Tensorflow Incompatable Shapes Error in Tutorial -

i've been trying create convolutional network tensorflow tutorial, i've been having trouble. reason, i'm getting errors size of y_conv 4x larger size of y_, , have no idea why. found this question, appears different problem mine, though looks similar.

to clear, batch size in below code 50, error it's coming is

tensorflow.python.framework.errors.invalidargumenterror: incompatible shapes: [200] vs. [50]

and when change batch size 10, get

tensorflow.python.framework.errors.invalidargumenterror: incompatible shapes: [40] vs. [10]

so it's related batch size somehow, can't figure out. can tell me what's wrong code? it's pretty straight tutorial linked above.

from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets('mnist_data', one_hot=true)  import tensorflow tf sess = tf.interactivesession()  def weight_variable(shape):   initial = tf.truncated_normal(shape, stddev=0.1)   return tf.variable(initial)  def bias_variable(shape):   initial = tf.constant(0.1, shape=shape)   return tf.variable(initial)  def conv2d(x, w):   return tf.nn.conv2d(x, w, strides=[1, 1, 1, 1], padding='same')  def max_pool_2x2(x):   return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides = [1, 2, 2, 1], padding='same')  x = tf.placeholder("float", shape=[none, 784]) y_ = tf.placeholder("float", shape=[none, 10])  w_conv1 = weight_variable([5, 5, 1, 32]) b_conv1 = bias_variable([32]) x_image = tf.reshape(x, [-1, 28, 28, 1]) h_conv1 = tf.nn.relu(conv2d(x_image, w_conv1) + b_conv1) h_pool1 = max_pool_2x2(h_conv1)  w_conv2 = weight_variable([5, 5, 32, 64]) b_conv2 = bias_variable([64]) h_conv2 = tf.nn.relu(conv2d(h_conv1, w_conv2) + b_conv2) h_pool2 = max_pool_2x2(h_conv2)  w_fc1 = weight_variable([7*7*64, 1024]) b_fc1 = bias_variable([1024]) h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64]) h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, w_fc1) + b_fc1)  keep_prob = tf.placeholder("float") h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)  w_fc2 = weight_variable([1024, 10]) b_fc2 = bias_variable([10])  y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, w_fc2) + b_fc2)  cross_entropy = -tf.reduce_sum(y_ * tf.log(tf.clip_by_value(y_conv, 1e-10, 1.0))) train_step = tf.train.adamoptimizer(1e-4).minimize(cross_entropy) correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) sess.run(tf.initialize_all_variables()) in range(20000):   batch = mnist.train.next_batch(50)   if i%100 == 0:     train_accuracy = accuracy.eval(feed_dict={x:batch[0], y_: batch[1], keep_prob: 1.0})     print("step %d, training accuracy %g"%(i, train_accuracy))   train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})  print("test accuracy %g"%accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0})) 

the reshapes -1's clues. it's not batch size that's wrong it's image size. you're flattening out batch dimension.

why image wrong size? problem pooling?:

yes. on second conv you're passing conv1 instead of pool1 conv2d(h_conv1, w_conv2).

personally pipelines like use 1 name data flows through.

start using debugger, it's worth it!