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# tf.keras.layers.Dot

Layer that computes a dot product between samples in two tensors.

Inherits From: `Layer`, `Module`

### Used in the notebooks

Used in the tutorials

E.g. if applied to a list of two tensors `a` and `b` of shape `(batch_size, n)`, the output will be a tensor of shape `(batch_size, 1)` where each entry `i` will be the dot product between `a[i]` and `b[i]`.

````x = np.arange(10).reshape(1, 5, 2)`
`print(x)`
`[[[0 1]`
`  [2 3]`
`  [4 5]`
`  [6 7]`
`  [8 9]]]`
`y = np.arange(10, 20).reshape(1, 2, 5)`
`print(y)`
`[[[10 11 12 13 14]`
`  [15 16 17 18 19]]]`
`tf.keras.layers.Dot(axes=(1, 2))([x, y])`
`<tf.Tensor: shape=(1, 2, 2), dtype=int64, numpy=`
`array([[[260, 360],`
`        [320, 445]]])>`
```
````x1 = tf.keras.layers.Dense(8)(np.arange(10).reshape(5, 2))`
`x2 = tf.keras.layers.Dense(8)(np.arange(10, 20).reshape(5, 2))`
`dotted = tf.keras.layers.Dot(axes=1)([x1, x2])`
`dotted.shape`
`TensorShape([5, 1])`
```

`axes` Integer or tuple of integers, axis or axes along which to take the dot product. If a tuple, should be two integers corresponding to the desired axis from the first input and the desired axis from the second input, respectively. Note that the size of the two selected axes must match.
`normalize` Whether to L2-normalize samples along the dot product axis before taking the dot product. If set to True, then the output of the dot product is the cosine proximity between the two samples.
`**kwargs` Standard layer keyword arguments.