def run_tests(): import problem_unittests as t t.test_folder_path(cifar10_dataset_folder_path) t.test_normalize(normalize) t.test_one_hot_encode(one_hot_encode) t.test_nn_image_inputs(neural_net_image_input) t.test_nn_label_inputs(neural_net_label_input) t.test_nn_keep_prob_inputs(neural_net_keep_prob_input) t.test_con_pool(conv2conv2d_maxpool) t.test_flatten(flatten) t.test_fully_conn(fully_conn) t.test_output(output) t.test_conv_net(conv_net) t.test_train_nn(train_neural_network)
def one_hot_encode(x): """ One hot encode a list of sample labels. Return a one-hot encoded vector for each label. : x: List of sample Labels : return: Numpy array of one-hot encoded labels """ # TODO: Implement Function one_hot = np.eye(10)[x] return one_hot """ DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE """ tests.test_one_hot_encode(one_hot_encode) # ### Randomize Data # As you saw from exploring the data above, the order of the samples are randomized. It doesn't hurt to randomize it again, but you don't need to for this dataset. # ## Preprocess all the data and save it # Running the code cell below will preprocess all the Fashion-MNIST data and save it to file. The code below also uses 10% of the training data for validation. # In[ ]: """ DON'T MODIFY ANYTHING IN THIS CELL """ # Preprocess Training, Validation, and Testing Data
def runOneHotEncodeTests(self): tests.test_one_hot_encode(one_hot_encode) print("Model OneHotEncodeTests Ran Successfully")
lb.fit([0,1,2,3,4,5,6,7,8,9]) def one_hot_encode(x): """ One hot encode a list of sample labels. Return a one-hot encoded vector for each label. : x: List of sample Labels : return: Numpy array of one-hot encoded labels """ # TODO: Implement Function return lb.transform(x) """ DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE """ tests.test_one_hot_encode(one_hot_encode) # ### Randomize Data # As you saw from exploring the data above, the order of the samples are randomized. It doesn't hurt to randomize it again, but you don't need to for this dataset. # ## Preprocess all the data and save it # Running the code cell below will preprocess all the CIFAR-10 data and save it to file. The code below also uses 10% of the training data for validation. # In[5]: """ DON'T MODIFY ANYTHING IN THIS CELL """ # Preprocess Training, Validation, and Testing Data
def test(): tests.test_normalize(normalize) tests.test_one_hot_encode(one_hot_encode)