轉載：https://blog.csdn.net/wang2008start/article/details/71516198 問題一：TypeError: Expected int32, got list containing Tensors of type ‘_Message’ instead. tensorflow 函式tf.cocat([fw,bw],2)出錯:

Expected int32, got list containing Tensors of type ‘_Message’ inst 檢視原因是11版本的函式形式為:tf.concat(2,[fw,bw]),即應把串聯的維度與串聯值位置調換即可.

問題二：Input ‘split_dim’ of ‘Split’ Op has type float32 that does not match expected type of int32 This is because in Tensorflow versions < 0.12.0 the split function takes the arguments as:

x = tf.split(0, n_steps, x) # tf.split(axis, num_or_size_splits, value) The tutorial you are working from was written for versions > 0.12.0, which has been changed to be consistent with Numpy’s split syntax:

x = tf.split(x, n_steps, 0) # tf.split(value, num_or_size_splits, axis)

問題三：TypeError: concat() got an unexpected keyword argument ‘axis’ tf.concat(concat_dim=axis, values=inputs, name=name) 修改為： tf.concat(inputs,1,name=name)

問題四：ValueError: ‘size’ must be a 1-D Tensor of 2 elements img = tf.image.resize_images(img, new_shape[0], new_shape[1]) 改為 img = tf.image.resize_images(img, new_shape)

問題五： ‘module’ object has no attribute ‘pack’ 因為TF後面的版本修改了這個函式的名稱，把 tf.pack 改為 tf.stack。

問題六：The value of a feed cannot be a tf.Tensor object. Acceptable feed values include Python scalars, strings, lists, or numpy ndarrays 資料集是feed輸入的，feed的資料格式是有要求的 解決：img,label = sess.run[img,label],用返回值

問題七：AttributeError: ‘module’ object has no attribute ‘per_image_whitening’ For anyone else who has this problem, per_image_whitening was replaced by per_image_standardization in v0.12.

問題八：AttributeError: ‘module’ object has no attribute ‘image_summary’ tf.image_summary should be renamed to tf.summary.image;

問題九：AttributeError: ‘module’ object has no attribute ‘mul’ tf.mul(a,b) 這裡的矩陣a和矩陣b的shape必須相等 tf.mul()是矩陣的element-wise相乘（即Hadamard乘積） tf.matmul(a,b) 這裡的矩陣a和矩陣b的shape應是a的行數對等與b的列數，tf.matmul()是矩陣的一般相乘。 解決：[tf.mul，tf.sub ] 和 [tf.neg] 不再使用，改為 [tf.multiply]，[tf.subtract] 和 [tf.negative]。

問題十：AttributeError: ‘module’ object has no attribute ‘scalar_summary’ 修改為:tf.summary.scalar(‘batch_loss’, loss)原因:新版本做了調整 …