title: Tensorflow - Saving and restoring a model
body: <p>I have came across <a href="https://stackoverflow.com/questions/33759623/tensorflow-how-to-save-restore-a-model-python">this question</a> in Stackoverflow that shows how one can save and restore a model.</p>
  
  <p>My question is how can I do that within my code below, as I'm not sure how to integrate it with my code:</p>
  
  <pre><code>import numpy as np
  import matplotlib.pyplot as plt
  import cifar_tools
  import tensorflow as tf
  
  data, labels = cifar_tools.read_data('C:\\Users\\abc\\Desktop\\Testing')
  
  x = tf.placeholder(tf.float32, [None, 150 * 150])
  y = tf.placeholder(tf.float32, [None, 2])
  
  w1 = tf.Variable(tf.random_normal([5, 5, 1, 64]))
  b1 = tf.Variable(tf.random_normal([64]))
  
  w2 = tf.Variable(tf.random_normal([5, 5, 64, 64]))
  b2 = tf.Variable(tf.random_normal([64]))
  
  w3 = tf.Variable(tf.random_normal([38*38*64, 1024]))
  b3 = tf.Variable(tf.random_normal([1024]))
  
  w_out = tf.Variable(tf.random_normal([1024, 2]))
  b_out = tf.Variable(tf.random_normal([2]))
  
  def conv_layer(x,w,b):
      conv = tf.nn.conv2d(x,w,strides=[1,1,1,1], padding = 'SAME')
      conv_with_b = tf.nn.bias_add(conv,b)
      conv_out = tf.nn.relu(conv_with_b)
      return conv_out
  
  def maxpool_layer(conv,k=2):
      return tf.nn.max_pool(conv, ksize=[1,k,k,1], strides=[1,k,k,1], padding='SAME')
  
  def model():
      x_reshaped = tf.reshape(x, shape=[-1, 150, 150, 1])
  
      conv_out1 = conv_layer(x_reshaped, w1, b1)
      maxpool_out1 = maxpool_layer(conv_out1)
      norm1 = tf.nn.lrn(maxpool_out1, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75)
      conv_out2 = conv_layer(norm1, w2, b2)
      norm2 = tf.nn.lrn(conv_out2, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75)
      maxpool_out2 = maxpool_layer(norm2)
  
      maxpool_reshaped = tf.reshape(maxpool_out2, [-1, w3.get_shape().as_list()[0]])
      local = tf.add(tf.matmul(maxpool_reshaped, w3), b3)
      local_out = tf.nn.relu(local)
  
      out = tf.add(tf.matmul(local_out, w_out), b_out)
      return out
  
  model_op = model()
  
  cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(model_op, y))
  train_op = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)
  
  correct_pred = tf.equal(tf.argmax(model_op, 1), tf.argmax(y,1))
  accuracy = tf.reduce_mean(tf.cast(correct_pred,tf.float32))
  
  with tf.Session() as sess:
      sess.run(tf.global_variables_initializer())
      onehot_labels = tf.one_hot(labels, 2, on_value=1.,off_value=0.,axis=-1)
      onehot_vals = sess.run(onehot_labels)
      batch_size = 1
      for j in range(0, 5):
          print('EPOCH', j)
          for i in range(0, len(data), batch_size):
              batch_data = data[i:i+batch_size, :]
              batch_onehot_vals = onehot_vals[i:i+batch_size, :]
              _, accuracy_val = sess.run([train_op, accuracy], feed_dict={x: batch_data, y: batch_onehot_vals})
              print(i, accuracy_val)
  
          print('DONE WITH EPOCH')
  </code></pre>
  
  <p>Thanks.</p>