import tensorflow as tf
from tensorflow.python.training import rmsprop,ftrl

n = 10


x = tf.Variable(tf.zeros([n,n]),trainable = True)


col = tf.Variable(tf.zeros([n,3]), trainable = True)

maxs = tf.Variable(tf.zeros([3]), trainable = True)


temp = tf.Variable(1.)

@tf.function
def f_x():


	col[0][0] = 1.
	col[0][1] = 1.
	col[0][2] = 1.


	for i in range(1,n):
		temp = 1.
		for j in range(i):
			if (x[j][i] <= 1.):
				if (temp<1.+col[j][0]):
					temp = 1.+col[j][0]
		col[i][0] =temp

		temp = 1.
		for j in range(i):
			if (x[j][i] < 0.) or (x[j][i] > 1.):
				if (temp < 1.+col[j][1]):
					temp =1.+col[j][1]
		col[i][1] =temp

		temp = 1.
		for j in range(i):
			if (x[j][i] >=0.):
				if (temp<1.+col[j][2]):
					temp=1.+col[j][2]
		col[i][2]=temp



	maxs[0]=0.
	maxs[1]=0.
	maxs[2]=0.

	for i in range(n):
		for j in range(3):
			if (maxs[j]<col[i][j]):
				maxs[j]=col[i][j]

	return maxs[0]*maxs[1]*maxs[2]

for _ in range(100):
	print([x.numpy(), f_x().numpy()])
	opt = ftrl.FtrlOptimizer(0.1).minimize(f_x)