# placeholders
x_data = tf.placeholder(shape = [None, x.shape[1]], dtype = tf.float32)
y_target = tf.placeholder(shape = [None, 1], dtype = tf.float32)

# prediction
W = tf.Variable(tf.random_normal(shape = [x.shape[1], 1]))
b = tf.Variable(tf.random_normal(shape = [1]))
y_logits = tf.matmul(x_data, W) + b
y_hat = tf.round(tf.sigmoid(y_logits))

# loss function 
loss = tf.reduce_sum(tf.nn.sigmoid_cross_entropy_with_logits(
    logits = y_logits, 
    labels = y_target))
regularizer = tf.reduce_sum(tf.abs(W))
loss = loss + 1. * regularizer

# training
train_step = tf.train.AdamOptimizer(0.1).minimize(loss)

sess = tf.InteractiveSession()
init = tf.global_variables_initializer()
sess.run(init)

# loop
n_epochs = 10
batch_size = 1024
for _ in range(n_epochs): 
    for i in range(0, X.shape[1], batch_size): 
        xs = x_[i:i+batch_size,:]
        ys = y_[i:i+batch_size]
        sess.run(train_step, feed_dict = {x_data: xs, y_target: ys})

y_pred = sess.run(y_hat, feed_dict = {x_data: x_, y_target: y_})
print(sk.metrics.classification_report(y.values.astype(int), y_pred))
    
sess.close()