def equivGradientDescentTest_FtrlPart(self, steps, dtype):
    (var0, var1, grads0, grads1) = self.initVariableAndGradient(dtype)
    opt = ftrl.FtrlOptimizer(3.0, learning_rate_power=(- 0.0), initial_accumulator_value=0.1, l1_regularization_strength=0.0, l2_regularization_strength=0.0)
    ftrl_update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
    variables.global_variables_initializer().run()
    self.assertAllClose([0.0, 0.0], var0.eval())
    self.assertAllClose([0.0, 0.0], var1.eval())
    for _ in range(steps):
        ftrl_update.run()
    return (var0.eval(), var1.eval())
def equivGradientDescentTest_FtrlPart(self, steps, dtype):
    (var0, var1, grads0, grads1) = self.initVariableAndGradient(dtype)
    opt = ftrl.FtrlOptimizer(3.0, learning_rate_power=(- 0.0), initial_accumulator_value=0.1, l1_regularization_strength=0.0, l2_regularization_strength=0.0)
    ftrl_update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
    variables.global_variables_initializer().run()
    self.assertAllClose([0.0, 0.0], var0.eval())
    self.assertAllClose([0.0, 0.0], var1.eval())
    for _ in range(steps):
        ftrl_update.run()
    return (var0.eval(), var1.eval())