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

Here's the equivalent implementation in pure JAX: 

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import jax
import jax.numpy as jnp
import numpy as np

class MLP:
    def __init__(self, input_size, hidden_size, output_size, key):
        key1, key2 = jax.random.split(key)
        
        # Initialize weights using Glorot uniform initialization
        self.linear1_weight = jax.random.uniform(key1, 
            shape=(hidden_size, input_size), 
            minval=-jnp.sqrt(6 / (input_size + hidden_size)), 
            maxval=jnp.sqrt(6 / (input_size + hidden_size))
        )
        self.linear1_bias = jnp.zeros(hidden_size)
        
        self.linear2_weight = jax.random.uniform(key2, 
            shape=(output_size, hidden_size), 
            minval=-jnp.sqrt(6 / (hidden_size + output_size)), 
            maxval=jnp.sqrt(6 / (hidden_size + output_size))
        )
        self.linear2_bias = jnp.zeros(output_size)

    def __call__(self, x):
        # First linear layer
        x = jnp.dot(x, self.linear1_weight.T) + self.linear1_bias
        
        # ReLU activation
        x = jnp.maximum(x, 0)
        
        # Second linear layer
        x = jnp.dot(x, self.linear2_weight.T) + self.linear2_bias
        
        # Softmax
        return jax.nn.softmax(x, axis=1)

# Set a random key for reproducibility
key = jax.random.PRNGKey(0)

# Create the model
model = MLP(10, 20, 30, key)

# Generate random input
x = jax.random.normal(key, (5, 10))

# Forward pass
y = model(x)

# Print sum along rows
print(list(y.sum(1)))
Key differences from the PyTorch version: 1. Weight initialization is manual in JAX. I've used Glorot (Xavier) uniform initialization, similar to PyTorch's default. 2. Linear layers are implemented manually using `jnp.dot()` and adding biases. 3. Activation functions are done with standard NumPy/JAX operations (`jnp.maximum()` for ReLU). 4. Softmax is done using `jax.nn.softmax()`. 5. Random number generation uses JAX's `random` module and requires explicit key management. 6. The model is a pure Python class without inheriting from a base module class. The core functionality remains the same: a simple MLP with ReLU activation and softmax output. Note: While this is a pure JAX implementation, in practice you might want to use `flax` or `haiku` for more convenient neural network implementations in JAX.

Extract Code

I extracted the following code from that output: 

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import jax
import jax.numpy as jnp
import numpy as np

class MLP:
    def __init__(self, input_size, hidden_size, output_size, key):
        key1, key2 = jax.random.split(key)
        
        # Initialize weights using Glorot uniform initialization
        self.linear1_weight = jax.random.uniform(key1, 
            shape=(hidden_size, input_size), 
            minval=-jnp.sqrt(6 / (input_size + hidden_size)), 
            maxval=jnp.sqrt(6 / (input_size + hidden_size))
        )
        self.linear1_bias = jnp.zeros(hidden_size)
        
        self.linear2_weight = jax.random.uniform(key2, 
            shape=(output_size, hidden_size), 
            minval=-jnp.sqrt(6 / (hidden_size + output_size)), 
            maxval=jnp.sqrt(6 / (hidden_size + output_size))
        )
        self.linear2_bias = jnp.zeros(output_size)

    def __call__(self, x):
        # First linear layer
        x = jnp.dot(x, self.linear1_weight.T) + self.linear1_bias
        
        # ReLU activation
        x = jnp.maximum(x, 0)
        
        # Second linear layer
        x = jnp.dot(x, self.linear2_weight.T) + self.linear2_bias
        
        # Softmax
        return jax.nn.softmax(x, axis=1)

# Set a random key for reproducibility
key = jax.random.PRNGKey(0)

# Create the model
model = MLP(10, 20, 30, key)

# Generate random input
x = jax.random.normal(key, (5, 10))

# Forward pass
y = model(x)

# Print sum along rows
print(list(y.sum(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
import numpy as np

class MLP:
    def __init__(self, input_size, hidden_size, output_size, key):
        key1, key2 = jax.random.split(key)
        
        # Initialize weights using Glorot uniform initialization
        self.linear1_weight = jax.random.uniform(key1, 
            shape=(hidden_size, input_size), 
            minval=-jnp.sqrt(6 / (input_size + hidden_size)), 
            maxval=jnp.sqrt(6 / (input_size + hidden_size))
        )
        self.linear1_bias = jnp.zeros(hidden_size)
        
        self.linear2_weight = jax.random.uniform(key2, 
            shape=(output_size, hidden_size), 
            minval=-jnp.sqrt(6 / (hidden_size + output_size)), 
            maxval=jnp.sqrt(6 / (hidden_size + output_size))
        )
        self.linear2_bias = jnp.zeros(output_size)

    def __call__(self, x):
        # First linear layer
        x = jnp.dot(x, self.linear1_weight.T) + self.linear1_bias
        
        # ReLU activation
        x = jnp.maximum(x, 0)
        
        # Second linear layer
        x = jnp.dot(x, self.linear2_weight.T) + self.linear2_bias
        
        # Softmax
        return jax.nn.softmax(x, axis=1)

# Set a random key for reproducibility
key = jax.random.PRNGKey(0)

# Create the model
model = MLP(10, 20, 30, key)

# Generate random input
x = jax.random.normal(key, (5, 10))

# Forward pass
y = model(x)

# Print sum along rows
print(list(y.sum(1)))
And got the output: [Array(1.0000001, dtype=float32), Array(1., dtype=float32), Array(0.99999994, dtype=float32), Array(0.9999999, dtype=float32), Array(1.0000001, dtype=float32)]

Check if any of the following conditions are true:

Substring Evaluation

Testing if the previous output contains the string 0.99999: True

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: True

Substring Evaluation

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

Final Answer: True

Check this condition is not true:

Substring Evaluation

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

Final Answer: True

Final Answer: True

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

Here's the equivalent implementation in pure JAX: 

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

class MLP:
    def __init__(self, input_size, hidden_size, output_size, key):
        # Initialize weights and biases using Xavier initialization
        key1, key2 = random.split(key)
        
        # First layer weights and biases
        std1 = jnp.sqrt(2.0 / (input_size + hidden_size))
        self.w1 = random.normal(key1, (input_size, hidden_size)) * std1
        self.b1 = jnp.zeros((hidden_size,))
        
        # Second layer weights and biases
        std2 = jnp.sqrt(2.0 / (hidden_size + output_size))
        self.w2 = random.normal(key2, (hidden_size, output_size)) * std2
        self.b2 = jnp.zeros((output_size,))

    def __call__(self, x):
        # First layer
        x = jnp.dot(x, self.w1) + self.b1
        x = jax.nn.relu(x)
        
        # Second layer
        x = jnp.dot(x, self.w2) + self.b2
        
        # Softmax
        return jax.nn.softmax(x)

# Set random seed for reproducibility
key = random.PRNGKey(0)

# Create model and input
model = MLP(10, 20, 30, key)
x = random.normal(key, (5, 10))

# Compute forward pass
y = model(x)

# Print sum along last dimension
print(list(y.sum(axis=1)))
Key differences from the PyTorch version: 1. Weight initialization is manual in JAX, unlike PyTorch's automatic initialization. - I used Xavier initialization by scaling the random normal weights. 2. Linear layers are manually implemented using `jnp.dot()` and adding biases. 3. Activation and softmax functions are from `jax.nn` module. 4. Random number generation uses JAX's `random` module with explicit PRNG keys. 5. The class uses manual weight and bias attributes instead of `torch.nn.Linear`. 6. Softmax is applied along the last axis by default in JAX. The functionality is equivalent to the PyTorch version, creating a simple MLP with ReLU activation and softmax output. The main principles remain the same: two linear layers with ReLU activation and softmax at the end.

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

class MLP:
    def __init__(self, input_size, hidden_size, output_size, key):
        # Initialize weights and biases using Xavier initialization
        key1, key2 = random.split(key)
        
        # First layer weights and biases
        std1 = jnp.sqrt(2.0 / (input_size + hidden_size))
        self.w1 = random.normal(key1, (input_size, hidden_size)) * std1
        self.b1 = jnp.zeros((hidden_size,))
        
        # Second layer weights and biases
        std2 = jnp.sqrt(2.0 / (hidden_size + output_size))
        self.w2 = random.normal(key2, (hidden_size, output_size)) * std2
        self.b2 = jnp.zeros((output_size,))

    def __call__(self, x):
        # First layer
        x = jnp.dot(x, self.w1) + self.b1
        x = jax.nn.relu(x)
        
        # Second layer
        x = jnp.dot(x, self.w2) + self.b2
        
        # Softmax
        return jax.nn.softmax(x)

# Set random seed for reproducibility
key = random.PRNGKey(0)

# Create model and input
model = MLP(10, 20, 30, key)
x = random.normal(key, (5, 10))

# Compute forward pass
y = model(x)

# Print sum along last dimension
print(list(y.sum(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

class MLP:
    def __init__(self, input_size, hidden_size, output_size, key):
        # Initialize weights and biases using Xavier initialization
        key1, key2 = random.split(key)
        
        # First layer weights and biases
        std1 = jnp.sqrt(2.0 / (input_size + hidden_size))
        self.w1 = random.normal(key1, (input_size, hidden_size)) * std1
        self.b1 = jnp.zeros((hidden_size,))
        
        # Second layer weights and biases
        std2 = jnp.sqrt(2.0 / (hidden_size + output_size))
        self.w2 = random.normal(key2, (hidden_size, output_size)) * std2
        self.b2 = jnp.zeros((output_size,))

    def __call__(self, x):
        # First layer
        x = jnp.dot(x, self.w1) + self.b1
        x = jax.nn.relu(x)
        
        # Second layer
        x = jnp.dot(x, self.w2) + self.b2
        
        # Softmax
        return jax.nn.softmax(x)

# Set random seed for reproducibility
key = random.PRNGKey(0)

# Create model and input
model = MLP(10, 20, 30, key)
x = random.normal(key, (5, 10))

# Compute forward pass
y = model(x)

# Print sum along last dimension
print(list(y.sum(axis=1)))
And got the output: [Array(1., dtype=float32), Array(1., dtype=float32), Array(0.9999999, dtype=float32), Array(0.99999994, dtype=float32), Array(0.9999999, dtype=float32)]

Check if any of the following conditions are true:

Substring Evaluation

Testing if the previous output contains the string 0.99999: True

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: True

Check this condition is not true:

Substring Evaluation

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

Final Answer: True

Final Answer: True

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

Here's the equivalent JAX implementation: 

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

class MLP:
    def __init__(self, input_size, hidden_size, output_size, key):
        # Initialize weights using Glorot (Xavier) initialization
        key1, key2 = random.split(key)
        
        # First layer weights and biases
        stddev1 = jnp.sqrt(2.0 / (input_size + hidden_size))
        self.w1 = random.normal(key1, (input_size, hidden_size)) * stddev1
        self.b1 = jnp.zeros((hidden_size,))
        
        # Second layer weights and biases
        stddev2 = jnp.sqrt(2.0 / (hidden_size + output_size))
        self.w2 = random.normal(key2, (hidden_size, output_size)) * stddev2
        self.b2 = jnp.zeros((output_size,))

    def __call__(self, x):
        # First layer
        x = jnp.dot(x, self.w1) + self.b1
        x = jax.nn.relu(x)
        
        # Second layer
        x = jnp.dot(x, self.w2) + self.b2
        
        # Softmax
        return jax.nn.softmax(x, axis=1)

# Create random key for initialization
key = random.PRNGKey(0)

# Create model
model = MLP(10, 20, 30, key)

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

# Forward pass
y = model(x)

# Print sum along rows
print(list(y.sum(1)))
Key differences from the PyTorch version: 1. Uses `jax.numpy` instead of `torch` 2. Manual weight initialization instead of `torch.nn.Linear` 3. Uses `jax.nn.relu` and `jax.nn.softmax` 4. Uses `random.PRNGKey` for randomness 5. Explicitly computes matrix multiplications with `jnp.dot()` 6. Passes a random key for initialization The core logic remains the same: a simple MLP with ReLU activation and softmax output. Note: In JAX, you would typically use `jax.random.normal()` for initialization and weight generation, which provides better random number generation compared to PyTorch's default methods.

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

class MLP:
    def __init__(self, input_size, hidden_size, output_size, key):
        # Initialize weights using Glorot (Xavier) initialization
        key1, key2 = random.split(key)
        
        # First layer weights and biases
        stddev1 = jnp.sqrt(2.0 / (input_size + hidden_size))
        self.w1 = random.normal(key1, (input_size, hidden_size)) * stddev1
        self.b1 = jnp.zeros((hidden_size,))
        
        # Second layer weights and biases
        stddev2 = jnp.sqrt(2.0 / (hidden_size + output_size))
        self.w2 = random.normal(key2, (hidden_size, output_size)) * stddev2
        self.b2 = jnp.zeros((output_size,))

    def __call__(self, x):
        # First layer
        x = jnp.dot(x, self.w1) + self.b1
        x = jax.nn.relu(x)
        
        # Second layer
        x = jnp.dot(x, self.w2) + self.b2
        
        # Softmax
        return jax.nn.softmax(x, axis=1)

# Create random key for initialization
key = random.PRNGKey(0)

# Create model
model = MLP(10, 20, 30, key)

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

# Forward pass
y = model(x)

# Print sum along rows
print(list(y.sum(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

class MLP:
    def __init__(self, input_size, hidden_size, output_size, key):
        # Initialize weights using Glorot (Xavier) initialization
        key1, key2 = random.split(key)
        
        # First layer weights and biases
        stddev1 = jnp.sqrt(2.0 / (input_size + hidden_size))
        self.w1 = random.normal(key1, (input_size, hidden_size)) * stddev1
        self.b1 = jnp.zeros((hidden_size,))
        
        # Second layer weights and biases
        stddev2 = jnp.sqrt(2.0 / (hidden_size + output_size))
        self.w2 = random.normal(key2, (hidden_size, output_size)) * stddev2
        self.b2 = jnp.zeros((output_size,))

    def __call__(self, x):
        # First layer
        x = jnp.dot(x, self.w1) + self.b1
        x = jax.nn.relu(x)
        
        # Second layer
        x = jnp.dot(x, self.w2) + self.b2
        
        # Softmax
        return jax.nn.softmax(x, axis=1)

# Create random key for initialization
key = random.PRNGKey(0)

# Create model
model = MLP(10, 20, 30, key)

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

# Forward pass
y = model(x)

# Print sum along rows
print(list(y.sum(1)))
And got the output: [Array(1., dtype=float32), Array(1., dtype=float32), Array(0.9999999, dtype=float32), Array(0.99999994, dtype=float32), Array(0.9999999, dtype=float32)]

Check if any of the following conditions are true:

Substring Evaluation

Testing if the previous output contains the string 0.99999: True

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: True

Check this condition is not true:

Substring Evaluation

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

Final Answer: True

Final Answer: True

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

Here's the equivalent implementation in pure JAX: 

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

class MLP:
    def __init__(self, input_size, hidden_size, output_size, key):
        # Initialize weights using Xavier initialization
        key1, key2 = random.split(key)
        
        # First layer weights and biases
        std1 = jnp.sqrt(2.0 / (input_size + hidden_size))
        self.w1 = random.normal(key1, (input_size, hidden_size)) * std1
        self.b1 = jnp.zeros((hidden_size,))
        
        # Second layer weights and biases
        std2 = jnp.sqrt(2.0 / (hidden_size + output_size))
        self.w2 = random.normal(key2, (hidden_size, output_size)) * std2
        self.b2 = jnp.zeros((output_size,))

    def __call__(self, x):
        # First layer
        x = jnp.dot(x, self.w1) + self.b1
        x = jax.nn.relu(x)
        
        # Second layer
        x = jnp.dot(x, self.w2) + self.b2
        
        # Softmax
        return jax.nn.softmax(x, axis=1)

# Set random seed for reproducibility
key = random.PRNGKey(42)

# Create model and input
model = MLP(10, 20, 30, key)
x = random.normal(key, (5, 10))

# Compute output
y = model(x)

# Print sum along last dimension
print(list(y.sum(1)))
Key differences from the PyTorch version: 1. Weight initialization is manual in JAX, unlike PyTorch's automatic initialization 2. Use `jax.nn.relu` instead of `torch.relu` 3. Use `jax.nn.softmax` instead of `torch.nn.functional.softmax` 4. Manual matrix multiplication with `jnp.dot` instead of `Linear` layers 5. Random number generation uses JAX's `random` module 6. Explicit weight and bias attributes instead of `nn.Module` The code follows JAX's functional programming paradigm while maintaining similar structure to the original PyTorch implementation.

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

class MLP:
    def __init__(self, input_size, hidden_size, output_size, key):
        # Initialize weights using Xavier initialization
        key1, key2 = random.split(key)
        
        # First layer weights and biases
        std1 = jnp.sqrt(2.0 / (input_size + hidden_size))
        self.w1 = random.normal(key1, (input_size, hidden_size)) * std1
        self.b1 = jnp.zeros((hidden_size,))
        
        # Second layer weights and biases
        std2 = jnp.sqrt(2.0 / (hidden_size + output_size))
        self.w2 = random.normal(key2, (hidden_size, output_size)) * std2
        self.b2 = jnp.zeros((output_size,))

    def __call__(self, x):
        # First layer
        x = jnp.dot(x, self.w1) + self.b1
        x = jax.nn.relu(x)
        
        # Second layer
        x = jnp.dot(x, self.w2) + self.b2
        
        # Softmax
        return jax.nn.softmax(x, axis=1)

# Set random seed for reproducibility
key = random.PRNGKey(42)

# Create model and input
model = MLP(10, 20, 30, key)
x = random.normal(key, (5, 10))

# Compute output
y = model(x)

# Print sum along last dimension
print(list(y.sum(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

class MLP:
    def __init__(self, input_size, hidden_size, output_size, key):
        # Initialize weights using Xavier initialization
        key1, key2 = random.split(key)
        
        # First layer weights and biases
        std1 = jnp.sqrt(2.0 / (input_size + hidden_size))
        self.w1 = random.normal(key1, (input_size, hidden_size)) * std1
        self.b1 = jnp.zeros((hidden_size,))
        
        # Second layer weights and biases
        std2 = jnp.sqrt(2.0 / (hidden_size + output_size))
        self.w2 = random.normal(key2, (hidden_size, output_size)) * std2
        self.b2 = jnp.zeros((output_size,))

    def __call__(self, x):
        # First layer
        x = jnp.dot(x, self.w1) + self.b1
        x = jax.nn.relu(x)
        
        # Second layer
        x = jnp.dot(x, self.w2) + self.b2
        
        # Softmax
        return jax.nn.softmax(x, axis=1)

# Set random seed for reproducibility
key = random.PRNGKey(42)

# Create model and input
model = MLP(10, 20, 30, key)
x = random.normal(key, (5, 10))

# Compute output
y = model(x)

# Print sum along last dimension
print(list(y.sum(1)))
And got the output: [Array(1., dtype=float32), Array(1., dtype=float32), Array(1., dtype=float32), Array(0.99999994, dtype=float32), Array(1., dtype=float32)]

Check if any of the following conditions are true:

Substring Evaluation

Testing if the previous output contains the string 0.99999: True

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: True

Check this condition is not true:

Substring Evaluation

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

Final Answer: True

Final Answer: True

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

Here's the equivalent implementation in JAX: 

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

class MLP:
    def __init__(self, input_size, hidden_size, output_size, key):
        # Use random.PRNGKey for random initialization
        key1, key2 = random.split(key)
        
        # Initialize weights with random values
        self.linear1_w = random.normal(key1, (input_size, hidden_size)) * 0.01
        self.linear1_b = jnp.zeros((hidden_size,))
        
        self.linear2_w = random.normal(key2, (hidden_size, output_size)) * 0.01
        self.linear2_b = jnp.zeros((output_size,))

    def __call__(self, x):
        # First layer: linear + ReLU
        x = jnp.dot(x, self.linear1_w) + self.linear1_b
        x = jax.nn.relu(x)
        
        # Second layer: linear + softmax
        x = jnp.dot(x, self.linear2_w) + self.linear2_b
        return jax.nn.softmax(x, axis=1)

# Set a seed for reproducibility
key = random.PRNGKey(0)

# Create model and input
model = MLP(10, 20, 30, key)
x = random.normal(key, (5, 10))

# Compute output
y = model(x)
print(list(y.sum(1)))
Key differences from the PyTorch version: 1. Weight initialization is manual using `jax.random` 2. Matrix multiplication uses `jnp.dot()` instead of `torch.nn.Linear` 3. Activation functions are from `jax.nn` instead of `torch.nn` 4. Use of `jax.random.PRNGKey` for random number generation 5. Explicit bias addition with `+` 6. Softmax and ReLU use JAX equivalents The core logic remains the same: a two-layer neural network with ReLU activation and softmax output.

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

class MLP:
    def __init__(self, input_size, hidden_size, output_size, key):
        # Use random.PRNGKey for random initialization
        key1, key2 = random.split(key)
        
        # Initialize weights with random values
        self.linear1_w = random.normal(key1, (input_size, hidden_size)) * 0.01
        self.linear1_b = jnp.zeros((hidden_size,))
        
        self.linear2_w = random.normal(key2, (hidden_size, output_size)) * 0.01
        self.linear2_b = jnp.zeros((output_size,))

    def __call__(self, x):
        # First layer: linear + ReLU
        x = jnp.dot(x, self.linear1_w) + self.linear1_b
        x = jax.nn.relu(x)
        
        # Second layer: linear + softmax
        x = jnp.dot(x, self.linear2_w) + self.linear2_b
        return jax.nn.softmax(x, axis=1)

# Set a seed for reproducibility
key = random.PRNGKey(0)

# Create model and input
model = MLP(10, 20, 30, key)
x = random.normal(key, (5, 10))

# Compute output
y = model(x)
print(list(y.sum(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

class MLP:
    def __init__(self, input_size, hidden_size, output_size, key):
        # Use random.PRNGKey for random initialization
        key1, key2 = random.split(key)
        
        # Initialize weights with random values
        self.linear1_w = random.normal(key1, (input_size, hidden_size)) * 0.01
        self.linear1_b = jnp.zeros((hidden_size,))
        
        self.linear2_w = random.normal(key2, (hidden_size, output_size)) * 0.01
        self.linear2_b = jnp.zeros((output_size,))

    def __call__(self, x):
        # First layer: linear + ReLU
        x = jnp.dot(x, self.linear1_w) + self.linear1_b
        x = jax.nn.relu(x)
        
        # Second layer: linear + softmax
        x = jnp.dot(x, self.linear2_w) + self.linear2_b
        return jax.nn.softmax(x, axis=1)

# Set a seed for reproducibility
key = random.PRNGKey(0)

# Create model and input
model = MLP(10, 20, 30, key)
x = random.normal(key, (5, 10))

# Compute output
y = model(x)
print(list(y.sum(1)))
And got the output: [Array(0.9999998, dtype=float32), Array(1., dtype=float32), Array(1., dtype=float32), Array(1.0000001, dtype=float32), Array(1.0000001, dtype=float32)]

Check if any of the following conditions are true:

Substring Evaluation

Testing if the previous output contains the string 0.99999: True

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: True

Substring Evaluation

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

Final Answer: True

Check this condition is not true:

Substring Evaluation

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

Final Answer: True

Final Answer: True