Numpy 为什么tensorflow没有'；我不接受np.float？

我正在使用tensorflow运行cnn深度学习程序，但失败了？我已将输入数据“images”转换为np.float32，但它仍报告数据类型错误：

E tensorflow/core/client/tensor_c_api.cc:485] You must feed a value for placeholder tensor 'Placeholder_2' with dtype float
 [[Node: Placeholder_2 = Placeholder[dtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]

以下是我的代码：

import dataset
import numpy as np
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

class CNN(object):

    def __init__(self):
        self.x = tf.placeholder(tf.float32, [None, 784])
        self.y_  = tf.placeholder(tf.float32, [None, 10])


        # First Convolutional Layer
        W_conv1 = self.weight_variable([5, 5, 1, 32])
        b_conv1 = self.bias_variable([32])

        x_image = tf.reshape(self.x, [-1, 28, 28, 1])

        h_conv1 = tf.nn.relu(self.conv2d(x_image, W_conv1) + b_conv1)
        h_pool1 = self.max_pool_2x2(h_conv1)

        # Second Convolutional Layer
        W_conv2 = self.weight_variable([5, 5, 32, 64])
        b_conv2 = self.bias_variable([64])

        h_conv2 = tf.nn.relu(self.conv2d(h_pool1, W_conv2) + b_conv2)
        h_pool2 = self.max_pool_2x2(h_conv2)

        # Densely Connected Layer
        W_fc1 = self.weight_variable([7 * 7 * 64, 1024])
        b_fc1 = self.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)

        # Dropout
        self.keep_prob = tf.placeholder(tf.float32)
        h_fc1_drop = tf.nn.dropout(h_fc1, self.keep_prob)

        # Readout Layer
        W_fc2 = self.weight_variable([1024, 10])
        b_fc2 = self.bias_variable([10])

        self.y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2

        # Train and Evaluate the Model
        self.cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(self.y_conv, self.y_))
        self.train_step = tf.train.AdamOptimizer(1e-4).minimize(self.cross_entropy)
        self.correct_prediction = tf.equal(tf.argmax(self.y_conv, 1), tf.argmax(self.y_, 1))
        self.accuracy = tf.reduce_mean(tf.cast(self.correct_prediction, tf.float32))

        self.saver = tf.train.Saver()
        self.sess = tf.Session()
        self.sess.run(tf.initialize_all_variables())
        print("cnn initial finished!")


    def weight_variable(self, shape):
        initial = tf.truncated_normal(shape, stddev=0.1)
        return tf.Variable(initial)

    def bias_variable(self, shape):
        initial = tf.constant(0.1, shape=shape)
        return tf.Variable(initial)

    def conv2d(self, x, W):
        return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

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


    def train(self):
        # 初始化数据集
        self.trainset = dataset.Train()
        # mnist_softmax.py 中使用的是在sess中通过run方法执行train_step, accuracy
        # mnist_cnn.py中 使用的是直接执行train_step, accuracy.eval，所以必须要传入session参数

        for i in range(20000):
            batch_xs, batch_ys = mnist.train.next_batch(50)
            if i%100 == 0:
                #print(batch_xs[0])
                #print(batch_ys[0])
                self.train_accuracy = self.accuracy.eval(session=self.sess, feed_dict={self.x: batch_xs, self.y_: batch_ys, self.keep_prob: 1.0})
                print("step %d, trainning accuracy %g" % (i, self.train_accuracy))
            self.train_step.run(session=self.sess, feed_dict={self.x: batch_xs, self.y_: batch_ys, self.keep_prob: 0.5})
        # Save the variables to disk.
        save_path = self.saver.save(self.sess, "CNN_data/model.ckpt")
        print("Model saved in file: %s" % save_path)
        #print("test accuracy %g" % self.accuracy.eval(session=self.sess, feed_dict={self.x: mnist.test.images, self.y_: mnist.test.labels, self.keep_prob: 1.0}))

    def predict(self, images):
        images = np.reshape(images, (1, 784))
        images = images.astype(np.float32)
        print(images)
        ckpt = tf.train.get_checkpoint_state("CNN_data/")
        if ckpt and ckpt.model_checkpoint_path:
            self.saver.restore(self.sess, ckpt.model_checkpoint_path)
        else:
            print("No checkpoint found!")

        predictions = self.sess.run(self.y_conv, feed_dict={self.x: images})

        return predictions


if __name__ == '__main__':
    cnn = CNN()
    #cnn.train()
    images = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 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, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
    cnn.predict(images)

train方法没有问题，但是当调用predict时，总是会出现占位符数据类型错误，就像上面所说的那样？我不知道为什么，因为我已经检查过我的imgae是float32数据类型

如果在这里失败：

predictions = self.sess.run(self.y_conv, feed_dict={self.x: images})

---

这是因为你需要通过一个self.keep_prob

在

tf.float32

和

np.float32

之间有什么区别吗。它可能有相同的4个字节，但对象包装器不同？是的，但这并不重要！我通过添加参数自我修复了它。保留问题。非常感谢您的回答！！我是CNN的新手，我只是运行演示，所以我不知道有一个愚蠢的错误！谢谢你的回答！