Question

我正在尝试使用tensorflow 1.13.1在python中编写CNN。由于某些原因，即使将模型简化为仅一个仿射层，我仍然会遇到尺寸错误。这是相关代码：

tf.reset_default_graph()
X = tf.placeholder(tf.float32, [None, X_SHAPE[1], X_SHAPE[2], 1]) # X_SHAPE is the shape of the input image types I am workig with
y = tf.placeholder(tf.int64, [None])
is_training = tf.placeholder(tf.bool)

def my_model(X, y, is_training):
    output = X
    output = tf.reshape(output, [-1, output.shape[1] * output.shape[2] * 
    output.shape[3]])
    output = tf.layers.dense(output, 2) # makes the error
    output = tf.contrib.layers.batch_norm(output) 
    return output

y_out = my_model(X, y, is_training)
total_loss = tf.losses.softmax_cross_entropy(tf.one_hot(y, 2), logits=y_out)
mean_loss = tf.reduce_mean(total_loss)
optimizer = tf.train.RMSPropOptimizer(1e-3)

# batch normalization in tensorflow requires this extra dependency
extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(extra_update_ops):
    train_step = optimizer.minimize(mean_loss)

sess = tf.Session()

sess.run(tf.global_variables_initializer())
print('Training')
run_model(sess,y_out,mean_loss,X_train,y_train,8,64,100,train_step,True)
print('Validation')
run_model(sess,y_out,mean_loss,X_val,y_val,1,64)

我得到的错误如下：

Traceback (most recent call last):
  File "C:/Users/t8484200/Documents/fanta/dicom_snippet.py", line 189, in <module>
    y_out = my_model(X, y, is_training)
  File "C:/Users/t8484200/Documents/fanta/dicom_snippet.py", line 181, in my_model
    output = tf.layers.dense(output, 2)
  File "C:\Users\t8484200\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\util\deprecation.py", line 324, in new_func
    return func(*args, **kwargs)
  File "C:\Users\t8484200\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\layers\core.py", line 188, in dense
    return layer.apply(inputs)
  File "C:\Users\t8484200\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\keras\engine\base_layer.py", line 1227, in apply
    return self.__call__(inputs, *args, **kwargs)
  File "C:\Users\t8484200\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\layers\base.py", line 530, in __call__
    outputs = super(Layer, self).__call__(inputs, *args, **kwargs)
  File "C:\Users\t8484200\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\keras\engine\base_layer.py", line 554, in __call__
    outputs = self.call(inputs, *args, **kwargs)
  File "C:\Users\t8484200\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\keras\layers\core.py", line 975, in call
    outputs = gen_math_ops.mat_mul(inputs, self.kernel)
  File "C:\Users\t8484200\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\ops\gen_math_ops.py", line 5629, in mat_mul
    name=name)
  File "C:\Users\t8484200\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 350, in _apply_op_helper
    g = ops._get_graph_from_inputs(_Flatten(keywords.values()))
  File "C:\Users\t8484200\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\ops.py", line 5713, in _get_graph_from_inputs
    _assert_same_graph(original_graph_element, graph_element)
  File "C:\Users\t8484200\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\ops.py", line 5649, in _assert_same_graph
    original_item))
ValueError: Tensor("dense/kernel:0", shape=(262144, 2), dtype=float32_ref) must be from the same graph as Tensor("Reshape:0", shape=(?, 262144), dtype=float32).

但是尺寸似乎还不错，所以我非常感谢您对此提供的帮助！

Answer 1

您没有收到尺寸错误。尺寸只是作为有关两个张量的信息的一部分提到的。我使用tf 1.13.1运行了此代码，它对我有用。

例如，当我将前4行替换为：（相同的行，但顺序不同）时，我能够得到相同的错误

X = tf.placeholder(tf.float32, [None, X_SHAPE[1], X_SHAPE[2], 1]) # X_SHAPE is the shape of the input image types I am workig with
y = tf.placeholder(tf.int64, [None])
is_training = tf.placeholder(tf.bool)
tf.reset_default_graph()

原因是在现有图形中创建了X，然后通过reset命令创建了新图形。然后在新图中创建密集张量，但不允许使用旧图中的X。（看起来reshape命令在旧图中，可能是因为它没有创建新变量。）

因此，与定义占位符的位置相比，您需要检查将图形重置的位置。

值错误张量必须与张量相同的图，但尺寸似乎很好

1 个答案: