我成功地在TensorFlow中训练了自己的模型，使用的图表如下：
在Python中看起来是这样的：
with tf.name_scope("Reshaping_data") as scope: x = tf.reshape(x, shape=[-1, imgSize, imgSize, 1], name="inp") #(?, 48, 48, 1)with tf.name_scope("Conv1") as scope: conv1 = conv2d(x, weights['wc1'], biases['bc1']) #(?, 48, 48, 32) conv1 = maxpool2d(conv1, k=2) #(?, 24, 24, 32)…（更多卷积和全连接层）…
out = tf.add(tf.matmul(fc1, weights['out']), biases['out'], name="out") #(?, 43)我使用GTSRB 数据集进行了训练，并保存了模型。现在我想用这个模型标记新图像。我当前的label.py文件如下：
import tensorflow as tfcheckpoint_file = tf.train.latest_checkpoint("saved_models")graph = tf.Graph()with graph.as_default(): sess = tf.Session() with sess.as_default(): saver = tf.train.import_meta_graph("{}.meta".format(checkpoint_file)) saver.restore(sess,checkpoint_file) inp = graph.get_operation_by_name("Reshaping_data/inp").outputs[0] prediction=graph.get_operation_by_name("out").outputs[0] input_img = tf.image.decode_jpeg(tf.read_file("/home/DB/GTSRB/Test/00021/07406.jpg"), channels=3) reshaped_image = tf.image.resize_image_with_crop_or_pad(tf.cast(input_img, tf.float32), 48, 48) float_image = tf.image.per_image_standardization(reshaped_image) images = tf.expand_dims(float_image, 0) print(sess.run(prediction,feed_dict={inp:images}))但是当读取feed_dict时失败了。我做错了什么？
Traceback (most recent call last): File "label.py", line 23, in <module> print(sess.run(prediction,feed_dict={inp:images})) File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/client/session.py", line 767, in runrun_metadata_ptr) File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/client/session.py", line 925, in _run raise TypeError('The value of a feed cannot be a tf.Tensor object. ' TypeError: The value of a feed cannot be a tf.Tensor object. Acceptable feed values include Python scalars, strings, lists, or numpy ndarrays.非常感谢！
回答：
我这样解决了这个问题：
checkpoint_file = tf.train.latest_checkpoint("saved_models")imgSize = 48graph = tf.Graph()with graph.as_default(): sess = tf.Session() with sess.as_default(): saver = tf.train.import_meta_graph("{}.meta".format(checkpoint_file)) saver.restore(sess,checkpoint_file) inp = graph.get_operation_by_name("Reshaping_data/inp").outputs[0] prediction=graph.get_operation_by_name("out").outputs[0] img = imread(imagepath, flatten=True) img = imresize(img, [imgSize, imgSize]) img = img.astype('float32') img_mean = np.mean(img) img_var = np.std(img) img = (img - img_mean)/img_var #img = (48, 48) img = np.expand_dims(img, axis=2) #img = (48, 48, 1) img = np.expand_dims(img, axis=0) #img = (1, 48, 48, 1) #inp expects (?, 48, 48, 1) res = sess.run(prediction,feed_dict={inp:img}) print(res) print(res.argmax(axis=1))