tensorflow InvalidArgumEnterror：您必须用dtype float为占位符张量提供一个值

Question

我是tensorflow的新手，想训练一个用于分类的logistic模型。

# Set model weights
W = tf.Variable(tf.zeros([30, 16]))
b = tf.Variable(tf.zeros([16]))
train_X, train_Y, X, Y = input('train.csv')

#construct model
pred = model(X, W, b)
# Minimize error using cross entropy
cost = tf.reduce_mean(-tf.reduce_sum(Y*tf.log(pred), reduction_indices=1))
# Gradient Descent
learning_rate = 0.1
#optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost)
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
# Initializing the variables
init = tf.initialize_all_variables()

get_ipython().magic(u'matplotlib inline')
import collections
import matplotlib.pyplot as plt

training_epochs = 200
batch_size = 300
train_X, train_Y, X, Y = input('train.csv')
acc = []
x = tf.placeholder(tf.float32, [None, 30]) 
y = tf.placeholder(tf.float32, [None, 16])
with tf.Session() as sess:
     sess.run(init)
     # Training cycle
     for epoch in range(training_epochs):
         avg_cost = 0.0
         #print(type(y_train[0][0]))
         print(type(train_X))
         print(type(train_X[0][0]))
         print X
         _, c = sess.run([optimizer, cost], feed_dict = {x: train_X, y: train_Y})

feef_dict方法不起作用，complay：

  train_X type: <type 'numpy.ndarray'>
  train_X[0][0]: <type 'numpy.float32'>
  train_X size: (300, 30)
  place_holder info : Tensor("Placeholder_56:0", shape=(?, 30), dtype=float32)

Answer 1

从错误消息来看，丢失的占位符的名称'placeholder_54'-是可疑的，因为这表明在当前解释器会话中至少创建了54个占位符。

没有足够的细节可以肯定，但我有些怀疑。您是否在同一个解释器会话中多次运行相同的代码（例如，使用ipython/jupyter或Python shell）？假设是这样，我怀疑您的cost张量依赖于在先前执行该代码时创建的占位符。

实际上，在构建完模型的其余部分之后，您的代码创建了两个TF.placeholder()张量X和Y，因此，看起来可能是：

input（）函数在内部调用tf.placeholder()，正是这些占位符（可能是张量x和y？）你应该喂食。