Tensorflow function(二)

tf.get_default_graph:
Returns the default graph for the current thread.


The returned graph will be the innermost graph on which a Graph.as_default() context has been entered, or a global default graph if none has been explicitly created.


NOTE: The default graph is a property of the current thread. If you create a new thread, and wish to use the default graph in that thread, you must explicitly add a with g.as_default(): in that thread's function.


Returns:
The default Graph being used in the current thread




get_operations:
get_operations()
Return the list of operations in the graph.


You can modify the operations in place, but modifications to the list such as inserts/delete have no effect on the list of operations known to the graph.


This method may be called concurrently from multiple threads.


get_tensor_by_name:
get_tensor_by_name(name)
Returns the Tensor with the given name.


This method may be called concurrently from multiple threads.


Args:
name: The name of the Tensor to return.
Returns:
The Tensor with the given name




tf.slice:
slice(
    input_,
    begin,
    size,
    name=None
)


Extracts a slice from a tensor.


This operation extracts a slice of size size from a tensor input starting at the location specified by begin. The slice size is represented as a tensor shape, where size[i] is the number of elements of the 'i'th dimension of input that you want to slice. The starting location (begin) for the slice is represented as an offset in each dimension of input. In other words, begin[i] is the offset into the 'i'th dimension of input that you want to slice from.


t = tf.constant([[[1, 1, 1], [2, 2, 2]],
                 [[3, 3, 3], [4, 4, 4]],
                 [[5, 5, 5], [6, 6, 6]]])


tf.slice(t, [1, 0, 0], [1, 1, 3])  # [[[3, 3, 3]]]


tf.slice(t, [1, 0, 0], [1, 2, 3])  # [[[3, 3, 3],
                                       #   [4, 4, 4]]]


tf.slice(t, [1, 0, 0], [2, 1, 3])  # [[[3, 3, 3]],
                                   #  [[5, 5, 5]]]




tf.expand_dims:
expand_dims(
    input,
    axis=None,
    name=None,
    dim=None
)


Inserts a dimension of 1 into a tensor's shape.


Given a tensor input, this operation inserts a dimension of 1 at the dimension index axis of input's shape. The dimension index axis starts at zero; if you specify a negative number for axis it is counted backward from the end.


This operation is useful if you want to add a batch dimension to a single element. For example, if you have a single image of shape [height, width, channels], you can make it a batch of 1 image with expand_dims(image, 0), which will make the shape [1, height, width, channels].


# 't' is a tensor of shape [2]
tf.shape(tf.expand_dims(t, 0))  # [1, 2]
tf.shape(tf.expand_dims(t, 1))  # [2, 1]
tf.shape(tf.expand_dims(t, -1))  # [2, 1]


# 't2' is a tensor of shape [2, 3, 5]
tf.shape(tf.expand_dims(t2, 0))  # [1, 2, 3, 5]
tf.shape(tf.expand_dims(t2, 2))  # [2, 3, 1, 5]
tf.shape(tf.expand_dims(t2, 3))  # [2, 3, 5, 1]


Returns:
A Tensor with the same data as input, but its shape has an additional dimension of size 1 added.




tf.squeeze:
squeeze(
    input,
    axis=None,
    name=None,
    squeeze_dims=None
)
Removes dimensions of size 1 from the shape of a tensor.


Given a tensor input, this operation returns a tensor of the same type with all dimensions of size 1 removed. If you don't want to remove all size 1 dimensions, you can remove specific size 1 dimensions by specifying axis.


# 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
tf.shape(tf.squeeze(t))  # [2, 3]


# 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
tf.shape(tf.squeeze(t, [2, 4]))  # [1, 2, 3, 1]


Returns:
A Tensor. Has the same type as input. Contains the same data as input, but has one or more dimensions of size 1 removed.