class TestMultiLayer(unittest.TestCase):
"""
test a multilayer network (check gradients)
"""
def setUp(self):
"""
Set up a small sequential network
"""
self.layer1 = FullLayer(5, 10)
self.relu1 = ReluLayer()
self.layer2 = FullLayer(10, 2)
self.softmax = SoftMaxLayer()
self.loss = CrossEntropyLayer()
self.model = Sequential(
(self.layer1,
self.relu1,
self.layer2,
self.softmax),
self.loss
)
def test_forward(self):
"""
Test forward pass with some fake input
"""
# make some fake input
x = np.array([[0.5, 0.2, 0.1, 0.3, 0.7],
[0.3, 0.1, 0.05, 0.8, 0.9]])
y = np.array([[0, 1],
[1, 0]])
# test layers individually
y1 = self.layer1.forward(x)
relu = self.relu1.forward(y1)
y2 = self.layer2.forward(relu)
soft = self.softmax.forward(y2)
loss = self.loss.forward(soft, y)
# test sequential model
y_model = self.model.forward(x, y)
self.assertAlmostEquals(loss, y_model)
def test_backward(self):
"""
Test the backward function using the numerical gradient
"""
# make some fake input
x = np.array([[0.5, 0.2, 0.1, 0.3, 0.7],
[0.3, 0.1, 0.05, 0.8, 0.9],
])
y = np.array([[0, 1],
[1, 0]])
out = self.model.forward(x, y)
grads = self.model.backward()
# test some gradients at the input
h = 0.001
for i in range(x.shape[0]):
for j in range(x.shape[1]):
new_x = np.copy(x)
new_x[i, j] += h
out2 = self.model.forward(new_x, y)
diff = (out2 - out) / h
print "######"
print diff
print grads[i, j]
print "######"
self.assertTrue(np.abs(diff - grads[i,j]) < 0.001)
ReluLayer(),
MaxPoolLayer(size=2),
FlattenLayer(),
FullLayer(n_i=12*12*32,n_o=7),
SoftMaxLayer()),
loss=CrossEntropyLayer())
myNet.load()
"""
pred = myNet.predict(val_x)
accuracy = np.mean(pred == val_y)
print('At learning rate = '+str(lr))
print('Validation Accuracy of Convolutional Neural Network = '+str(accuracy))
"""
forw = myNet.forward(test_x)
pred = myNet.predict(test_x)
accuracy = np.mean(pred == test_y)
print('At learning rate = '+str(lr))
print('Testing Accuracy of Convolutional Neural Network = '+str(accuracy))
ty = np.argmax(test_y,axis=1)
prediction = np.argmax(pred,axis=1)
###Confusion MAtrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(ty, prediction)
print(cm)
"""
###Precision Recall
from sklearn.metrics import precision_recall_curve