Losses are increasing in Binary classification using gradient descent optimization method - binary

Question

This my program for Binary classification using gradient descent optimization method. I am not sure about my loss function. The error in my case is incresing when plotted
def sigmoid_activation(x):
return 1.0 / (1 + np.exp(-x))
def predict(testX, W):
preds= sigmoid_activation(np.dot(testX,W))
# apply a step function to threshold (=0.5) the outputs to binary class
#labels
#start your code here
for i in range(len(preds)):
if preds[i]<0.5:
p.append(0)
if preds[i]>=0.5:
p.append(1)
return p
epochs = 50
alpha = 0.01
(X,y)=make_moons(n_samples=1000, noise = 0.15)
y=y.reshape(y.shape[0],1)
X = np.c_[X, np.ones((X.shape[0]))]
(trainX, testX, trainY, testY) = train_test_split(X, y,
test_size=0.5, random_state=42)
print("[INFO] training...")
W = np.random.randn(X.shape[1], 1)
losses = []
for epoch in np.arange(0, epochs):
#start your code here
Z=np.dot(trainX, W)
yhat= sigmoid_activation(Z)
error=trainY-yhat
loss = np.sum(error ** 2)
losses.append(loss)
gradient = trainX.T.dot(error) / trainX.shape[0]
W= W-alpha*gradient #moving in -ve direction
# check to see if an update should be displayed
if epoch == 0 or (epoch + 1) % 5 == 0:
print("[INFO] epoch={}, loss={:.7f}".format(int(epoch + 1),
loss))
# evaluate our model
print("[INFO] evaluating...")
preds = predict(testX, W)
print(classification_report(testY, preds))
# plot the (testing) classification data
plt.style.use("ggplot")
plt.figure()
plt.title("Data")
plt.scatter(testX[:, 0], testX[:, 1], marker="o", c=testY[:,0], s=30)
# construct a figure that plots the loss over time
plt.style.use("ggplot")
plt.figure()
plt.plot(np.arange(0, epochs), losses)
plt.title("Training Loss")
plt.xlabel("Epoch #")
plt.ylabel("Loss")
plt.show()