My tensorflow code does not work good -bank analysis - tensorflow

Question

I'm studding tensorflow and I wrote some code but it does not work good.
the data was downloaded from uci:http://archive.ics.uci.edu/ml/datasets/Bank+Marketing
I want to find the client who will subscribe a term deposit,but the result matched is 0.
I use 3 layers neural network and sigmod for output.
My code is like this,please help me.
hidden_layer1 = 200
hidden_layer2 = 200
x = tf.placeholder(tf.float32,[None,16])
y = tf.placeholder(tf.float32,[None,1])
Weights_L1 = tf.Variable(tf.random_normal([16,hidden_layer1]))
biases_L1 = tf.Variable(tf.random_normal([1,hidden_layer1]))
Wx_plus_b_L1 = tf.matmul(x,Weights_L1) + biases_L1
L1=tf.nn.relu(Wx_plus_b_L1)
Weights_L2 = tf.Variable( tf.random_normal([hidden_layer1,1]))
biases_L2 = tf.Variable( tf.random_normal([1,1]))
Wx_plus_b_L2 = tf.matmul(L1,Weights_L2) + biases_L2
pred = tf.nn.sigmoid(Wx_plus_b_L2)
loss = tf.reduce_mean(tf.square(y-pred))
learning_rate=0.05
train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)
pred_correct = tf.equal(y,pred)
accuracy = tf.reduce_mean(tf.cast(pred_correct,tf.float32))
batch_num = 0
with tf.Session() as ss:
ss.run(tf.global_variables_initializer())
for step in range(500):
ss.run(train_step,feed_dict={x:bank_train_x,y:bank_train_y})
if step%100==0:
batch_num = batch_num +1
acc1 = ss.run(accuracy,feed_dict={x:bank_train_x,y:bank_train_y})
print("train acc"+ str(step) + ", " + str(acc1) +" , batch_num:" + str(batch_num))
#print(ss.run(learning_rate,feed_dict={global_:step}))
p = ss.run(pred,feed_dict={x:bank_train_x,y:bank_train_y})
acc2 = ss.run(accuracy,feed_dict={x:bank_test_x,y:bank_test_y})
print("test acc" + str(acc2))
def calc(pred,y):
l = y.shape[0]
a = 0
b=0
c=0
d=0
for i in range(l):
if (p[i] >0.5 and y[i] == 0):
a = a +1
elif (p[i] >0.5 and y[i] == 1):
b = b+1
elif (p[i] <0.5 and y[i] == 0):
c = c+1
elif (p[i] <0.5 and y[i] == 1):
d = d +1
print(a,b,c,d)
calc(p,bank_train_y)
#the result is 169 0 34959 4629