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]
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]
The bug in the original code is in the way the summation of the output is being computed. Specifically, the `flatten().sum(1)` operation is incorrect for summing the output probabilities since the output of the softmax function is already a 2D array where each row represents the output probabilities for each input. Instead, we want to sum across the columns (for each row) to check that the output probabilities sum to 1. Here is the corrected version of the code:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | 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, (input_size, hidden_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, (hidden_size, output_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']) + params['b1'] # Relu activation x = jax.nn.relu(x) # Second linear layer x = jnp.dot(x, params['w2']) + 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) # Correctly summing the output probabilities along the correct axis output_sums = y.sum(axis=1) print(output_sums) # The output sum should be very close to [1, 1, 1, 1, 1] |
I extracted the following code from that output:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | 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, (input_size, hidden_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, (hidden_size, output_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']) + params['b1'] # Relu activation x = jax.nn.relu(x) # Second linear layer x = jnp.dot(x, params['w2']) + 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) # Correctly summing the output probabilities along the correct axis output_sums = y.sum(axis=1) print(output_sums) # The output sum should be very close to [1, 1, 1, 1, 1] |
Running the following program:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | 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, (input_size, hidden_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, (hidden_size, output_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']) + params['b1'] # Relu activation x = jax.nn.relu(x) # Second linear layer x = jnp.dot(x, params['w2']) + 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) # Correctly summing the output probabilities along the correct axis output_sums = y.sum(axis=1) print(output_sums) # The output sum should be very close to [1, 1, 1, 1, 1] |
[1. 1. 1. 1. 0.99999994]
Testing if the previous output contains the string 0.99999
: True
Testing if the previous output contains the string 1.0
: False
Testing if the previous output contains the string 1.00000
: False
Testing if the previous output contains the string 1.0,
: False
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]
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]
The issue in the provided code arises from the way the output sum is calculated in the print statement. The `flatten()` method is applied to the entire output tensor, which does not give the expected result. Instead, the sum should be computed for each row of the output tensor individually. Here's the corrected version of the code:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | 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) # Correct way to compute the output sum for each row output_sums = y.sum(axis=1) # Sum across the columns (axis=1) for each row print(output_sums) # The output sum should be very close to [1, 1, 1, 1, 1] |
I extracted the following code from that output:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | 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) # Correct way to compute the output sum for each row output_sums = y.sum(axis=1) # Sum across the columns (axis=1) for each row print(output_sums) # The output sum should be very close to [1, 1, 1, 1, 1] |
Running the following program:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | 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) # Correct way to compute the output sum for each row output_sums = y.sum(axis=1) # Sum across the columns (axis=1) for each row print(output_sums) # The output sum should be very close to [1, 1, 1, 1, 1] |
[1. 1. 0.9999999 1. 0.9999999]
Testing if the previous output contains the string 0.99999
: True
Testing if the previous output contains the string 1.0
: False
Testing if the previous output contains the string 1.00000
: False
Testing if the previous output contains the string 1.0,
: False
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]
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]
The provided code has a bug in the way the output is printed. The `flatten().sum(1)` will raise an error because `flatten()` converts the array into a 1D array, and then attempting to sum along axis 1 is invalid since a 1D array has no such axis. Instead, we should sum along axis 0 to ensure that each row sums to 1 after applying softmax. Here is the corrected version of the code:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | 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(y.sum(axis=1)) # The output sum should be very close to [1, 1, 1, 1, 1] |
I extracted the following code from that output:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | 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(y.sum(axis=1)) # The output sum should be very close to [1, 1, 1, 1, 1] |
Running the following program:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | 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(y.sum(axis=1)) # The output sum should be very close to [1, 1, 1, 1, 1] |
[1. 1. 0.9999999 1. 0.9999999]
Testing if the previous output contains the string 0.99999
: True
Testing if the previous output contains the string 1.0
: False
Testing if the previous output contains the string 1.00000
: False
Testing if the previous output contains the string 1.0,
: False
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]
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]
The code you provided has a small bug in the final print statement. The `flatten()` function returns a 1D array, and summing along axis 1 in that case will raise an error since there is no axis 1 in a 1D array. The correct way to check if the output of the softmax is summing to 1 for each sample is to sum along the correct axis (which is 0 for a 2D array). Here’s the corrected code with all the necessary parts included:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | 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) # Correctly summing the output across the right axis print(y.sum(axis=1)) # The output sum should be very close to [1, 1, 1, 1, 1] |
I extracted the following code from that output:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | 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) # Correctly summing the output across the right axis print(y.sum(axis=1)) # The output sum should be very close to [1, 1, 1, 1, 1] |
Running the following program:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | 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) # Correctly summing the output across the right axis print(y.sum(axis=1)) # The output sum should be very close to [1, 1, 1, 1, 1] |
[1. 1. 0.9999999 1. 0.9999999]
Testing if the previous output contains the string 0.99999
: True
Testing if the previous output contains the string 1.0
: False
Testing if the previous output contains the string 1.00000
: False
Testing if the previous output contains the string 1.0,
: False
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]
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]
The code you provided has a minor bug in the way the output is printed. The `flatten().sum(1)` operation is not correctly applied because `flatten()` returns a 1-D array, and then calling `sum(1)` on it does not make sense. Instead, you should sum along the appropriate axis for your output. Here's the corrected version of the code:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | 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(y.sum(axis=1)) # The output sum should be very close to [1, 1, 1, 1, 1] |
I extracted the following code from that output:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | 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(y.sum(axis=1)) # The output sum should be very close to [1, 1, 1, 1, 1] |
Running the following program:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | 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(y.sum(axis=1)) # The output sum should be very close to [1, 1, 1, 1, 1] |
[1. 1. 0.9999999 1. 0.9999999]
Testing if the previous output contains the string 0.99999
: True
Testing if the previous output contains the string 1.0
: False
Testing if the previous output contains the string 1.00000
: False
Testing if the previous output contains the string 1.0,
: False