models.py 文件源码

def __init__(self, field_sizes=None, embed_size=10, layer_sizes=None, layer_acts=None, drop_out=None,
                 embed_l2=None, layer_l2=None, init_path=None, opt_algo='gd', learning_rate=1e-2, random_seed=None):
        Model.__init__(self)
        init_vars = []
        num_inputs = len(field_sizes)
        for i in range(num_inputs):
            init_vars.append(('embed_%d' % i, [field_sizes[i], embed_size], 'xavier', dtype))
        node_in = num_inputs * embed_size
        for i in range(len(layer_sizes)):
            init_vars.append(('w%d' % i, [node_in, layer_sizes[i]], 'xavier', dtype))
            init_vars.append(('b%d' % i, [layer_sizes[i]], 'zero', dtype))
            node_in = layer_sizes[i]
        self.graph = tf.Graph()
        with self.graph.as_default():
            if random_seed is not None:
                tf.set_random_seed(random_seed)
            self.X = [tf.sparse_placeholder(dtype) for i in range(num_inputs)]
            self.y = tf.placeholder(dtype)
            self.keep_prob_train = 1 - np.array(drop_out)
            self.keep_prob_test = np.ones_like(drop_out)
            self.layer_keeps = tf.placeholder(dtype)
            self.vars = utils.init_var_map(init_vars, init_path)
            w0 = [self.vars['embed_%d' % i] for i in range(num_inputs)]
            xw = tf.concat([tf.sparse_tensor_dense_matmul(self.X[i], w0[i]) for i in range(num_inputs)], 1)
            l = xw

            for i in range(len(layer_sizes)):
                wi = self.vars['w%d' % i]
                bi = self.vars['b%d' % i]
                print(l.shape, wi.shape, bi.shape)
                l = tf.nn.dropout(
                    utils.activate(
                        tf.matmul(l, wi) + bi,
                        layer_acts[i]),
                    self.layer_keeps[i])

            l = tf.squeeze(l)
            self.y_prob = tf.sigmoid(l)

            self.loss = tf.reduce_mean(
                tf.nn.sigmoid_cross_entropy_with_logits(logits=l, labels=self.y))
            if layer_l2 is not None:
                self.loss += embed_l2 * tf.nn.l2_loss(xw)
                for i in range(len(layer_sizes)):
                    wi = self.vars['w%d' % i]
                    self.loss += layer_l2[i] * tf.nn.l2_loss(wi)
            self.optimizer = utils.get_optimizer(opt_algo, learning_rate, self.loss)

            config = tf.ConfigProto()
            config.gpu_options.allow_growth = True
            self.sess = tf.Session(config=config)
            tf.global_variables_initializer().run(session=self.sess)