Summary table

Following table contains the list of the operators that can be imported, if the constraints or limitations are met. The 28 custom operators are listed in the next table.

• supported optional fused activation (or non-linearity): gelu, linear, relu, quantized_relu, relu_n1_to_1, relu_0_to_1, leaky_relu, relu6, elu, selu, sigmoid, hard_sigmoid, hard_swish, exponential, tanh, softmax, softplus, softsign, abs, acos, acosh, asin, asinh, atan, atanh, ceil, clip, cos, cosh, erf, flexerf, exp, floor, identity, log, logistic, neg, logical_not, prelu, probit, reciprocal, relu_generic, relu_thresholded, round, sign, sin, sinh, softmax_zero, sqrt, swish, tan
  
• supported optional fused integer activation (or non-linearity): prelu, relu, clip, lut, swish, identity, relu6
  
• integer operation for Keras models are only supported through QKeras and Larq extensions
  
• if an operator is not supported in integer, floating point version is used. Converters are automatically added by the code generator.

Custom operators

Following table contains the list of the custom Keras 2 operators that can be imported.

Common constraints

• input and output tensors must be not dynamic.
  • variable-length batch dimension (i.e. (None,)) is considered as equal to 1
    
  • must not be greater than 6D
    
  • dimension must be in the range [0, 65536[
    
  • batch dimension is partially supported in the axis/axes parameters
    
• data type for the weights/activations tensors must be:
  • float32, int8, uint8
    
  • only int32 for the bias tensor is considered
    
  • for some operators, bool and binary types are also supported
    
• 1D operator is mapped on the respective 2D operator by adding a singleton dimension on the input: (12,3) -> (12, 1, 3)

ops.absolute

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• Supported only in Keras 3 models

layers.Activation

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8
  
• fused activations (if present): gelu, linear, relu, quantized_relu, relu_n1_to_1, relu_0_to_1, leaky_relu, relu6, elu, selu, sigmoid, hard_sigmoid, hard_swish, exponential, tanh, softmax, softplus, softsign

Specific constraints/recommendations:

• softmax is always conserved in float32, converters are added if necessary

layers.ActivityRegularization

Applies Dropout to the input

• category: regularization layers
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• operator is dropped during the conversion

layers.Add

Performs element-wise operation

• category: eltwise operator
  
• input data types: float32
  
• output data types: float32

layers.AlphaDropout

Applies Dropout to the input

• category: regularization layers
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• operator is dropped during the conversion

ops.arccos

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• Supported only in Keras 3 models

ops.arccosh

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• Supported only in Keras 3 models

ops.arcsin

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• Supported only in Keras 3 models

ops.arcsinh

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• Supported only in Keras 3 models

ops.arctan

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• Supported only in Keras 3 models

ops.arctan2

Performs element-wise operation

• category: eltwise operator
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• Supported only in Keras 3 models

ops.arctanh

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• Supported only in Keras 3 models

ops.argmax

• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• Supported only in Keras 3 models

ops.argmin

• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• Supported only in Keras 3 models

layers.Average

Performs element-wise operation

• category: eltwise operator
  
• input data types: float32
  
• output data types: float32

layers.AveragePooling1D

Downsamples the input

• category: pooling layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

Specific constraints/recommendations:

• arbitrary strides, provided that they are smaller than the input size
  
• arbitrary pool sizes, provided that they are smaller than the input size

layers.AveragePooling2D

Downsamples the input

• category: pooling layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

Specific constraints/recommendations:

• arbitrary strides, provided that they are smaller than the input size
  
• arbitrary pool sizes, provided that they are smaller than the input size

layers.BatchNormalization

Performs the normalization of the input

• category: normalization layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• Only normalization on the last axis (channels) is supported

layers.Bidirectional

Bidirectionnal wrapper for RNNs

• category: recurrent layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• supported layers: LSTM, GRU and SimpleRNN
  
• supported merge mode: concat, mul, ave and sum

ops.ceil

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• Supported only in Keras 3 models

layers.Concatenate

Performs concatenation of a list of inputs

• category: merge operator
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• concatenating on the batch dimension is not supported

layers.Conv1D

Performs convolution operation

• category: convolutional layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8
  
• fused activations (if present): gelu, linear, relu, quantized_relu, relu_n1_to_1, relu_0_to_1, leaky_relu, relu6, elu, selu, sigmoid, hard_sigmoid, hard_swish, exponential, tanh, softmax, softplus, softsign
  
• integer schemes: weights / activations
  • Signed Symmetric / Signed Asymmetric (SSSA)
    
  • Signed Symmetric per channel (or per-axis) / Signed Asymmetric (SSSA_CH)
    
  • Signed Symmetric / Unsigned Asymmetric (SSUA)
    
  • Signed Symmetric per channel (or per-axis) / Unsigned Asymmetric (SSUA_CH)
    
  • Unsigned Asymmetric / Unsigned Asymmetric (UAUA)
    
  • Unsigned Asymmetric per channel (or per-axis) / Unsigned Asymmetric (UAUA_CH)

Specific constraints/recommendations:

• arbitrary strides, provided that they are smaller than the input size
  
• arbitrary filter kernel sizes, provided that they are smaller than the input size
  
• dilation factors different from 1 are not supported for int8 model

layers.Conv1DTranspose

Transposed convolutional layer

• category: convolutional layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8
  
• fused activations (if present): gelu, linear, relu, quantized_relu, relu_n1_to_1, relu_0_to_1, leaky_relu, relu6, elu, selu, sigmoid, hard_sigmoid, hard_swish, exponential, tanh, softmax, softplus, softsign

Specific constraints/recommendations:

• arbitrary strides, provided that they are smaller than the input size
  
• arbitrary filter kernel sizes, provided that they are smaller than the input size

layers.Conv2D

Performs convolution operation

• category: convolutional layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8
  
• fused activations (if present): gelu, linear, relu, quantized_relu, relu_n1_to_1, relu_0_to_1, leaky_relu, relu6, elu, selu, sigmoid, hard_sigmoid, hard_swish, exponential, tanh, softmax, softplus, softsign
  
• integer schemes: weights / activations
  • Signed Symmetric / Signed Asymmetric (SSSA)
    
  • Signed Symmetric per channel (or per-axis) / Signed Asymmetric (SSSA_CH)
    
  • Signed Symmetric / Unsigned Asymmetric (SSUA)
    
  • Signed Symmetric per channel (or per-axis) / Unsigned Asymmetric (SSUA_CH)
    
  • Unsigned Asymmetric / Unsigned Asymmetric (UAUA)
    
  • Unsigned Asymmetric per channel (or per-axis) / Unsigned Asymmetric (UAUA_CH)

Specific constraints/recommendations:

• arbitrary strides, provided that they are smaller than the input size
  
• arbitrary filter kernel sizes, provided that they are smaller than the input size
  
• dilation factors different from 1 are not supported for int8 model

layers.Conv2DTranspose

Transposed convolutional layer

• category: convolutional layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8
  
• fused activations (if present): gelu, linear, relu, quantized_relu, relu_n1_to_1, relu_0_to_1, leaky_relu, relu6, elu, selu, sigmoid, hard_sigmoid, hard_swish, exponential, tanh, softmax, softplus, softsign

Specific constraints/recommendations:

• arbitrary strides, provided that they are smaller than the input size
  
• arbitrary filter kernel sizes, provided that they are smaller than the input size

ops.cos

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• Supported only in Keras 3 models

ops.cosh

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• Supported only in Keras 3 models

layers.Cropping1D

Crops the input

• category: reshaping layer
  
• input data types: float32
  
• output data types: float32

layers.Cropping2D

Crops the input

• category: reshaping layer
  
• input data types: float32
  
• output data types: float32

CustomDoReFa

Computes element-wise data conversion full precision to low precision, based on the scale/zeropoint parameters

• category: conversion layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

CustomDoReFaQuantizer

Computes element-wise data conversion full precision to low precision, based on the scale/zeropoint parameters

• category: conversion layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

layers.Lambda

Wraps arbitrary expressions

• category: custom layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• The wrapped operation is uncompiled, this conversion may fail if the tf operator is unsupported

layers.Dense

Fully Connected operation

• category: core layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8
  
• fused activations (if present): gelu, linear, relu, quantized_relu, relu_n1_to_1, relu_0_to_1, leaky_relu, relu6, elu, selu, sigmoid, hard_sigmoid, hard_swish, exponential, tanh, softmax, softplus, softsign
  
• integer schemes: weights / activations
  • Signed Symmetric / Signed Asymmetric (SSSA)
    
  • Signed Symmetric per channel (or per-axis) / Signed Asymmetric (SSSA_CH)
    
  • Signed Symmetric / Unsigned Asymmetric (SSUA)
    
  • Signed Symmetric per channel (or per-axis) / Unsigned Asymmetric (SSUA_CH)
    
  • Unsigned Asymmetric / Unsigned Asymmetric (UAUA)
    
  • Unsigned Asymmetric per channel (or per-axis) / Unsigned Asymmetric (UAUA_CH)

Specific constraints/recommendations:

• for the floating point model, weights and/or bias can be compressed during the code generation

layers.DepthwiseConv2D

Performs convolution operation

• category: convolutional layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8
  
• fused activations (if present): gelu, linear, relu, quantized_relu, relu_n1_to_1, relu_0_to_1, leaky_relu, relu6, elu, selu, sigmoid, hard_sigmoid, hard_swish, exponential, tanh, softmax, softplus, softsign
  
• integer schemes: weights / activations
  • Signed Symmetric / Signed Asymmetric (SSSA)
    
  • Signed Symmetric per channel (or per-axis) / Signed Asymmetric (SSSA_CH)
    
  • Signed Symmetric / Unsigned Asymmetric (SSUA)
    
  • Signed Symmetric per channel (or per-axis) / Unsigned Asymmetric (SSUA_CH)
    
  • Unsigned Asymmetric / Unsigned Asymmetric (UAUA)
    
  • Unsigned Asymmetric per channel (or per-axis) / Unsigned Asymmetric (UAUA_CH)

Specific constraints/recommendations:

• arbitrary strides, provided that they are smaller than the input size
  
• arbitrary filter kernel sizes, provided that they are smaller than the input size
  
• dilation factors different from 1 are not supported for int8 model

ops.divide

Performs element-wise operation

• category: eltwise operator
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• Supported only in Keras 3 models

layers.Dropout

Applies Dropout to the input

• category: regularization layers
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• operator is dropped during the conversion

layers.ELU

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

ops.equal

Performs logical element-wise operation

• category: eltwise operator
  
• input data types: float32
  
• output data types: bool

Specific constraints/recommendations:

• Supported only in Keras 3 models

ops.erf

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• Supported only in Keras 3 models

layers.Flatten

Flattens the non-batch input dimensions to a vector

• category: Reshaping operation
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

Specific constraints/recommendations:

• Flatten on the batch dimension is not supported
  
• operator is dropped during the conversion

ops.floor

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• Supported only in Keras 3 models

ops.floor_divide

Performs element-wise operation

• category: eltwise operator
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• Supported only in Keras 3 models

layers.GaussianDropout

Applies Dropout to the input

• category: regularization layers
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• operator is dropped during the conversion

layers.GaussianNoise

Applies Dropout to the input

• category: regularization layers
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• operator is dropped during the conversion

layers.GlobalAveragePooling1D

Downsamples the input

• category: pooling layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

layers.GlobalAveragePooling2D

Downsamples the input

• category: pooling layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

layers.GlobalMaxPooling1D

Downsamples the input

• category: pooling layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

layers.GlobalMaxPooling2D

Downsamples the input

• category: pooling layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

ops.greater

Performs logical element-wise operation

• category: eltwise operator
  
• input data types: float32
  
• output data types: bool

ops.greater_equal

Performs element-wise operation

• category: eltwise operator
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• Supported only in Keras 3 models

layers.GRU

Gated Recurrent Unit

• category: recurrent layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• stateless and stateful (batch=1 only) mode support
  
• fused activation: gelu, linear, relu, quantized_relu, relu_n1_to_1, relu_0_to_1, leaky_relu, relu6, elu, selu, sigmoid, hard_sigmoid, hard_swish, exponential, tanh, softmax, softplus, softsign
  
• fused recurrent activation: gelu, linear, relu, quantized_relu, relu_n1_to_1, relu_0_to_1, leaky_relu, relu6, elu, selu, sigmoid, hard_sigmoid, hard_swish, exponential, tanh, softmax, softplus, softsign
  
• return_state not supported

ops.hard_silu

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• Supported only in Keras 3 models

layers.InputLayer

Optional placeholder for the network’s input

• category: core layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

Specific constraints/recommendations:

• operator is dropped during the conversion

layers.LeakyReLU

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

ops.leaky_relu

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• Supported only in Keras 3 models

ops.less

Performs logical element-wise operation

• category: eltwise operator
  
• input data types: float32
  
• output data types: bool

Specific constraints/recommendations:

• Supported only in Keras 3 models

ops.less_equal

Performs element-wise operation

• category: eltwise operator
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• Supported only in Keras 3 models

ops.log

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• Supported only in Keras 3 models

ops.logical_and

Performs element-wise operation

• category: eltwise operator
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• Supported only in Keras 3 models

ops.logical_not

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• Supported only in Keras 3 models

ops.logical_or

Performs element-wise operation

• category: eltwise operator
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• Supported only in Keras 3 models

ops.logical_xor

Performs element-wise operation

• category: eltwise operator
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• Supported only in Keras 3 models

layers.LSTM

Computes a multi-layer long short-term memory (LSTM) RNN to an input sequence (batch=1, timesteps, features)

• category: recurrent layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• stateless and stateful (batch=1 only) mode support
  
• in stateful mode the user is requested to define two C routines to allocate and deallocate internal layer state.
  • initial state must be provided as part of the allocation routine implementation
    
  • the two functions to implement are:
    • void _allocate_lstm_states(ai_float **states, ai_u32 size_in_bytes)
      
    • void _deallocate_lstm_states(ai_float **states)
      
• fused activation: gelu, linear, relu, quantized_relu, relu_n1_to_1, relu_0_to_1, leaky_relu, relu6, elu, selu, sigmoid, hard_sigmoid, hard_swish, exponential, tanh, softmax, softplus, softsign
  
• fused recurrent activation: gelu, linear, relu, quantized_relu, relu_n1_to_1, relu_0_to_1, leaky_relu, relu6, elu, selu, sigmoid, hard_sigmoid, hard_swish, exponential, tanh, softmax, softplus, softsign
  
• return_state not supported

ops.maximum

Computes the maximum (element-wise) a list of inputs

• category: eltwise operator
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• Supported only in Keras 3 models

layers.MaxPooling1D

Downsamples the input

• category: pooling layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

Specific constraints/recommendations:

• arbitrary strides, provided that they are smaller than the input size
  
• arbitrary pool sizes, provided that they are smaller than the input size

layers.MaxPooling2D

Downsamples the input

• category: pooling layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

Specific constraints/recommendations:

• arbitrary strides, provided that they are smaller than the input size
  
• arbitrary pool sizes, provided that they are smaller than the input size

ops.minimum

Computes the minimum (element-wise) a list of inputs

• category: eltwise operator
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• Supported only in Keras 3 models

ops.mod

Performs element-wise operation

• category: eltwise operator
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• Supported only in Keras 3 models

layers.Multiply

Performs element-wise operation

• category: eltwise operator
  
• input data types: float32
  
• output data types: float32

ops.negative

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• Supported only in Keras 3 models

ops.not_equal

Performs element-wise operation

• category: eltwise operator
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• Supported only in Keras 3 models

layers.Permute

Permutes the dimensions of the input according to a given pattern

• category: reshaping layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

Specific constraints/recommendations:

• transposing the batch dimension is not supported

layers.PReLU

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

Specific constraints/recommendations:

• shared axes in PReLU supported only for the leading dimensions

qkeras.QActivation

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32, int8, binary
  
• output data types: binary

Specific constraints/recommendations:

• Custom Keras layer from QKeras framework
  
• For detailed information, see Deep Quantized Neural Network [DQNN] support article

qkeras.QBatchNormalization

Performs the normalization of the input

• category: normalization layer
  
• input data types: float32, int8, binary
  
• output data types: float32, int8, binary

Specific constraints/recommendations:

• Custom Keras layer from QKeras framework
  
• Only normalization on the last axis (channels) is supported
  
• For detailed information, see Deep Quantized Neural Network [DQNN] support article

qkeras.QConv1D

Performs convolution operation

• category: convolutional layer
  
• input data types: float32, int8, binary
  
• output data types: float32, int8, binary

Specific constraints/recommendations:

• arbitrary strides, provided that they are smaller than the input size
  
• arbitrary filter kernel sizes, provided that they are smaller than the input size
  
• Custom Keras layer from QKeras framework
  
• For detailed information, see Deep Quantized Neural Network [DQNN] support article

qkeras.QConv2D

Performs convolution operation

• category: convolutional layer
  
• input data types: float32, int8, binary
  
• output data types: float32, int8, binary

Specific constraints/recommendations:

• arbitrary strides, provided that they are smaller than the input size
  
• arbitrary filter kernel sizes, provided that they are smaller than the input size
  
• Custom Keras layer from QKeras framework
  
• For detailed information, see Deep Quantized Neural Network [DQNN] support article

qkeras.QConv2DTranspose

Transposed convolutional layer

• category: convolutional layer
  
• input data types: float32, int8, binary
  
• output data types: float32, int8, binary

Specific constraints/recommendations:

• Custom Keras layer from QKeras framework
  
• Padding must be valid
  
• Stride must be (1, 1)
  
• For detailed information, see Deep Quantized Neural Network [DQNN] support article

qkeras.QDense

• category: core layer
  
• input data types: float32, int8, binary
  
• output data types: float32, int8, binary

Specific constraints/recommendations:

• Custom Keras layer from QKeras framework
  
• Only 2D input shape is supported: [batch_size, input_dim]. A rank greater than 2 is not supported, Flatten layer before the QuantDense/QDense operator should be added
  
• For detailed information, see Deep Quantized Neural Network [DQNN] support article

qkeras.QDepthwiseConv2D

Performs convolution operation

• category: convolutional layer
  
• input data types: float32, int8, binary
  
• output data types: float32, int8, binary

Specific constraints/recommendations:

• arbitrary strides, provided that they are smaller than the input size
  
• arbitrary filter kernel sizes, provided that they are smaller than the input size
  
• Custom Keras layer from QKeras framework
  
• For detailed information, see Deep Quantized Neural Network [DQNN] support article

larq.layers.QuantConv2D

Performs convolution operation

• category: convolutional layer
  
• input data types: float32, int8, binary
  
• output data types: float32, int8, binary

Specific constraints/recommendations:

• arbitrary strides, provided that they are smaller than the input size
  
• arbitrary filter kernel sizes, provided that they are smaller than the input size
  
• Custom Keras layer from LARQ framework
  
• For detailed information, see Deep Quantized Neural Network [DQNN] support article

larq.layers.QuantDense

• category: core layer
  
• input data types: float32, int8, binary
  
• output data types: float32, int8, binary

Specific constraints/recommendations:

• Custom Keras layer from LARQ framework
  
• Only 2D input shape is supported: [batch_size, input_dim]. A rank greater than 2 is not supported, Flatten layer before the QuantDense/QDense operator should be added
  
• For detailed information, see Deep Quantized Neural Network [DQNN] support article

larq.layers.QuantDepthwiseConv2D

Performs convolution operation

• category: convolutional layer
  
• input data types: float32, int8, binary
  
• output data types: float32, int8, binary

Specific constraints/recommendations:

• arbitrary strides, provided that they are smaller than the input size
  
• arbitrary filter kernel sizes, provided that they are smaller than the input size
  
• Custom Keras layer from LARQ framework
  
• For detailed information, see Deep Quantized Neural Network [DQNN] support article

ops.relu

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• Supported only in Keras 3 models

layers.ReLU

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

ops.relu6

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• Supported only in Keras 3 models

layers.RepeatVector

Repeats the input n times

• category: reshaping layer
  
• input data types: float32
  
• output data types: float32

layers.Reshape

Reshapes a tensor

• category: Reshaping operation
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

ops.round

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• Supported only in Keras 3 models

ops.rsqrt

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• Supported only in Keras 3 models

ops.selu

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• Supported only in Keras 3 models

layers.SeparableConv1D

Performs convolution operation

• category: convolutional layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8
  
• fused activations (if present): gelu, linear, relu, quantized_relu, relu_n1_to_1, relu_0_to_1, leaky_relu, relu6, elu, selu, sigmoid, hard_sigmoid, hard_swish, exponential, tanh, softmax, softplus, softsign
  
• integer schemes: weights / activations
  • Signed Symmetric / Signed Asymmetric (SSSA)
    
  • Signed Symmetric per channel (or per-axis) / Signed Asymmetric (SSSA_CH)
    
  • Signed Symmetric / Unsigned Asymmetric (SSUA)
    
  • Signed Symmetric per channel (or per-axis) / Unsigned Asymmetric (SSUA_CH)
    
  • Unsigned Asymmetric / Unsigned Asymmetric (UAUA)
    
  • Unsigned Asymmetric per channel (or per-axis) / Unsigned Asymmetric (UAUA_CH)

Specific constraints/recommendations:

• arbitrary strides, provided that they are smaller than the input size
  
• arbitrary filter kernel sizes, provided that they are smaller than the input size
  
• dilation factors different from 1 are not supported for int8 model

layers.SeparableConv2D

Performs convolution operation

• category: convolutional layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8
  
• fused activations (if present): gelu, linear, relu, quantized_relu, relu_n1_to_1, relu_0_to_1, leaky_relu, relu6, elu, selu, sigmoid, hard_sigmoid, hard_swish, exponential, tanh, softmax, softplus, softsign
  
• integer schemes: weights / activations
  • Signed Symmetric / Signed Asymmetric (SSSA)
    
  • Signed Symmetric per channel (or per-axis) / Signed Asymmetric (SSSA_CH)
    
  • Signed Symmetric / Unsigned Asymmetric (SSUA)
    
  • Signed Symmetric per channel (or per-axis) / Unsigned Asymmetric (SSUA_CH)
    
  • Unsigned Asymmetric / Unsigned Asymmetric (UAUA)
    
  • Unsigned Asymmetric per channel (or per-axis) / Unsigned Asymmetric (UAUA_CH)

Specific constraints/recommendations:

• arbitrary strides, provided that they are smaller than the input size
  
• arbitrary filter kernel sizes, provided that they are smaller than the input size
  
• dilation factors different from 1 are not supported for int8 model

ops.sign

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• Supported only in Keras 3 models

ops.sin

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• Supported only in Keras 3 models

layers.Softmax

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• It is supported only for 1D tensor and only on the channel dimension
  
• The value 1 is supported as the default value of the axis attribute, not the value -1

layers.SpatialDropout1D

Applies Dropout to the input

• category: regularization layers
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• operator is dropped during the conversion

layers.SpatialDropout2D

Applies Dropout to the input

• category: regularization layers
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• operator is dropped during the conversion

ops.sqrt

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• Supported only in Keras 3 models

STCustomDoReFa

Computes element-wise data conversion full precision to low precision, based on the scale/zeropoint parameters

• category: conversion layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

layers.Subtract

Performs element-wise operation

• category: eltwise operator
  
• input data types: float32
  
• output data types: float32

ops.tan

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• Supported only in Keras 3 models

ops.tanh

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• Supported only in Keras 3 models

layers.TFOpLambda

Wraps arbitrary expressions

• category: custom layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• The wrapped operation is uncompiled, this conversion may fail if the tf operator is unsupported

layers.ThresholdedReLU

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

layers.TimeDistributed

Applies a layer to every temporal slice of an input

• category: wrapper layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• Supported layers: Conv2D, Dense, Flatten, MaxPooling2D, ZeroPadding2D, Dropout

layers.UpSampling1D

• category: resizing operation
  
• input data types: float32
  
• output data types: float32

layers.UpSampling2D

• category: resizing operation
  
• input data types: float32
  
• output data types: float32

layers.ZeroPadding1D

Pads an input tensor

• category: Reshaping layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

layers.ZeroPadding2D

Pads an input tensor

• category: Reshaping layer
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

Abs

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• related TF operator: tf.math.abs

Acos

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• related TF operator: tf.math.acos

Acosh

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• related TF operator: tf.math.acosh

Asin

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• related TF operator: tf.math.asin

Asinh

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• related TF operator: tf.math.asinh

Atan

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• related TF operator: tf.math.atan

Atanh

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• related TF operator: tf.math.atanh

Ceil

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• related TF operator: tf.math.ceil

Clip

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• related TF operator: tf.clip_by_value

Cos

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• related TF operator: tf.math.cos

Exp

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• related TF operator: tf.math.exp

Fill

Generates a tensor with given value and shape

• category: constant layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• related TF operator: tf.fill

FloorDiv

Performs element-wise operation

• category: eltwise operator
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• related TF operator: tf.math.floordiv

FloorMod

Performs element-wise operation

• category: eltwise operator
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• related TF operator: tf.math.floormod

Log

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• related TF operator: tf.math.log

Pow

Performs element-wise operation

• category: eltwise operator
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• related TF operator: tf.math.pow

Reciprocal

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• related TF operator: tf.reciprocal

Reshape

Reshapes a tensor

• category: Reshaping operation
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

Specific constraints/recommendations:

• related TF operator: tf.reshape
  
• operator is dropped during the conversion

Round

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• related TF operator: tf.math.round

Shape

Returns a tensor containing the shape of the input tensor

• category: Reshaping operation
  
• input data types: float32, int8, uint8
  
• output data types: int32

Specific constraints/recommendations:

• related TF operator: tf.shape

Sign

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• related TF operator: tf.math.sign

Sin

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• related TF operator: tf.math.sin

Split

Splits a tensor into a list of sub tensors

• category: split operator
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

Specific constraints/recommendations:

• Only supported if the number of splits is equal to the size of the splitting dimension
  
• related TF operator: tf.split

Sqrt

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• related TF operator: tf.math.sqrt

Square

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• related TF operator: tf.math.square

Tanh

Applies an activation function to the input tensor

• category: activation layer
  
• input data types: float32
  
• output data types: float32

Specific constraints/recommendations:

• related TF operator: tf.math.tanh

Unpack

Unpacks num tensors from values along specified axis

• category: split operator
  
• input data types: float32, int8, uint8
  
• output data types: float32, int8, uint8

Specific constraints/recommendations:

• related TF operator: tf.unpack

Where

Where layer

• category: generic layer
  
• input data types: float32, int8, uint8, int16, uint16, int32, uint32, bool
  
• output data types: float32, int8, uint8, int16, uint16, int32, uint32, bool

Specific constraints/recommendations:

• related TF operator: tf.where