Почему tenorflow не принимает np.float?

Question

Я использую shadoworflow для запуска программы глубокого обучения cnn, но это не удалось? я перевел мои входные данные «изображение» в np.float32, но до сих пор сообщает DTYPE ошибки:

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"]()]] 

Ниже мой код:

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) 

метод поезд это не проблема, но когда вызов предсказывать, всегда есть ошибка в ошибке-заполнителе dtype, как и выше? Я не знаю, почему, потому что я проверил, что мои imgaes - float32 dtype.

Answer 1

, если он терпит неудачу здесь:

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

это потому, что вам нужно пройти в self.keep_prob