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 normalize(x):
"""
Normalize a list of sample image data in the range of 0 to 1
: x: List of image data. The image shape is (28, 28, 1)
: return: Numpy array of normalize data
"""
# TODO: Implement Function
output = np.array([image/255 for image in x])
return output
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_normalize(normalize)
# ### One-hot encode
# Just like the previous code cell, you'll be implementing a function for preprocessing. This time, you'll implement the `one_hot_encode` function. The input, `x`, are a list of labels. Implement the function to return the list of labels as One-Hot encoded Numpy array. The possible values for labels are 0 to 9. The one-hot encoding function should return the same encoding for each value between each call to `one_hot_encode`. Make sure to save the map of encodings outside the function.
#
# Hint: Don't reinvent the wheel. You have multiple ways to attempt this: Numpy, TF, or even sklearn's preprocessing package.
# In[ ]:
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
def runNormalisationTests(self):
tests.test_normalize(normalize)
print("Model NormalisationTests Ran Successfully")
"""
Normalize a list of sample image data in the range of 0 to 1
: x: List of image data. The image shape is (32, 32, 3)
: return: Numpy array of normalize data
"""
# TODO: Implement Function
scale_max = 255
scale_min = 0
return (x-scale_min)/(scale_max-scale_min)
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_normalize(normalize)
# ### One-hot encode
# Just like the previous code cell, you'll be implementing a function for preprocessing. This time, you'll implement the `one_hot_encode` function. The input, `x`, are a list of labels. Implement the function to return the list of labels as One-Hot encoded Numpy array. The possible values for labels are 0 to 9. The one-hot encoding function should return the same encoding for each value between each call to `one_hot_encode`. Make sure to save the map of encodings outside the function.
#
# Hint: Don't reinvent the wheel.
# In[4]:
from sklearn import preprocessing
lb = preprocessing.LabelBinarizer()
lb.fit([0,1,2,3,4,5,6,7,8,9])
def one_hot_encode(x):
def test():
tests.test_normalize(normalize)
tests.test_one_hot_encode(one_hot_encode)
