class IrisTest(tf.test.TestCase):
def setUp(self):
self.iris = Iris()
def test_download_function(self):
filepath = self.iris.download_data()[-17:]
self.assertAllEqual(filepath, 'iris_training.csv')
def test_download_test_data_function(self):
filepath = self.iris.download_test_data()[-13:]
self.assertAllEqual(filepath, 'iris_test.csv')
def test_format_data_features_and_label_are_tensors(self):
features, label, dataset = self.iris.format_data(
self.iris.download_data())
self.assertTrue(isinstance(features[0], tf.Tensor))
self.assertTrue(isinstance(label[0], tf.Tensor))
def test_test_function_returns_accuracy(self):
test_accuracy_result = self.iris.test()
self.assertAllEqual(test_accuracy_result[-1:], '%')
def test_train_function_adds_to_loss_and_accuracy_array(self):
train_loss_results, train_accuracy_results = self.iris.train()
self.assertAllEqual(len(train_loss_results), 201)
self.assertAllEqual(len(train_accuracy_results), 201)
def test_predict_function(self):
self.iris.train()
possible_answers = [
'Example 1 prediction: Iris setosa',
'Example 1 prediction: Iris versicolor',
'Example 1 prediction: Iris virginica'
]
predictions = self.iris.predict([[5.1, 3.3, 1.7, 0.5]])
self.assertTrue(predictions in possible_answers)
def test_graph_creates_file(self):
self.iris.graph([[3, 2, 4, 5], [2, 7, 1, 0]], 'test_graphs')
my_file = Path('./test_graphs/figure.png')
self.assertTrue(my_file.is_file())
from __future__ import absolute_import, division, print_function
import os
import matplotlib.pyplot as plt
import tensorflow as tf
import tensorflow.contrib.eager as tfe
tf.enable_eager_execution()
from iris import Iris
global_step = tf.train.get_or_create_global_step()
summary_writer = tf.contrib.summary.create_file_writer(
'iris_model', flush_millis=10000)
with summary_writer.as_default(), tf.contrib.summary.always_record_summaries():
iris = Iris()
trained_iris = iris.train()
iris.graph(trained_iris, 'test_graphs')
model = iris.model
optimizer = tf.train.AdamOptimizer(learning_rate=0.001)
checkpoint_dir = './iris_model'
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt")
root = tfe.Checkpoint(optimizer=optimizer,
model=model,
optimizer_step=tf.train.get_or_create_global_step())
root.save(file_prefix=checkpoint_prefix)
tf.contrib.summary.scalar("loss", my_loss)