fxn_approx.py 文件源码

def __init__(self, session, ob_dim=None, n_epochs=10, stepsize=1e-3):
        """ 
        They provide us with an ob_dim in the code so I assume we can use it;
        makes it easy to define the layers anyway. This gets constructed upon
        initialization so future calls to self.fit should remember this. I
        actually use the pre-processed version, though.
        """
        self.n_epochs    = n_epochs
        self.lrate       = stepsize
        self.sy_ytarg    = tf.placeholder(shape=[None], name="nnvf_y", dtype=tf.float32)
        self.sy_ob_no    = tf.placeholder(shape=[None, ob_dim+1], name="nnvf_ob", dtype=tf.float32)
        self.sy_h1       = utils.lrelu(utils.dense(self.sy_ob_no, 32, "nnvf_h1", weight_init=utils.normc_initializer(1.0)), leak=0.0)
        self.sy_h2       = utils.lrelu(utils.dense(self.sy_h1, 32, "nnvf_h2", weight_init=utils.normc_initializer(1.0)), leak=0.0)
        self.sy_final_n  = utils.dense(self.sy_h2, 1, "nnvf_final", weight_init=utils.normc_initializer(1.0))
        self.sy_ypred    = tf.reshape(self.sy_final_n, [-1])
        self.sy_l2_error = tf.reduce_mean(tf.square(self.sy_ypred - self.sy_ytarg))
        self.fit_op      = tf.train.AdamOptimizer(stepsize).minimize(self.sy_l2_error)
        self.sess = session