class_docstr: Testing DNN model from tflearn/models/dnn.py
code: def test_dnn(self):
      with tf.Graph().as_default():
          X = [
              3.3,
              4.4,
              5.5,
              6.71,
              6.93,
              4.168,
              9.779,
              6.182,
              7.59,
              2.167,
              7.042,
              10.791,
              5.313,
              7.997,
              5.654,
              9.27,
              3.1,
          ]
          Y = [
              1.7,
              2.76,
              2.09,
              3.19,
              1.694,
              1.573,
              3.366,
              2.596,
              2.53,
              1.221,
              2.827,
              3.465,
              1.65,
              2.904,
              2.42,
              2.94,
              1.3,
          ]
          input = tflearn.input_data(shape=[None])
          linear = tflearn.single_unit(input)
          regression = tflearn.regression(
              linear,
              optimizer="sgd",
              loss="mean_square",
              metric="R2",
              learning_rate=0.01,
          )
          m = tflearn.DNN(regression)
          # Testing fit and predict
          m.fit(
              X, Y, n_epoch=1000, show_metric=True, snapshot_epoch=False
          )
          res = m.predict([3.2])[0]
          self.assertGreater(
              res,
              1.3,
              "DNN test (linear regression) failed! with score: "
              + str(res)
              + " expected > 1.3",
          )
          self.assertLess(
              res,
              1.8,
              "DNN test (linear regression) failed! with score: "
              + str(res)
              + " expected < 1.8",
          )
  
          # Testing save method
          m.save("test_dnn.tflearn")
          self.assertTrue(os.path.exists("test_dnn.tflearn.index"))
  
      with tf.Graph().as_default():
          input = tflearn.input_data(shape=[None])
          linear = tflearn.single_unit(input)
          regression = tflearn.regression(
              linear,
              optimizer="sgd",
              loss="mean_square",
              metric="R2",
              learning_rate=0.01,
          )
          m = tflearn.DNN(regression)
  
          # Testing load method
          m.load("test_dnn.tflearn")
          res = m.predict([3.2])[0]
          self.assertGreater(
              res,
              1.3,
              "DNN test (linear regression) failed after loading model! score: "
              + str(res)
              + " expected > 1.3",
          )
          self.assertLess(
              res,
              1.8,
              "DNN test (linear regression) failed after loading model! score: "
              + str(res)
              + " expected < 1.8",
          )
  
file_path: tflearn/tests/test_models.py
imports: import tensorflow.compat.v1
  import numpy
  import tflearn
  import unittest
  import os
label_desc: Use image node_id 1 for calling the TestModels obj's underlying member method code with example usage: obj.test_dnn() without return types