message: [tfgan] Replace deprecated ops.
diff: @@ -387,7 +387,7 @@ def classifier_score_from_logits(logits):
  # Use maximum precision for best results.
  logits_dtype = logits.dtype
  if logits_dtype != dtypes.float64:
  - logits = math_ops.to_double(logits)
  + logits = math_ops.cast(logits, dtypes.float64)
  p = nn_ops.softmax(logits)
  q = math_ops.reduce_mean(p, axis=0)
  @@ -562,8 +562,8 @@ def mean_only_frechet_classifier_distance_from_activations(
  activations_dtype = real_activations.dtype
  if activations_dtype != dtypes.float64:
  - real_activations = math_ops.to_double(real_activations)
  - generated_activations = math_ops.to_double(generated_activations)
  + real_activations = math_ops.cast(real_activations, dtypes.float64)
  + generated_activations = math_ops.cast(generated_activations, dtypes.float64)
  # Compute means of activations.
  m = math_ops.reduce_mean(real_activations, 0)
  @@ -623,8 +623,8 @@ def diagonal_only_frechet_classifier_distance_from_activations(
  activations_dtype = real_activations.dtype
  if activations_dtype != dtypes.float64:
  - real_activations = math_ops.to_double(real_activations)
  - generated_activations = math_ops.to_double(generated_activations)
  + real_activations = math_ops.cast(real_activations, dtypes.float64)
  + generated_activations = math_ops.cast(generated_activations, dtypes.float64)
  # Compute mean and covariance matrices of activations.
  m, var = nn_impl.moments(real_activations, axes=[0])
  @@ -698,15 +698,16 @@ def frechet_classifier_distance_from_activations(real_activations,
  activations_dtype = real_activations.dtype
  if activations_dtype != dtypes.float64:
  - real_activations = math_ops.to_double(real_activations)
  - generated_activations = math_ops.to_double(generated_activations)
  + real_activations = math_ops.cast(real_activations, dtypes.float64)
  + generated_activations = math_ops.cast(generated_activations, dtypes.float64)
  # Compute mean and covariance matrices of activations.
  m = math_ops.reduce_mean(real_activations, 0)
  m_w = math_ops.reduce_mean(generated_activations, 0)
  - num_examples_real = math_ops.to_double(array_ops.shape(real_activations)[0])
  - num_examples_generated = math_ops.to_double(
  - array_ops.shape(generated_activations)[0])
  + num_examples_real = math_ops.cast(
  + array_ops.shape(real_activations)[0], dtypes.float64)
  + num_examples_generated = math_ops.cast(
  + array_ops.shape(generated_activations)[0], dtypes.float64)
  # sigma = (1 / (n - 1)) * (X - mu) (X - mu)^T
  real_centered = real_activations - m
  