CODEWITHSUNDEEP
tensorflow
Yone
Yone

Reputation: 2134

Tensorflow error while trying to fill a placeholder

I am practising with the mnist data and I am having trouble feeding the placeholder because of this error:

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

My code until now is:

    import gzip
#https://stackoverflow.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://stackoverflow.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://stackoverflow.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????

I would really appreciate any help. Also I have read this topic:

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

and

Tensorflow error using my own data

but I need help.

Upvotes: 0

Views: 380

Answers (1)

Miriam Farber
Miriam Farber

Reputation: 19634

You did not apply one_hot on the elements of train_y (as indicated be the row #y_data = one_hot(train_set[:, 785].astype(int), 10), which is just a comment, and the only place in your code where you used one_hot).

Hence batch_ys is an array of numbers, and you need to transform it into an array of one_hot's if you want to feed it into feed_dict, because y_ is a placeholder that corresponds to the one_hot's:

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

Also, remove the row tf.reshape(batch_ys, [2, 10]), as you don't need to reshape batch_ys. Instead, you need to transform it using one_hot as discussed above.

Upvotes: 2

Related Questions