mnist_softmax.py 文件源码

def _build(self):
        # ?????????? --- build
        self._lin = photinia.Linear('LINEAR', self._input_size, self._num_classes).build()
        # ????
        x = tf.placeholder(dtype=photinia.D_TYPE, shape=[None, self._input_size])
        y_ = tf.placeholder(dtype=photinia.D_TYPE, shape=[None, self._num_classes])
        # ?????? --- setup
        y = self._lin.setup(x)
        # ??????? softmax?????
        loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))
        # accuracy??
        correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, photinia.D_TYPE))
        # ????????slot
        self._add_slot(
            'train',
            outputs=loss,
            inputs=(x, y_),
            updates=tf.train.GradientDescentOptimizer(0.5).minimize(loss)
        )
        self._add_slot(
            'predict',
            outputs=accuracy,
            inputs=(x, y_)
        )