train.py
def generate_z(sample_size, z_dim):
    """Helper function to generate noise vector.
    Can replace this with a different noise function.

    Args:
        sample_size: sample/batch size
        z_dim: dimensionality of z noise

    Returns:
        random noise, dimensionality is (sample_size, z_dim)
    """
    return np.random.uniform(-1, 1,
        size=(sample_size, z_dim)).astype(np.float32)
Helper function to generate noise vector.
    Can replace this with a different noise function.

    Args:
        sample_size: sample/batch size
        z_dim: dimensionality of z noise

    Returns:
        random noise, dimensionality is (sample_size, z_dim)
    
Helper function to generate noise vector.
Can replace this with a different noise function.
Helper function to generate noise vector . Can replace this with a different noise function .
def generate_z(sample_size, z_dim):
    return np.random.uniform(-1, 1,
        size=(sample_size, z_dim)).astype(np.float32)
def generate_z ( sample_size , z_dim ) : return np . random . uniform ( - 1 , 1 , size = ( sample_size , z_dim ) ) . astype ( np . float32 )
Helper function to generate noise vector.
Helper function to generate noise vector .
"""Helper function to generate noise vector.
    Can replace this with a different noise function.

    Args:
        sample_size: sample/batch size
        z_dim: dimensionality of z noise

    Returns:
        random noise, dimensionality is (sample_size, z_dim)
    """
random noise, dimensionality is (sample_size, z_dim)
random noise dimensionality is ( sample_size z_dim )
sample_size
None
z_dim
dimensionality of z noise
dimensionality of z noise