title: TensorFlow - How to predict with trained model on a different test dataset?
body: <p>I am new using TensorFlow and I don't know how to classify a picture with a trained model. I have already built a train, validation and test dataset for my training and all works but I want to predict on second test dataset (called test2). I am classifying pictures of digits.</p>
  
  <p>I have tried this but it doesn't work :</p>
  
  <pre><code>def train_and_predict(restore=False, test_set=None):
      """
      Training of the model, posibility to restore a trained model and predict on another dataset. 
      """
      batch_size = 50
      # Regular datasets for training
      train_dataset, train_labels, test_dataset, test_labels, valid_dataset, valid_labels = load_dataset(dataset_size)
      if restore:
         # change the testset if restoring the trained model
         test_dataset, test_labels = create_dataset(test_set)
         test_dataset, test_labels = reformat(test_dataset, test_labels)
         batch_size = number_predictions
  
      graph = tf.Graph()
      with graph.as_default():
  
         # Input data.
         tf_train_dataset = tf.placeholder(tf.float32, shape=(batch_size, image_size, image_size, num_channels))
         tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels))
         tf_valid_dataset = tf.constant(valid_dataset)
         tf_test_dataset = tf.constant(test_dataset)
  
         # Variables.
         K = 32  # first convolutional layer output depth
         L = 64  # second convolutional layer output depth
         N = 1024  # fully connected layer
  
         W1 = tf.Variable(tf.truncated_normal([5, 5, 1, K], stddev=0.1))  # 5x5 patch, 1 input channel
         B1 = tf.Variable(tf.constant(0.1, tf.float32, [K]))
         W2 = tf.Variable(tf.truncated_normal([5, 5, K, L], stddev=0.1))
         B2 = tf.Variable(tf.constant(0.1, tf.float32, [L]))
  
         W3 = tf.Variable(tf.truncated_normal([7 * 7 * L, N], stddev=0.1))
         B3 = tf.Variable(tf.constant(0.1, tf.float32, [N]))
         W4 = tf.Variable(tf.truncated_normal([N, 10], stddev=0.1))
         B4 = tf.Variable(tf.constant(0.1, tf.float32, [10]))
  
         # Model.
         def model(data, train = True):
             stride = 1 
             Y1 = tf.nn.relu(tf.nn.conv2d(data, W1, strides=[1, stride, stride, 1], padding='SAME') + B1)
             Y1 = tf.nn.max_pool(Y1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
             Y2 = tf.nn.relu(tf.nn.conv2d(Y1, W2, strides=[1, stride, stride, 1], padding='SAME') + B2)
             Y2 = tf.nn.max_pool(Y2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
             Y3 = tf.reshape(Y2, [-1, 7*7*64])
             Y4 = tf.nn.relu(tf.matmul(Y3, W3) + B3)
             if train:
                 # drop-out during training
                 Y4 = tf.nn.dropout(Y4, 0.5)
             return tf.matmul(Y4, W4) + B4
  
         # Training computation.
         logits = model(tf_train_dataset)
         loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits, tf_train_labels))
  
         # Optimizer.
         optimizer = tf.train.AdamOptimizer(1e-4).minimize(loss)
  
         # Predictions for the training, validation, and test data.
         train_prediction = tf.nn.softmax(logits)
         valid_prediction = tf.nn.softmax(model(tf_valid_dataset, False))
         test_prediction = tf.nn.softmax(model(tf_test_dataset, False))
  
         # Saver
         saver = tf.train.Saver()
  
       num_steps = 1001
       with tf.Session(graph=graph) as session:
          if restore:
              ckpt = tf.train.get_checkpoint_state('./model/')
              saver.restore(session, ckpt.model_checkpoint_path)
              _, l, predictions = session.run([optimizer, loss, test_prediction])
          else:
              tf.global_variables_initializer().run()
              for step in range(num_steps):
                  offset = (step * batch_size) % (train_labels.shape[0] - batch_size)
                  batch_data = train_dataset[offset:(offset + batch_size), :, :, :]
                  batch_labels = train_labels[offset:(offset + batch_size), :]
                  feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels}
                  _, l, predictions = session.run([optimizer, loss, train_prediction], feed_dict=feed_dict)
                  if (step % 100 ==0):
                      saver.save(session, './model/' + 'model.ckpt', global_step=step+1)
                  if (step % 1000 == 0):
                      print('\nMinibatch loss at step %d: %f' % (step, l))
          test_accuracy = accuracy(test_prediction.eval(), test_labels)
      return test_accuracy , predictions
  </code></pre>
  
  <p>So the first time, I trained the model and test, then I want to predict on the other test set:</p>
  
  <pre><code>t,p = train_and_predict() #training
  t_test2, p_test2 = train_and_predict(restore=True, test_set='./test2')
  </code></pre>
  
  <p>Functions <code>load_dataset</code>, <code>create_dataset</code>and <code>reformat</code> give me datasets with shape : (nb_pictures, 28, 28, 1) and labels with shape : (nb_pictures, 10).</p>
  
  <p>Thank you very much for any help</p>