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nn_impl.py cleanup: used keepdims instead of deprecated keep_dims.

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PiperOrigin-RevId: 177972555
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A. Unique TensorFlower 2017-12-05 10:17:45 -08:00 committed by TensorFlower Gardener
parent f88cd91955
commit c72bb97541
1 changed files with 8 additions and 8 deletions
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16
tensorflow/python/ops/nn_impl.py
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@ -341,7 +341,7 @@ def l2_normalize(x, axis=None, epsilon=1e-12, name=None, dim=None):
with ops.name_scope(name, "l2_normalize", [x]) as name: with ops.name_scope(name, "l2_normalize", [x]) as name:
axis = deprecated_argument_lookup("axis", axis, "dim", dim) axis = deprecated_argument_lookup("axis", axis, "dim", dim)
x = ops.convert_to_tensor(x, name="x") x = ops.convert_to_tensor(x, name="x")
square_sum = math_ops.reduce_sum(math_ops.square(x), axis, keep_dims=True) square_sum = math_ops.reduce_sum(math_ops.square(x), axis, keepdims=True)
x_inv_norm = math_ops.rsqrt(math_ops.maximum(square_sum, epsilon)) x_inv_norm = math_ops.rsqrt(math_ops.maximum(square_sum, epsilon))
return math_ops.multiply(x, x_inv_norm, name=name) return math_ops.multiply(x, x_inv_norm, name=name)
@ -593,8 +593,8 @@ def sufficient_statistics(x, axes, shift=None, keep_dims=False, name=None):
else: # no shift. else: # no shift.
m_ss = x m_ss = x
v_ss = math_ops.square(x) v_ss = math_ops.square(x)
m_ss = math_ops.reduce_sum(m_ss, axes, keep_dims=keep_dims, name="mean_ss") m_ss = math_ops.reduce_sum(m_ss, axes, keepdims=keep_dims, name="mean_ss")
v_ss = math_ops.reduce_sum(v_ss, axes, keep_dims=keep_dims, name="var_ss") v_ss = math_ops.reduce_sum(v_ss, axes, keepdims=keep_dims, name="var_ss")
return counts, m_ss, v_ss, shift return counts, m_ss, v_ss, shift
@ -664,12 +664,12 @@ def moments(x, axes,
# on 32-bit floats before converting the mean and variance back to fp16 # on 32-bit floats before converting the mean and variance back to fp16
y = math_ops.cast(x, dtypes.float32) if x.dtype == dtypes.float16 else x y = math_ops.cast(x, dtypes.float32) if x.dtype == dtypes.float16 else x
# Compute true mean while keeping the dims for proper broadcasting. # Compute true mean while keeping the dims for proper broadcasting.
mean = math_ops.reduce_mean(y, axes, keep_dims=True, name="mean") mean = math_ops.reduce_mean(y, axes, keepdims=True, name="mean")
# sample variance, not unbiased variance # sample variance, not unbiased variance
variance = math_ops.reduce_mean( variance = math_ops.reduce_mean(
math_ops.squared_difference(y, array_ops.stop_gradient(mean)), math_ops.squared_difference(y, array_ops.stop_gradient(mean)),
axes, axes,
keep_dims=True, keepdims=True,
name="variance") name="variance")
if not keep_dims: if not keep_dims:
mean = array_ops.squeeze(mean, axes) mean = array_ops.squeeze(mean, axes)
@ -714,7 +714,7 @@ def weighted_moments(x, axes, frequency_weights, name=None, keep_dims=False):
# Note that we use keep_dims=True for our reductions regardless of the arg; # Note that we use keep_dims=True for our reductions regardless of the arg;
# this is so that the results remain broadcast-compatible with the inputs. # this is so that the results remain broadcast-compatible with the inputs.
weighted_input_sum = math_ops.reduce_sum( weighted_input_sum = math_ops.reduce_sum(
frequency_weights * x, axes, name="weighted_input_sum", keep_dims=True) frequency_weights * x, axes, name="weighted_input_sum", keepdims=True)
# The shape of the weights isn't necessarily the same as x's # The shape of the weights isn't necessarily the same as x's
# shape, just broadcast-compatible with it -- so this expression # shape, just broadcast-compatible with it -- so this expression
@ -725,7 +725,7 @@ def weighted_moments(x, axes, frequency_weights, name=None, keep_dims=False):
broadcasted_weights = frequency_weights + array_ops.zeros_like(x) broadcasted_weights = frequency_weights + array_ops.zeros_like(x)
sum_of_weights = math_ops.reduce_sum( sum_of_weights = math_ops.reduce_sum(
broadcasted_weights, axes, name="sum_of_weights", keep_dims=True) broadcasted_weights, axes, name="sum_of_weights", keepdims=True)
divisor = math_ops.reciprocal(sum_of_weights, name="inv_weight_sum") divisor = math_ops.reciprocal(sum_of_weights, name="inv_weight_sum")
@ -736,7 +736,7 @@ def weighted_moments(x, axes, frequency_weights, name=None, keep_dims=False):
frequency_weights * math_ops.squared_difference(x, weighted_mean), frequency_weights * math_ops.squared_difference(x, weighted_mean),
axes, axes,
name="weighted_distsq", name="weighted_distsq",
keep_dims=True) keepdims=True)
weighted_variance = math_ops.multiply(weighted_distsq, divisor) weighted_variance = math_ops.multiply(weighted_distsq, divisor)