【发布时间】：2018-08-04 19:45:59
【问题描述】：
我正在尝试用 1000 个示例测试数据，而我得到了 5500 个训练数据示例。虽然我将测试部分的占位符参数 (1000,784) 从用于训练部分的 (5500, 784) 更改，但我仍然收到错误 ValueError: Cannot feed value of shape (1000, 784) for Tensor 'Placeholder: 0'，其形状为 '(5500, 784)'，有人可以帮忙吗？
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()
【问题讨论】：
标签： python tensorflow mnist