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@ -1516,7 +1516,8 @@ def batch_norm(input,
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in_place=False,
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name=None,
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moving_mean_name=None,
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moving_variance_name=None):
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moving_variance_name=None,
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do_model_average_for_mean_and_var=False):
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"""
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This function helps create an operator to implement
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the BatchNorm layer using the configurations from the input parameters.
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@ -1547,7 +1548,10 @@ def batch_norm(input,
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mean = helper.create_parameter(
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attr=ParamAttr(
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name=moving_mean_name, initializer=Constant(0.0), trainable=False),
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name=moving_mean_name,
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initializer=Constant(0.0),
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trainable=False,
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do_model_average=do_model_average_for_mean_and_var),
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shape=param_shape,
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dtype=input.dtype)
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mean.stop_gradient = True
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@ -1556,7 +1560,8 @@ def batch_norm(input,
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attr=ParamAttr(
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name=moving_variance_name,
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initializer=Constant(1.0),
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trainable=False),
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trainable=False,
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do_model_average=do_model_average_for_mean_and_var),
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shape=param_shape,
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dtype=input.dtype)
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variance.stop_gradient = True
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@ -3374,14 +3379,14 @@ def reshape(x, shape, actual_shape=None, act=None, inplace=True, name=None):
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Here are some examples to explain it.
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1. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape
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is [6, 8], the reshape operator will transform x into a 2-D tensor with
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is [6, 8], the reshape operator will transform x into a 2-D tensor with
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shape [6, 8] and leaving x's data unchanged.
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2. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape
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specified is [2, 3, -1, 2], the reshape operator will transform x into a
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4-D tensor with shape [2, 3, 4, 2] and leaving x's data unchanged. In this
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case, one dimension of the target shape is set to -1, the value of this
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dimension is inferred from the total element number of x and remaining
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case, one dimension of the target shape is set to -1, the value of this
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dimension is inferred from the total element number of x and remaining
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dimensions.
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3. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape
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@ -3615,7 +3620,7 @@ def lrn(input, n=5, k=1.0, alpha=1e-4, beta=0.75, name=None):
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def pad(x, paddings, pad_value=0., name=None):
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"""
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Pads a tensor with a constant value given by :attr:`pad_value`, and the
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padded width is specified by :attr:`paddings`.
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padded width is specified by :attr:`paddings`.
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Specifically, the number of values padded before the contents of :attr:`x`
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in dimension :attr:`i` is indicated by :attr:`paddings[i]`, and the number
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@ -3643,7 +3648,7 @@ def pad(x, paddings, pad_value=0., name=None):
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x (Variable): The input tensor variable.
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paddings (list): A list of integers. Its elements specify the padded
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width before and after for each dimension in turn.
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The length of :attr:paddings must be
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The length of :attr:paddings must be
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:math:`rank(x) \\times 2`.
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pad_value (float): The constant value used to pad.
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name(str|None): A name for this layer(optional). If set None, the layer
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weixing02
7 years ago