一个 TensorFlow 简单示例答案

【问题标题】：A TensorFlow simple example一个 TensorFlow 简单示例
【发布时间】：2018-01-19 08:38:17
【问题描述】：

我正在尝试运行这个 TensorFlow 示例。我使用的占位符似乎不正确。运行时错误信息对新手没有多大帮助:-)

# Building a neuronal network with TensorFlow

import tensorflow as tf

def multilayer_perceptron( x, weights, biases ):
    # Hidden layer with RELU activation
    layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])
    layer_1 = tf.nn.relu(layer_1)
    # Output layer with linear activation
    out_layer = tf.matmul(layer_1, weights['out']) + biases['out']
    return out_layer

session = tf.Session()

nInputs = 7  # Number of inputs to the neuronal network
nHiddenPerceptrons = 5
nTypes = 10  # seven posible types of values in the output
nLearningRate = 0.001
nTrainingEpochs = 15

aInputs = [ [ 1, 1, 1, 0, 1, 1, 1 ],  # zero                 2
            [ 1, 0, 0, 0, 0, 0, 1 ],  # one               ------- 
            [ 1, 1, 0, 1, 1, 1, 0 ],  # two            3  |     |  1
            [ 1, 1, 0, 1, 0, 1, 1 ],  # three             |  4  |  
            [ 1, 0, 1, 1, 0, 0, 1 ],  # four              -------
            [ 0, 1, 1, 1, 0, 1, 1 ],  # five              |     |  
            [ 0, 1, 1, 1, 1, 1, 1 ],  # six            5  |     |  7     
            [ 1, 1, 0, 0, 0, 0, 1 ],  # seven             -------   
            [ 1, 1, 1, 1, 1, 1, 1 ],  # eight                6
            [ 1, 1, 1, 1, 0, 1, 1 ] ] # nine

aOutputs = [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 ]

weights = { 'h1': tf.Variable( tf.random_normal( [ nInputs, nHiddenPerceptrons ] ) ),
            'out': tf.Variable( tf.random_normal( [ nHiddenPerceptrons, nTypes ] ) ) }
biases = { 'b1': tf.Variable( tf.random_normal( [ nHiddenPerceptrons ] ) ),
           'out': tf.Variable( tf.random_normal( [ nTypes ] ) ) }

x = tf.placeholder( "float", shape=[ None,] )
y = tf.placeholder( "float" )

network = multilayer_perceptron( x, weights, biases )
loss = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits( logits=network, labels=tf.placeholder( "float" ) ) )
optimizer = tf.train.AdamOptimizer( learning_rate = nLearningRate ).minimize( loss )
init = tf.global_variables_initializer()

with tf.Session() as session :
   session.run( init )

   # Training cycle
   for epoch in range( nTrainingEpochs ) :
      avg_loss = 0.
      for n in range( len( aInputs ) ) :
         c = session.run( [ optimizer, loss ], { x: aInputs[ n ], y: aOutputs[ n ] } )
         # Compute average loss
         avg_loss += c / total_batch
         print("Epoch:", '%04d' % ( epoch + 1 ), "cost=", "{:.9f}".format( avg_loss ) )

      print("Optimization Finished!")

但是我遇到了一些运行时错误，我不知道如何解决它们。感谢您的帮助，谢谢

文件“C:\Users\Administrator\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\common_shapes.py”，第 671 行，在 _call_cpp_shape_fn_impl input_tensors_as_shapes，状态）退出中的文件“C:\Users\Administrator\AppData\Local\Programs\Python\Python36\lib\contextlib.py”，第 88 行下一个（self.gen）文件“C:\Users\Administrator\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\errors_impl.py”，第 466 行，在 raise_exception_on_not_ok_status pywrap_tensorflow.TF_GetCode（状态）） tensorflow.python.framework.errors_impl.InvalidArgumentError：形状必须为 2 级，但对于输入形状为 [?]、[7,5] 的“MatMul”（操作：“MatMul”）为 1 级。在处理上述异常的过程中，又出现了一个异常：回溯（最近一次通话最后）：文件“tf_nn.py”，第 42 行，在网络 = 多层感知器（x，权重，偏差）多层感知器中的文件“tf_nn.py”，第 7 行 layer_1 = tf.add(tf.matmul(x, weights['h1']), 偏差['b1']) 文件“C:\Users\Administrator\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\ops\math_ops.py”，第 1816 行，在 matmul a, b, transpose_a=transpose_a, transpose_b=transpose_b, name=name) 文件“C:\Users\Administrator\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\ops\gen_math_ops.py”，第 1217 行，在 _mat_mul transpose_b=transpose_b，名称=名称）文件“C:\Users\Administrator\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\op_def_library.py”，第 767 行，在 apply_op op_def=op_def) 文件“C:\Users\Administrator\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\ops.py”，第 2508 行，在 create_op set_shapes_for_outputs(ret) 文件“C:\Users\Administrator\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\ops.py”，第 1873 行，在 set_shapes_for_outputs 形状 = shape_func(op) 文件“C:\Users\Administrator\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\ops.py”，第 1823 行，在 call_with_requiring 返回 call_cpp_shape_fn(op, require_shape_fn=True) 文件“C:\Users\Administrator\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\common_shapes.py”，第 610 行，在 call_cpp_shape_fn debug_python_shape_fn, require_shape_fn) _call_cpp_shape_fn_impl 中的文件“C:\Users\Administrator\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\common_shapes.py”，第 676 行引发 ValueError(err.message) ValueError：形状必须为 2 级，但对于输入形状为 [?]、[7,5] 的“MatMul”（操作：“MatMul”）为 1 级。

【问题讨论】：

如果您希望我们能够提供帮助，您必须向我们提供您看到的错误。

标签： tensorflow

【解决方案1】：

错误信息说 x 的形状不正确。

您需要设置形状参数的第二个维度。

x = tf.placeholder("float", shape=[None, nInputs])

【讨论】：

我很抱歉将错误发布为新的消息。不会再发生了
不，不是，因为出现了另一个错误，我无法将其作为评论发布，因为 stackoverflow 报告“...字符太长”
形状必须为 2 级，但对于输入形状为 [?]、[7,5] 的“MatMul”（操作：“MatMul”）为 1 级

【解决方案2】：

这样解决：

# Building a neuronal network with TensorFlow

import tensorflow as tf

def multilayer_perceptron( x, weights, biases ):
    # Hidden layer with RELU activation
    layer_1 = tf.add( tf.matmul( x, weights[ 'h1' ] ), biases[ 'b1' ] )
    layer_1 = tf.nn.relu(layer_1)

    # Output layer with linear activation
    out_layer = tf.matmul( layer_1, weights[ 'out' ] ) + biases[ 'out' ] 
    return out_layer

session = tf.Session()

nInputs = 7  # Number of inputs to the neuronal network
nHiddenPerceptrons = 12
nTypes = 10  # Number of different types in the output
nLearningRate = 0.002
nTrainingEpochs = 500

# Input data
aInputs = [ [ 1, 1, 1, 0, 1, 1, 1 ],  # zero                 2
            [ 1, 0, 0, 0, 0, 0, 1 ],  # one               ------- 
            [ 1, 1, 0, 1, 1, 1, 0 ],  # two            3  |     |  1
            [ 1, 1, 0, 1, 0, 1, 1 ],  # three             |  4  |  
            [ 1, 0, 1, 1, 0, 0, 1 ],  # four              -------
            [ 0, 1, 1, 1, 0, 1, 1 ],  # five              |     |  
            [ 0, 1, 1, 1, 1, 1, 1 ],  # six            5  |     |  7     
            [ 1, 1, 0, 0, 0, 0, 1 ],  # seven             -------   
            [ 1, 1, 1, 1, 1, 1, 1 ],  # eight                6
            [ 1, 1, 1, 1, 0, 1, 1 ] ] # nine

aOutputs = [ [ 1, 0, 0, 0, 0, 0, 0, 0, 0, 0 ],
             [ 0, 1, 0, 0, 0, 0, 0, 0, 0, 0 ],
             [ 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 ],
             [ 0, 0, 0, 1, 0, 0, 0, 0, 0, 0 ],
             [ 0, 0, 0, 0, 1, 0, 0, 0, 0, 0 ],
             [ 0, 0, 0, 0, 0, 1, 0, 0, 0, 0 ],
             [ 0, 0, 0, 0, 0, 0, 1, 0, 0, 0 ],
             [ 0, 0, 0, 0, 0, 0, 0, 1, 0, 0 ],
             [ 0, 0, 0, 0, 0, 0, 0, 0, 1, 0 ],
             [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 1 ] ]

input = tf.placeholder( "float", shape=( None, nInputs ) )
output = tf.placeholder( "float", shape=( None, nTypes ) )

# Store layers weight & bias
weights = { 'h1': tf.Variable(tf.random_normal( [ nInputs, nHiddenPerceptrons ] ) ),
            'out': tf.Variable(tf.random_normal( [ nHiddenPerceptrons, nTypes ] ) ) }
biases = { 'b1': tf.Variable( tf.random_normal( [ nHiddenPerceptrons ] ) ),
           'out': tf.Variable( tf.random_normal( [ nTypes ] ) ) }

# Create model
network = multilayer_perceptron( input, weights, biases )
loss = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits( logits=network, labels=output ) )
optimizer = tf.train.AdamOptimizer( learning_rate = nLearningRate ).minimize( loss )
init = tf.global_variables_initializer()

with tf.Session() as session:
   session.run( init )

   # Training cycle
   for epoch in range( nTrainingEpochs ) :
       avg_error = 0
       for n in range( len( aInputs ) ) :
          cost = session.run( [ optimizer, loss ], { input: [ aInputs[ n ] ], output: [ aOutputs[ n ] ] } )
          # Compute average error
          avg_error += cost[ 1 ] / len( aInputs )

       print( "Epoch:", '%04d' % ( epoch + 1 ), "error=", "{:.9f}".format( avg_error ) )

   print( "Optimization Finished" )

   # Test model on train data
   print( "Testing:" )
   for n in range( len( aInputs ) ) :
      print( tf.argmax( network, 1 ).eval( { input: [ aInputs[ n ] ] } )[ 0 ] )

【讨论】：