Stacks a list of rank-R tensors into one rank-(R+1) tensor.

# Aliases:

• tf.compat.v1.stack
• tf.compat.v2.stack

 tf.stack(
    values,
    axis=0,
    name='stack'
)

# Used in the guide:

• The Keras functionalAPIin TensorFlow``
• tf.data:BuildTensorFlow input pipelines

# Used in the tutorials:

• Customtraining:walkthrough``
• LoadCSVdata
• Loadapandas.DataFrame
• Pix2Pix Packs the list of tensors in values into a tensor with rank one higher than each tensor in values, by packing them along the axis dimension. Given a list of length N of tensors of shape (A, B, C); if axis == 0 then the output tensor will have the shape (N, A, B, CN, A, B, C). if axis == 1 then the output tensor will have the shape (``). Etc.

# For example:

 x = tf.constant([1, 4])
y = tf.constant([2, 5])
z = tf.constant([3, 6])
tf.stack([x, y, z])  # [[1, 4], [2, 5], [3, 6]] (Pack along first dim.)
tf.stack([x, y, z], axis=1)  # [[1, 2, 3], [4, 5, 6]]

This is the opposite of unstack. The numpy equivalent is

 tf.stack([x, y, z]) = np.stack([x, y, z])

# Args:

• values: A list of Tensor objects with the same shape and type.

# Returns:

• output: A stacked Tensor with the same type as values.

# Raises:

• ValueError: If axis is out of the range [-(R+1), R+1).