MNIST數據-無法為形狀為（（5500，784））的張量'占位符：0'輸入形狀（1000，784）的值

Question

我嘗試使用1000個示例來測試數據，而獲得5500個訓練數據示例。 盡管我從用於訓練部分的（5500，784）更改了測試部分的（1000,784）的占位符參數，但仍然出現錯誤ValueError：無法為Tensor'占位符提供形狀的值（1000，784）： 0'，形狀為（（5500，784）'，有人可以幫忙嗎？

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

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

def TRAIN_SIZE(num):
    print ('Total Training Images in Dataset = ' + str(mnist.train.images.shape))
    print ('--------------------------------------------------')
    x_train = mnist.train.images[:num,:]
    print ('x_train Examples Loaded = ' + str(x_train.shape))
    y_train = mnist.train.labels[:num,:]
    print ('y_train Examples Loaded = ' + str(y_train.shape))
    print('')
    return x_train, y_train

def TEST_SIZE(num):
    print ('Total Test Examples in Dataset = ' + str(mnist.test.images.shape))
    print ('--------------------------------------------------')
    x_test = mnist.test.images[:num,:]
    print ('x_test Examples Loaded = ' + str(x_test.shape))
    y_test = mnist.test.labels[:num,:]
    print ('y_test Examples Loaded = ' + str(y_test.shape))
    return x_test, y_test

def display_digit(num,x_train,y_train):
    print(y_train[num])
    label = y_train[num].argmax(axis=0)
    image = x_train[num].reshape([28,28])
    plt.title('Example: %d  Label: %d' % (num, label))
    plt.imshow(image, cmap=plt.get_cmap('gray_r'))
   # plt.show()

X_train, Y_train = TRAIN_SIZE(5500)
X_test, Y_test = TEST_SIZE(1000)
learning_rate = 0.01
train_steps = 2500

# Display 30 random digits
for i in range(30):
    selected = random.randint(0,5500-1)
    display_digit(selected, X_train, Y_train)

# n features, k classes
n = 784
k = 10

batch_size = 5500
X = tf.placeholder(tf.float32, [batch_size, 784])
Y = tf.placeholder(tf.float32, [batch_size, 10])

W = tf.Variable(tf.random_normal(shape=[784,10],stddev=0.01),name="weights")
b = tf.Variable(tf.zeros([1,10]),name="bias")
logits = tf.matmul(X, W) + b

entropy = tf.nn.softmax_cross_entropy_with_logits(logits= logits, labels=Y)
loss = tf.reduce_mean(entropy) #computes the mean over examples in the batch

optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(loss)


X_ = tf.placeholder(tf.float32, [1000, 784])
preds = tf.nn.softmax(tf.matmul(X_, W) + b)
Y_ = tf.placeholder(tf.float32, [1000, 10])
correct_prediction = tf.equal(tf.argmax(preds, 1), tf.argmax(Y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)

    # Train model
    for i in range(train_steps + 1):
        sess.run([optimizer, loss], feed_dict={X: X_train, Y: Y_train})
        if i % 100 == 0:
            print('Training Step: ', i)
            print('Accuracy:', sess.run(accuracy, feed_dict={X: X_test, Y: Y_test}))

    # Obtain weights
    W_values = sess.run(W)

# Display the weights
for i in range(10):
    plt.subplot(2, 5, i+1)
    weight = W_values[:,i]
    plt.title(i)
    plt.imshow(weight.reshape([28,28]), cmap=plt.get_cmap('seismic'))
    frame1 = plt.gca()
    frame1.axes.get_xaxis().set_visible(False)
    frame1.axes.get_yaxis().set_visible(False)
plt.show()

Answer 1

我發現了你的錯誤。 您已將測試占位符聲明為X_和Y_，並且要提供X，Y，它們是應該提供測試占位符的地方的火車占位符。 您在2個地方提供X，Y火車占位符（火車，同時測試這兩個地方）。在代碼中更改以下行即可正常工作。

sess.run(accuracy,feed_dict={X_:X_test,Y_:Y_test})

我運行了您的代碼，並得到以下答案。 該程序仍在運行。

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
Total Training Images in Dataset = (55000, 784)
--------------------------------------------------
x_train Examples Loaded = (5500, 784)
y_train Examples Loaded = (5500, 10)

Total Test Examples in Dataset = (10000, 784)
--------------------------------------------------
x_test Examples Loaded = (1000, 784)
y_test Examples Loaded = (1000, 10)
[0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]
[0. 0. 0. 1. 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. 1. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]
[0. 1. 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. 1. 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. 0. 0. 0. 0. 0. 1. 0.]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]
[0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 1. 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. 1. 0.]
[0. 0. 0. 0. 1. 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. 1. 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. 1. 0.]
[0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]
[0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]
[1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
('Training Step: ', 0)
('Accuracy:', 0.098)
('Training Step: ', 100)
('Accuracy:', 0.716)
('Training Step: ', 200)
('Accuracy:', 0.763)
('Training Step: ', 300)
('Accuracy:', 0.778)
('Training Step: ', 400)
('Accuracy:', 0.79)
('Training Step: ', 500)
('Accuracy:', 0.802)
('Training Step: ', 600)
('Accuracy:', 0.81)
('Training Step: ', 700)
('Accuracy:', 0.821)
('Training Step: ', 800)
('Accuracy:', 0.829)
('Training Step: ', 900)
('Accuracy:', 0.833)
('Training Step: ', 1000)
('Accuracy:', 0.836)
('Training Step: ', 1100)
('Accuracy:', 0.84)
('Training Step: ', 1200)
('Accuracy:', 0.846)
('Training Step: ', 1300)
('Accuracy:', 0.85)
('Training Step: ', 1400)
('Accuracy:', 0.853)
('Training Step: ', 1500)
('Accuracy:', 0.853)

Answer 2

占位符的形狀應為（None，784）