draw_attn_window.py

def linear(x,output_dim):
    """
    affine transformation Wx+b
    assumes x.shape = (batch_size, num_features)
    """
    w=tf.get_variable("w", [x.get_shape()[1], output_dim])
    b=tf.get_variable("b", [output_dim], initializer=tf.constant_initializer(0.0))
    return tf.matmul(x,w)+b

def attn_window(scope,h_dec,N):
    with tf.variable_scope(scope,reuse=DO_SHARE):
        params=linear(h_dec,5)
    gx_,gy_,log_sigma2,log_delta,log_gamma=tf.split(1,5,params)
    gx=(A+1)/2*(gx_+1)
    gy=(B+1)/2*(gy_+1)
    sigma2=tf.exp(log_sigma2)
    delta=(max(A,B)-1)/(N-1)*tf.exp(log_delta) # batch x N
    return filterbank(gx,gy,sigma2,delta,N)+(tf.exp(log_gamma),)