def reduce_sum(input_tensor,
axis=None,
keep_dims=False,
name=None,
reduction_indices=None):
if reduction_indices is not None:
if axis is not None:
raise ValueError(
"cannot specify both 'axis' and 'reduction_indices'.")
axis = reduction_indices
elif axis is None:
axis = -1 # reduce all
if isinstance(axis, list) or isinstance(axis,
tuple): # reduce continuously
if len(axis) < 1:
raise RuntimeError('reduce axes should at least have one.')
if len(axis) == 1:
return ops.Sum(input_tensor, axis=axis[0], keep_dims=keep_dims)
else:
ret = ops.Sum(input_tensor, axis=axis[0], keep_dims=True)
for i in xrange(1, len(axis) - 1):
ret = ops.Sum(ret, axis=axis[i], keep_dims=True)
return ops.Sum(ret, axis=axis[len(axis) - 1], keep_dims=keep_dims)
else:
return ops.Sum(input_tensor, axis=axis, keep_dims=keep_dims)
def reduce_sum(input_tensor,
axis=None,
keep_dims=False,
name=None,
reduction_indices=None):
"""
Computes the sum of elements across dimensions of a tensor.
Reduces `input_tensor` along the dimensions given in `axis`.
Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each
entry in `axis`. If `keep_dims` is true, the reduced dimensions
are retained with length 1.
If `axis` has no entries, all dimensions are reduced, and a
tensor with a single element is returned.
For example:
```python
# 'x' is [[1, 1, 1]
# [1, 1, 1]]
tf.reduce_sum(x) ==> 6
tf.reduce_sum(x, 0) ==> [2, 2, 2]
tf.reduce_sum(x, 1) ==> [3, 3]
tf.reduce_sum(x, 1, keep_dims=True) ==> [[3], [3]]
tf.reduce_sum(x, [0, 1]) ==> 6
```
Args:
input_tensor: The tensor to reduce. Should have numeric type.
axis: The dimensions to reduce. If `None` (the default),
reduces all dimensions.
keep_dims: If true, retains reduced dimensions with length 1.
name: A name for the operation (optional).
reduction_indices: The old (deprecated) name for axis.
Returns:
The reduced tensor.
"""
if reduction_indices is not None:
if axis is not None:
raise ValueError(
"cannot specify both 'axis' and 'reduction_indices'.")
axis = reduction_indices
elif axis is None:
axis = -1 # reduce all
if isinstance(axis, list) or isinstance(axis,
tuple): # reduce continuously
if len(axis) < 1:
raise RuntimeError('reduce axes should at least have one.')
if len(axis) == 1:
return ops.Sum(input_tensor, axis=axis[0], keep_dims=keep_dims)
else:
ret = ops.Sum(input_tensor, axis=axis[0], keep_dims=True)
for i in xrange(1, len(axis) - 1):
ret = ops.Sum(ret, axis=axis[i], keep_dims=True)
return ops.Sum(ret, axis=axis[len(axis) - 1], keep_dims=keep_dims)
else:
return ops.Sum(input_tensor, axis=axis, keep_dims=keep_dims)
def _create_graph(self):
self.x = Tensor(shape=[None, self.img_channels, self.img_height, self.img_width]).Variable()
self.y_r = Tensor(shape=[None], name='Yr').Variable()
# As implemented in A3C paper
self.n1 = ops.Relu(ops.Conv2D([self.x] + self.weight_bias(), kernel_size=8, stride=4, num_output=16))
self.n2 = ops.Relu(ops.Conv2D([self.n1] + self.weight_bias(), kernel_size=4, stride=2, num_output=32))
self.action_index = Tensor(shape=[None, self.num_actions]).Variable()
self.d1 = ops.Relu(ops.InnerProduct([self.n2] + self.weight_bias(), num_output=256))
self.logits_v = ops.InnerProduct([self.d1] + self.weight_bias(), num_output=1)
self.cost_v = ops.L2Loss([self.y_r, self.logits_v])
self.logits_p = ops.InnerProduct([self.d1] + self.weight_bias(), num_output=self.num_actions)
if Config.USE_LOG_SOFTMAX: raise NotImplementedError()
else:
self.softmax_p = ops.Softmax(self.logits_p)
self.selected_action_prob = ops.Sum(self.softmax_p * self.action_index, axis=1)
self.cost_p_1 = ops.Log(ops.Clip(self.selected_action_prob, self.log_epsilon, None)) * \
(self.y_r - ops.StopGradient(self.logits_v))
self.cost_p_2 = ops.Sum(ops.Log(ops.Clip(self.softmax_p, self.log_epsilon, None)) *
self.softmax_p, axis=1) * (-self.beta)
self.cost_p_1_agg = ops.Sum(self.cost_p_1)
self.cost_p_2_agg = ops.Sum(self.cost_p_2)
self.cost_p = -(self.cost_p_1_agg + self.cost_p_2_agg)
self.cost_all = self.cost_p + self.cost_v
if Config.DUAL_RMSPROP: raise NotImplementedError()
else:
if Config.USE_GRAD_CLIP:
self.opt = updaters.RMSPropUpdater(decay=Config.RMSPROP_DECAY,
eps=Config.RMSPROP_EPSILON,
clip_gradient=Config.GRAD_CLIP_NORM)
else:
self.opt = updaters.RMSPropUpdater(decay=Config.RMSPROP_DECAY,
eps=Config.RMSPROP_EPSILON)
grads = T.grad(self.cost_all, self.network_params)
for p, g in zip(self.network_params, grads):
self.opt.append((p, g), lr_mult=1.0)
def reduce_sum(
input_tensor,
axis=None,
keep_dims=False,
name=None,
reduction_indices=None,
):
if reduction_indices is not None:
if axis is not None:
raise ValueError(
"Cannot specify both 'axis' and 'reduction_indices'.")
axis = reduction_indices
return _ops.Sum(
input_tensor,
axes=axis,
keep_dims=keep_dims,
name=name,
)
def categorical_crossentropy(coding_dist, true_dist, axis=1):
"""Compute the categorical cross-entropy between input and target distribution.
Parameters
----------
coding_dist : Tensor
The distribution of input.
true_dist : Tensor
The distribution of target.
axis : int
The axis of category.
Returns
-------
Tensor
The categorical cross-entropy.
"""
return -ops.Sum(true_dist * ops.Log(coding_dist), axis=axis)
def sum(input, axis=None, keepdims=False, **kwargs):
"""Compute the sum along the given axis.
Parameters
----------
input : Tensor
The input tensor.
axis : int
The axis to compute. Default is ``None`` (Along all axes).
keep_dims : boolean
Whether to keep dims after computing.
Returns
-------
Tensor
The sum result.
"""
if axis is None: axis = -1
return ops.Sum(input, axis=axis, keep_dims=keepdims)