我正在尝试运行这个TensorFlow示例。似乎我使用的占位符不正确。运行时错误信息对新手没有太大帮助：-）
# 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。谢谢你的帮助，谢谢
错误消息说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])
通过这种方式解决：
# 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 ] )
