unit test lab 1

Author: aamini

mitdeeplearning/lab1.py

@@ -77,3 +77,10 @@ def test_batch_func_next_step(func, args):
     assert (x[:,1:] == y[:,:-1]).all(), "[FAIL] test_batch_func_next_step: x_{t} must equal y_{t-1} for all t"
     print("[PASS] test_batch_func_next_step")
     return True
+
+def test_custom_dense_layer_output(y):
+  true_y = np.array([[0.2697859,  0.45750418, 0.66536945]],dtype='float32')
+  assert tf.shape(y).numpy().tolist() == list(true_y.shape), "[FAIL] output is of incorrect shape. expected {} but got {}".format(true_y.shape, y.numpy().shape)
+  assert np.array_equal(y.numpy(), true_y), "[FAIL] output is of incorrect value. expected {} but got {}".format(y.numpy(), y.numpy())
+  print("[PASS] test_custom_dense_layer_output")
+  return True

setup.py

@@ -22,13 +22,13 @@ def get_dist(pkgname):
 setup(
   name = 'mitdeeplearning',         # How you named your package folder (MyLib)
   packages = ['mitdeeplearning'],   # Chose the same as "name"
-  version = '1.0.0',      # Start with a small number and increase it with every change you make
+  version = '0.1.0',      # Start with a small number and increase it with every change you make
   license='MIT',        # Chose a license from here: https://help.github.com/articles/licensing-a-repository
   description = 'Official software labs for MIT Introduction to Deep Learning (http://introtodeeplearning.com)',   # Give a short description about your library
   author = 'Alexander Amini',                   # Type in your name
   author_email = '[email protected]',      # Type in your E-Mail
   url = 'http://introtodeeplearning.com',   # Provide either the link to your github or to your website
-  download_url = 'https://github.com/aamini/introtodeeplearning/archive/v1.0.0.tar.gz',    # I explain this later on
+  download_url = 'https://github.com/aamini/introtodeeplearning/archive/v0.1.0.tar.gz',    # I explain this later on
   keywords = ['deep learning', 'neural networks', 'tensorflow', 'introduction'],   # Keywords that define your package best
   install_requires=install_deps,
   classifiers=[