Tensorflow Dimensions are not compatible in CNN
<p>This is main.py:</p>

<pre><code># pylint: disable=missing-docstring
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import time

from six.moves import xrange  # pylint: disable=redefined-builtin
import tensorflow as tf
from pylab import *

import cnn

# Basic model parameters as external flags.
flags = tf.app.flags
FLAGS = flags.FLAGS
flags.DEFINE_float('learning_rate', 0.01, 'Initial learning rate.')
flags.DEFINE_integer('max_steps', 2000, 'Number of steps to run trainer.')
flags.DEFINE_integer('batch_size', 1000, 'Batch size. Must divide evenly into the dataset sizes.')


def placeholder_inputs(batch_size):

  images_placeholder = tf.placeholder(tf.float32, shape=(batch_size, cnn.IMAGE_WIDTH, cnn.IMAGE_HEIGHT, 1))
  labels_placeholder = tf.placeholder(tf.int32, shape=(batch_size))
  return images_placeholder, labels_placeholder


def fill_feed_dict(data_set, images_pl, labels_pl):

  data_set = loadtxt("../dataset/images")
  images = data_set[:,:115*25]
  labels_feed = data_set[:,115*25:]
  images_feed = tf.reshape(images, [batch_size, cnn.IMAGE_WIDTH, cnn.IMAGE_HEIGHT, 1])

  feed_dict = {
      images_pl: images_feed,
      labels_pl: labels_feed,
  }
  return feed_dict

def run_training():

  with tf.Graph().as_default():

    images_placeholder, labels_placeholder = placeholder_inputs(FLAGS.batch_size)
    logits = cnn.inference(images_placeholder)
    loss = cnn.loss(logits, labels_placeholder)
    train_op = cnn.training(loss, FLAGS.learning_rate)
    eval_correct = cnn.evaluation(logits, labels_placeholder)
    summary_op = tf.merge_all_summaries()
    init = tf.initialize_all_variables()

    saver = tf.train.Saver()
    sess = tf.Session()
    summary_writer = tf.train.SummaryWriter(FLAGS.train_dir, sess.graph)

    sess.run(init)
    feed_dict = fill_feed_dict(data_sets.train, images_placeholder, labels_placeholder)
    # Start the training loop.
    for step in xrange(FLAGS.max_steps):
      start_time = time.time()

      _, loss_value = sess.run([train_op, loss], feed_dict=feed_dict)

      duration = time.time() - start_time

      # Write the summaries and print an overview fairly often.
      if step % 100 == 0:
        # Print status to stdout.
        print('Step %d: loss = %.2f (%.3f sec)' % (step, loss_value, duration))
        # Update the events file.
        summary_str = sess.run(summary_op, feed_dict=feed_dict)
        summary_writer.add_summary(summary_str, step)
        summary_writer.flush()

  predictions = sess.run(logits, feed_dict=feed_dict)
  savetxt("predictions", predictions)

def main(_):
  run_training()

if __name__ == '__main__':
  tf.app.run()
</code></pre>

<p>then, cnn.py:</p>

<pre><code>from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import math

import tensorflow as tf

NUM_OUTPUT = 4

IMAGE_WIDTH = 115
IMAGE_HEIGHT = 25
IMAGE_PIXELS = IMAGE_WIDTH * IMAGE_HEIGHT


def inference(images):
    # Conv 1
    with tf.name_scope('conv1'):        
        kernel = tf.Variable(tf.truncated_normal(stddev = 1.0 / math.sqrt(float(IMAGE_PIXELS)), name='weights', shape=[5, 5, 1, 10]))
        conv = tf.nn.conv2d(images, kernel, [1, 1, 1, 1], padding='VALID')
        biases = tf.Variable(tf.constant(0.0, name='biases', shape=[10]))
        bias = tf.nn.bias_add(conv, biases)
        conv1 = tf.nn.relu(bias, name='conv1')

    # Pool1
    pool1 = tf.nn.max_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 3, 3, 1], padding='VALID', name='pool1')    

    # Conv 2
    with tf.name_scope('conv2'):    
        kernel = tf.Variable(tf.truncated_normal(stddev = 1.0 / math.sqrt(float(IMAGE_PIXELS)), name='weights', shape=[5, 5, 10, 20]))
        conv = tf.nn.conv2d(images, kernel, [1, 1, 1, 1], padding='VALID')
        biases = tf.Variable(tf.constant(0.1, name='biases', shape=[20]))
        bias = tf.nn.bias_add(conv, biases)
        conv2 = tf.nn.relu(bias, name='conv2')

    # Pool2
    pool2 = tf.nn.max_pool(conv2, ksize=[1, 3, 3, 1], strides=[1, 3, 3, 1], padding='VALID', name='pool2')

    # Identity  
    with tf.name_scope('identity'):
        weights = tf.Variable(tf.truncated_normal([11, NUM_OUTPUT], stddev=1.0 / math.sqrt(float(11))), name='weights')
        biases = tf.Variable(tf.zeros([NUM_OUTPUT], name='biases'))
        logits = tf.matmul(pool2, weights) + biases

    return output


def loss(outputs, labels):

  rmse = tf.sqrt(tf.reduce_mean(tf.square(tf.sub(targets, outputs))), name="rmse")
  return rmse


def training(loss, learning_rate):

  tf.scalar_summary(loss.op.name, loss)
  optimizer = tf.train.GradientDescentOptimizer(learning_rate)
  global_step = tf.Variable(0, name='global_step', trainable=False)
  train_op = optimizer.minimize(loss, global_step=global_step)
  return train_op
</code></pre>

<p>and I get this error:</p>

<pre><code>Traceback (most recent call last):
  File "main.py", line 84, in &lt;module&gt;
    tf.app.run()
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/platform/app.py", line 30, in run
    sys.exit(main(sys.argv))
  File "main.py", line 81, in main
    run_training()
  File "main.py", line 47, in run_training
    logits = cnn.inference(images_placeholder)
  File "/home/andrea/test/python/cnn.py", line 31, in inference
    conv = tf.nn.conv2d(images, kernel, [1, 1, 1, 1], padding='VALID')
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_nn_ops.py", line 394, in conv2d
    data_format=data_format, name=name)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/op_def_library.py", line 704, in apply_op
    op_def=op_def)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2262, in create_op
    set_shapes_for_outputs(ret)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1702, in set_shapes_for_outputs
    shapes = shape_func(op)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/common_shapes.py", line 230, in conv2d_shape
    input_shape[3].assert_is_compatible_with(filter_shape[2])
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/tensor_shape.py", line 108, in assert_is_compatible_with
    % (self, other))
ValueError: Dimensions 1 and 10 are not compatible
</code></pre>

<p>Don't understand why. Dimensions seem all fine to me. Input image are 1000 samples of 115(width)x25(height)x1(color). I am using 'VALID' as padding and I double checked the calculation by hand. Not sure where the mismatch come from. Anyone can help?
TensorFlow rc0.9 on Ubuntu 14.04 (Note: in the code there might be other errors which I am not aware of yet, forget them)</p>