How can I use Sonnet to train a neural network from scratch

The key steps are:

1. Define your neural network architecture using Sonnet modules. Sonnet provides a set of predefined modules like `snt.Linear`, `snt.Conv2D`, etc. that you can compose to build your model.

2. Initialize the model parameters randomly.

3. Define a loss function that measures the error between the model's predictions and the ground truth.

4. Use an optimization algorithm like gradient descent to update the model parameters and minimize the loss.

5. Repeat steps 3-4 for multiple training iterations.

Here's some sample code to illustrate this process:

python
import sonnet as snt
import tensorflow as tf

# 1. Define the model architecture
class MyModel(snt.Module):
    def __init__(self, output_size, name=None):
        super().__init__(name=name)
        self.fc1 = snt.Linear(256)
        self.fc2 = snt.Linear(output_size)

    def __call__(self, x):
        x = tf.nn.relu(self.fc1(x))
        return self.fc2(x)

# 2. Initialize the model parameters
model = MyModel(10)  # 10 output classes
initial_params = model.trainable_variables

# 3. Define the loss function
labels = tf.one_hot(y_true, 10)
logits = model(x_input)
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=labels, logits=logits))

# 4. Optimize the model using gradient descent
optimizer = tf.optimizers.Adam(learning_rate=0.001)
for _ in range(num_steps):
    with tf.GradientTape() as tape:
        logits = model(x_input)
        loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=labels, logits=logits))
    grads = tape.gradient(loss, model.trainable_variables)
    optimizer.apply_gradients(zip(grads, model.trainable_variables))

# 5. Repeat steps 3-4 for multiple training iterations

The key aspects are:

- Defining the model architecture using Sonnet modules
- Initializing the model parameters randomly
- Defining a loss function to measure the error
- Using gradient descent to optimize the model parameters

This allows you to train a neural network from scratch using Sonnet, without relying on pre-built models or high-level APIs. The flexibility of Sonnet enables you to customize the architecture and training process as needed for your specific problem.