Question

我正在阅读tensorflow教程并不断收到此错误：

InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'Placeholder' with dtype float
     [[Node: Placeholder = Placeholder[dtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]

这是追溯：

Traceback (most recent call last):
  File "/home/mddrill/anaconda2/envs/python35/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 972, in _do_call
    return fn(*args)
  File "/home/mddrill/anaconda2/envs/python35/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 954, in _run_fn
    status, run_metadata)
  File "/home/mddrill/anaconda2/envs/python35/lib/python3.5/contextlib.py", line 66, in __exit__
    next(self.gen)
  File "/home/mddrill/anaconda2/envs/python35/lib/python3.5/site-packages/tensorflow/python/framework/errors.py", line 463, in raise_exception_on_not_ok_status
    pywrap_tensorflow.TF_GetCode(status))
tensorflow.python.framework.errors.InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder' with dtype float
     [[Node: Placeholder = Placeholder[dtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "neural_network.py", line 48, in <module>
    print(sess.run(loss), feed_dict={xs:x_data, ys:y_data})
  File "/home/mddrill/anaconda2/envs/python35/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 717, in run
    run_metadata_ptr)
  File "/home/mddrill/anaconda2/envs/python35/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 915, in _run
    feed_dict_string, options, run_metadata)
  File "/home/mddrill/anaconda2/envs/python35/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 965, in _do_run
    target_list, options, run_metadata)
  File "/home/mddrill/anaconda2/envs/python35/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 985, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors.InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder' with dtype float
     [[Node: Placeholder = Placeholder[dtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]

Caused by op 'Placeholder', defined at:
  File "neural_network.py", line 21, in <module>
    xs = tf.placeholder(tf.float32,[None,1])
  File "/home/mddrill/anaconda2/envs/python35/lib/python3.5/site-packages/tensorflow/python/ops/array_ops.py", line 1332, in placeholder
    name=name)
  File "/home/mddrill/anaconda2/envs/python35/lib/python3.5/site-packages/tensorflow/python/ops/gen_array_ops.py", line 1748, in _placeholder
    name=name)
  File "/home/mddrill/anaconda2/envs/python35/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py", line 749, in apply_op
    op_def=op_def)
  File "/home/mddrill/anaconda2/envs/python35/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 2380, in create_op
    original_op=self._default_original_op, op_def=op_def)
  File "/home/mddrill/anaconda2/envs/python35/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 1298, in __init__
    self._traceback = _extract_stack()

这是我的代码：

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

def add_layer(inputs, in_size, out_size,activation_function=None):
    Weights = tf.Variable(tf.random_normal([in_size, out_size]))
    biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)
    Wx_plus_b = tf.matmul(inputs, Weights) + biases
    if activation_function is None:
        outputs = Wx_plus_b
    else:
        outputs = activation_function(Wx_plus_b)
    return outputs

# Make up some data
x_data = np.linspace(-1,1,300, dtype=np.float32)[:,np.newaxis]
noise = np.random.normal(0,0.05,x_data.shape).astype(np.float32)
y_data = np.square(x_data) - 0.5 + noise

# define placeholder for inputs to network
xs = tf.placeholder(tf.float32,[None,1])
ys = tf.placeholder(tf.float32,[None,1])

# add hidden layer
lay1 = add_layer(xs, 1, 10, activation_function=tf.nn.relu)

#add output layer
prediction = add_layer(lay1, 10, 1, activation_function=None)

# the error between prediction and real data
loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction), reduction_indices=[1]))
### if we have [[3,4],[5,6],[7,8]] and reduction_indices is 1
### then we are taking f(3,4) f(5,6) and f(7,8)
### if reduction_indices is 0, then we are taking
### f(3,5,7) and f(4,6,8)
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

# important step
init = tf.initialize_all_variables()

with tf.Session() as sess:
    sess.run(init)
    for i in range(1000):
        # training
        sess.run(train_step, feed_dict={xs:x_data, ys:y_data})
        if i% 50 == 0:
            # to see the step improvement
            print(sess.run(loss), feed_dict={xs:x_data, ys:y_data})

正如你在这里看到的那样，x_data和y_data都是花车，所以我不知道为什么我会收到这个错误。

在print(type(x_data[0][0]),type(y_data[0][0]))之前添加打印语句sess.run(train_step, feed_dict={xs:x_data, ys:y_data})时。我明白了：<class 'numpy.float32'> <class 'numpy.float32'>很明显它们都是漂浮物

我在这里缺少什么？

Answer 1

print()声明中有错误。幸运的是，当我使用TensorFlow r0.11时，它被SyntaxError选中。

替换，

print(sess.run(loss), feed_dict={xs:x_data, ys:y_data})

用，

print(sess.run(loss, feed_dict={xs:x_data, ys:y_data}))

Tensorflow：您必须为占位符张量和占位符＆＃39;提供一个值。用dtype float [但值是浮点数]

1 个答案: