Initial Query

Convert the following PyTorch code to pure JAX. Do not use any other libraries. 

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import torch

class MLP(torch.nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super().__init__()
        self.linear1 = torch.nn.Linear(input_size, hidden_size)
        self.linear2 = torch.nn.Linear(hidden_size, output_size)

    def __call__(self, x):
        x = self.linear1(x)
        x = torch.relu(x)
        x = self.linear2(x)
        return torch.nn.functional.softmax(x, dim=1)

model = MLP(10, 20, 30)
x = torch.randn(5, 10)
y = model(x)
print(list(y.flatten().numpy().sum(1)))

LLM Generation

Query

Convert the following PyTorch code to pure JAX. Do not use any other libraries. 

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import torch

class MLP(torch.nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super().__init__()
        self.linear1 = torch.nn.Linear(input_size, hidden_size)
        self.linear2 = torch.nn.Linear(hidden_size, output_size)

    def __call__(self, x):
        x = self.linear1(x)
        x = torch.relu(x)
        x = self.linear2(x)
        return torch.nn.functional.softmax(x, dim=1)

model = MLP(10, 20, 30)
x = torch.randn(5, 10)
y = model(x)
print(list(y.flatten().numpy().sum(1)))

Output

To convert the given PyTorch code to pure JAX, we need to replace PyTorch-specific constructs with their JAX equivalents. Here's the equivalent code in JAX: 

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import jax
import jax.numpy as jnp
from jax import random

# Define the MLP model using JAX
class MLP:
    def __init__(self, input_size, hidden_size, output_size, key):
        key1, key2 = random.split(key)
        self.linear1 = self._initialize_linear(key1, input_size, hidden_size)
        self.linear2 = self._initialize_linear(key2, hidden_size, output_size)

    def _initialize_linear(self, key, in_features, out_features):
        # Initialize weights and biases
        w_key, b_key = random.split(key)
        weights = random.normal(w_key, (in_features, out_features)) * jnp.sqrt(2.0 / in_features)
        biases = random.normal(b_key, (out_features,))
        return (weights, biases)

    def __call__(self, x):
        # Apply the first linear layer
        x = jnp.dot(x, self.linear1[0]) + self.linear1[1]
        # Apply ReLU activation
        x = jax.nn.relu(x)
        # Apply the second linear layer
        x = jnp.dot(x, self.linear2[0]) + self.linear2[1]
        # Apply softmax along the last dimension
        return jax.nn.softmax(x, axis=-1)

# Initialize the model
key = random.PRNGKey(0)
model = MLP(10, 20, 30, key)

# Create a random input
x = random.normal(key, (5, 10))

# Forward pass through the model
y = model(x)

# Print the sum of each row in the output
print(jnp.sum(y, axis=1))
### Explanation: 1. **Initialization**: - In JAX, we use `random.PRNGKey` to generate random keys for initializing weights and biases. - The `_initialize_linear` method initializes the weights and biases for each linear layer. 2. **Linear Layer**: - The linear layer is implemented using matrix multiplication (`jnp.dot`) and addition of biases. 3. **Activation Functions**: - ReLU is applied using `jax.nn.relu`. - Softmax is applied using `jax.nn.softmax` along the last dimension (`axis=-1`). 4. **Forward Pass**: - The `__call__` method performs the forward pass through the network. 5. **Output**: - The output is summed along the rows to verify that the softmax output sums to 1 for each row. This code is a direct translation of the PyTorch code to JAX, using only JAX's core functionality.

Extract Code

I extracted the following code from that output: 

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import jax
import jax.numpy as jnp
from jax import random

# Define the MLP model using JAX
class MLP:
    def __init__(self, input_size, hidden_size, output_size, key):
        key1, key2 = random.split(key)
        self.linear1 = self._initialize_linear(key1, input_size, hidden_size)
        self.linear2 = self._initialize_linear(key2, hidden_size, output_size)

    def _initialize_linear(self, key, in_features, out_features):
        # Initialize weights and biases
        w_key, b_key = random.split(key)
        weights = random.normal(w_key, (in_features, out_features)) * jnp.sqrt(2.0 / in_features)
        biases = random.normal(b_key, (out_features,))
        return (weights, biases)

    def __call__(self, x):
        # Apply the first linear layer
        x = jnp.dot(x, self.linear1[0]) + self.linear1[1]
        # Apply ReLU activation
        x = jax.nn.relu(x)
        # Apply the second linear layer
        x = jnp.dot(x, self.linear2[0]) + self.linear2[1]
        # Apply softmax along the last dimension
        return jax.nn.softmax(x, axis=-1)

# Initialize the model
key = random.PRNGKey(0)
model = MLP(10, 20, 30, key)

# Create a random input
x = random.normal(key, (5, 10))

# Forward pass through the model
y = model(x)

# Print the sum of each row in the output
print(jnp.sum(y, axis=1))

Check if all of the following conditions are true:

Run Code Interpreter

Running the following program: 

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import jax
import jax.numpy as jnp
from jax import random

# Define the MLP model using JAX
class MLP:
    def __init__(self, input_size, hidden_size, output_size, key):
        key1, key2 = random.split(key)
        self.linear1 = self._initialize_linear(key1, input_size, hidden_size)
        self.linear2 = self._initialize_linear(key2, hidden_size, output_size)

    def _initialize_linear(self, key, in_features, out_features):
        # Initialize weights and biases
        w_key, b_key = random.split(key)
        weights = random.normal(w_key, (in_features, out_features)) * jnp.sqrt(2.0 / in_features)
        biases = random.normal(b_key, (out_features,))
        return (weights, biases)

    def __call__(self, x):
        # Apply the first linear layer
        x = jnp.dot(x, self.linear1[0]) + self.linear1[1]
        # Apply ReLU activation
        x = jax.nn.relu(x)
        # Apply the second linear layer
        x = jnp.dot(x, self.linear2[0]) + self.linear2[1]
        # Apply softmax along the last dimension
        return jax.nn.softmax(x, axis=-1)

# Initialize the model
key = random.PRNGKey(0)
model = MLP(10, 20, 30, key)

# Create a random input
x = random.normal(key, (5, 10))

# Forward pass through the model
y = model(x)

# Print the sum of each row in the output
print(jnp.sum(y, axis=1))
And got the output: [1. 1. 1. 1. 1.]

Check if any of the following conditions are true:

Substring Evaluation

Testing if the previous output contains the string 0.99999: False

Substring Evaluation

Testing if the previous output contains the string 1.0: False

Substring Evaluation

Testing if the previous output contains the string 1.00000: False

Substring Evaluation

Testing if the previous output contains the string 1.0,: False

Final Answer: False

Check this condition is not true:

Substring Evaluation

Testing if the previous output contains the string import torch: False

Final Answer: True

Final Answer: False