Tensorflow - ValueError：形状はランク0である必要がありますが、入力の形状が[範囲]（ '範囲'）の '制限'の場合はランク1です。[]、[10]、[]

Question

ビルド方法最近の単純なニューラルネットワーク。氏のMoのチュートリアル後
は、私はステップによって、コードのステップを記述します。

from __future__ import print_function 
import tensorflow as tf 
from tensorflow.examples.tutorials.mnist import input_data 
# number 1 to 10 data 
mnist = input_data.read_data_sets('MNIST_data', one_hot=True) 

def compute_accuracy(v_xs, v_ys): 
    global prediction 
    y_pre = sess.run(prediction, feed_dict={xs: v_xs, keep_prob: 1}) 
    correct_prediction = tf.equal(tf.argmax(y_pre,1), tf.argmax(v_ys,1)) 
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) 
    result = sess.run(accuracy, feed_dict={xs: v_xs, ys: v_ys, keep_prob:1}) 
    return result 

def weight_variable(shape): 
    initial = tf.truncated_normal(shape, stddev=0.1) 
    return tf.Variable(initial) 

def bias_variable(shape): 
    initial = tf.constant(0.1, shape=shape) 
    return tf.Variable(initial) 

def conv2d(x, W): 
    # stride [1, x_movement, y_movement, 1] 
    # Must have strides[0] = strides[3] = 1 
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') 

def max_pool_2x2(x): 
    # stride [1, x_movement, y_movement, 1] 
    return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1],padding='SAME') 

# define placeholder for inputs to network 
xs = tf.placeholder(tf.float32, [None, 784]) # 28x28 
ys = tf.placeholder(tf.float32, [None, 10]) 
keep_prob = tf.placeholder(tf.float32) 
x_image = tf.reshape(xs, [-1,28,28,1]) 

## conv1 layer ## 
W_conv1 = weight_variable([5,5,1,32]) 
b_conv1 = bias_variable([32]) 
h_conv1 = tf.nn.relu(conv2d(x_image,W_conv1) + b_conv1) 
h_pool1 = max_pool_2x2(h_conv1) 
## conv2 layer ## 
W_conv2=weight_variable([5,5,32,64]) 
b_conv2=bias_variable([64]) 
h_conv2=tf.nn.relu(conv2d(h_pool1,W_conv2)+b_conv2) 
h_pool2=max_pool_2x2(h_conv2) 
## func1 layer ## 
W_fc1=weight_variable([7*7*64,1024]) 
b_fc1=bias_variable([1024]) 
#[n_samples,7,7,64]->>[n_samples,7*7*64] 
h_pool2_flat=tf.reshape(h_pool2,[-1,7*7*64]) 
h_fc1=tf.nn.relu(tf.matmul(h_pool2_flat,W_fc1)+b_fc1) 
h_fc1_drop=tf.nn.dropout(h_fc1,keep_prob) 
## func2 layer ## 
W_fc2=weight_variable([1024,10]) 
b_fc2=bias_variable([10]) 
prediction=tf.nn.softmax(tf.matmul(h_fc1_drop,W_fc2),b_fc2) 
# the error between prediction and real data 
cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction), 
               reduction_indices=[1]))  # loss 
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) 

sess = tf.Session() 
init = tf.global_variables_initializer() 
sess.run(init) 

for i in range(1000): 
    batch_xs, batch_ys = mnist.train.next_batch(100) 
    sess.run(train_step, feed_dict={xs: batch_xs, ys: batch_ys, keep_prob: 0.5}) 
    if i % 50 == 0: 
     print(compute_accuracy(
      mnist.test.images[:1000], mnist.test.labels[:1000])) 

しかし、私はエラーを取得：

runfile('C:/Users/220/tutorials-master/tensorflowTUT/tf18_CNN2/full_code.py', wdir='C:/Users/220/tutorials-master/tensorflowTUT/tf18_CNN2') 
Extracting MNIST_data\train-images-idx3-ubyte.gz 
Extracting MNIST_data\train-labels-idx1-ubyte.gz 
Extracting MNIST_data\t10k-images-idx3-ubyte.gz 
Extracting MNIST_data\t10k-labels-idx1-ubyte.gz 
Traceback (most recent call last): 

    File "<ipython-input-1-b66fc51270cf>", line 1, in <module> 
    runfile('C:/Users/220/tutorials-master/tensorflowTUT/tf18_CNN2/full_code.py', wdir='C:/Users/220/tutorials-master/tensorflowTUT/tf18_CNN2') 

    File "C:\ProgramData\Anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 880, in runfile 
execfile(filename, namespace) 

    File "C:\ProgramData\Anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 102, in execfile 
exec(compile(f.read(), filename, 'exec'), namespace) 

    File "C:/Users/220/tutorials-master/tensorflowTUT/tf18_CNN2/full_code.py", line 66, in <module> 
prediction=tf.nn.softmax(tf.matmul(h_fc1_drop,W_fc2),b_fc2) 

    File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\ops\nn_ops.py", line 1531, in softmax 
return _softmax(logits, gen_nn_ops._softmax, dim, name) 

    File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\ops\nn_ops.py", line 1491, in _softmax 
logits = _swap_axis(logits, dim, math_ops.subtract(input_rank, 1)) 

    File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\ops\nn_ops.py", line 1463, in _swap_axis 
math_ops.range(dim_index), [last_index], 

    File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\ops\math_ops.py", line 1163, in range 
return gen_math_ops._range(start, limit, delta, name=name) 

    File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\ops\gen_math_ops.py", line 1740, in _range 
delta=delta, name=name) 

    File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 768, in apply_op 
op_def=op_def) 

    File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 2338, in create_op 
set_shapes_for_outputs(ret) 

    File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 1719, in set_shapes_for_outputs 
shapes = shape_func(op) 

    File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 1669, in call_with_requiring 
return call_cpp_shape_fn(op, require_shape_fn=True) 

    File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\framework\common_shapes.py", line 610, in call_cpp_shape_fn 
debug_python_shape_fn, require_shape_fn) 

    File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\framework\common_shapes.py", line 676, in _call_cpp_shape_fn_impl 
raise ValueError(err.message) 

ValueError: Shape must be rank 0 but is rank 1 
    for 'limit' for 'range' (op: 'Range') with input shapes: [], [10], []. 

私はいくつかの類似した質問とその解決策を見つけます。たとえば、「1D Tesnorとして学習率を宣言しましたが、スカラーでなければなりません」。残念ながら、私はそれが実際に何を意味するのか、どのように私の問題を解決するのかはわかりません。

ありがとうございました！この行で

Answer 1

：

prediction=tf.nn.softmax(tf.matmul(h_fc1_drop,W_fc2), b_fc2) 

それは次のようになります。

prediction=tf.nn.softmax(tf.matmul(h_fc1_drop,W_fc2) + b_fc2)