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源自：3-7 TensorFlow结合mnist进行卷积模型训练（4）

错误 TypeError: Value passed to parameter 'input' has DataType int32 not in list of allowed values: float16, bfloat16, float32, float64

with tf.variable_scope("convolutional"):
    x = tf.placeholder(tf.float32, [None, 784], name='x')
    keep_prob = tf.placeholder(tf.float32)
    y, variables = model.convolutional(x, keep_prob)

y_ = tf.placeholder(tf.float32, [None, 10], name='y')
cross_entropy = -tf.reduce_sum(y_ * tf.log(y))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

saver = tf.train.Saver(variables)

with tf.Session() as sess:
    merged_summary_op = tf.summary.merge_all()
    summay_writer = tf.summary.FileWriter('/tmp/mnist_log/1', sess.graph)
    sess.run(tf.global_variables_initializer())

    for i in range(20000):
        batch = mnist.train.next_batch(50)
        if i % 100 == 0:
            train_accuracy = accuracy.eval(feed_dict={x: batch[0], y_: batch[1], keep_prob: 1.0})
            print("step %d, training accuracy %g" % (i, train_accuracy))
        sess.run(train_step, feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})

    print((sess.run(accuracy, feed_dict={x:mnist.test.images, y_:mnist.test.labels, keep_prob: 1.0})))

    path = saver.save(
        sess,os.path.join(os.path.dirname(__file__), 'data', 'convolutional.ckpt'),
        write_meta_graph=False, write_state=False)
    print("Saved:", path)
    
TypeError: Value passed to parameter 'input' has DataType int32 not in list of allowed values: float16, bfloat16, float32, float64


提问者：qq_Wuli臭_0 2019-07-17 10:46

个回答

  • weixin_慕桂英5294027
    2019-08-15 21:17:46

    找到在哪儿错了

    model.py conv2d那个文件改一下下面这行

    把

    return tf.nn.conv2d([1, 1, 1, 1], padding='SAME')
改成
    return tf.nn.conv2d(x, W, [1, 1, 1, 1], padding='SAME')

    没有传入参数当然出错喽

    2
  • weixin_慕桂英5294027
    2019-08-15 20:13:05


    Traceback (most recent call last):  
    File "/home/mnist/convolutional.py", line 11, in <module>
        y, variables = model.convolutional(x, keep_prob)
    File "/home/mnist/model.py", line 22, in convolutional
        h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
    File "/home/mnist/model.py", line 10, in conv2d    
        return tf.nn.conv2d([1, 1, 1, 1], padding='SAME')
TypeError: Value passed to parameter 'input' has DataType int32 not in list of allowed values: float16, bfloat16, float32, float64


    0
  • weixin_慕桂英5294027
    2019-08-15 20:10:24

    同是这个错误 求解答

    import os
import model
import tensorflow as tf
import input_data

data = input_data.read_data_sets('MNIST_data', one_hot=True)
# model
with tf.variable_scope("convolutional"):
    x = tf.placeholder(tf.float32, [None, 784], name='x')
    keep_prob = tf.placeholder(tf.float32)
    y, variables = model.convolutional(x, keep_prob)
# train
y_ = tf.placeholder(tf.float32, [None, 10], name='y')
cross_entropy = -tf.reduce_sum(y_ * tf.log(y))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

saver = tf.train.Saver(variables)
with tf.Session() as sess:
    merged_summary_op = tf.summary.merge_all()
    summary_writer = tf.summary.FileWriter('/home/mnist_log/1', sess.graph)
    summary_writer.add_graph(sess.graph)

    sess.run(tf.global_variables_initializer())
    for i in range(20000):
        batch = data.train.next_batch(50)
        if i % 100 == 0:
            train_accuracy = accuracy.eval(feed_dict={x: batch[0], y_: batch[1], keep_prob: 1.0})
            print("step %d, training accuracy %g" % (i, train_accuracy))
        sess.run(train_step, feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})

    print(sess.run(accuracy, feed_dict={x: data.test.images, y_: data.test.labels, keep_prob: 1.0}))

    path = saver.save(sess, os.path.join(os.path.dirname(__file__), 'data', 'convolutional.ckpt'),
                      write_meta_graph=False, write_state=False)
    print("Saved:", path)


    0