src/layers.py
def linear_network(x_in: int, in_dim: int, out_dim: int, mu_in=mu, sigma_in=sigma):
    """
    Create a linear network layer with the input parameters provided.
    """
    W = tf.Variable(
        tf.truncated_normal(shape=(in_dim, out_dim), mean=mu_in, stddev=sigma_in)
    )
    B = tf.Variable(tf.zeros(shape=(1, out_dim)))
    y_out = tf.matmul(x_in, W) + B
    return y_out

    Create a linear network layer with the input parameters provided.
    
Create a linear network layer with the input parameters provided.
Create a linear network layer with the input parameters provided .
def linear_network(x_in: int, in_dim: int, out_dim: int, mu_in=mu, sigma_in=sigma):
    W = tf.Variable(
        tf.truncated_normal(shape=(in_dim, out_dim), mean=mu_in, stddev=sigma_in)
    )
    B = tf.Variable(tf.zeros(shape=(1, out_dim)))
    y_out = tf.matmul(x_in, W) + B
    return y_out
def linear_network ( x_in : int , in_dim : int , out_dim : int , mu_in = mu , sigma_in = sigma ) : W = tf . Variable ( tf . truncated_normal ( shape = ( in_dim , out_dim ) , mean = mu_in , stddev = sigma_in ) ) B = tf . Variable ( tf . zeros ( shape = ( 1 , out_dim ) ) ) y_out = tf . matmul ( x_in , W ) + B return y_out
Create a linear network layer with the input parameters provided.
Create a linear network layer with the input parameters provided .
"""
    Create a linear network layer with the input parameters provided.
    """
x_in
int
in_dim
int
out_dim
int
mu_in
sigma_in