Initial Query

Fix the bug in this jnp function. Write out the full correct version of the code. import jax import jax.numpy as jnp def init_mlp_params(input_size, hidden_size, output_size, key): k1, k2 = jax.random.split(key) # Initialize weights and biases for the first linear layer w1 = jax.random.normal(k1, (hidden_size, input_size)) * jnp.sqrt(2.0 / input_size) b1 = jnp.zeros(hidden_size) # Initialize weights and biases for the second linear layer w2 = jax.random.normal(k2, (output_size, hidden_size)) * jnp.sqrt(2.0 / hidden_size) b2 = jnp.zeros(output_size) return {'w1': w1, 'b1': b1, 'w2': w2, 'b2': b2} def mlp_apply(params, x): # First linear layer x = jnp.dot(x, params['w1'].T) + params['b1'] # Relu activation x = jax.nn.relu(x) # Second linear layer x = jnp.dot(x, params['w2'].T) + params['b2'] # Softmax activation return jax.nn.softmax(x, axis=1) # Example usage: key = jax.random.PRNGKey(0) params = init_mlp_params(10, 20, 30, key) x = jax.random.normal(key, (5, 10)) # random input similar to torch.randn y = mlp_apply(params, x) print(list(y.flatten().sum(1))) # The output sum should be very close to [1, 1, 1, 1, 1]

LLM Generation

Query

Fix the bug in this jnp function. Write out the full correct version of the code. import jax import jax.numpy as jnp def init_mlp_params(input_size, hidden_size, output_size, key): k1, k2 = jax.random.split(key) # Initialize weights and biases for the first linear layer w1 = jax.random.normal(k1, (hidden_size, input_size)) * jnp.sqrt(2.0 / input_size) b1 = jnp.zeros(hidden_size) # Initialize weights and biases for the second linear layer w2 = jax.random.normal(k2, (output_size, hidden_size)) * jnp.sqrt(2.0 / hidden_size) b2 = jnp.zeros(output_size) return {'w1': w1, 'b1': b1, 'w2': w2, 'b2': b2} def mlp_apply(params, x): # First linear layer x = jnp.dot(x, params['w1'].T) + params['b1'] # Relu activation x = jax.nn.relu(x) # Second linear layer x = jnp.dot(x, params['w2'].T) + params['b2'] # Softmax activation return jax.nn.softmax(x, axis=1) # Example usage: key = jax.random.PRNGKey(0) params = init_mlp_params(10, 20, 30, key) x = jax.random.normal(key, (5, 10)) # random input similar to torch.randn y = mlp_apply(params, x) print(list(y.flatten().sum(1))) # The output sum should be very close to [1, 1, 1, 1, 1]

Output

Below is the corrected version. The bug was that the code was calling .flatten() before summing along axis 1. Once flattened the array becomes one dimensional so trying to sum along axis 1 causes an error. Instead, we directly sum over axis 1 of y. ------------------------------------------------------------ #!/usr/bin/env python3 import jax import jax.numpy as jnp def init_mlp_params(input_size, hidden_size, output_size, key): k1, k2 = jax.random.split(key) # Initialize weights and biases for the first linear layer w1 = jax.random.normal(k1, (hidden_size, input_size)) * jnp.sqrt(2.0 / input_size) b1 = jnp.zeros((hidden_size,)) # Initialize weights and biases for the second linear layer w2 = jax.random.normal(k2, (output_size, hidden_size)) * jnp.sqrt(2.0 / hidden_size) b2 = jnp.zeros((output_size,)) return {'w1': w1, 'b1': b1, 'w2': w2, 'b2': b2} def mlp_apply(params, x): # Apply first linear layer x = jnp.dot(x, params['w1'].T) + params['b1'] # Apply ReLU activation x = jax.nn.relu(x) # Apply second linear layer x = jnp.dot(x, params['w2'].T) + params['b2'] # Apply softmax activation along the correct axis return jax.nn.softmax(x, axis=1) # Example usage: key = jax.random.PRNGKey(0) params = init_mlp_params(input_size=10, hidden_size=20, output_size=30, key=key) x = jax.random.normal(key, (5, 10)) # random input, 5 examples of size 10 y = mlp_apply(params, x) # Instead of flattening, sum over each row (axis=1). The sums # should be very close to 1 if softmax is working correctly. print(list(y.sum(axis=1))) ------------------------------------------------------------ When you run this code, you should see output close to [1, 1, 1, 1, 1].

Extract Code

I extracted the following code from that output: 

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#!/usr/bin/env python3
import jax
import jax.numpy as jnp

def init_mlp_params(input_size, hidden_size, output_size, key):
    k1, k2 = jax.random.split(key)
    # Initialize weights and biases for the first linear layer
    w1 = jax.random.normal(k1, (hidden_size, input_size)) * jnp.sqrt(2.0 / input_size)
    b1 = jnp.zeros((hidden_size,))
    # Initialize weights and biases for the second linear layer
    w2 = jax.random.normal(k2, (output_size, hidden_size)) * jnp.sqrt(2.0 / hidden_size)
    b2 = jnp.zeros((output_size,))
    return {'w1': w1, 'b1': b1, 'w2': w2, 'b2': b2}

def mlp_apply(params, x):
    # Apply first linear layer
    x = jnp.dot(x, params['w1'].T) + params['b1']
    # Apply ReLU activation
    x = jax.nn.relu(x)
    # Apply second linear layer
    x = jnp.dot(x, params['w2'].T) + params['b2']
    # Apply softmax activation along the correct axis
    return jax.nn.softmax(x, axis=1)

def main():
    key = jax.random.PRNGKey(0)
    params = init_mlp_params(input_size=10, hidden_size=20, output_size=30, key=key)
    x = jax.random.normal(key, (5, 10))  # random input, 5 examples of size 10
    y = mlp_apply(params, x)
    # Instead of flattening, sum over each row (axis=1). The sums
    # should be very close to 1 if softmax is working correctly.
    print(list(y.sum(axis=1)))

if __name__ == "__main__":
    main()

Run Code Interpreter

Running the following program: 

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#!/usr/bin/env python3
import jax
import jax.numpy as jnp

def init_mlp_params(input_size, hidden_size, output_size, key):
    k1, k2 = jax.random.split(key)
    # Initialize weights and biases for the first linear layer
    w1 = jax.random.normal(k1, (hidden_size, input_size)) * jnp.sqrt(2.0 / input_size)
    b1 = jnp.zeros((hidden_size,))
    # Initialize weights and biases for the second linear layer
    w2 = jax.random.normal(k2, (output_size, hidden_size)) * jnp.sqrt(2.0 / hidden_size)
    b2 = jnp.zeros((output_size,))
    return {'w1': w1, 'b1': b1, 'w2': w2, 'b2': b2}

def mlp_apply(params, x):
    # Apply first linear layer
    x = jnp.dot(x, params['w1'].T) + params['b1']
    # Apply ReLU activation
    x = jax.nn.relu(x)
    # Apply second linear layer
    x = jnp.dot(x, params['w2'].T) + params['b2']
    # Apply softmax activation along the correct axis
    return jax.nn.softmax(x, axis=1)

def main():
    key = jax.random.PRNGKey(0)
    params = init_mlp_params(input_size=10, hidden_size=20, output_size=30, key=key)
    x = jax.random.normal(key, (5, 10))  # random input, 5 examples of size 10
    y = mlp_apply(params, x)
    # Instead of flattening, sum over each row (axis=1). The sums
    # should be very close to 1 if softmax is working correctly.
    print(list(y.sum(axis=1)))

if __name__ == "__main__":
    main()
And got the output: [Array(1., dtype=float32), Array(1., dtype=float32), Array(0.9999999, dtype=float32), Array(1., 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

Initial Query

Fix the bug in this jnp function. Write out the full correct version of the code. import jax import jax.numpy as jnp def init_mlp_params(input_size, hidden_size, output_size, key): k1, k2 = jax.random.split(key) # Initialize weights and biases for the first linear layer w1 = jax.random.normal(k1, (hidden_size, input_size)) * jnp.sqrt(2.0 / input_size) b1 = jnp.zeros(hidden_size) # Initialize weights and biases for the second linear layer w2 = jax.random.normal(k2, (output_size, hidden_size)) * jnp.sqrt(2.0 / hidden_size) b2 = jnp.zeros(output_size) return {'w1': w1, 'b1': b1, 'w2': w2, 'b2': b2} def mlp_apply(params, x): # First linear layer x = jnp.dot(x, params['w1'].T) + params['b1'] # Relu activation x = jax.nn.relu(x) # Second linear layer x = jnp.dot(x, params['w2'].T) + params['b2'] # Softmax activation return jax.nn.softmax(x, axis=1) # Example usage: key = jax.random.PRNGKey(0) params = init_mlp_params(10, 20, 30, key) x = jax.random.normal(key, (5, 10)) # random input similar to torch.randn y = mlp_apply(params, x) print(list(y.flatten().sum(1))) # The output sum should be very close to [1, 1, 1, 1, 1]

LLM Generation

Query

Fix the bug in this jnp function. Write out the full correct version of the code. import jax import jax.numpy as jnp def init_mlp_params(input_size, hidden_size, output_size, key): k1, k2 = jax.random.split(key) # Initialize weights and biases for the first linear layer w1 = jax.random.normal(k1, (hidden_size, input_size)) * jnp.sqrt(2.0 / input_size) b1 = jnp.zeros(hidden_size) # Initialize weights and biases for the second linear layer w2 = jax.random.normal(k2, (output_size, hidden_size)) * jnp.sqrt(2.0 / hidden_size) b2 = jnp.zeros(output_size) return {'w1': w1, 'b1': b1, 'w2': w2, 'b2': b2} def mlp_apply(params, x): # First linear layer x = jnp.dot(x, params['w1'].T) + params['b1'] # Relu activation x = jax.nn.relu(x) # Second linear layer x = jnp.dot(x, params['w2'].T) + params['b2'] # Softmax activation return jax.nn.softmax(x, axis=1) # Example usage: key = jax.random.PRNGKey(0) params = init_mlp_params(10, 20, 30, key) x = jax.random.normal(key, (5, 10)) # random input similar to torch.randn y = mlp_apply(params, x) print(list(y.flatten().sum(1))) # The output sum should be very close to [1, 1, 1, 1, 1]

Output

Below is the full corrected version. The only bug was in the final print statement where y was unnecessarily flattened (making it a 1D array on which an axis‑wise sum is not defined). Instead, we can directly sum each row of y by calling y.sum(axis=1). Here is the complete corrected code: ------------------------------------------------------------ import jax import jax.numpy as jnp def init_mlp_params(input_size, hidden_size, output_size, key): k1, k2 = jax.random.split(key) # Initialize weights and biases for the first linear layer w1 = jax.random.normal(k1, (hidden_size, input_size)) * jnp.sqrt(2.0 / input_size) b1 = jnp.zeros(hidden_size) # Initialize weights and biases for the second linear layer w2 = jax.random.normal(k2, (output_size, hidden_size)) * jnp.sqrt(2.0 / hidden_size) b2 = jnp.zeros(output_size) return {'w1': w1, 'b1': b1, 'w2': w2, 'b2': b2} def mlp_apply(params, x): # First linear layer x = jnp.dot(x, params['w1'].T) + params['b1'] # ReLU activation x = jax.nn.relu(x) # Second linear layer x = jnp.dot(x, params['w2'].T) + params['b2'] # Softmax activation return jax.nn.softmax(x, axis=1) # Example usage: key = jax.random.PRNGKey(0) params = init_mlp_params(10, 20, 30, key) # For randomness, it's better to split the key for different purposes. key, subkey = jax.random.split(key) x = jax.random.normal(subkey, (5, 10)) y = mlp_apply(params, x) # Each row of the softmax output sums to 1.0. print(list(y.sum(axis=1))) # Should print values very close to [1, 1, 1, 1, 1] ------------------------------------------------------------ This version fixes the bug in the print statement (replacing y.flatten().sum(1) with y.sum(axis=1)) so that each row’s probabilities correctly sum to 1.

Extract Code

I extracted the following code from that output: 

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#!/usr/bin/env python
import jax
import jax.numpy as jnp

def init_mlp_params(input_size, hidden_size, output_size, key):
    k1, k2 = jax.random.split(key)
    # Initialize weights and biases for the first linear layer
    w1 = jax.random.normal(k1, (hidden_size, input_size)) * jnp.sqrt(2.0 / input_size)
    b1 = jnp.zeros(hidden_size)
    # Initialize weights and biases for the second linear layer
    w2 = jax.random.normal(k2, (output_size, hidden_size)) * jnp.sqrt(2.0 / hidden_size)
    b2 = jnp.zeros(output_size)
    return {'w1': w1, 'b1': b1, 'w2': w2, 'b2': b2}

def mlp_apply(params, x):
    # First linear layer
    x = jnp.dot(x, params['w1'].T) + params['b1']
    # ReLU activation
    x = jax.nn.relu(x)
    # Second linear layer
    x = jnp.dot(x, params['w2'].T) + params['b2']
    # Softmax activation
    return jax.nn.softmax(x, axis=1)

if __name__ == '__main__':
    # Example usage:
    key = jax.random.PRNGKey(0)
    params = init_mlp_params(10, 20, 30, key)
    # For randomness, it's better to split the key for different purposes.
    key, subkey = jax.random.split(key)
    x = jax.random.normal(subkey, (5, 10))
    y = mlp_apply(params, x)
    # Each row of the softmax output sums to 1.0.
    print(list(y.sum(axis=1)))  # Should print values very close to [1, 1, 1, 1, 1]

Run Code Interpreter

Running the following program: 

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#!/usr/bin/env python
import jax
import jax.numpy as jnp

def init_mlp_params(input_size, hidden_size, output_size, key):
    k1, k2 = jax.random.split(key)
    # Initialize weights and biases for the first linear layer
    w1 = jax.random.normal(k1, (hidden_size, input_size)) * jnp.sqrt(2.0 / input_size)
    b1 = jnp.zeros(hidden_size)
    # Initialize weights and biases for the second linear layer
    w2 = jax.random.normal(k2, (output_size, hidden_size)) * jnp.sqrt(2.0 / hidden_size)
    b2 = jnp.zeros(output_size)
    return {'w1': w1, 'b1': b1, 'w2': w2, 'b2': b2}

def mlp_apply(params, x):
    # First linear layer
    x = jnp.dot(x, params['w1'].T) + params['b1']
    # ReLU activation
    x = jax.nn.relu(x)
    # Second linear layer
    x = jnp.dot(x, params['w2'].T) + params['b2']
    # Softmax activation
    return jax.nn.softmax(x, axis=1)

if __name__ == '__main__':
    # Example usage:
    key = jax.random.PRNGKey(0)
    params = init_mlp_params(10, 20, 30, key)
    # For randomness, it's better to split the key for different purposes.
    key, subkey = jax.random.split(key)
    x = jax.random.normal(subkey, (5, 10))
    y = mlp_apply(params, x)
    # Each row of the softmax output sums to 1.0.
    print(list(y.sum(axis=1)))  # Should print values very close to [1, 1, 1, 1, 1]
And got the output: [Array(0.99999994, dtype=float32), Array(0.99999994, dtype=float32), Array(0.99999994, dtype=float32), Array(1., 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