# How to use from_tensors method in tf.data.Dataset| TensorFlow

`from_tensors` method of `tf.data.Dataset` creates a Dataset with single element. There is no slicing operation along first dimesion as it is done in method `from_tensor_slices `. Lets understand use of `from_tensors` with some examples.

###### Create 1D tensor and use `tf.data.Dataset.from_tensors on it.`

``````
import tensorflow as tf
t1_1D = tf.constant(value = [3,6,7])
print(t1_1D.shape)
dataset = tf.data.Dataset.from_tensors(t1_1D)
print(list(dataset))
```
```

###### Example Output:
Note there is no change in shape of produced tensor

``````
(3,)
[<tf.Tensor: shape=(3,), dtype=int32, numpy=array([3, 6, 7], dtype=int32)>]
```
```

###### Create 2D tensor and use `tf.data.Dataset.from_tensors`

``````
import tensorflow as tf
t2_2D = tf.constant(value = [[2,3,4], [4,5,6]])
print(t2_2D.shape)
dataset = tf.data.Dataset.from_tensors(t2_2D)
print(list(dataset))
```
```

###### Example Output:
Note there is no change in shape of produced tensor

``````
(2, 3)
[<tf.Tensor: shape=(2, 3), dtype=int32, numpy=
array([[2, 3, 4],
[4, 5, 6]], dtype=int32)>]
```
```

###### Create 3D tensor and use `tf.data.Dataset.from_tensors` on this tensor

``````
import tensorflow as tf
t3_3D = tf.constant(value = [[[2,3,4]], [[4,5,6]]])
print(t3_3D.shape)
dataset = tf.data.Dataset.from_tensors(t3_3D)
print(list(dataset))
```
```

###### Example Output:
Note there is no change in shape of produced tensor

``````
(2, 1, 3)
[<tf.Tensor: shape=(2, 1, 3), dtype=int32, numpy=
array([[[2, 3, 4]],

[[4, 5, 6]]], dtype=int32)>]
```
```

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