neurons and an activation function of “relu”, followed by a dropout layer with probability 0.5 to prevent overfitting, and finally another dense layer with 10 outputs and an activation function of “softmax” for the output layer.
To use this model in practice, we can create an instance of it using Flax’s `Module` class:
# Import necessary libraries
import flax
import jax
import jax.numpy as jnp
from flax import nn
# Define our neural network model
class MyModel(nn.Module):
def apply(self, x):
# Define our first dense layer with 64 outputs and an activation function of "relu"
x = nn.Dense(x, features=64, activation="relu")
# Add a dropout layer with probability 0.5 to prevent overfitting
x = nn.Dropout(x, rate=0.5)
# Define our second dense layer with 10 outputs and an activation function of "softmax" for the output layer
x = nn.Dense(x, features=10, activation="softmax")
return x
# Create an instance of our defined neural network model
model = MyModel()
# To use this model in practice, we can create an instance of it using Flax's `Module` class:
# Create a new instance of our defined neural network model
model = MyModel()
We can then pass input data through this model to get output predictions. For example:
# Define some sample input data (in this case, a one-dimensional array of numbers)
input_data = [1, 2, 3]
# Pass the input data through our defined neural network model to get output predictions
output_predictions = model(inputs=input_data)
In this example, we have passed an array of three numbers as input data and received output predictions from our model. These output predictions can be used for various purposes such as classification or regression depending on your specific use case.