model_abs.py 文件源码

def training_graph(loss, learning_rate=1.0, max_grad_norm=5.0):
    ''' Builds training graph. '''
    global_step = tf.Variable(0, name='global_step', trainable=False)

    with tf.variable_scope('SGD_Training'):
        # SGD learning parameter
        learning_rate = tf.Variable(learning_rate, trainable=False, name='learning_rate')

        # collect all trainable variables
        tvars = tf.trainable_variables()
        grads, global_norm = tf.clip_by_global_norm(tf.gradients(loss, tvars), max_grad_norm)

        optimizer = tf.train.GradientDescentOptimizer(learning_rate)
        train_op = optimizer.apply_gradients(zip(grads, tvars), global_step=global_step)

    return adict(
        learning_rate=learning_rate,
        global_step=global_step,
        global_norm=global_norm,
        train_op=train_op)