我正在TensorFlow中恢复预先训练的模型,并想知道我的代码是否按预期工作 . 这是下面的代码 . 看起来它没有使用预先训练的模型,因为来自下面的代码的损失高于训练误差,例如200个东西 . 请告诉我如何修改它 . 先感谢您 .
def main(_):
with tf.Graph().as_default() as g:
x, img, rows, cols = load_image(FLAGS.input, FLAGS.image_size)
with slim.arg_scope(resnet_v2.resnet_arg_scope(weight_decay=0.0005)):
logits, endpoints = resnet_v2.resnet_v2_152(x, nb_classes, is_training=False)
predictions = tf.argmax(logits, 1)
saver = tf.train.Saver()
with tf.Session(graph=g) as sess:
sess.run(tf.global_variables_initializer())
# Load a pretrained model.
print("\nLoading a model...")
saver.restore(sess, checkpoint_path)
print("\nFeedforwarding...")
pred, loss_out, output, grads_val = sess.run([predictions, loss, conv_layer, norm_grads])