# Supporting TensorFlow Layers

You can use the following TensorFlow layers to train deep learning models that are supported by AWS DeepLens.

Supported TensorFlow Layers | |
---|---|

Layer | Description |

Add |
Computes element-wise addition |

AvgPool |
Performs average pooling on the input |

BatchToSpaceND |
Rearranges data from batch into blocks of spatial data |

BiasAdd |
Adds bias |

Const |
Creates a constant tensor |

Conv2D |
Computes a 2-D convolution |

Conv2DBackpropInput |
Computes the gradients of convolution with respect to the input |

Identity |
Returns a tensor with the same shape and contents as input |

Maximum |
Computes element-wise maximization. |

MaxPool |
Performs the max pooling on the input |

Mean |
Computes the mean of elements across dimensions of a tensor |

Mul |
Computes element-wise multiplication |

Neg |
Computes numerical negative value element-wise |

Pad |
Pads a tensor |

Placeholder |
Inserts a placeholder for a tensor that will be always fed |

Prod |
Computes the product of elements across dimensions of a tensor |

RandomUniform |
Outputs random values from a uniform distribution |

Range |
Creates a sequence of numbers |

Relu |
Computes rectified linear activations |

Reshape |
Reshapes a tensor |

Rsqrt |
Computes reciprocal of square root |

Shape |
Returns the shape of a tensor |

Softmax |
Computes Softmax activations |

SpaceToBatchND |
Zero-pads and then rearranges blocks of spatial data into batch |

Square |
Computes element-wise square |

Squeeze |
Removes dimensions of size 1 from the shape of a tensor |

StopGradient |
Stops gradient computation |

Sub |
Computes element-wise subtraction |

Sum |
Computes the sum of elements across dimensions of a tensor |

Tile |
Constructs a tensor by tiling a given tensor |

For more information about TensorFlow layers, see TensorFlow Layers .