TensorFlow cannot feed value error - python-3.x

Question

I am implementing a logistic regression function. It is quite simple and work properly up until I get to the part where I want to calculate its accuracy. Here is my logistic regression...
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
# tf Graph Input
x = tf.get_variable("input_image", shape=[100,784], dtype=tf.float32)
x_placeholder = tf.placeholder(tf.float32, shape=[100, 784])
assign_x_op = x.assign(x_placeholder).op
y = tf.placeholder(shape=[100,10], name='input_label', dtype=tf.float32) # 0-9 digits recognition => 10 classes
# set model weights
W = tf.get_variable("weights", shape=[784, 10], dtype=tf.float32, initializer=tf.random_normal_initializer())
b = tf.get_variable("biases", shape=[1, 10], dtype=tf.float32, initializer=tf.zeros_initializer())
# construct model
logits = tf.matmul(x, W) + b
pred = tf.nn.softmax(logits) # Softmax
# minimize error using cross entropy
cost = tf.reduce_mean(-tf.reduce_sum(y * tf.log(pred), reduction_indices=1))
# Gradient Descent
optimizer = tf.train.GradientDescentOptimizer(FLAGS.learning_rate).minimize(cost)
# initializing the variables
init = tf.global_variables_initializer()
saver = tf.train.Saver()
# launch the graph
with tf.Session() as sess:
sess.run(init)
# training cycle
for epoch in range(FLAGS.training_epochs):
avg_cost = 0
total_batch = int(mnist.train.num_examples/FLAGS.batch_size)
# loop over all batches
for i in range(total_batch):
batch_xs, batch_ys = mnist.train.next_batch(FLAGS.batch_size)
# Assign the contents of `batch_xs` to variable `x`.
sess.run(assign_x_op, feed_dict={x_placeholder: batch_xs})
_, c = sess.run([optimizer, cost], feed_dict={y: batch_ys})
# compute average loss
avg_cost += c / total_batch
# display logs per epoch step
if (epoch + 1) % FLAGS.display_step == 0:
print("Epoch:", '%04d' % (epoch + 1), "cost=", "{:.9f}".format(avg_cost))
save_path = saver.save(sess, "/tmp/model.ckpt")
print("Model saved in file: %s" % save_path)
print("Optimization Finished!")
As you can see it is a basic logistic regression and function and it works perfectly.
It is important to not that batch_size is 100.
Now, after the code snipped above, I try the following...
# list of booleans to determine the correct predictions
correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
print(correct_prediction.eval({x_placeholder:mnist.test.images, y:mnist.test.labels}))
# calculate total accuracy
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print("Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))
However the code fails on correct_prediction. I get the following error...
% (np_val.shape, subfeed_t.name, str(subfeed_t.get_shape())))
ValueError: Cannot feed value of shape (10000, 784) for Tensor 'Placeholder:0', which has shape '(100, 784)'
I believe I get this error because of the value I am trying to assign the placeholder for x. How can I fix this? Do I need to reshape the array?

Answer 1

In
x_placeholder = tf.placeholder(tf.float32, shape=[100, 784])
y = tf.placeholder(shape=[100,10], name='input_label', dtype=tf.float32) # 0-9
avoid fixing the first dimension as 100, since it prohibits you from using any other batch size (so if the number of images in mnist.test.images is different from 100, you'll get an error). Instead specify them as None:
x_placeholder = tf.placeholder(tf.float32, shape=[None, 784])
y = tf.placeholder(shape=[None,10], name='input_label', dtype=tf.float32) #
Then you can use any batch size