tensorflow_predictint_function.py

import sys
import tensorflow as tf

def predictint(imvalue):
  """
  This function returns the predicted integer.
  The imput is the pixel values from the imageprepare() function.
  """
  
  # Define the model (same as when creating the model file)
  x = tf.placeholder(tf.float32, [None, 784])
  W = tf.Variable(tf.zeros([784, 10]))
  b = tf.Variable(tf.zeros([10]))
  
  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')   
  
  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_pool1, 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(tf.float32)
  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)
  
  init_op = tf.initialize_all_variables()
  saver = tf.train.Saver()
  
  """
  Load the model2.ckpt file
  file is stored in the same directory as this python script is started
  Use the model to predict the integer. Integer is returend as list.

  Based on the documentatoin at
  https://www.tensorflow.org/versions/master/how_tos/variables/index.html
  """
  with tf.Session() as sess:
    sess.run(init_op)
    saver.restore(sess, "model2.ckpt")
    #print ("Model restored.")
   
    prediction=tf.argmax(y_conv,1)
    return prediction.eval(feed_dict={x: [imvalue],keep_prob: 1.0}, session=sess)
    
def main(argv):
  """
  Main function.
  """
  imvalue = imageprepare(argv)
  predint = predictint(imvalue)
  print (predint[0]) #first value in list
    
if __name__ == "__main__":
  main(sys.argv[1])