diff --git a/mitdeeplearning/lab1.py b/mitdeeplearning/lab1.py index 94f6bae0..6e66b926 100644 --- a/mitdeeplearning/lab1.py +++ b/mitdeeplearning/lab1.py @@ -81,7 +81,7 @@ def test_batch_func_next_step(func, args): def test_custom_dense_layer_output(y): # define the ground truth value for the array - true_y = np.array([[0.27064407 0.1826951 0.50374055]],dtype='float32') + true_y = np.array([[0.27064407, 0.1826951, 0.50374055]],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) np.testing.assert_almost_equal(y.numpy(), true_y, decimal=7, err_msg="[FAIL] output is of incorrect value. expected {} but got {}".format(true_y, y.numpy()), verbose=True) print("[PASS] test_custom_dense_layer_output") diff --git a/setup.py b/setup.py index f3d7941f..44d10014 100644 --- a/setup.py +++ b/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 = '0.6.0', # Start with a small number and increase it with every change you make + version = '0.6.1', # 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 = 'introtodeeplearning-staff@mit.edu', # 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/v0.6.0.tar.gz', # I explain this later on + download_url = 'https://github.com/aamini/introtodeeplearning/archive/v0.6.1.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=[