# 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 GradientDescent."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import numpy as np
import tensorflow as tf


class GradientDescentOptimizerTest(tf.test.TestCase):

  def testBasic(self):
    for dtype in [tf.half, tf.float32]:
      with self.test_session():
        var0 = tf.Variable([1.0, 2.0], dtype=dtype)
        var1 = tf.Variable([3.0, 4.0], dtype=dtype)
        grads0 = tf.constant([0.1, 0.1], dtype=dtype)
        grads1 = tf.constant([0.01, 0.01], dtype=dtype)
        sgd_op = tf.train.GradientDescentOptimizer(3.0).apply_gradients(zip(
            [grads0, grads1], [var0, var1]))
        tf.initialize_all_variables().run()
        # Fetch params to validate initial values
        self.assertAllCloseAccordingToType([1.0, 2.0], var0.eval())
        self.assertAllCloseAccordingToType([3.0, 4.0], var1.eval())
        # Run 1 step of sgd
        sgd_op.run()
        # Validate updated params
        self.assertAllCloseAccordingToType(
            [1.0 - 3.0 * 0.1, 2.0 - 3.0 * 0.1], var0.eval())
        self.assertAllCloseAccordingToType(
            [3.0 - 3.0 * 0.01, 4.0 - 3.0 * 0.01], var1.eval())

  def testTensorLearningRate(self):
    for dtype in [tf.half, tf.float32]:
      with self.test_session():
        var0 = tf.Variable([1.0, 2.0], dtype=dtype)
        var1 = tf.Variable([3.0, 4.0], dtype=dtype)
        grads0 = tf.constant([0.1, 0.1], dtype=dtype)
        grads1 = tf.constant([0.01, 0.01], dtype=dtype)
        lrate = tf.constant(3.0)
        sgd_op = tf.train.GradientDescentOptimizer(lrate).apply_gradients(zip(
            [grads0, grads1], [var0, var1]))
        tf.initialize_all_variables().run()
        # Fetch params to validate initial values
        self.assertAllCloseAccordingToType([1.0, 2.0], var0.eval())
        self.assertAllCloseAccordingToType([3.0, 4.0], var1.eval())
        # Run 1 step of sgd
        sgd_op.run()
        # Validate updated params
        self.assertAllCloseAccordingToType(
            [1.0 - 3.0 * 0.1, 2.0 - 3.0 * 0.1], var0.eval())
        self.assertAllCloseAccordingToType(
            [3.0 - 3.0 * 0.01, 4.0 - 3.0 * 0.01], var1.eval())

  def testFloat64(self):
    with self.test_session():
      opt = tf.train.GradientDescentOptimizer(3.0)

      # compute_gradients.
      values = [1.0, 3.0]
      good_vars = [tf.Variable([v]) for v in values]
      bad_loss = tf.constant(2.0, tf.float64, name="bad_loss")
      self.assertRaisesRegexp(
          ValueError, r"Invalid type.*float64.*bad_loss.*expected.*float32",
          opt.compute_gradients, bad_loss, good_vars)
      bad_vars = [
          tf.Variable(np.array([v], np.float64), name="bad_var")
          for v in values
      ]
      self.assertRaisesRegexp(
          ValueError, r"Invalid type.*float64.*bad_var.*expected.*float32",
          opt.compute_gradients, tf.cast(bad_vars[0] + bad_vars[1], tf.float32),
          bad_vars)
      opt.compute_gradients(good_vars[0] + good_vars[1], good_vars)

      # apply_gradients.
      bad_grads = [
          tf.constant([0.1], dtype=np.float64, name="bad_grad"),
          tf.constant([0.01])
      ]
      self.assertRaisesRegexp(
          ValueError, r"Invalid type.*float64.*bad_grad.*expected.*float32",
          opt.apply_gradients, zip(bad_grads, good_vars))
      good_grads = [tf.constant([0.01]), tf.constant([0.02])]
      self.assertRaisesRegexp(
          ValueError, r"Invalid type.*float64.*bad_var.*expected.*float32",
          opt.apply_gradients, zip(good_grads, bad_vars))
      opt.apply_gradients(zip(good_grads, good_vars))

  def testGradWrtRef(self):
    for dtype in [tf.half, tf.float32]:
      with self.test_session():
        opt = tf.train.GradientDescentOptimizer(3.0)
        values = [1.0, 3.0]
        vars_ = [tf.Variable([v], dtype=dtype) for v in values]
        grads_and_vars = opt.compute_gradients(vars_[0].ref() + vars_[1], vars_)
        tf.initialize_all_variables().run()
        for grad, _ in grads_and_vars:
          self.assertAllCloseAccordingToType([1.0], grad.eval())

  def testWithGlobalStep(self):
    for dtype in [tf.half, tf.float32]:
      with self.test_session():
        global_step = tf.Variable(0, trainable=False)
        var0 = tf.Variable([1.0, 2.0], dtype=dtype)
        var1 = tf.Variable([3.0, 4.0], dtype=dtype)
        grads0 = tf.constant([0.1, 0.1], dtype=dtype)
        grads1 = tf.constant([0.01, 0.01], dtype=dtype)
        sgd_op = tf.train.GradientDescentOptimizer(3.0).apply_gradients(
            zip([grads0, grads1], [var0, var1]),
            global_step=global_step)
        tf.initialize_all_variables().run()
        # Fetch params to validate initial values
        self.assertAllCloseAccordingToType([1.0, 2.0], var0.eval())
        self.assertAllCloseAccordingToType([3.0, 4.0], var1.eval())
        # Run 1 step of sgd
        sgd_op.run()
        # Validate updated params and global_step
        self.assertAllCloseAccordingToType(
            [1.0 - 3.0 * 0.1, 2.0 - 3.0 * 0.1], var0.eval())
        self.assertAllCloseAccordingToType(
            [3.0 - 3.0 * 0.01, 4.0 - 3.0 * 0.01], var1.eval())
        self.assertAllCloseAccordingToType(1, global_step.eval())

  def testSparseBasic(self):
    for dtype in [tf.half, tf.float32]:
      with self.test_session():
        var0 = tf.Variable([[1.0], [2.0]], dtype=dtype)
        var1 = tf.Variable([[3.0], [4.0]], dtype=dtype)
        grads0 = tf.IndexedSlices(
            tf.constant([0.1], shape=[1, 1], dtype=dtype),
            tf.constant([0]),
            tf.constant([2, 1]))
        grads1 = tf.IndexedSlices(
            tf.constant([0.01], shape=[1, 1], dtype=dtype),
            tf.constant([1]),
            tf.constant([2, 1]))
        sgd_op = tf.train.GradientDescentOptimizer(3.0).apply_gradients(
            zip([grads0, grads1], [var0, var1]))
        tf.initialize_all_variables().run()
        # Fetch params to validate initial values
        self.assertAllCloseAccordingToType([[1.0], [2.0]], var0.eval())
        self.assertAllCloseAccordingToType([[3.0], [4.0]], var1.eval())
        # Run 1 step of sgd
        sgd_op.run()
        # Validate updated params
        self.assertAllCloseAccordingToType(
            [[1.0 - 3.0 * 0.1], [2.0]], var0.eval())
        self.assertAllCloseAccordingToType(
            [[3.0], [4.0 - 3.0 * 0.01]], var1.eval())


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