"""Tests for tensorflow.ops.nn."""
import math

import tensorflow.python.platform

import numpy as np

from tensorflow.python.framework import test_util
from tensorflow.python.framework import types
from tensorflow.python.kernel_tests import gradient_checker as gc
from tensorflow.python.ops import constant_op
from tensorflow.python.ops import gen_nn_ops
from tensorflow.python.ops import gradients
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn
from tensorflow.python.ops import nn_grad
from tensorflow.python.platform import googletest

exp = math.exp
log = math.log


class SigmoidCrossEntropyWithLogitsTest(test_util.TensorFlowTestCase):

  def _SigmoidCrossEntropyWithLogits(self, logits, targets):
    assert len(logits) == len(targets)
    pred = [1 / (1 + exp(-x)) for x in logits]
    eps = 0.0001
    pred = [min(max(p, eps), 1 - eps) for p in pred]
    return [-z * log(y) - (1 - z) * log(1 - y) for y, z in zip(pred, targets)]

  def _Inputs(self, x=None, y=None, dtype=types.float64, sizes=None):
    x = [-100, -2, -2, 0, 2, 2, 2, 100] if x is None else x
    y = [0, 0, 1, 0, 0, 1, 0.5, 1] if y is None else y
    assert len(x) == len(y)
    sizes = sizes if sizes else [len(x)]
    logits = constant_op.constant(x, shape=sizes, dtype=dtype, name="logits")
    targets = constant_op.constant(y, shape=sizes, dtype=dtype, name="targets")
    losses = np.array(self._SigmoidCrossEntropyWithLogits(x, y)).reshape(*sizes)
    return logits, targets, losses

  def testConstructionNamed(self):
    with self.test_session():
      logits, targets, _ = self._Inputs()
      loss = nn.sigmoid_cross_entropy_with_logits(logits, targets,
                                                  name="mylogistic")
    self.assertEqual("mylogistic", loss.op.name)

  def testLogisticOutput(self):
    for use_gpu in [True, False]:
      with self.test_session(use_gpu=use_gpu):
        logits, targets, losses = self._Inputs(dtype=types.float32)
        loss = nn.sigmoid_cross_entropy_with_logits(logits, targets)
        np_loss = np.array(losses).astype(np.float32)
        tf_loss = loss.eval()
      self.assertAllClose(np_loss, tf_loss, atol=0.001)

  def testLogisticOutputMultiDim(self):
    for use_gpu in [True, False]:
      with self.test_session(use_gpu=use_gpu):
        logits, targets, losses = self._Inputs(dtype=types.float32,
                                               sizes=[2, 2, 2])
        loss = nn.sigmoid_cross_entropy_with_logits(logits, targets)
        np_loss = np.array(losses).astype(np.float32)
        tf_loss = loss.eval()
      self.assertAllClose(np_loss, tf_loss, atol=0.001)

  def testGradient(self):
    sizes = [4, 2]
    with self.test_session():
      logits, targets, _ = self._Inputs(sizes=sizes)
      loss = nn.sigmoid_cross_entropy_with_logits(logits, targets)
      err = gc.ComputeGradientError(logits, sizes, loss, sizes)
    print "logistic loss gradient err = ", err
    self.assertLess(err, 1e-7)


class ZeroFractionTest(test_util.TensorFlowTestCase):

  def _ZeroFraction(self, x):
    assert x.shape
    total_elements = float(np.prod(x.shape))
    nonzeros = float(np.count_nonzero(x.flatten()))
    return 1.0 - (nonzeros / total_elements)

  def testZeroFraction(self):
    x_shape = [5, 17]
    x_np = np.random.randint(0, 2, size=x_shape).astype(np.float32)
    y_np = self._ZeroFraction(x_np)
    with self.test_session():
      x_tf = constant_op.constant(x_np)
      x_tf.set_shape(x_shape)
      y_tf = nn.zero_fraction(x_tf)
      y_tf_np = y_tf.eval()
    eps = 1e-8
    self.assertAllClose(y_tf_np, y_np, eps)

  def testZeroFractionEmpty(self):
    with self.test_session():
      x = np.zeros(0)
      y = nn.zero_fraction(x).eval()
      self.assertTrue(np.isnan(y))


class SoftmaxTest(test_util.TensorFlowTestCase):

  def _softmax(self, x):
    assert len(x.shape) == 2
    m = x.max(1)[:, np.newaxis]
    u = np.exp(x - m)
    z = u.sum(1)[:, np.newaxis]
    return u / z

  def testSoftmax(self):
    x_shape = [5, 10]
    x_np = np.random.randn(*x_shape).astype(np.float32)
    y_np = self._softmax(x_np)
    with self.test_session():
      x_tf = constant_op.constant(x_np)
      y_tf = nn.softmax(x_tf)
      y_tf_np = y_tf.eval()
    eps = 1e-3
    self.assertAllClose(y_tf_np, y_np, eps)

  def testGradient(self):
    x_shape = [5, 10]
    x_np = np.random.randn(*x_shape).astype(np.float64)
    with self.test_session():
      x_tf = constant_op.constant(x_np)
      y_tf = nn.softmax(x_tf)
      err = gc.ComputeGradientError(x_tf, x_shape, y_tf, x_shape)
    eps = 1e-8
    self.assertLess(err, eps)


class DeConv2DTest(test_util.TensorFlowTestCase):

  def testDeConv2DSingleStride(self):
    with self.test_session():
      strides = [1, 1, 1, 1]

      # Input, output: [batch, height, width, depth]
      x_shape = [2, 6, 4, 3]
      y_shape = [2, 6, 4, 2]

      # Filter: [kernel_height, kernel_width, output_depth, input_depth]
      f_shape = [3, 3, 2, 3]

      x = constant_op.constant(1.0, shape=x_shape, name="x",
                               dtype=types.float32)
      f = constant_op.constant(1.0, shape=f_shape, name="filter",
                               dtype=types.float32)
      output = nn.deconv2d(x, f, y_shape, strides=strides, padding="SAME")
      value = output.eval()

      # We count the number of cells being added at the locations in the output.
      # At the center, #cells=kernel_height * kernel_width
      # At the corners, #cells=ceil(kernel_height/2) * ceil(kernel_width/2)
      # At the borders, #cells=ceil(kernel_height/2)*kernel_width or
      #                        kernel_height * ceil(kernel_width/2)

      for n in xrange(x_shape[0]):
        for k in xrange(f_shape[2]):
          for w in xrange(y_shape[2]):
            for h in xrange(y_shape[1]):
              target = 4 * 3.0
              h_in = h > 0 and h < y_shape[1] - 1
              w_in = w > 0 and w < y_shape[2] - 1
              if h_in and w_in:
                target += 5 * 3.0
              elif h_in or w_in:
                target += 2 * 3.0
              self.assertAllClose(target, value[n, h, w, k])

  def testDeConv2DSame(self):
    with self.test_session():
      strides = [1, 2, 2, 1]

      # Input, output: [batch, height, width, depth]
      x_shape = [2, 6, 4, 3]
      y_shape = [2, 12, 8, 2]

      # Filter: [kernel_height, kernel_width, output_depth, input_depth]
      f_shape = [3, 3, 2, 3]

      x = constant_op.constant(1.0, shape=x_shape, name="x",
                               dtype=types.float32)
      f = constant_op.constant(1.0, shape=f_shape, name="filter",
                               dtype=types.float32)
      output = nn.deconv2d(x, f, y_shape, strides=strides, padding="SAME")
      value = output.eval()

      for n in xrange(x_shape[0]):
        for k in xrange(f_shape[2]):
          for w in xrange(y_shape[2]):
            for h in xrange(y_shape[1]):
              target = 3.0
              # We add a case for locations divisible by the stride.
              h_in = h % strides[1] == 0 and h > 0 and h < y_shape[1] - 1
              w_in = w % strides[2] == 0 and w > 0 and w < y_shape[2] - 1
              if h_in and w_in:
                target += 9.0
              elif h_in or w_in:
                target += 3.0
              self.assertAllClose(target, value[n, h, w, k])

  def testDeConv2DValid(self):
    with self.test_session():
      strides = [1, 2, 2, 1]

      # Input, output: [batch, height, width, depth]
      x_shape = [2, 6, 4, 3]
      y_shape = [2, 13, 9, 2]

      # Filter: [kernel_height, kernel_width, output_depth, input_depth]
      f_shape = [3, 3, 2, 3]

      x = constant_op.constant(1.0, shape=x_shape, name="x",
                               dtype=types.float32)
      f = constant_op.constant(1.0, shape=f_shape, name="filter",
                               dtype=types.float32)
      output = nn.deconv2d(x, f, y_shape, strides=strides, padding="VALID")
      value = output.eval()

      cache_values = np.zeros(y_shape, dtype=np.float32)

      # The amount of padding added
      pad = 1

      for n in xrange(x_shape[0]):
        for k in xrange(f_shape[2]):
          for w in xrange(pad, y_shape[2] - pad):
            for h in xrange(pad, y_shape[1] - pad):
              target = 3.0
              # We add a case for locations divisible by the stride.
              h_in = h % strides[
                  1] == 0 and h > pad and h < y_shape[1] - 1 - pad
              w_in = w % strides[
                  2] == 0 and w > pad and w < y_shape[2] - 1 - pad
              if h_in and w_in:
                target += 9.0
              elif h_in or w_in:
                target += 3.0
              cache_values[n, h, w, k] = target

          # copy values in the border
          cache_values[n, :, 0, k] = cache_values[n, :, 1, k]
          cache_values[n, :, -1, k] = cache_values[n, :, -2, k]
          cache_values[n, 0, :, k] = cache_values[n, 1, :, k]
          cache_values[n, -1, :, k] = cache_values[n, -2, :, k]

    self.assertAllClose(cache_values, value)

  def testGradient(self):
    x_shape = [2, 6, 4, 3]
    f_shape = [3, 3, 2, 3]
    y_shape = [2, 12, 8, 2]
    strides = [1, 2, 2, 1]
    np.random.seed(1)  # Make it reproducible.
    x_val = np.random.random_sample(x_shape).astype(np.float64)
    f_val = np.random.random_sample(f_shape).astype(np.float64)
    with self.test_session():
      x = constant_op.constant(x_val, name="x", dtype=types.float32)
      f = constant_op.constant(f_val, name="f", dtype=types.float32)
      output = nn.deconv2d(x, f, y_shape, strides=strides, padding="SAME")
      err = gc.ComputeGradientError([x, f], [x_shape, f_shape], output, y_shape)
    print "DeConv gradient err = %g " % err
    err_tolerance = 0.0005
    self.assertLess(err, err_tolerance)


class L2LossTest(test_util.TensorFlowTestCase):

  def testL2Loss(self):
    with self.test_session():
      x = constant_op.constant([1.0, 0.0, 3.0, 2.0], shape=[2, 2], name="x")
      l2loss = nn.l2_loss(x)
      value = l2loss.eval()
    self.assertAllClose(7.0, value)

  def testGradient(self):
    x_shape = [20, 7, 3]
    np.random.seed(1)  # Make it reproducible.
    x_val = np.random.random_sample(x_shape).astype(np.float64)
    with self.test_session():
      x = constant_op.constant(x_val, name="x")
      output = nn.l2_loss(x)
      err = gc.ComputeGradientError(x, x_shape, output, [1])
    print "L2Loss gradient err = %g " % err
    err_tolerance = 1e-11
    self.assertLess(err, err_tolerance)


class L2NormalizeTest(test_util.TensorFlowTestCase):

  def _l2Normalize(self, x, dim):
    norm = np.apply_along_axis(np.linalg.norm, dim, x)
    return x / np.expand_dims(norm, dim)

  def testL2Normalize(self):
    x_shape = [20, 7, 3]
    np.random.seed(1)
    x_np = np.random.random_sample(x_shape).astype(np.float32)
    for dim in range(len(x_shape)):
      y_np = self._l2Normalize(x_np, dim)
      with self.test_session():
        x_tf = constant_op.constant(x_np, name="x")
        y_tf = nn.l2_normalize(x_tf, dim)
        self.assertAllClose(y_np, y_tf.eval())

  def testL2NormalizeGradient(self):
    x_shape = [20, 7, 3]
    np.random.seed(1)
    x_np = np.random.random_sample(x_shape).astype(np.float64)
    for dim in range(len(x_shape)):
      with self.test_session():
        x_tf = constant_op.constant(x_np, name="x")
        y_tf = nn.l2_normalize(x_tf, dim)
        err = gc.ComputeGradientError(x_tf, x_shape, y_tf, x_shape)
      print "L2Normalize gradient err = %g " % err
      self.assertLess(err, 1e-4)


class DropoutTest(test_util.TensorFlowTestCase):

  def testDropout(self):
    # Runs dropout with 0-1 tensor 10 times, sum the number of ones and validate
    # that it is producing approximately the right number of ones over a large
    # number of samples, based on the keep probability.
    x_dim = 40
    y_dim = 30
    num_iter = 10
    for keep_prob in [0.1, 0.5, 0.8]:
      with self.test_session():
        t = constant_op.constant(1.0,
                                 shape=[x_dim, y_dim],
                                 dtype=types.float32)
        dropout = nn.dropout(t, keep_prob)
        final_count = 0
        self.assertEqual([x_dim, y_dim], dropout.get_shape())
        for _ in xrange(0, num_iter):
          value = dropout.eval()
          final_count += np.count_nonzero(value)
          # Verifies that there are only two values: 0 and 1/keep_prob.
          sorted_value = np.unique(np.sort(value))
          self.assertEqual(0, sorted_value[0])
          self.assertAllClose(1 / keep_prob, sorted_value[1])
      # Check that we are in the 15% error range
      expected_count = x_dim * y_dim * keep_prob * num_iter
      rel_error = math.fabs(final_count - expected_count) / expected_count
      print rel_error
      self.assertTrue(rel_error < 0.15)

  def testShapedDropout(self):
    # Runs dropout with 0-1 tensor 10 times, sum the number of ones and validate
    # that it is producing approximately the right number of ones over a large
    # number of samples, based on the keep probability. This time with shaped
    # noise.
    x_dim = 40 * 30
    y_dim = 3
    num_iter = 10
    for keep_prob in [0.1, 0.5, 0.8]:
      with self.test_session():
        t = constant_op.constant(1.0,
                                 shape=[x_dim, y_dim],
                                 dtype=types.float32)
        dropout = nn.dropout(t, keep_prob, noise_shape=[x_dim, 1])
        self.assertEqual([x_dim, y_dim], dropout.get_shape())
        final_count = 0
        for _ in xrange(0, num_iter):
          value = dropout.eval()
          final_count += np.count_nonzero(value)
          # Verifies that there are only two values: 0 and 1/keep_prob.
          sorted_value = np.unique(np.sort(value))
          self.assertEqual(0, sorted_value[0])
          self.assertAllClose(1 / keep_prob, sorted_value[1])
      # Check that we are in the 15% error range
      expected_count = x_dim * y_dim * keep_prob * num_iter
      rel_error = math.fabs(final_count - expected_count) / expected_count
      print rel_error
      self.assertTrue(rel_error < 0.15)

  def testShapedDropoutCorrelation(self):
    # Runs a shaped dropout and tests that the correlations are correct.
    x_dim = 40
    y_dim = 30
    num_iter = 10
    for keep_prob in [0.1, 0.5, 0.8]:
      with self.test_session():
        t = constant_op.constant(1.0,
                                 shape=[x_dim, y_dim],
                                 dtype=types.float32)
        dropout = nn.dropout(t, keep_prob, noise_shape=[x_dim, 1])
        self.assertEqual([x_dim, y_dim], dropout.get_shape())
        for _ in xrange(0, num_iter):
          value = dropout.eval()
          # Verifies that each y column as only one type of activation.
          for i in xrange(x_dim):
            sorted_value = np.unique(np.sort(value[i, :]))
            self.assertEqual(sorted_value.size, 1)

  def testShapedDropoutShapeError(self):
    # Runs shaped dropout and verifies an error is thrown on misshapen noise.
    x_dim = 40
    y_dim = 30
    keep_prob = 0.5
    with self.test_session():
      t = constant_op.constant(1.0,
                               shape=[x_dim, y_dim],
                               dtype=types.float32)
      with self.assertRaises(ValueError):
        _ = nn.dropout(t, keep_prob, noise_shape=[x_dim, y_dim + 10])
      with self.assertRaises(ValueError):
        _ = nn.dropout(t, keep_prob, noise_shape=[x_dim, y_dim, 5])
      with self.assertRaises(ValueError):
        _ = nn.dropout(t, keep_prob, noise_shape=[x_dim + 3])
      with self.assertRaises(ValueError):
        _ = nn.dropout(t, keep_prob, noise_shape=[x_dim])
      # test that broadcasting proceeds
      _ = nn.dropout(t, keep_prob, noise_shape=[y_dim])
      _ = nn.dropout(t, keep_prob, noise_shape=[1, y_dim])
      _ = nn.dropout(t, keep_prob, noise_shape=[x_dim, 1])
      _ = nn.dropout(t, keep_prob, noise_shape=[1, 1])


class BatchNormWithGlobalNormalizationTest(test_util.TensorFlowTestCase):

  def _npBatchNorm(self, x, m, v, beta, gamma, epsilon,
                   scale_after_normalization):
    y = (x - m) / np.sqrt(v + epsilon)
    y = y * gamma if scale_after_normalization else y
    y += beta
    return y

  def _opsBatchNorm(self, x, m, v, beta, gamma, epsilon,
                    scale_after_normalization):
    y = (x - m) * math_ops.rsqrt(v + epsilon)
    if scale_after_normalization:
      y = gamma * y
    y += beta
    return y

  def testBatchNorm(self):
    x_shape = [3, 5, 4, 2]
    param_shape = [2]
    x_val = np.random.random_sample(x_shape).astype(np.float32)
    m_val = np.random.random_sample(param_shape).astype(np.float32)
    v_val = np.random.random_sample(param_shape).astype(np.float32)
    beta_val = np.random.random_sample(param_shape).astype(np.float32)
    gamma_val = np.random.random_sample(param_shape).astype(np.float32)
    for use_gpu in [True, False]:
      with self.test_session(use_gpu=use_gpu) as sess:
        x = constant_op.constant(x_val, name="x")
        m = constant_op.constant(m_val, name="m")
        v = constant_op.constant(v_val, name="v")
        beta = constant_op.constant(beta_val, name="beta")
        gamma = constant_op.constant(gamma_val, name="gamma")
        epsilon = 0.001
        for scale_after_normalization in [True, False]:
          bn = nn.batch_norm_with_global_normalization(
              x, m, v, beta, gamma, epsilon, scale_after_normalization)
          on = self._opsBatchNorm(
              x, m, v, beta, gamma, epsilon, scale_after_normalization)
          np_batch_norm = self._npBatchNorm(
              x_val, m_val, v_val, beta_val, gamma_val, epsilon,
              scale_after_normalization)
          tf_batch_norm, ops_batch_norm = sess.run([bn, on])
          self.assertAllClose(np_batch_norm, tf_batch_norm, atol=0.000001)
          self.assertAllClose(np_batch_norm, ops_batch_norm, atol=0.000001)
          self.assertAllClose(tf_batch_norm, ops_batch_norm, atol=0.000001)

  def _testBatchNormGradient(self, param_index, tag, scale_after_normalization,
                             err_tolerance=1e-11):
    x_shape = [3, 5, 4, 5]
    param_shape = [5]
    np.random.seed(1)  # Make it reproducible.
    x_val = np.random.random_sample(x_shape).astype(np.float64)
    m_val = np.random.random_sample(param_shape).astype(np.float64)
    v_val = np.random.random_sample(param_shape).astype(np.float64)
    beta_val = np.random.random_sample(param_shape).astype(np.float64)
    gamma_val = np.random.random_sample(param_shape).astype(np.float64)
    with self.test_session():
      x = constant_op.constant(x_val, name="x")
      m = constant_op.constant(m_val, name="m")
      v = constant_op.constant(v_val, name="v")
      beta = constant_op.constant(beta_val, name="beta")
      gamma = constant_op.constant(gamma_val, name="gamma")
      epsilon = 0.001
      # If scale_after_normalization is False, backprop for gamma
      # will be 0. gamma is unchanged.
      output = nn.batch_norm_with_global_normalization(
          x, m, v, beta, gamma, epsilon, scale_after_normalization)
      all_params = [x, m, v, beta, gamma]
      all_shapes = [x_shape, param_shape, param_shape, param_shape, param_shape]
      err = gc.ComputeGradientError(all_params[param_index],
                                    all_shapes[param_index], output, x_shape)
    print "Batch normalization %s gradient %s scale err = " % (
        tag, "with" if scale_after_normalization else "without"
    ), err
    self.assertLess(err, err_tolerance)

  def testBatchNormInputGradient(self):
    for scale_after_normalization in [True, False]:
      self._testBatchNormGradient(0, "x", scale_after_normalization)

  def testBatchNormMeanGradient(self):
    for scale_after_normalization in [True, False]:
      self._testBatchNormGradient(1, "mean", scale_after_normalization)

  def testBatchNormVarianceGradient(self):
    for scale_after_normalization in [True, False]:
      self._testBatchNormGradient(2, "variance", scale_after_normalization,
                                  err_tolerance=1e-03)

  def testBatchNormBetaGradient(self):
    for scale_after_normalization in [True, False]:
      self._testBatchNormGradient(3, "beta", scale_after_normalization)

  def testBatchNormGammaGradient(self):
    for scale_after_normalization in [True, False]:
      self._testBatchNormGradient(4, "gamma", scale_after_normalization)

  def testBatchNormGradImpl(self):
    x_shape = [7, 5, 4, 6]
    param_shape = [6]
    np.random.seed(1)  # Make it reproducible.
    x_val = np.random.random_sample(x_shape).astype(np.float32)
    m_val = np.random.random_sample(param_shape).astype(np.float32)
    v_val = np.random.random_sample(param_shape).astype(np.float32)
    beta_val = np.random.random_sample(param_shape).astype(np.float32)
    gamma_val = np.random.random_sample(param_shape).astype(np.float32)
    backprop_val = np.random.random_sample(x_shape).astype(np.float32)
    for use_gpu in [False, True]:
      with self.test_session(use_gpu=use_gpu) as sess:
        x = constant_op.constant(x_val, name="x")
        m = constant_op.constant(m_val, name="m")
        v = constant_op.constant(v_val, name="v")
        beta = constant_op.constant(beta_val, name="beta")
        gamma = constant_op.constant(gamma_val, name="gamma")
        backprop = constant_op.constant(backprop_val, name="backprop")
        epsilon = 0.001
        for scale_after_normalization in [True, False]:
          dx, dm, dv, db, dg = (
              gen_nn_ops._batch_norm_with_global_normalization_grad(
              x, m, v, gamma, backprop, epsilon, scale_after_normalization))
          on = self._opsBatchNorm(
              x, m, v, beta, gamma, epsilon, scale_after_normalization)
          odx, odm, odv, odb, odg = gradients.gradients(
              [on], [x, m, v, beta, gamma], [backprop])
          if scale_after_normalization:
            all_grads = sess.run([dx, dm, dv, db, dg, odx, odm, odv, odb, odg])
            to_check = ["dx", "dm", "dv", "db", "dg"]
          else:
            all_grads = sess.run([dx, dm, dv, db, odx, odm, odv, odb])
            to_check = ["dx", "dm", "dv", "db"]
          for i, n in enumerate(to_check):
            print n
            self.assertAllClose(
                all_grads[i + len(to_check)], all_grads[i], atol=0.000001)


class MomentsTest(test_util.TensorFlowTestCase):

  def RunMomentTest(self, shape, global_norm):
    with self.test_session():
      # shape = [batch, width, height, depth]
      assert len(shape) == 4

      x_numpy = np.random.normal(size=shape).astype(np.float32)
      x = constant_op.constant(x_numpy)
      x.set_shape(shape)
      axes = [0, 1, 2] if global_norm else [0]
      mean, var = nn.moments(x, axes)

      num_elements = np.prod([shape[i] for i in axes])

      ax = (0, 1, 2) if global_norm else (0)
      expected_mean = np.sum(x_numpy, axis=ax) / num_elements
      expected_mean_squared = np.multiply(expected_mean, expected_mean)
      expected_x_squared = np.sum(
          np.multiply(x_numpy, x_numpy), axis=ax) / num_elements
      expected_variance = expected_x_squared - expected_mean_squared

      # Check that the moments are correct.
      self.assertAllClose(expected_mean, mean.eval())
      self.assertAllClose(expected_variance, var.eval())

  def testBasic(self):
    self.RunMomentTest(shape=[2, 3, 5, 4], global_norm=False)

  def testGlobalNormalization(self):
    self.RunMomentTest(shape=[2, 3, 5, 4], global_norm=True)

  def _testGlobalGradient(self, from_y="mean"):
    with self.test_session():
      x_shape = [3, 5, 4, 2]
      x_val = np.random.random_sample(x_shape).astype(np.float64)
      x = constant_op.constant(x_val)
      x.set_shape(x_shape)

      axes = [0, 1, 2]
      y_shape = [2]  # Depth of x
      out_mean, out_var = nn.moments(x, axes)
      if from_y == "mean":
        y = out_mean
      elif from_y == "var":
        y = out_var
      err = gc.ComputeGradientError(x, x_shape, y, y_shape)
      print "Moments %s gradient err = %g" % (from_y, err)
      self.assertLess(err, 1e-11)

  def testMeanGlobalGradient(self):
    self._testGlobalGradient(from_y="mean")

  def testVarGlobalGradient(self):
    self._testGlobalGradient(from_y="var")


class ComputeSampledLogitsTest(test_util.TensorFlowTestCase):

  def setUp(self):
    self._num_classes = 5
    self._dim = 10
    self._batch_size = 3

  def _GenerateTestInputs(self):
    np.random.seed(0)
    weights = np.random.randn(self._num_classes, self._dim).astype(np.float32)
    biases = np.random.randn(self._num_classes).astype(np.float32)
    hidden_acts = np.random.randn(self._batch_size, self._dim).astype(
        np.float32)

    return weights, biases, hidden_acts

  def _ComputeSampledLogitsNP(self, true_w, true_b, sampled_w, sampled_b,
                              hidden_acts,
                              num_true=1,
                              true_expected=None,
                              sampled_expected=None):

    batch_size, dim = hidden_acts.shape
    true_logits = np.sum(
        hidden_acts.reshape((batch_size, 1, dim)) * true_w.reshape(
            (batch_size, num_true, dim)),
        axis=2)
    true_b = true_b.reshape((batch_size, num_true))
    true_logits += true_b
    sampled_logits = np.dot(hidden_acts, sampled_w.T) + sampled_b

    if true_expected is not None:
      true_logits -= np.log(true_expected)
    if sampled_expected is not None:
      sampled_logits -= np.log(sampled_expected[np.newaxis, :])

    out_logits = np.concatenate([true_logits, sampled_logits], axis=1)
    out_labels = np.hstack((np.ones_like(true_logits) / num_true,
                            np.zeros_like(sampled_logits)))

    return out_logits, out_labels

  def _ComputeSampledLogitsTF(self, weights, biases, hidden_acts, labels,
                              num_sampled, num_classes, num_true, sampled_vals,
                              subtract_log_q, remove_accidental_hits,
                              name="sampled_loss_TF"):
    # Should be called from within a `with test_session():` block
    weights_tf = constant_op.constant(weights)
    biases_tf = constant_op.constant(biases)
    hidden_acts_tf = constant_op.constant(hidden_acts,
                                          shape=(self._batch_size, self._dim))
    labels_tf = constant_op.constant(labels, dtype=types.int64,
                                     shape=(self._batch_size, num_true))

    pred_logits_tf, pred_labels_tf = nn._compute_sampled_logits(
        weights_tf, biases_tf, hidden_acts_tf, labels_tf, num_sampled,
        num_classes, num_true, sampled_vals,
        subtract_log_q=subtract_log_q,
        remove_accidental_hits=remove_accidental_hits,
        name=name)
    return pred_logits_tf, pred_labels_tf

  def testComputeSampledLogitsShapes(self):
    # We just check that the shapes of the returned values are correct.
    weights, biases, hidden_acts = self._GenerateTestInputs()
    sampled = [1, 0, 2, 3]
    num_sampled = len(sampled)
    true_exp = sampled_exp = [1., 1., 1., 1.]
    test_sampled_vals = (sampled, true_exp, sampled_exp)
    sampled_w, sampled_b = weights[sampled], biases[sampled]

    with self.test_session() as sess:
      for num_true_test in range(1, 5):
        labels = np.random.randint(low=0, high=self._num_classes,
                                   size=self._batch_size * num_true_test)
        true_w, true_b = weights[labels], biases[labels]

        logits_np, labels_np = self._ComputeSampledLogitsNP(
            true_w, true_b, sampled_w, sampled_b, hidden_acts,
            num_true=num_true_test)

        logits_tf, labels_tf = self._ComputeSampledLogitsTF(
            weights, biases, hidden_acts, labels, num_sampled,
            self._num_classes,
            num_true=num_true_test,
            sampled_vals=test_sampled_vals,
            remove_accidental_hits=True,
            subtract_log_q=False)

      logits_tf_val, labels_tf_val = sess.run([logits_tf, labels_tf])
      self.assertEqual(logits_np.shape, logits_tf_val.shape)
      self.assertEqual(labels_np.shape, labels_tf_val.shape)

  def testComputeSampledLogitsValues(self):
    # Here we check the actual numerics.
    weights, biases, hidden_acts = self._GenerateTestInputs()
    eps = 1e-3
    sampled = [1, 0, 2, 3]
    num_sampled = len(sampled)
    true_exp = np.empty([self._batch_size, 1], dtype=np.float32)
    true_exp.fill(0.5)
    sampled_exp = np.empty([num_sampled], dtype=np.float32)
    sampled_exp.fill(0.5)
    sampled_w, sampled_b = weights[sampled], biases[sampled]
    test_sampled_vals = (sampled, true_exp, sampled_exp)

    with self.test_session() as sess:
      for num_true_test in range(1, 5):
        # Generate test data for this run
        labels = np.random.randint(low=0, high=self._num_classes,
                                   size=self._batch_size * num_true_test)
        true_w, true_b = weights[labels], biases[labels]

        # Test 1: Without accidental hit removal or subtract_log_q
        logits_np, labels_np = self._ComputeSampledLogitsNP(
            true_w, true_b, sampled_w, sampled_b, hidden_acts,
            num_true=num_true_test)
        logits_tf, labels_tf = self._ComputeSampledLogitsTF(
            weights, biases, hidden_acts, labels, num_sampled,
            self._num_classes,
            num_true=num_true_test,
            sampled_vals=test_sampled_vals,
            subtract_log_q=False,
            remove_accidental_hits=False,
            name="sampled_loss_test1_num_true%d" % num_true_test)

        logits_tf_val, labels_tf_val = sess.run([logits_tf, labels_tf])
        self.assertAllClose(logits_np, logits_tf_val, eps)
        self.assertAllClose(labels_np, labels_tf_val, eps)

        # Test 2: With accidental hit removal, no subtract_log_q
        logits_tf, labels_tf = self._ComputeSampledLogitsTF(
            weights, biases, hidden_acts, labels, num_sampled,
            self._num_classes,
            num_true=num_true_test,
            sampled_vals=test_sampled_vals,
            subtract_log_q=False,
            remove_accidental_hits=True,
            name="sampled_loss_test2_num_true%d" % num_true_test)

        # Test that the exponentiated logits of accidental hits are near 0.
        # First we need to find the hits in this random test run:
        labels_reshape = labels.reshape((self._batch_size, num_true_test))
        logits_tf_np = logits_tf.eval()
        for row in xrange(self._batch_size):
          row_labels = labels_reshape[row, :]
          for col in xrange(num_sampled):
            if sampled[col] in row_labels:
              # We need to add the num_true_test offset into logits_*
              self.assertNear(
                  np.exp(logits_tf_np[row, col + num_true_test]), 0., eps)

        # Test 3: With subtract_log_q, no accidental hit removal
        logits_np, labels_np = self._ComputeSampledLogitsNP(
            true_w, true_b, sampled_w, sampled_b, hidden_acts,
            num_true=num_true_test,
            true_expected=true_exp,
            sampled_expected=sampled_exp)
        logits_tf, labels_tf = self._ComputeSampledLogitsTF(
            weights, biases, hidden_acts, labels, num_sampled,
            self._num_classes,
            num_true=num_true_test,
            sampled_vals=test_sampled_vals,
            subtract_log_q=True,
            remove_accidental_hits=False,
            name="sampled_loss_test3_num_true%d" % num_true_test)

        logits_tf_val, labels_tf_val = sess.run([logits_tf, labels_tf])
        self.assertAllClose(logits_np, logits_tf_val, eps)
        self.assertAllClose(labels_np, labels_tf_val, eps)

  def testNCELoss(self):
    # A simple test to verify the numerics.

    def _SigmoidCrossEntropyWithLogits(logits, targets):
      # logits, targets: float arrays of the same shape.
      assert logits.shape == targets.shape
      pred = 1. / (1. + np.exp(-logits))
      eps = 0.0001
      pred = np.minimum(np.maximum(pred, eps), 1 - eps)
      return -targets * np.log(pred) - (1. - targets) * np.log(1. - pred)

    weights, biases, hidden_acts = self._GenerateTestInputs()
    labels = [0, 1, 2]
    true_w, true_b = weights[labels], biases[labels]
    sampled = [1, 0, 2, 3]
    num_sampled = len(sampled)
    true_exp = np.empty([self._batch_size, 1], dtype=np.float32)
    true_exp.fill(0.5)
    sampled_exp = np.empty([num_sampled], dtype=np.float32)
    sampled_exp.fill(0.5)
    sampled_w, sampled_b = weights[sampled], biases[sampled]
    test_sampled_vals = (sampled, true_exp, sampled_exp)

    with self.test_session():
      logits_np, labels_np = self._ComputeSampledLogitsNP(
          true_w, true_b, sampled_w, sampled_b, hidden_acts,
          true_expected=true_exp,
          sampled_expected=sampled_exp)
      nce_loss_np = np.sum(
          _SigmoidCrossEntropyWithLogits(logits_np, labels_np), 1)

      labels_tf = constant_op.constant(labels, shape=(self._batch_size, 1))
      weights_tf = constant_op.constant(weights)
      biases_tf = constant_op.constant(biases)
      inputs_tf = constant_op.constant(hidden_acts)

      nce_loss_tf = nn.nce_loss(
          weights_tf, biases_tf, inputs_tf, labels_tf,
          num_sampled=1,
          num_classes=self._num_classes,
          num_true=1,
          sampled_values=test_sampled_vals)

      self.assertAllClose(nce_loss_np, nce_loss_tf.eval(), 1e-4)

  def testSampledSoftmaxLoss(self):
    # A simple test to verify the numerics.

    def _SoftmaxCrossEntropyWithLogits(logits, targets):
      # logits, targets: float arrays of the same shape.
      assert logits.shape == targets.shape
      stable_exp_logits = np.exp(logits - np.amax(
          logits, axis=1, keepdims=True))
      pred = stable_exp_logits / np.sum(stable_exp_logits, 1, keepdims=True)
      return -np.sum(targets * np.log(pred + 1.0e-20), axis=1)

    weights, biases, hidden_acts = self._GenerateTestInputs()
    labels = [0, 1, 2]
    true_w, true_b = weights[labels], biases[labels]
    sampled = [1, 0, 2, 3]
    num_sampled = len(sampled)
    true_exp = np.full([self._batch_size, 1], fill_value=0.5, dtype=np.float32)
    sampled_exp = np.full([num_sampled], fill_value=0.5, dtype=np.float32)
    sampled_w, sampled_b = weights[sampled], biases[sampled]
    test_sampled_vals = (sampled, true_exp, sampled_exp)

    with self.test_session():
      logits_np, labels_np = self._ComputeSampledLogitsNP(
          true_w, true_b, sampled_w, sampled_b, hidden_acts,
          true_expected=true_exp,
          sampled_expected=sampled_exp)
      sampled_softmax_loss_np = _SoftmaxCrossEntropyWithLogits(logits_np,
                                                               labels_np)

      labels_tf = constant_op.constant(labels, shape=(self._batch_size, 1))
      weights_tf = constant_op.constant(weights)
      biases_tf = constant_op.constant(biases)
      inputs_tf = constant_op.constant(hidden_acts)

      sampled_softmax_loss_tf = nn.sampled_softmax_loss(
          weights_tf, biases_tf, inputs_tf, labels_tf,
          num_sampled=1,
          num_classes=self._num_classes,
          num_true=1,
          sampled_values=test_sampled_vals,
          remove_accidental_hits=False)

      self.assertAllClose(
          sampled_softmax_loss_np, sampled_softmax_loss_tf.eval(), 1e-4)


if __name__ == "__main__":
  googletest.main()