model.py 文件源码

def mixture_loss(pred, y, n_mixtures, batch_size):
    pred = tf.verify_tensor_all_finite(pred, "Pred not finite!")
    out_pi, out_sigma, out_mu, out_rho = splitMix(pred, n_mixtures, batch_size)
    result_binorm, result_delta = tf_bivariate_normal(y, out_mu, out_sigma, out_rho, n_mixtures, batch_size)

    result_binorm = tf.verify_tensor_all_finite(result_binorm, "Result not finite1!")
    result_weighted = tf.mul(result_binorm, out_pi)
    result_weighted = tf.verify_tensor_all_finite(result_weighted, "Result not finite2!")
    result_raw = tf.reduce_sum(result_weighted + epsilon, 1, keep_dims=True)
    result_raw = tf.Print(result_raw, [tf.reduce_sum(result_raw)], "Sum of weighted density. If zero, sigma is too small: ")
    result_raw = tf.Print(result_raw, [tf.reduce_max(result_raw)], "Max of weighted density. If zero, sigma is too small: ")
    result_raw = tf.verify_tensor_all_finite(result_raw, "Result not finite3!")
    result = -tf.log(result_raw + e)
    result = tf.verify_tensor_all_finite(result, "Result not finite4!")
    result = tf.reduce_sum(result)
    result = tf.verify_tensor_all_finite(result, "Result not finite5!")
    return result

# Returns the LSTM stack created based on the parameters.
# Processes several batches at once.
# Input shape is: (parameters['batch_size'], parameters['n_steps'], parameters['n_input'])