lenet.py 文件源码

def __init__(self):
        self.raw_input_image = tf.placeholder(tf.float32, [None, 784])
        self.input_images = tf.reshape(self.raw_input_image, [-1, 28, 28, 1])
        self.raw_input_label = tf.placeholder("float", [None, 10])
        self.input_labels = tf.cast(self.raw_input_label,tf.int32)
        self.dropout = cfg.KEEP_PROB

        with tf.variable_scope("Lenet") as scope:
            self.train_digits = self.construct_net(True)
            scope.reuse_variables()
            self.pred_digits = self.construct_net(False)

        self.prediction = tf.argmax(self.pred_digits, 1)
        self.correct_prediction = tf.equal(tf.argmax(self.pred_digits, 1), tf.argmax(self.input_labels, 1))
        self.train_accuracy = tf.reduce_mean(tf.cast(self.correct_prediction, "float"))


        self.loss = slim.losses.softmax_cross_entropy(self.train_digits, self.input_labels)
        self.lr = cfg.LEARNING_RATE
        self.train_op = tf.train.AdamOptimizer(self.lr).minimize(self.loss)