# Copyright 2015 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

"""Functional test for optimizer."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf


class OptimizerTest(tf.test.TestCase):

  def testBasic(self):
    for dtype in [tf.half, tf.float32, tf.float64]:
      with self.test_session():
        var0 = tf.Variable([1.0, 2.0], dtype=dtype)
        var1 = tf.Variable([3.0, 4.0], dtype=dtype)
        cost = 5 * var0 + 3 * var1
        global_step = tf.Variable(tf.zeros([], tf.int64), name='global_step')
        sgd_op = tf.train.GradientDescentOptimizer(3.0)
        opt_op = sgd_op.minimize(cost, global_step, [var0, var1])

        tf.initialize_all_variables().run()
        # Fetch params to validate initial values
        self.assertAllClose([1.0, 2.0], var0.eval())
        self.assertAllClose([3.0, 4.0], var1.eval())
        # Run 1 step of sgd through optimizer
        opt_op.run()
        # Validate updated params
        self.assertAllClose([-14., -13.], var0.eval())
        self.assertAllClose([-6., -5.], var1.eval())

  def testAggregationMethod(self):
    for dtype in [tf.half, tf.float32, tf.float64]:
      with self.test_session():
        var0 = tf.Variable([1.0, 2.0], dtype=dtype)
        var1 = tf.Variable([3.0, 4.0], dtype=dtype)
        cost = 5 * var0 + 3 * var1
        global_step = tf.Variable(tf.zeros([], tf.int64), name='global_step')
        sgd_op = tf.train.GradientDescentOptimizer(3.0)
        opt_op = sgd_op.minimize(
            cost,
            global_step,
            [var0, var1],
            aggregation_method=tf.AggregationMethod.EXPERIMENTAL_ACCUMULATE_N)

        tf.initialize_all_variables().run()
        # Fetch params to validate initial values
        self.assertAllClose([1.0, 2.0], var0.eval())
        self.assertAllClose([3.0, 4.0], var1.eval())
        # Run 1 step of sgd through optimizer
        opt_op.run()
        # Validate updated params
        self.assertAllClose([-14., -13.], var0.eval())
        self.assertAllClose([-6., -5.], var1.eval())

  def testPrecomputedGradient(self):
    for dtype in [tf.half, tf.float32, tf.float64]:
      with self.test_session():
        var0 = tf.Variable([1.0, 2.0], dtype=dtype)
        var1 = tf.Variable([3.0, 4.0], dtype=dtype)
        cost = 5 * var0 + 3 * var1
        grad_loss = tf.constant([42, -42], dtype=dtype)
        global_step = tf.Variable(tf.zeros([], tf.int64), name='global_step')
        sgd_op = tf.train.GradientDescentOptimizer(3.0)
        opt_op = sgd_op.minimize(cost,
                                 global_step, [var0, var1],
                                 grad_loss=grad_loss)

        tf.initialize_all_variables().run()
        # Fetch params to validate initial values
        self.assertAllClose([1.0, 2.0], var0.eval())
        self.assertAllClose([3.0, 4.0], var1.eval())
        # Run 1 step of sgd through optimizer
        opt_op.run()
        # Validate updated params
        self.assertAllClose(
            [1.0 - 3 * 5 * 42.0, 2.0 - 3 * 5 * (-42.0)], var0.eval())
        self.assertAllClose(
            [3.0 - 3 * 3 * 42.0, 4.0 - 3 * 3 * (-42.0)], var1.eval())

  def testNoVariables(self):
    for dtype in [tf.half, tf.float32, tf.float64]:
      with self.test_session():
        var0 = tf.Variable([1.0, 2.0], dtype=dtype, trainable=False)
        var1 = tf.Variable([3.0, 4.0], dtype=dtype, trainable=False)
        cost = 5 * var0 + var1
        sgd_op = tf.train.GradientDescentOptimizer(3.0)
        with self.assertRaisesRegexp(ValueError, 'No variables'):
          sgd_op.minimize(cost)

  def testNoGradients(self):
    for dtype in [tf.half, tf.float32, tf.float64]:
      with self.test_session():
        var0 = tf.Variable([1.0, 2.0], dtype=dtype)
        var1 = tf.Variable([3.0, 4.0], dtype=dtype)
        cost = 5 * var0
        global_step = tf.Variable(tf.zeros([], tf.int64), name='global_step')
        sgd_op = tf.train.GradientDescentOptimizer(3.0)
        with self.assertRaisesRegexp(ValueError, 'No gradients'):
          # var1 has no gradient
          sgd_op.minimize(cost, global_step, [var1])


if __name__ == '__main__':
  tf.test.main()
