def test_1(self):
classes = ('smiley', 'frowny')
arrs = []
labels = []
file_names = []
for c in classes:
files = [name for name in os.listdir('../library/' + c)]
for el in files:
img = Image.open('../library/' + c + '/' + el).convert('L')
img = img.resize((50, 50), Image.ANTIALIAS)
arrs.append(img.getdata())
labels.append(c)
file_names.append(el)
name = '../Ann-models/model_n_i_2500_n_o_34_n_h_2 2015-05-27 22:15:24.990089.annm'
model = pickle.load(open(name, 'rb'))[0][0]
ann = Ann(model)
print(ann.h(arrs[0]))
def test_1(self):
classes = ('smiley', 'frowny')
arrs = []
labels = []
file_names = []
for c in classes:
files = [name for name in os.listdir('../library/' + c)]
for el in files:
img = Image.open('../library/' + c + '/' + el).convert('L')
img = img.resize((50, 50), Image.ANTIALIAS)
arrs.append(img.getdata())
labels.append(c)
file_names.append(el)
name = '../Ann-models/model_n_i_2500_n_o_34_n_h_2 2015-05-27 22:15:24.990089.annm'
model = pickle.load(open(name, 'rb'))[0][0]
ann = Ann(model)
print(ann.h(arrs[0]))
def test_2(self):
# Test for forward-propagation#
# First architecture test#
# Logistic regression (0 hidden layers) forward propagation test#
n_i1 = 4 # Number of input neurons
n_h1 = 0 # Number of hidden layers
n_o1 = 1 # Number of output neurons
ann1 = Ann(n_i=n_i1, n_h=n_h1, n_o=n_o1) # Create this architecture
x1 = [1, 2, 3, 4] # Array as first example
x2 = [-1, -1, -1, -1] # Array as second example
# Set all weights to zero#
for i in range(0, len(ann1.Thetas)):
shape = ann1.Thetas[i].shape
self.assertEqual(shape, (1, 5))
ann1.Thetas[i] = np.zeros(shape)
self.assertEqual(ann1.h(x1), 0.5)
self.assertEqual(ann1.h(x2), 0.5)
# Set all weights to one#
for i in range(0, len(ann1.Thetas)):
shape = ann1.Thetas[i].shape
self.assertEqual(shape, (1, 5))
ann1.Thetas[i] = np.ones(shape)
self.assertAlmostEqual(ann1.h(x1), 0.999, delta=0.001)
self.assertAlmostEqual(ann1.h(x2), 0.0474, delta=0.0001)
# Set all weights randomly between -1 and 1 (and test the range of output)#
ann1 = Ann(n_i=n_i1, n_h=n_h1, n_o=n_o1) # Create this architecture
self.assertAlmostEqual(ann1.h(x1), 0.5, delta=0.5) # Sigmoid always gives values between 0 and 1
self.assertAlmostEqual(ann1.h(x2), 0.5, delta=0.5)
# Custom Thetas weights#
M = np.matrix([[1, -1, 0.5, -0.3, 2]])
ann1.Thetas[0] = M
self.assertAlmostEqual(ann1.h(x1), 0.786, delta=0.001)
self.assertAlmostEqual(ann1.h(x2), 0.858, delta=0.001)
# Second architecture test#
# 1 hidden layer forward propagation test#
n_i1 = 4 # Number of input neurons
n_h1 = 1 # Number of hidden layers
n_o1 = 1 # Number of output neurons
ann1 = Ann(n_i=n_i1, n_h=n_h1, n_o=n_o1) # Create this architecture
x1 = [1, 2, 3, 4] # Array as first example
x2 = [-1, -1, -1, -1] # Array as second example
# Set all weights to zero#
for i in range(0, len(ann1.Thetas)):
shape = ann1.Thetas[i].shape
ann1.Thetas[i] = np.zeros(shape)
self.assertEqual(ann1.h(x1), 0.5)
self.assertEqual(ann1.h(x2), 0.5)
# Set all weights to one#
for i in range(0, len(ann1.Thetas)):
shape = ann1.Thetas[i].shape
ann1.Thetas[i] = np.ones(shape)
self.assertAlmostEqual(ann1.h(x1), 0.993, delta=0.001)
self.assertAlmostEqual(ann1.h(x2), 0.767, delta=0.001)
# Set all weights randomly between -1 and 1 (and test the range of output)#
ann1 = Ann(n_i=n_i1, n_h=n_h1, n_o=n_o1) # Create this architecture
self.assertAlmostEqual(ann1.h(x1), 0.5, delta=0.5) # Sigmoid always gives values between 0 and 1
self.assertAlmostEqual(ann1.h(x2), 0.5, delta=0.5)
# Custom Thetas weights#
M1 = np.matrix([[1, -1, 0.5, -0.3, 2],
[1, -1, 0.5, -0.3, 2],
[1, -1, 0.5, -0.3, 2],
[1, -1, 0.5, -0.3, 2]])
M2 = np.matrix([[1, 1, -1, 0.5, -1]])
ann1.Thetas[0] = M1
ann1.Thetas[1] = M2
# a^(1) Should be [0.786 0.786 0.786 0.786 1]^T#
self.assertAlmostEqual(ann1.h(x1), 0.545, delta=0.001)
# a^(1) Should be [0.858 0.858 0.858 0.858 1]^T#
self.assertAlmostEqual(ann1.h(x2), 0.571, delta=0.001)
def test_2(self):
# Test for forward-propagation#
# First architecture test#
# Logistic regression (0 hidden layers) forward propagation test#
n_i1 = 4 # Number of input neurons
n_h1 = 0 # Number of hidden layers
n_o1 = 1 # Number of output neurons
ann1 = Ann(n_i=n_i1, n_h=n_h1, n_o=n_o1) # Create this architecture
x1 = [1, 2, 3, 4] # Array as first example
x2 = [-1, -1, -1, -1] # Array as second example
# Set all weights to zero#
for i in range(0, len(ann1.Thetas)):
shape = ann1.Thetas[i].shape
self.assertEqual(shape, (1, 5))
ann1.Thetas[i] = np.zeros(shape)
self.assertEqual(ann1.h(x1), 0.5)
self.assertEqual(ann1.h(x2), 0.5)
# Set all weights to one#
for i in range(0, len(ann1.Thetas)):
shape = ann1.Thetas[i].shape
self.assertEqual(shape, (1, 5))
ann1.Thetas[i] = np.ones(shape)
self.assertAlmostEqual(ann1.h(x1), 0.999, delta=0.001)
self.assertAlmostEqual(ann1.h(x2), 0.0474, delta=0.0001)
# Set all weights randomly between -1 and 1 (and test the range of output)#
ann1 = Ann(n_i=n_i1, n_h=n_h1, n_o=n_o1) # Create this architecture
self.assertAlmostEqual(
ann1.h(x1), 0.5,
delta=0.5) # Sigmoid always gives values between 0 and 1
self.assertAlmostEqual(ann1.h(x2), 0.5, delta=0.5)
# Custom Thetas weights#
M = np.matrix([[1, -1, 0.5, -0.3, 2]])
ann1.Thetas[0] = M
self.assertAlmostEqual(ann1.h(x1), 0.786, delta=0.001)
self.assertAlmostEqual(ann1.h(x2), 0.858, delta=0.001)
# Second architecture test#
# 1 hidden layer forward propagation test#
n_i1 = 4 # Number of input neurons
n_h1 = 1 # Number of hidden layers
n_o1 = 1 # Number of output neurons
ann1 = Ann(n_i=n_i1, n_h=n_h1, n_o=n_o1) # Create this architecture
x1 = [1, 2, 3, 4] # Array as first example
x2 = [-1, -1, -1, -1] # Array as second example
# Set all weights to zero#
for i in range(0, len(ann1.Thetas)):
shape = ann1.Thetas[i].shape
ann1.Thetas[i] = np.zeros(shape)
self.assertEqual(ann1.h(x1), 0.5)
self.assertEqual(ann1.h(x2), 0.5)
# Set all weights to one#
for i in range(0, len(ann1.Thetas)):
shape = ann1.Thetas[i].shape
ann1.Thetas[i] = np.ones(shape)
self.assertAlmostEqual(ann1.h(x1), 0.993, delta=0.001)
self.assertAlmostEqual(ann1.h(x2), 0.767, delta=0.001)
# Set all weights randomly between -1 and 1 (and test the range of output)#
ann1 = Ann(n_i=n_i1, n_h=n_h1, n_o=n_o1) # Create this architecture
self.assertAlmostEqual(
ann1.h(x1), 0.5,
delta=0.5) # Sigmoid always gives values between 0 and 1
self.assertAlmostEqual(ann1.h(x2), 0.5, delta=0.5)
# Custom Thetas weights#
M1 = np.matrix([[1, -1, 0.5, -0.3, 2], [1, -1, 0.5, -0.3, 2],
[1, -1, 0.5, -0.3, 2], [1, -1, 0.5, -0.3, 2]])
M2 = np.matrix([[1, 1, -1, 0.5, -1]])
ann1.Thetas[0] = M1
ann1.Thetas[1] = M2
# a^(1) Should be [0.786 0.786 0.786 0.786 1]^T#
self.assertAlmostEqual(ann1.h(x1), 0.545, delta=0.001)
# a^(1) Should be [0.858 0.858 0.858 0.858 1]^T#
self.assertAlmostEqual(ann1.h(x2), 0.571, delta=0.001)
