尝试填充占位符时出现Tensorflow错误

Posted 2023-03-13

【中文标题】尝试填充占位符时出现Tensorflow错误

【英文标题】：Tensorflow error while trying to fill a placeholder

【发布时间】：2017-11-12 11:06:31

【问题描述】：

我正在使用 mnist 数据进行练习，但由于此错误，我无法输入占位符：

ValueError: Cannot feed value of shape (20,) for Tensor 'Placeholder_1:0', which has shape '(?, 10)'

到目前为止我的代码是：

    import gzip
#https://***.com/questions/37132899/installing-cpickle-with-python-3-5
import _pickle as cPickle

import tensorflow as tf
import numpy as np


# Translate a list of labels into an array of 0's and one 1.
# i.e.: 4 -> [0,0,0,0,1,0,0,0,0,0]
def one_hot(x, n):
    """
    :param x: label (int)
    :param n: number of bits
    :return: one hot code
    """
    if type(x) == list:
        x = np.array(x)
    x = x.flatten()
    o_h = np.zeros((len(x), n))
    o_h[np.arange(len(x)), x] = 1
    return o_h


f = gzip.open('mnist.pkl.gz', 'rb')
#https://***.com/questions/40493856/python-pickle-unicodedecodeerror
train_set, valid_set, test_set = cPickle.load(f, encoding='latin1')
f.close()



train_x, train_y = train_set



# ---------------- Visualizing some element of the MNIST dataset --------------

import matplotlib.cm as cm
import matplotlib.pyplot as plt

plt.imshow(train_x[57].reshape((28, 28)), cmap=cm.Greys_r)
plt.show()  # Let's see a sample
print (train_y[57])


# TODO: the neural net!!

# OJO hace falta formatear los datos.
#x_data = train_set[:, 0:784].astype('f4')
#y_data = one_hot(train_set[:, 785].astype(int), 10)

#Conocemos que las imagenes son de 28x28 entonces las columnas son 784, las filas se dejan para el momento del relleno
x = tf.placeholder("float", [None, 784])

#Necesitamos albergar las etiquetas reales del 0-9 para luego comparar y hallar el error.
y_ = tf.placeholder("float", [None, 10])

#Recibimos las 784 entradas y las sumamos a trav�s de 10 neuronas
W1 = tf.Variable(np.float32(np.random.rand(784, 10)) * 0.1)
#El umbral es 10 porque queremos que todas las neuronas participen �? AND �?
b1 = tf.Variable(np.float32(np.random.rand(10)) * 0.1)
#La funcion que clasifica la aplicamos a las entradas x con los pesos W1 adicionando el b1
y = tf.nn.softmax(tf.matmul(x, W1) + b1)

#Nuestro error es la diferencia entre las etiquetas reales de los n y las predichas por la red, al cuadrado; haciendo la media.
loss = tf.reduce_sum(tf.square(y_ - y))

#Minimizamos el error con un factor de aprendizaje de 0.01
train = tf.train.GradientDescentOptimizer(0.01).minimize(loss)

init = tf.initialize_all_variables()

sess = tf.Session()
sess.run(init)

print ("----------------------")
print ("   Start training...  ")
print ("----------------------")

batch_size = 20

for epoch in range(100):
    #https://***.com/questions/19824721/i-keep-getting-this-error-for-my-simple-python-program-typeerror-float-obje
    for jj in range(len(train_x) // batch_size):
        batch_xs = train_x[jj * batch_size: jj * batch_size + batch_size]
        batch_ys = train_y[jj * batch_size: jj * batch_size + batch_size]
        tf.reshape(batch_ys, [2, 10])
        sess.run(train, feed_dict=x: batch_xs, y_: batch_ys)

    print ("Epoch #:", epoch, "Error: ", sess.run(loss, feed_dict=x: batch_xs, y_: batch_ys))
    result = sess.run(y, feed_dict=x: batch_xs)
    for b, r in zip(batch_ys, result):
        print (b, "-->", r)
    print ("----------------------------------------------------------------------------------")

###�Como usamos el conjunto de validacion????

我非常感谢任何帮助。我也读过这个话题：

TensorFlow ValueError: Cannot feed value of shape (64, 64, 3) for Tensor u'Placeholder:0', which has shape '(?, 64, 64, 3)'

和

Tensorflow error using my own data

但我需要帮助。

【参考方案1】：

您没有将one_hot 应用于train_y 的元素（如#y_data = one_hot(train_set[:, 785].astype(int), 10) 行所示，这只是一个注释，也是您代码中唯一使用one_hot 的地方）。

因此batch_ys是一个数字数组，如果你想将它输入feed_dict，你需要将它转换成一个one_hot的数组，因为y_是一个占位符，对应于one_hot的：

y_ = tf.placeholder("float", [None, 10])

另外，删除tf.reshape(batch_ys, [2, 10]) 行，因为您不需要重塑batch_ys。相反，您需要使用one_hot 对其进行转换，如上所述。

【讨论】：

谢谢 Miriam Farber，您帮助了我，因为我试图将 0-9 的数字强制输入一个占位符，称为 y_，它需要一个包含 10 个元素的热数组