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Python class

Weight

Weight​

class max.graph.Weight(*args, **kwargs)

Bases: TensorValue

Represents a value in a Graph that can be loaded at a later time.

Weights can be initialized outside of a Graph and are lazily-added to the parent graph when used. If there is no parent graph when a weight is used, an error will be raised.

align​

align*: int | None*

dtype​

property dtype*: DType*

Returns the tensor data type.

matrix = np.array([[1, 2], [3, 4]], dtype=np.float32)

with Graph("dtype_demo") as graph:
    # Create a constant tensor from the matrix
    tensor = ops.constant(matrix, dtype=DType.float32)
    print(f"Data type: {tensor.dtype}")  # Output: Data type: DType.float32
matrix = np.array([[1, 2], [3, 4]], dtype=np.float32)

with Graph("dtype_demo") as graph:
    # Create a constant tensor from the matrix
    tensor = ops.constant(matrix, dtype=DType.float32)
    print(f"Data type: {tensor.dtype}")  # Output: Data type: DType.float32

quantization_encoding​

quantization_encoding*: QuantizationEncoding | None*

shape​

property shape*: Shape*

Returns the shape of the TensorValue.

matrix = np.array([[1, 2], [3, 4]], dtype=np.float32)

# Create a Graph context to work with tensors
with Graph("shape_demo") as graph:
    # Create a constant tensor from the matrix
    tensor = ops.constant(matrix, dtype=DType.float32)

    # Access tensor properties
    print(f"Shape: {tensor.shape}")  # Output: Shape: (2, 2)
matrix = np.array([[1, 2], [3, 4]], dtype=np.float32)

# Create a Graph context to work with tensors
with Graph("shape_demo") as graph:
    # Create a constant tensor from the matrix
    tensor = ops.constant(matrix, dtype=DType.float32)

    # Access tensor properties
    print(f"Shape: {tensor.shape}")  # Output: Shape: (2, 2)