modules.py 文件源码

def compile(self, in_x, train_feed, eval_feed):
        n = np.product(self.in_d)
        m, param_init_fn = [dom[i] for (dom, i) in zip(self.domains, self.chosen)]

        #sc = np.sqrt(6.0) / np.sqrt(m + n)
        #W = tf.Variable(tf.random_uniform([n, m], -sc, sc))
        W = tf.Variable( param_init_fn( [n, m] ) )
        b = tf.Variable(tf.zeros([m]))

        # if the number of input dimensions is larger than one, flatten the 
        # input and apply the affine transformation. 
        if len(self.in_d) > 1:
            in_x_flat = tf.reshape(in_x, shape=[-1, n])
            out_y = tf.add(tf.matmul(in_x_flat, W), b)
        else:
            out_y = tf.add(tf.matmul(in_x, W), b)
        return out_y

# computes the output dimension based on the padding scheme used.
# this comes from the tensorflow documentation