def test_9(self):
# function 1 (XOR function) on 1 hidden layers
arrs = []
arrs.append([0, 0])
arrs.append([0, 1])
arrs.append([1, 0])
arrs.append([1, 1])
labels = []
labels.append('false')
labels.append('true')
labels.append('true')
labels.append('false')
ann = Ann(arrs, labels, n_h=1)
# Train and save model
model = ann.train()[0][0] # Take the first model from the list of models in the tuple
ann.validate_train()
# Check to see if train_accuracy is over 90%
self.assertTrue(ann.train_accuracy() > 0.9)
# Load the trained model into a new neural network
ann_from_model = Ann(model)
# Evaluate some vectors using this neural network initialized only with a model
self.assertEqual(ann_from_model.h_by_class(arrs[0]), 'false')
self.assertEqual(ann_from_model.h_by_class(arrs[1]), 'true')
x = [1.1, 0.9]
self.assertEqual(ann_from_model.h_by_class(x), 'false')
# function 2 on 2 hidden layers
arrs2 = []
arrs2.append([1, 1])
arrs2.append([2, 2])
arrs2.append([1, 3])
arrs2.append([2, 10])
arrs2.append([1, -1])
arrs2.append([-2, -2])
arrs2.append([1, -3])
arrs2.append([-2, -10])
labels2 = []
labels2.append('false')
labels2.append('false')
labels2.append('false')
labels2.append('false')
labels2.append('true')
labels2.append('true')
labels2.append('true')
labels2.append('true')
ann = Ann(arrs2, labels2, n_h=2)
model2 = ann.train()[0][0]
ann.validate_train()
# Load the second model
ann_from_model = Ann(model2)
# Evaluate some vectors using this neural network initialized only with a model
self.assertEqual(ann_from_model.h_by_class(arrs2[0]), 'false')
self.assertEqual(ann_from_model.h_by_class(arrs2[len(arrs2) - 1]), 'true')
x = [1, -5]
self.assertEqual(ann_from_model.h_by_class(x), 'true')
# Load the first model again
ann_from_model = Ann(model)
# Evaluate some vectors using this neural network initialized only with a model
self.assertEqual(ann_from_model.h_by_class(arrs[0]), 'false')
self.assertEqual(ann_from_model.h_by_class(arrs[1]), 'true')
x = [1.1, 0.9]
self.assertEqual(ann_from_model.h_by_class(x), 'false')
# Try pickling our model into a sister folder
model_name = model.name
directory = '../Ann-models'
path_to_file = directory + '/' + model_name
if not os.path.exists(directory):
os.makedirs(directory)
pickle.dump(model, open(path_to_file, 'wb'))
# Try unpickling our model
unpickled_model = pickle.load(open(path_to_file, 'rb'))
# Load unpickled model and test
ann_from_pickle = Ann(unpickled_model)
# Evaluate some vectors using this neural network initialized only with a model
self.assertEqual(ann_from_pickle.h_by_class(arrs[0]), 'false')
self.assertEqual(ann_from_pickle.h_by_class(arrs[1]), 'true')
x = [1.1, 0.9]
self.assertEqual(ann_from_pickle.h_by_class(x), 'false')
def test_9(self):
# function 1 (XOR function) on 1 hidden layers
arrs = []
arrs.append([0, 0])
arrs.append([0, 1])
arrs.append([1, 0])
arrs.append([1, 1])
labels = []
labels.append('false')
labels.append('true')
labels.append('true')
labels.append('false')
ann = Ann(arrs, labels, n_h=1)
# Train and save model
model = ann.train()[0][
0] # Take the first model from the list of models in the tuple
ann.validate_train()
# Check to see if train_accuracy is over 90%
self.assertTrue(ann.train_accuracy() > 0.9)
# Load the trained model into a new neural network
ann_from_model = Ann(model)
# Evaluate some vectors using this neural network initialized only with a model
self.assertEqual(ann_from_model.h_by_class(arrs[0]), 'false')
self.assertEqual(ann_from_model.h_by_class(arrs[1]), 'true')
x = [1.1, 0.9]
self.assertEqual(ann_from_model.h_by_class(x), 'false')
# function 2 on 2 hidden layers
arrs2 = []
arrs2.append([1, 1])
arrs2.append([2, 2])
arrs2.append([1, 3])
arrs2.append([2, 10])
arrs2.append([1, -1])
arrs2.append([-2, -2])
arrs2.append([1, -3])
arrs2.append([-2, -10])
labels2 = []
labels2.append('false')
labels2.append('false')
labels2.append('false')
labels2.append('false')
labels2.append('true')
labels2.append('true')
labels2.append('true')
labels2.append('true')
ann = Ann(arrs2, labels2, n_h=2)
model2 = ann.train()[0][0]
ann.validate_train()
# Load the second model
ann_from_model = Ann(model2)
# Evaluate some vectors using this neural network initialized only with a model
self.assertEqual(ann_from_model.h_by_class(arrs2[0]), 'false')
self.assertEqual(ann_from_model.h_by_class(arrs2[len(arrs2) - 1]),
'true')
x = [1, -5]
self.assertEqual(ann_from_model.h_by_class(x), 'true')
# Load the first model again
ann_from_model = Ann(model)
# Evaluate some vectors using this neural network initialized only with a model
self.assertEqual(ann_from_model.h_by_class(arrs[0]), 'false')
self.assertEqual(ann_from_model.h_by_class(arrs[1]), 'true')
x = [1.1, 0.9]
self.assertEqual(ann_from_model.h_by_class(x), 'false')
# Try pickling our model into a sister folder
model_name = model.name
directory = '../Ann-models'
path_to_file = directory + '/' + model_name
if not os.path.exists(directory):
os.makedirs(directory)
pickle.dump(model, open(path_to_file, 'wb'))
# Try unpickling our model
unpickled_model = pickle.load(open(path_to_file, 'rb'))
# Load unpickled model and test
ann_from_pickle = Ann(unpickled_model)
# Evaluate some vectors using this neural network initialized only with a model
self.assertEqual(ann_from_pickle.h_by_class(arrs[0]), 'false')
self.assertEqual(ann_from_pickle.h_by_class(arrs[1]), 'true')
x = [1.1, 0.9]
self.assertEqual(ann_from_pickle.h_by_class(x), 'false')
def test_7(self):
# Learn some basic functions#
# Linearly-separable data-sets#
# function 1 (AND function) on 0 hidden layers
arrs = []
arrs.append([0, 0])
arrs.append([0, 1])
arrs.append([1, 0])
arrs.append([1, 1])
labels = []
labels.append('false')
labels.append('true')
labels.append('true')
labels.append('true')
ann = Ann(arrs, labels, n_h=0)
ann.train()
ann.validate_train()
# Check to see if train_accuracy is over 90%
self.assertTrue(ann.train_accuracy() > 0.9)
# function 2 on 2 hidden layers
arrs = []
arrs.append([1, 1])
arrs.append([2, 2])
arrs.append([1, 3])
arrs.append([2, 10])
arrs.append([1, -1])
arrs.append([-2, -2])
arrs.append([1, -3])
arrs.append([-2, -10])
labels = []
labels.append('false')
labels.append('false')
labels.append('false')
labels.append('false')
labels.append('true')
labels.append('true')
labels.append('true')
labels.append('true')
ann = Ann(arrs, labels, n_h=2)
ann.train()
ann.validate_train()
# Check to see if train_accuracy is over 90%
self.assertTrue(ann.train_accuracy() > 0.9)
# Non-linearly-separable data-sets#
# function 1 (XOR function) on 1 hidden layers
arrs = []
arrs.append([0, 0])
arrs.append([0, 1])
arrs.append([1, 0])
arrs.append([1, 1])
labels = []
labels.append('false')
labels.append('true')
labels.append('true')
labels.append('false')
ann = Ann(arrs, labels, n_h=1)
ann.train(it=3000)
ann.validate_train()
# Check to see if train_accuracy is over 90%
self.assertTrue(ann.train_accuracy() > 0.9)
# function 1b (XOR function) on 1 hidden layers (with custom architecture)
arrs = []
arrs.append([0, 0])
arrs.append([0, 1])
arrs.append([1, 0])
arrs.append([1, 1])
labels = []
labels.append('false')
labels.append('true')
labels.append('true')
labels.append('false')
s = [4, 5] # Custom hidden layer architecture
ann = Ann(arrs, labels, n_h=len(s), s=s)
ann.train()
ann.validate_train()
# Check to see if train_accuracy is over 90%
self.assertTrue(ann.train_accuracy() > 0.9)
# function 1 (two nested sets) on 2 hidden layers
arrs = []
arrs.append([0, 0])
arrs.append([0, 1])
arrs.append([1, 1])
arrs.append([1, 1])
arrs.append([10, 0])
arrs.append([0, 10])
arrs.append([110, 10])
arrs.append([-10, 10])
labels = []
labels.append('false')
labels.append('false')
labels.append('false')
labels.append('false')
labels.append('true')
labels.append('true')
labels.append('true')
labels.append('true')
ann = Ann(arrs, labels, n_h=0)
ann.train()
ann.validate_train()
# Check to see if train_accuracy is over 90%
self.assertTrue(ann.train_accuracy() > 0.9)
def test_7(self):
# Learn some basic functions#
# Linearly-separable data-sets#
# function 1 (AND function) on 0 hidden layers
arrs = []
arrs.append([0, 0])
arrs.append([0, 1])
arrs.append([1, 0])
arrs.append([1, 1])
labels = []
labels.append('false')
labels.append('true')
labels.append('true')
labels.append('true')
ann = Ann(arrs, labels, n_h=0)
ann.train()
ann.validate_train()
# Check to see if train_accuracy is over 90%
self.assertTrue(ann.train_accuracy() > 0.9)
# function 2 on 2 hidden layers
arrs = []
arrs.append([1, 1])
arrs.append([2, 2])
arrs.append([1, 3])
arrs.append([2, 10])
arrs.append([1, -1])
arrs.append([-2, -2])
arrs.append([1, -3])
arrs.append([-2, -10])
labels = []
labels.append('false')
labels.append('false')
labels.append('false')
labels.append('false')
labels.append('true')
labels.append('true')
labels.append('true')
labels.append('true')
ann = Ann(arrs, labels, n_h=2)
ann.train()
ann.validate_train()
# Check to see if train_accuracy is over 90%
self.assertTrue(ann.train_accuracy() > 0.9)
# Non-linearly-separable data-sets#
# function 1 (XOR function) on 1 hidden layers
arrs = []
arrs.append([0, 0])
arrs.append([0, 1])
arrs.append([1, 0])
arrs.append([1, 1])
labels = []
labels.append('false')
labels.append('true')
labels.append('true')
labels.append('false')
ann = Ann(arrs, labels, n_h=1)
ann.train(it=3000)
ann.validate_train()
# Check to see if train_accuracy is over 90%
self.assertTrue(ann.train_accuracy() > 0.9)
# function 1b (XOR function) on 1 hidden layers (with custom architecture)
arrs = []
arrs.append([0, 0])
arrs.append([0, 1])
arrs.append([1, 0])
arrs.append([1, 1])
labels = []
labels.append('false')
labels.append('true')
labels.append('true')
labels.append('false')
s = [4, 5] # Custom hidden layer architecture
ann = Ann(arrs, labels, n_h=len(s), s=s)
ann.train()
ann.validate_train()
# Check to see if train_accuracy is over 90%
self.assertTrue(ann.train_accuracy() > 0.9)
# function 1 (two nested sets) on 2 hidden layers
arrs = []
arrs.append([0, 0])
arrs.append([0, 1])
arrs.append([1, 1])
arrs.append([1, 1])
arrs.append([10, 0])
arrs.append([0, 10])
arrs.append([110, 10])
arrs.append([-10, 10])
labels = []
labels.append('false')
labels.append('false')
labels.append('false')
labels.append('false')
labels.append('true')
labels.append('true')
labels.append('true')
labels.append('true')
ann = Ann(arrs, labels, n_h=0)
ann.train()
ann.validate_train()
# Check to see if train_accuracy is over 90%
self.assertTrue(ann.train_accuracy() > 0.9)
