TFBackprop.py 文件源码

def __init__(self, dim_in, dim_hidden, learning_rate, gate=Relu(),
                 initializer=tf.random_normal_initializer(), optimizer=None, name='BP'):
        dim_out = 1
        if optimizer is None:
            optimizer = tf.train.GradientDescentOptimizer(learning_rate)
        self.x = tf.placeholder(tf.float32, shape=(None, dim_in))
        self.target = tf.placeholder(tf.float32, shape=(None, dim_out))
        U, __, ___, phi = \
            fully_connected(name, 'fully_connected_layer1', self.x, dim_in, dim_hidden, initializer, gate.gate_fun)
        W, __, ___, y = \
            fully_connected(name, 'fully_connected_layer2', phi, dim_hidden, dim_out, initializer, tf.identity)
        self.loss = tf.scalar_mul(0.5, tf.reduce_mean(tf.squared_difference(y, self.target)))
        self.all_gradients = optimizer.compute_gradients(self.loss)
        self.train_op = optimizer.apply_gradients(self.all_gradients)
        self.outgoing_weight = W
        self.feature_matrix = U