CODEWITHSUNDEEP
pythontensorflow
Yaoi Dirty
Yaoi Dirty

Reputation: 109

TensorFlow: Cannot feed value of shape when using CSV file as input

I'm a newbie for TensorFlow. I have been trying to feed MNIST in CSV form. Each row contains (label,pixel i-j,...) which i = row, j = column. Then, I am trying to train model based on the tutorials in TensorFlow website.

However, I got the error

ValueError: Cannot feed value of shape (28, 28) for Tensor u'Placeholder_12:0', which has shape '(?, 784)'

Could you please suggest me what is wrong with my code? 

import tensorflow as tf
import numpy as np
x_file = open("mnist_train_100.csv",'r')
x_list = x_file.readlines()
x_file.close()
output_nodes = 10

for r in x_list:
    pixs = r.split(',')
    inp = (np.asfarray(pixs[1:])).reshape(28,28) 
    targets = np.zeros(output_nodes) 
    targets[int(pixs[0])] = 1 



test_file = open("mnist_test_10.csv",'r')
test_list = test_file.readlines()
test_file.close()


for i in test_list:
    pixels = i.split(',')
    test_input = (np.asfarray(pixels[1:])).reshape(28,28)   
    test_targets = np.zeros(output_nodes) 
    test_targets[int(pixels[0])] = 1 



x = tf.placeholder(tf.float32, [None, 784])

W = tf.Variable(tf.zeros([784, 10])) #weight
b = tf.Variable(tf.zeros([10])) 

y = tf.nn.softmax(tf.matmul(x, W) + b)

y_ = tf.placeholder(tf.float32, [None, 10])

cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))

train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

init = tf.initialize_all_variables()

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

for i in range(1000):
    sess.run(train_step, feed_dict={x: inp, y_: targets})

correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(sess.run(accuracy, feed_dict={x: test_input, y_: test_targets}))

Thank you in advance.

Upvotes: 0

Views: 798

Answers (1)

Wojtek
Wojtek

Reputation: 101

test_input = (np.asfarray(pixels[1:])).reshape(28,28)

Drop the reshape. Your x is of shape:

x = tf.placeholder(tf.float32, [None, 784])

Which does not compute:

sess.run(accuracy, feed_dict={x: test_input, y_: test_targets})

Upvotes: 0

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