Python TensorFlow 值错误 : Shape must be rank 1 but is rank 0

我是 Sentdex 教程的神经网络新手。我尝试运行该代码:

   import tensorflow as tf 
from tensorflow.examples.tutorials.mnist import input_data 
 
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) 
 
n_nodes_hl1 = 500 
n_nodes_hl2 = 500 
n_nodes_hl3 = 500 
 
n_classes = 0 
batch_size = 100 
 
x = tf.placeholder('float',[None, 784]) 
y = tf.placeholder('float') 
def neural_model(impuls): 
    hidden_1_layer = {'weights':tf.Variable(tf.random_normal([784, n_nodes_hl1])), 
                      'biases':tf.Variable(tf.random_normal(n_nodes_hl1))} 
    hidden_2_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])), 
                      'biases':tf.Variable(tf.random_normal(n_nodes_hl2))} 
    hidden_3_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl2, n_nodes_hl3])), 
                      'biases':tf.Variable(tf.random_normal(n_nodes_hl3))} 
    output_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl3, n_classes])), 
                      'biases':tf.Variable(tf.random_normal(n_classes))} 
 
    l1 = tf.add(tf.matmul(data, hidden_1_layer['weights']) + hidden_1_layer['biases']) 
    l1 = tf.nn.relu(l1) 
 
    l2 = tf.add(tf.matmul(l1, hidden_2_layer['weights']) + hidden_2_layer['biases']) 
    l2 = tf.nn.relu(l2) 
 
    l3 = tf.add(tf.matmul(l2, hidden_3_layer['weights']) + hidden_3_layer['biases']) 
    l3 = tf.nn.relu(l3) 
 
    output = tf.matmul(l3, output_layer['weights']) + output_layer['biases'] 
 
    return output 
 
def train_neural_network(x): 
    prediction = neural_model(x) 
    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=y)) 
    optimizer = tf.train.AdamOptimizer().minimize(cost) 
 
    hm_epochs = 10 
 
    with tf.Session as sess: 
        sess.run(tf.global_variables_initializer()) 
 
        for epoch in hm_epochs: 
            epoch_loss = 0 
            for _ in range(int(mnist.train.num_examples/batch_size)): 
                x, y = mnist.train.next_batch(batch_size) 
                _, c = sess.run([optimizer, cost], feed_dict={x: x, y:y}) 
                epoch_loss += c 
            print('Epoch: ', epoch, 'completed out of', hm_epochs, 'loss: ', epoch_loss) 
        correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y,1)) 
 
        ac = tf.reduce_mean(tf.cast(correct, 'float')) 
        print('acc: ', ac.eval({x:mnist.test_images, y:mnist.test_labels})) 
 
train_neural_network(x) 

但它引发了这个错误:

ValueError: shape must be rank 1 but is rank 0 for 'random_normal1/:...' with shapes[] 

编辑:这是完整的回溯:

Traceback (most recent call last): 
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/common_shapes.py", line 686, in _call_cpp_shape_fn_impl 
    input_tensors_as_shapes, status) 
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/errors_impl.py", line 473, in __exit__ 
    c_api.TF_GetCode(self.status.status)) 
tensorflow.python.framework.errors_impl.InvalidArgumentError: Shape must be rank 1 but is rank 0 for 'random_normal_1/RandomStandardNormal' (op: 'RandomStandardNormal') with input shapes: []. 
 
During handling of the above exception, another exception occurred: 
 
Traceback (most recent call last): 
  File "a.py", line 60, in <module> 
    train_neural_network(x) 
  File "a.py", line 39, in train_neural_network 
    prediction = neural_model(x) 
  File "a.py", line 17, in neural_model 
    'biases':tf.Variable(tf.random_normal(n_nodes_hl1))} 
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/random_ops.py", line 76, in random_normal 
    shape_tensor, dtype, seed=seed1, seed2=seed2) 
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gen_random_ops.py", line 420, in _random_standard_normal 
    seed2=seed2, name=name) 
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper 
    op_def=op_def) 
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 2958, in create_op 
    set_shapes_for_outputs(ret) 
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 2209, in set_shapes_for_outputs 
    shapes = shape_func(op) 
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 2159, in call_with_requiring 
    return call_cpp_shape_fn(op, require_shape_fn=True) 
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/common_shapes.py", line 627, in call_cpp_shape_fn 
    require_shape_fn) 
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/common_shapes.py", line 691, in _call_cpp_shape_fn_impl 
    raise ValueError(err.message) 
ValueError: Shape must be rank 1 but is rank 0 for 'random_normal_1/RandomStandardNormal' (op: 'RandomStandardNormal') with input shapes: []. 

我只是不确定我必须做什么。我猜一定没有错误......

感谢您的帮助，如果我有任何错误，请原谅我的英语不好。

EDIT2: 我现在添加了全部代码。我几乎可以确保我的代码与 senddex 的视频相同。这段代码对那个人有效……我哪里错了？

请您参考如下方法:

一旦您提供了 neural_model 的完整代码，我将根据需要更新此答案，因为错误在那里，但从回溯中我已经看到您在那里:

'biases':tf.Variable(tf.random_normal(n_nodes_hl1)) 

tf.random_normal需要一个 list 作为形状。

将 tf.random_normal(n_nodes_hl1) 更改为 tf.random_normal( [n_nodes_hl1] ) 它应该可以工作(或者至少转到下一个错误..)

更新:上面写的内容也适用于 tf.random_normal

更新 2: 关于 add() 问题，您有:

l1 = tf.add(tf.matmul(data, hidden_1_layer['weights']) + hidden_1_layer['biases']) 

+ 是错误的。您可以使用 tf.add(tensor1, tensor2) 或执行 tensor1 + tensor2(TF 负责其余部分)。您的代码是两者的混合，相当于 tf.add( tensor1 + tensor2 )，因此它会提示，因为 add 缺少第二个参数。