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

# Aliases:

• tf.compat.v2.expand_dims

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

# Used in the guide:

• Masking and padding withKeras``
• Recurrent NeuralNetworks (RNN) withKeras``

# Used in the tutorials:

• DeepDream
• Image captioningwith visualattention``
• Neuralmachinetranslationwith attention
• Pix2Pix
• Textclassification withan RNN
• Textgeneration withan RNN
• Transformer model for languageunderstandingGiven 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.

# Other examples:

 # '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]

This operation requires that: -1-input.dims() <= dim <= input.dims() This operation is related to squeeze(), which removes dimensions of size 1.

# Args:

• input: A Tensor.
• axis: 0-D (scalar). Specifies the dimension index at which to expand the shape of input. Must be in the range [-rank(input) - 1, rank(input)].
• : The of the output Tensor (optional).

# Returns:

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