深入浅出TensorFlow2函数—

分类目录：《深入浅出TensorFlow2函数》总目录

语法

tf.reshape(tensor, shape, name=None
)

参数

返回值

返回一个新的形状为shape的tf.Tensor且具有与tensor以同样的顺序和相同的值。

实例

输入：

t1 = [[1, 2, 3],[4, 5, 6]]
print(tf.shape(t1).numpy())        # [2 3]
t2 = tf.reshape(t1, [6])
t2        #<tf.Tensor: shape=(6,), dtype=int32, numpy=array([1, 2, 3, 4, 5, 6], dtype=int32)>
tf.reshape(t2, [3, 2])        # <tf.Tensor: shape=(3, 2), dtype=int32, numpy= array([[1, 2], [3, 4], [5, 6]], dtype=int32)>

如果shape的一个参数为是-1，则计算该维度的大小，使总大小保持不变。特别是，若shape为[-1]，则将tensor展平为一维。shape的参数最多只能有一个-1。此外，若t为仅含一个元素的tensor，则tf.reshape(t, [])将t转变为一个标量。

函数实现

@tf_export("reshape", v1=["reshape", "manip.reshape"])
@dispatch.add_dispatch_support
def reshape(tensor, shape, name=None):  # pylint: disable=redefined-outer-namer"""Reshapes a tensor.Given `tensor`, this operation returns a new `tf.Tensor` that has the samevalues as `tensor` in the same order, except with a new shape given by`shape`.>>> t1 = [[1, 2, 3],...       [4, 5, 6]]>>> print(tf.shape(t1).numpy())[2 3]>>> t2 = tf.reshape(t1, [6])>>> t2<tf.Tensor: shape=(6,), dtype=int32,numpy=array([1, 2, 3, 4, 5, 6], dtype=int32)>>>> tf.reshape(t2, [3, 2])<tf.Tensor: shape=(3, 2), dtype=int32, numpy=array([[1, 2],[3, 4],[5, 6]], dtype=int32)>The `tf.reshape` does not change the order of or the total number of elementsin the tensor, and so it can reuse the underlying data buffer. This makes ita fast operation independent of how big of a tensor it is operating on.>>> tf.reshape([1, 2, 3], [2, 2])Traceback (most recent call last):...InvalidArgumentError: Input to reshape is a tensor with 3 values, but therequested shape has 4To instead reorder the data to rearrange the dimensions of a tensor, see`tf.transpose`.>>> t = [[1, 2, 3],...      [4, 5, 6]]>>> tf.reshape(t, [3, 2]).numpy()array([[1, 2],[3, 4],[5, 6]], dtype=int32)>>> tf.transpose(t, perm=[1, 0]).numpy()array([[1, 4],[2, 5],[3, 6]], dtype=int32)If one component of `shape` is the special value -1, the size of thatdimension is computed so that the total size remains constant.  In particular,a `shape` of `[-1]` flattens into 1-D.  At most one component of `shape` canbe -1.>>> t = [[1, 2, 3],...      [4, 5, 6]]>>> tf.reshape(t, [-1])<tf.Tensor: shape=(6,), dtype=int32,numpy=array([1, 2, 3, 4, 5, 6], dtype=int32)>>>> tf.reshape(t, [3, -1])<tf.Tensor: shape=(3, 2), dtype=int32, numpy=array([[1, 2],[3, 4],[5, 6]], dtype=int32)>>>> tf.reshape(t, [-1, 2])<tf.Tensor: shape=(3, 2), dtype=int32, numpy=array([[1, 2],[3, 4],[5, 6]], dtype=int32)>`tf.reshape(t, [])` reshapes a tensor `t` with one element to a scalar.>>> tf.reshape([7], []).numpy()7More examples:>>> t = [1, 2, 3, 4, 5, 6, 7, 8, 9]>>> print(tf.shape(t).numpy())[9]>>> tf.reshape(t, [3, 3])<tf.Tensor: shape=(3, 3), dtype=int32, numpy=array([[1, 2, 3],[4, 5, 6],[7, 8, 9]], dtype=int32)>>>> t = [[[1, 1], [2, 2]],...      [[3, 3], [4, 4]]]>>> print(tf.shape(t).numpy())[2 2 2]>>> tf.reshape(t, [2, 4])<tf.Tensor: shape=(2, 4), dtype=int32, numpy=array([[1, 1, 2, 2],[3, 3, 4, 4]], dtype=int32)>>>> t = [[[1, 1, 1],...       [2, 2, 2]],...      [[3, 3, 3],...       [4, 4, 4]],...      [[5, 5, 5],...       [6, 6, 6]]]>>> print(tf.shape(t).numpy())[3 2 3]>>> # Pass '[-1]' to flatten 't'.>>> tf.reshape(t, [-1])<tf.Tensor: shape=(18,), dtype=int32,numpy=array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6],dtype=int32)>>>> # -- Using -1 to infer the shape -->>> # Here -1 is inferred to be 9:>>> tf.reshape(t, [2, -1])<tf.Tensor: shape=(2, 9), dtype=int32, numpy=array([[1, 1, 1, 2, 2, 2, 3, 3, 3],[4, 4, 4, 5, 5, 5, 6, 6, 6]], dtype=int32)>>>> # -1 is inferred to be 2:>>> tf.reshape(t, [-1, 9])<tf.Tensor: shape=(2, 9), dtype=int32, numpy=array([[1, 1, 1, 2, 2, 2, 3, 3, 3],[4, 4, 4, 5, 5, 5, 6, 6, 6]], dtype=int32)>>>> # -1 is inferred to be 3:>>> tf.reshape(t, [ 2, -1, 3])<tf.Tensor: shape=(2, 3, 3), dtype=int32, numpy=array([[[1, 1, 1],[2, 2, 2],[3, 3, 3]],[[4, 4, 4],[5, 5, 5],[6, 6, 6]]], dtype=int32)>Args:tensor: A `Tensor`.shape: A `Tensor`. Must be one of the following types: `int32`, `int64`.Defines the shape of the output tensor.name: Optional string. A name for the operation.Returns:A `Tensor`. Has the same type as `tensor`."""result = gen_array_ops.reshape(tensor, shape, name)tensor_util.maybe_set_static_shape(result, shape)return result

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深入浅出TensorFlow2函数——tf.reshape

语法

参数

返回值

实例

函数实现

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