class_docstr: Testing Validation Batch size specification
code: def test_vbs1(self):
      # Temp skip test
      return
  
      with tf.Graph().as_default():
          # Data loading and preprocessing
          import tflearn.datasets.mnist as mnist
  
          X, Y, testX, testY = mnist.load_data(one_hot=True)
          X = X.reshape([-1, 28, 28, 1])
          testX = testX.reshape([-1, 28, 28, 1])
          X = X[:20, :, :, :]
          Y = Y[:20, :]
          testX = testX[:10, :, :, :]
          testY = testY[:10, :]
  
          # Building convolutional network
          network = input_data(shape=[None, 28, 28, 1], name="input")
          network = conv_2d(
              network, 32, 3, activation="relu", regularizer="L2"
          )
          network = max_pool_2d(network, 2)
          network = local_response_normalization(network)
          network = conv_2d(
              network, 64, 3, activation="relu", regularizer="L2"
          )
          network = max_pool_2d(network, 2)
          network = local_response_normalization(network)
          network = fully_connected(network, 128, activation="tanh")
          network = dropout(network, 0.8)
          network = fully_connected(network, 256, activation="tanh")
          network = dropout(network, 0.8)
          network = fully_connected(network, 10, activation="softmax")
          network = regression(
              network,
              optimizer="adam",
              learning_rate=0.01,
              loss="categorical_crossentropy",
              name="target",
          )
  
          # Training
          model = tflearn.DNN(network, tensorboard_verbose=3)
          model.fit(
              {"input": X},
              {"target": Y},
              n_epoch=1,
              batch_size=10,
              validation_set=({"input": testX}, {"target": testY}),
              validation_batch_size=5,
              snapshot_step=10,
              show_metric=True,
              run_id="convnet_mnist_vbs",
          )
  
          self.assertEqual(model.train_ops[0].validation_batch_size, 5)
          self.assertEqual(model.train_ops[0].batch_size, 10)
  
file_path: tflearn/tests/test_validation_monitors.py
imports: from __future__ import division, print_function, absolute_import
  import tensorflow.compat.v1
  import tflearn
  import unittest
  import os
  from tflearn.layers.core import input_data, dropout, fully_connected
  from tflearn.layers.conv import conv_2d, max_pool_2d
  from tflearn.layers.normalization import local_response_normalization
  from tflearn.layers.estimator import regression
label_desc: Use image node_id 1 for calling the TestValidationBatch obj's underlying member method code with example usage: obj.test_vbs1() without return types