InvalidArgumentError（请参阅上面的回溯）：您必须使用dtype float输入占位符张量'Placeholder_8'的值

Question

我尝试运行一个用于CNN训练的python程序，抛出以下错误消息，我的Python版本是Python 3.5.3 :: Anaconda 4.1.1（64位），而tensorflow版本是1.0.1，

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 "C:\anaconda\Anaconda3_411\lib\site-packages\tensorflow\python\client\session.py", line 1022, in _do_call return fn(*args)
  File "C:\anaconda\Anaconda3_411\lib\site-packages\tensorflow\python\client\session.py", line 1004, in _run_fn status, run_metadata)
  File "C:\anaconda\Anaconda3_411\lib\contextlib.py", line 66, in __exit__ next(self.gen)
  File "C:\anaconda\Anaconda3_411\lib\site-packages\tensorflow\python\framework\errors_impl.py", line 466, in raise_exception_on_not_ok_status pywrap_tensorflow.TF_GetCode(status)) tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder_8' with dtype float [[Node: Placeholder_8 = 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 "<stdin>", line 1, in <module>
  File "C:\anaconda\Anaconda3_411\lib\site-packages\spyderlib\widgets\externalshell\sitecustomize.py", line 714, in runfile
    execfile(filename, namespace)
  File "C:\anaconda\Anaconda3_411\lib\site-packages\spyderlib\widgets\externalshell\sitecustomize.py", line 89, in execfile
    exec(compile(f.read(), filename, 'exec'), namespace)
  File "C:/development/workspace/tensorflow_ws/cmf_ts18.py", line 109, in <module>
    sess.run(train_step, feed_dict={xs:batch_xs, ys:batch_ys})
  File "C:\anaconda\Anaconda3_411\lib\site-packages\tensorflow\python\client\session.py", line 767, in run
    run_metadata_ptr)
  File "C:\anaconda\Anaconda3_411\lib\site-packages\tensorflow\python\client\session.py", line 965, in _run
    feed_dict_string, options, run_metadata)
  File "C:\anaconda\Anaconda3_411\lib\site-packages\tensorflow\python\client\session.py", line 1015, in _do_run
    target_list, options, run_metadata)
  File "C:\anaconda\Anaconda3_411\lib\site-packages\tensorflow\python\client\session.py", line 1035, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder_8' with dtype float
     [[Node: Placeholder_8 = Placeholder[dtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]

Caused by op 'Placeholder_8', defined at:
  File "<stdin>", line 1, in <module>
  File "C:\anaconda\Anaconda3_411\lib\site-packages\spyderlib\widgets\externalshell\sitecustomize.py", line 714, in runfile
    execfile(filename, namespace)
  File "C:\anaconda\Anaconda3_411\lib\site-packages\spyderlib\widgets\externalshell\sitecustomize.py", line 89, in execfile
    exec(compile(f.read(), filename, 'exec'), namespace)
  File "C:/development/workspace/tensorflow_ws/cmf_ts18.py", line 54, in <module>
    keep_prob = tf.placeholder(tf.float32, shape=None)
  File "C:\anaconda\Anaconda3_411\lib\site-packages\tensorflow\python\ops\array_ops.py", line 1502, in placeholder
    name=name)
  File "C:\anaconda\Anaconda3_411\lib\site-packages\tensorflow\python\ops\gen_array_ops.py", line 2149, in _placeholder
    name=name)
  File "C:\anaconda\Anaconda3_411\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 763, in apply_op
    op_def=op_def)
  File "C:\anaconda\Anaconda3_411\lib\site-packages\tensorflow\python\framework\ops.py", line 2327, in create_op
    original_op=self._default_original_op, op_def=op_def)
  File "C:\anaconda\Anaconda3_411\lib\site-packages\tensorflow\python\framework\ops.py", line 1226, in __init__
    self._traceback = _extract_stack()

InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'Placeholder_8' with dtype float
     [[Node: Placeholder_8 = Placeholder[dtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]

该程序的来源

import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_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):
    return tf.nn.conv2d(x, W, strides=[1,1,1,1], padding='SAME')

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

xs = tf.placeholder(tf.float32, shape=[None, 784])
ys = tf.placeholder(tf.float32, shape=[None, 10])
keep_prob = tf.placeholder(tf.float32, shape=None)
x_image = tf.reshape(xs, [-1,28,28,1])

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)

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)

W_fc1 = weight_variable([7*7*64, 1024])
b_fc1 = bias_variable([1024])
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)

W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
prediction = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)

cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction), reduction_indices=[1]))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

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

sess.run(init)

for i in range(500):
    batch_xs, batch_ys = mnist.train.next_batch(100)
    sess.run(train_step, feed_dict={xs:batch_xs, ys:batch_ys})
    if i % 50 == 0:
        print(compute_accuracy(mnist.test.images, mnist.test.labels))

Answer 1

在这一行中，您需要提供keep_prob ：

sess.run(train_step, feed_dict={xs:batch_xs, ys:batch_ys})

至

sess.run(train_step, feed_dict={xs:batch_xs, ys:batch_ys, keep_prob: 0.5})