# larq.layers¶

Each Quantized Layer requires a `input_quantizer`

and `kernel_quantizer`

that describes the way of quantizing the activation of the previous layer and the weights respectively.

If both `input_quantizer`

and `kernel_quantizer`

are `None`

the layer is equivalent to a full precision layer.

## QuantDense¶

QuantDense(units, activation=None, use_bias=True, input_quantizer=None, kernel_quantizer=None, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, metrics=None, **kwargs)

`QuantDense`

implements the operation: `output = activation(dot(input_quantizer(input), kernel_quantizer(kernel)) + bias)`

, where `activation`

is the element-wise activation function passed as the `activation`

argument, `kernel`

is a weights matrix created by the layer, and `bias`

is a bias vector created by the layer (only applicable if `use_bias`

is `True`

). `input_quantizer`

and `kernel_quantizer`

are the element-wise quantization functions to use. If both quantization functions are `None`

this layer is equivalent to `Dense`

.

If the input to the layer has a rank greater than 2, then it is flattened prior to the initial dot product with `kernel`

.

Example

# as first layer in a sequential model: model = Sequential() model.add( QuantDense( 32, input_quantizer="ste_sign", kernel_quantizer="ste_sign", kernel_constraint="weight_clip", input_shape=(16,), ) ) # now the model will take as input arrays of shape (*, 16) # and output arrays of shape (*, 32) # after the first layer, you don't need to specify # the size of the input anymore: model.add( QuantDense( 32, input_quantizer="ste_sign", kernel_quantizer="ste_sign", kernel_constraint="weight_clip", ) )

**Arguments**

`units`

: Positive integer, dimensionality of the output space.`activation`

: Activation function to use. If you don't specify anything, no activation is applied (`a(x) = x`

).`use_bias`

: Boolean, whether the layer uses a bias vector.`input_quantizer`

: Quantization function applied to the input of the layer.`kernel_quantizer`

: Quantization function applied to the`kernel`

weights matrix.`kernel_initializer`

: Initializer for the`kernel`

weights matrix.`bias_initializer`

: Initializer for the bias vector.`kernel_regularizer`

: Regularizer function applied to the`kernel`

weights matrix.`bias_regularizer`

: Regularizer function applied to the bias vector.`activity_regularizer`

: Regularizer function applied to the output of the layer (its "activation").`kernel_constraint`

: Constraint function applied to the`kernel`

weights matrix.`bias_constraint`

: Constraint function applied to the bias vector.`metrics`

: An array of metrics to add to the layer. If`None`

the metrics set in`larq.metrics.scope`

are used. Currently only the`flip_ratio`

metric is available.

**Input shape**

N-D tensor with shape: `(batch_size, ..., input_dim)`

. The most common situation would be a 2D input with shape `(batch_size, input_dim)`

.

**Output shape**

N-D tensor with shape: `(batch_size, ..., units)`

. For instance, for a 2D input with shape `(batch_size, input_dim)`

, the output would have shape `(batch_size, units)`

.

## QuantConv1D¶

QuantConv1D(filters, kernel_size, strides=1, padding='valid', data_format='channels_last', dilation_rate=1, activation=None, use_bias=True, input_quantizer=None, kernel_quantizer=None, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, metrics=None, **kwargs)

This layer creates a convolution kernel that is convolved with the layer input over a single spatial (or temporal) dimension to produce a tensor of outputs. `input_quantizer`

and `kernel_quantizer`

are the element-wise quantization functions to use. If both quantization functions are `None`

this layer is equivalent to `Conv1D`

. If `use_bias`

is True, a bias vector is created and added to the outputs. Finally, if `activation`

is not `None`

, it is applied to the outputs as well.

When using this layer as the first layer in a model, provide an `input_shape`

argument (tuple of integers or `None`

, e.g. `(10, 128)`

for sequences of 10 vectors of 128-dimensional vectors, or `(None, 128)`

for variable-length sequences of 128-dimensional vectors.

**Arguments**

`filters`

: Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).`kernel_size`

: An integer or tuple/list of a single integer, specifying the length of the 1D convolution window.`strides`

: An integer or tuple/list of a single integer, specifying the stride length of the convolution. Specifying any stride value != 1 is incompatible with specifying any`dilation_rate`

value != 1.`padding`

: One of`"valid"`

,`"causal"`

or`"same"`

(case-insensitive).`"causal"`

results in causal (dilated) convolutions, e.g. output[t] does not depend on`input[t+1`

:]. Useful when modeling temporal data where the model should not`violate the temporal order. See [WaveNet`

: A Generative Model for Raw Audio,`section 2.1](https`

://arxiv.org/abs/1609.03499).`data_format`

: A string, one of`channels_last`

(default) or`channels_first`

.`dilation_rate`

: an integer or tuple/list of a single integer, specifying the dilation rate to use for dilated convolution. Currently, specifying any`dilation_rate`

value != 1 is incompatible with specifying any`strides`

value != 1.`activation`

: Activation function to use. If you don't specify anything, no activation is applied (`a(x) = x`

).`use_bias`

: Boolean, whether the layer uses a bias vector.`input_quantizer`

: Quantization function applied to the input of the layer.`kernel_quantizer`

: Quantization function applied to the`kernel`

weights matrix.`kernel_initializer`

: Initializer for the`kernel`

weights matrix.`bias_initializer`

: Initializer for the bias vector.`kernel_regularizer`

: Regularizer function applied to the`kernel`

weights matrix.`bias_regularizer`

: Regularizer function applied to the bias vector.`activity_regularizer`

: Regularizer function applied to the output of the layer (its "activation").`kernel_constraint`

: Constraint function applied to the kernel matrix.`bias_constraint`

: Constraint function applied to the bias vector.`metrics`

: An array of metrics to add to the layer. If`None`

the metrics set in`larq.metrics.scope`

are used. Currently only the`flip_ratio`

metric is available.

**Input shape**

3D tensor with shape: `(batch_size, steps, input_dim)`

**Output shape**

3D tensor with shape: `(batch_size, new_steps, filters)`

. `steps`

value might have changed due to padding or strides.

## QuantConv2D¶

QuantConv2D(filters, kernel_size, strides=(1, 1), padding='valid', data_format=None, dilation_rate=(1, 1), activation=None, use_bias=True, input_quantizer=None, kernel_quantizer=None, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, metrics=None, **kwargs)

This layer creates a convolution kernel that is convolved with the layer input to produce a tensor of outputs. `input_quantizer`

and `kernel_quantizer`

are the element-wise quantization functions to use. If both quantization functions are `None`

this layer is equivalent to `Conv2D`

. If `use_bias`

is True, a bias vector is created and added to the outputs. Finally, if `activation`

is not `None`

, it is applied to the outputs as well.

When using this layer as the first layer in a model, provide the keyword argument `input_shape`

(tuple of integers, does not include the sample axis), e.g. `input_shape=(128, 128, 3)`

for 128x128 RGB pictures in `data_format="channels_last"`

.

**Arguments**

`filters`

: Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).`kernel_size`

: An integer or tuple/list of 2 integers, specifying the height and width of the 2D convolution window. Can be a single integer to specify the same value for all spatial dimensions.`strides`

: An integer or tuple/list of 2 integers, specifying the strides of the convolution along the height and width. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any`dilation_rate`

value != 1.`padding`

: one of`"valid"`

or`"same"`

(case-insensitive).`data_format`

: A string, one of`channels_last`

(default) or`channels_first`

. The ordering of the dimensions in the inputs.`channels_last`

corresponds to inputs with shape`(batch, height, width, channels)`

while`channels_first`

corresponds to inputs with shape`(batch, channels, height, width)`

. It defaults to the`image_data_format`

value found in your Keras config file at`~/.keras/keras.json`

. If you never set it, then it will be "channels_last".`dilation_rate`

: an integer or tuple/list of 2 integers, specifying the dilation rate to use for dilated convolution. Can be a single integer to specify the same value for all spatial dimensions. Currently, specifying any`dilation_rate`

value != 1 is incompatible with specifying any stride value != 1.`activation`

: Activation function to use. If you don't specify anything, no activation is applied (`a(x) = x`

).`use_bias`

: Boolean, whether the layer uses a bias vector.`input_quantizer`

: Quantization function applied to the input of the layer.`kernel_quantizer`

: Quantization function applied to the`kernel`

weights matrix.`kernel_initializer`

: Initializer for the`kernel`

weights matrix.`bias_initializer`

: Initializer for the bias vector.`kernel_regularizer`

: Regularizer function applied to the`kernel`

weights matrix.`bias_regularizer`

: Regularizer function applied to the bias vector.`activity_regularizer`

: Regularizer function applied to the output of the layer (its "activation").`kernel_constraint`

: Constraint function applied to the kernel matrix.`bias_constraint`

: Constraint function applied to the bias vector.`metrics`

: An array of metrics to add to the layer. If`None`

the metrics set in`larq.metrics.scope`

are used. Currently only the`flip_ratio`

metric is available.

**Input shape**

4D tensor with shape: `(samples, channels, rows, cols)`

if data_format='channels_first' or 4D tensor with shape: `(samples, rows, cols, channels)`

if data_format='channels_last'.

**Output shape**

4D tensor with shape: `(samples, filters, new_rows, new_cols)`

if data_format='channels_first' or 4D tensor with shape: `(samples, new_rows, new_cols, filters)`

if data_format='channels_last'. `rows`

and `cols`

values might have changed due to padding.

## QuantConv3D¶

QuantConv3D(filters, kernel_size, strides=(1, 1, 1), padding='valid', data_format=None, dilation_rate=(1, 1, 1), activation=None, use_bias=True, input_quantizer=None, kernel_quantizer=None, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, metrics=None, **kwargs)

This layer creates a convolution kernel that is convolved with the layer input to produce a tensor of outputs. `input_quantizer`

and `kernel_quantizer`

are the element-wise quantization functions to use. If both quantization functions are `None`

this layer is equivalent to `Conv3D`

. If `use_bias`

is True, a bias vector is created and added to the outputs. Finally, if `activation`

is not `None`

, it is applied to the outputs as well.

When using this layer as the first layer in a model, provide the keyword argument `input_shape`

(tuple of integers, does not include the sample axis), e.g. `input_shape=(128, 128, 128, 1)`

for 128x128x128 volumes with a single channel, in `data_format="channels_last"`

.

**Arguments**

`filters`

: Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).`kernel_size`

: An integer or tuple/list of 3 integers, specifying the depth, height and width of the 3D convolution window. Can be a single integer to specify the same value for all spatial dimensions.`strides`

: An integer or tuple/list of 3 integers, specifying the strides of the convolution along each spatial dimension. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any`dilation_rate`

value != 1.`padding`

: one of`"valid"`

or`"same"`

(case-insensitive).`data_format`

: A string, one of`channels_last`

(default) or`channels_first`

. The ordering of the dimensions in the inputs.`channels_last`

corresponds to inputs with shape`(batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)`

while`channels_first`

corresponds to inputs with shape`(batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)`

. It defaults to the`image_data_format`

value found in your Keras config file at`~/.keras/keras.json`

. If you never set it, then it will be "channels_last".`dilation_rate`

: an integer or tuple/list of 3 integers, specifying the dilation rate to use for dilated convolution. Can be a single integer to specify the same value for all spatial dimensions. Currently, specifying any`dilation_rate`

value != 1 is incompatible with specifying any stride value != 1.`activation`

: Activation function to use. If you don't specify anything, no activation is applied (`a(x) = x`

).`use_bias`

: Boolean, whether the layer uses a bias vector.`input_quantizer`

: Quantization function applied to the input of the layer.`kernel_quantizer`

: Quantization function applied to the`kernel`

weights matrix.`kernel_initializer`

: Initializer for the`kernel`

weights matrix.`bias_initializer`

: Initializer for the bias vector.`kernel_regularizer`

: Regularizer function applied to the`kernel`

weights matrix.`bias_regularizer`

: Regularizer function applied to the bias vector.`activity_regularizer`

: Regularizer function applied to the output of the layer (its "activation").`kernel_constraint`

: Constraint function applied to the kernel matrix.`bias_constraint`

: Constraint function applied to the bias vector.`metrics`

: An array of metrics to add to the layer. If`None`

the metrics set in`larq.metrics.scope`

are used. Currently only the`flip_ratio`

metric is available.

**Input shape**

5D tensor with shape: `(samples, channels, conv_dim1, conv_dim2, conv_dim3)`

if data_format='channels_first' or 5D tensor with shape: `(samples, conv_dim1, conv_dim2, conv_dim3, channels)`

if data_format='channels_last'.

**Output shape**

5D tensor with shape: `(samples, filters, new_conv_dim1, new_conv_dim2, new_conv_dim3)`

if data_format='channels_first' or 5D tensor with shape: `(samples, new_conv_dim1, new_conv_dim2, new_conv_dim3, filters)`

if data_format='channels_last'. `new_conv_dim1`

, `new_conv_dim2`

and `new_conv_dim3`

values might have changed due to padding.

## QuantDepthwiseConv2D¶

QuantDepthwiseConv2D(kernel_size, strides=(1, 1), padding='valid', depth_multiplier=1, data_format=None, activation=None, use_bias=True, input_quantizer=None, depthwise_quantizer=None, depthwise_initializer='glorot_uniform', bias_initializer='zeros', depthwise_regularizer=None, bias_regularizer=None, activity_regularizer=None, depthwise_constraint=None, bias_constraint=None, metrics=None, **kwargs)

`depth_multiplier`

argument controls how many output channels are generated per input channel in the depthwise step. **Arguments**

`kernel_size`

: An integer or tuple/list of 2 integers, specifying the height and width of the 2D convolution window. Can be a single integer to specify the same value for all spatial dimensions.`strides`

: An integer or tuple/list of 2 integers, specifying the strides of the convolution along the height and width. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any`dilation_rate`

value != 1.`padding`

: one of`'valid'`

or`'same'`

(case-insensitive).`depth_multiplier`

: The number of depthwise convolution output channels for each input channel. The total number of depthwise convolution output channels will be equal to`filters_in * depth_multiplier`

.`data_format`

: A string, one of`channels_last`

(default) or`channels_first`

. The ordering of the dimensions in the inputs.`channels_last`

corresponds to inputs with shape`(batch, height, width, channels)`

while`channels_first`

corresponds to inputs with shape`(batch, channels, height, width)`

. It defaults to the`image_data_format`

value found in your Keras config file at`~/.keras/keras.json`

. If you never set it, then it will be 'channels_last'.`activation`

: Activation function to use. If you don't specify anything, no activation is applied (ie.`a(x) = x`

).`use_bias`

: Boolean, whether the layer uses a bias vector.`input_quantizer`

: Quantization function applied to the input of the layer.`depthwise_quantizer`

: Quantization function applied to the`depthwise_kernel`

weights matrix.`depthwise_initializer`

: Initializer for the depthwise kernel matrix.`bias_initializer`

: Initializer for the bias vector.`depthwise_regularizer`

: Regularizer function applied to the depthwise kernel matrix.`bias_regularizer`

: Regularizer function applied to the bias vector.`activity_regularizer`

: Regularizer function applied to the output of the layer (its 'activation').`depthwise_constraint`

: Constraint function applied to the depthwise kernel matrix.`bias_constraint`

: Constraint function applied to the bias vector.`metrics`

: An array of metrics to add to the layer. If`None`

the metrics set in`larq.metrics.scope`

are used. Currently only the`flip_ratio`

metric is available.

**Input shape**

4D tensor with shape: `[batch, channels, rows, cols]`

if data_format='channels_first' or 4D tensor with shape: `[batch, rows, cols, channels]`

if data_format='channels_last'.

**Output shape**

4D tensor with shape: `[batch, filters, new_rows, new_cols]`

if data_format='channels_first' or 4D tensor with shape: `[batch, new_rows, new_cols, filters]`

if data_format='channels_last'. `rows`

and `cols`

values might have changed due to padding.

## QuantSeparableConv1D¶

QuantSeparableConv1D(filters, kernel_size, strides=1, padding='valid', data_format=None, dilation_rate=1, depth_multiplier=1, activation=None, use_bias=True, input_quantizer=None, depthwise_quantizer=None, pointwise_quantizer=None, depthwise_initializer='glorot_uniform', pointwise_initializer='glorot_uniform', bias_initializer='zeros', depthwise_regularizer=None, pointwise_regularizer=None, bias_regularizer=None, activity_regularizer=None, depthwise_constraint=None, pointwise_constraint=None, bias_constraint=None, metrics=None, **kwargs)

This layer performs a depthwise convolution that acts separately on channels, followed by a pointwise convolution that mixes channels. `input_quantizer`

, `depthwise_quantizer`

and `pointwise_quantizer`

are the element-wise quantization functions to use. If all quantization functions are `None`

this layer is equivalent to `SeparableConv1D`

. If `use_bias`

is True and a bias initializer is provided, it adds a bias vector to the output. It then optionally applies an activation function to produce the final output.

**Arguments**

`filters`

: Integer, the dimensionality of the output space (i.e. the number of filters in the convolution).`kernel_size`

: A single integer specifying the spatial dimensions of the filters.`strides`

: A single integer specifying the strides of the convolution. Specifying any`stride`

value != 1 is incompatible with specifying any`dilation_rate`

value != 1.`padding`

: One of`"valid"`

,`"same"`

, or`"causal"`

(case-insensitive).`data_format`

: A string, one of`channels_last`

(default) or`channels_first`

. The ordering of the dimensions in the inputs.`channels_last`

corresponds to inputs with shape`(batch, length, channels)`

while`channels_first`

corresponds to inputs with shape`(batch, channels, length)`

.`dilation_rate`

: A single integer, specifying the dilation rate to use for dilated convolution. Currently, specifying any`dilation_rate`

value != 1 is incompatible with specifying any stride value != 1.`depth_multiplier`

: The number of depthwise convolution output channels for each input channel. The total number of depthwise convolution output channels will be equal to`num_filters_in * depth_multiplier`

.`activation`

: Activation function. Set it to None to maintain a linear activation.`use_bias`

: Boolean, whether the layer uses a bias.`input_quantizer`

: Quantization function applied to the input of the layer.`depthwise_quantizer`

: Quantization function applied to the depthwise kernel.`pointwise_quantizer`

: Quantization function applied to the pointwise kernel.`depthwise_initializer`

: An initializer for the depthwise convolution kernel.`pointwise_initializer`

: An initializer for the pointwise convolution kernel.`bias_initializer`

: An initializer for the bias vector. If None, the default initializer will be used.`depthwise_regularizer`

: Optional regularizer for the depthwise convolution kernel.`pointwise_regularizer`

: Optional regularizer for the pointwise convolution kernel.`bias_regularizer`

: Optional regularizer for the bias vector.`activity_regularizer`

: Optional regularizer function for the output.`depthwise_constraint`

: Optional projection function to be applied to the depthwise kernel after being updated by an`Optimizer`

(e.g. used for norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training.`pointwise_constraint`

: Optional projection function to be applied to the pointwise kernel after being updated by an`Optimizer`

.`bias_constraint`

: Optional projection function to be applied to the bias after being updated by an`Optimizer`

.`metrics`

: An array of metrics to add to the layer. If`None`

the metrics set in`larq.metrics.scope`

are used. Currently only the`flip_ratio`

metric is available.`trainable`

: Boolean, if`True`

the weights of this layer will be marked as trainable (and listed in`layer.trainable_weights`

).`name`

: A string, the name of the layer.

## QuantSeparableConv2D¶

QuantSeparableConv2D(filters, kernel_size, strides=(1, 1), padding='valid', data_format=None, dilation_rate=(1, 1), depth_multiplier=1, activation=None, use_bias=True, input_quantizer=None, depthwise_quantizer=None, pointwise_quantizer=None, depthwise_initializer='glorot_uniform', pointwise_initializer='glorot_uniform', bias_initializer='zeros', depthwise_regularizer=None, pointwise_regularizer=None, bias_regularizer=None, activity_regularizer=None, depthwise_constraint=None, pointwise_constraint=None, bias_constraint=None, metrics=None, **kwargs)

Separable convolutions consist in first performing a depthwise spatial convolution (which acts on each input channel separately) followed by a pointwise convolution which mixes together the resulting output channels. The `depth_multiplier`

argument controls how many output channels are generated per input channel in the depthwise step. `input_quantizer`

, `depthwise_quantizer`

and `pointwise_quantizer`

are the element-wise quantization functions to use. If all quantization functions are `None`

this layer is equivalent to `SeparableConv1D`

. If `use_bias`

is True and a bias initializer is provided, it adds a bias vector to the output. It then optionally applies an activation function to produce the final output.

Intuitively, separable convolutions can be understood as a way to factorize a convolution kernel into two smaller kernels, or as an extreme version of an Inception block.

**Arguments**

`filters`

: Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).`kernel_size`

: An integer or tuple/list of 2 integers, specifying the height and width of the 2D convolution window. Can be a single integer to specify the same value for all spatial dimensions.`strides`

: An integer or tuple/list of 2 integers, specifying the strides of the convolution along the height and width. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any`dilation_rate`

value != 1.`padding`

: one of`"valid"`

or`"same"`

(case-insensitive).`data_format`

: A string, one of`channels_last`

(default) or`channels_first`

. The ordering of the dimensions in the inputs.`channels_last`

corresponds to inputs with shape`(batch, height, width, channels)`

while`channels_first`

corresponds to inputs with shape`(batch, channels, height, width)`

. It defaults to the`image_data_format`

value found in your Keras config file at`~/.keras/keras.json`

. If you never set it, then it will be "channels_last".`dilation_rate`

: An integer or tuple/list of 2 integers, specifying the dilation rate to use for dilated convolution. Currently, specifying any`dilation_rate`

value != 1 is incompatible with specifying any`strides`

value != 1.`depth_multiplier`

: The number of depthwise convolution output channels for each input channel. The total number of depthwise convolution output channels will be equal to`filters_in * depth_multiplier`

.`activation`

: Activation function to use. If you don't specify anything, no activation is applied (`a(x) = x`

).`use_bias`

: Boolean, whether the layer uses a bias vector.`input_quantizer`

: Quantization function applied to the input of the layer.`depthwise_quantizer`

: Quantization function applied to the depthwise kernel matrix.`pointwise_quantizer`

: Quantization function applied to the pointwise kernel matrix.`depthwise_initializer`

: Initializer for the depthwise kernel matrix.`pointwise_initializer`

: Initializer for the pointwise kernel matrix.`bias_initializer`

: Initializer for the bias vector.`depthwise_regularizer`

: Regularizer function applied to the depthwise kernel matrix.`pointwise_regularizer`

: Regularizer function applied to the pointwise kernel matrix.`bias_regularizer`

: Regularizer function applied to the bias vector.`activity_regularizer`

: Regularizer function applied to the output of the layer (its "activation").`depthwise_constraint`

: Constraint function applied to the depthwise kernel matrix.`pointwise_constraint`

: Constraint function applied to the pointwise kernel matrix.`bias_constraint`

: Constraint function applied to the bias vector.`metrics`

: An array of metrics to add to the layer. If`None`

the metrics set in`larq.metrics.scope`

are used. Currently only the`flip_ratio`

metric is available.

**Input shape**

4D tensor with shape: `(batch, channels, rows, cols)`

if data_format='channels_first' or 4D tensor with shape: `(batch, rows, cols, channels)`

if data_format='channels_last'.

**Output shape**

4D tensor with shape: `(batch, filters, new_rows, new_cols)`

if data_format='channels_first' or 4D tensor with shape: `(batch, new_rows, new_cols, filters)`

if data_format='channels_last'. `rows`

and `cols`

values might have changed due to padding.

## QuantConv2DTranspose¶

QuantConv2DTranspose(filters, kernel_size, strides=(1, 1), padding='valid', output_padding=None, data_format=None, dilation_rate=(1, 1), activation=None, use_bias=True, input_quantizer=None, kernel_quantizer=None, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, metrics=None, **kwargs)

The need for transposed convolutions generally arises from the desire to use a transformation going in the opposite direction of a normal convolution, i.e., from something that has the shape of the output of some convolution to something that has the shape of its input while maintaining a connectivity pattern that is compatible with said convolution. `input_quantizer`

and `kernel_quantizer`

are the element-wise quantization functions to use. If both quantization functions are `None`

this layer is equivalent to `Conv2DTranspose`

.

When using this layer as the first layer in a model, provide the keyword argument `input_shape`

(tuple of integers, does not include the sample axis), e.g. `input_shape=(128, 128, 3)`

for 128x128 RGB pictures in `data_format="channels_last"`

.

**Arguments**

`filters`

: Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).`kernel_size`

: An integer or tuple/list of 2 integers, specifying the height and width of the 2D convolution window. Can be a single integer to specify the same value for all spatial dimensions.`strides`

: An integer or tuple/list of 2 integers, specifying the strides of the convolution along the height and width. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any`dilation_rate`

value != 1.`padding`

: one of`"valid"`

or`"same"`

(case-insensitive).`output_padding`

: An integer or tuple/list of 2 integers, specifying the amount of padding along the height and width of the output tensor. Can be a single integer to specify the same value for all spatial dimensions. The amount of output padding along a given dimension must be lower than the stride along that same dimension. If set to`None`

(default), the output shape is inferred.`data_format`

: A string, one of`channels_last`

(default) or`channels_first`

. The ordering of the dimensions in the inputs.`channels_last`

corresponds to inputs with shape`(batch, height, width, channels)`

while`channels_first`

corresponds to inputs with shape`(batch, channels, height, width)`

. It defaults to the`image_data_format`

value found in your Keras config file at`~/.keras/keras.json`

. If you never set it, then it will be "channels_last".`dilation_rate`

: an integer or tuple/list of 2 integers, specifying the dilation rate to use for dilated convolution. Can be a single integer to specify the same value for all spatial dimensions. Currently, specifying any`dilation_rate`

value != 1 is incompatible with specifying any stride value != 1.`activation`

: Activation function to use. If you don't specify anything, no activation is applied (`a(x) = x`

).`use_bias`

: Boolean, whether the layer uses a bias vector.`input_quantizer`

: Quantization function applied to the input of the layer.`kernel_quantizer`

: Quantization function applied to the`kernel`

weights matrix.`kernel_initializer`

: Initializer for the`kernel`

weights matrix.`bias_initializer`

: Initializer for the bias vector.`kernel_regularizer`

: Regularizer function applied to the`kernel`

weights matrix.`bias_regularizer`

: Regularizer function applied to the bias vector.`activity_regularizer`

: Regularizer function applied to the output of the layer (its "activation").`kernel_constraint`

: Constraint function applied to the kernel matrix.`bias_constraint`

: Constraint function applied to the bias vector.`metrics`

: An array of metrics to add to the layer. If`None`

the metrics set in`larq.metrics.scope`

are used. Currently only the`flip_ratio`

metric is available.

**Input shape**

4D tensor with shape: `(batch, channels, rows, cols)`

if data_format='channels_first' or 4D tensor with shape: `(batch, rows, cols, channels)`

if data_format='channels_last'.

**Output shape**

4D tensor with shape: `(batch, filters, new_rows, new_cols)`

if data_format='channels_first' or 4D tensor with shape: `(batch, new_rows, new_cols, filters)`

if data_format='channels_last'. `rows`

and `cols`

values might have changed due to padding.

**References**

## QuantConv3DTranspose¶

QuantConv3DTranspose(filters, kernel_size, strides=(1, 1, 1), padding='valid', output_padding=None, data_format=None, activation=None, use_bias=True, input_quantizer=None, kernel_quantizer=None, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, metrics=None, **kwargs)

The need for transposed convolutions generally arises from the desire to use a transformation going in the opposite direction of a normal convolution, i.e., from something that has the shape of the output of some convolution to something that has the shape of its input while maintaining a connectivity pattern that is compatible with said convolution. `input_quantizer`

and `kernel_quantizer`

are the element-wise quantization functions to use. If both quantization functions are `None`

this layer is equivalent to `Conv3DTranspose`

.

When using this layer as the first layer in a model, provide the keyword argument `input_shape`

(tuple of integers, does not include the sample axis), e.g. `input_shape=(128, 128, 128, 3)`

for a 128x128x128 volume with 3 channels if `data_format="channels_last"`

.

**Arguments**

`filters`

: Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).`kernel_size`

: An integer or tuple/list of 3 integers, specifying the depth, height and width of the 3D convolution window. Can be a single integer to specify the same value for all spatial dimensions.`strides`

: An integer or tuple/list of 3 integers, specifying the strides of the convolution along the depth, height and width. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any`dilation_rate`

value != 1.`padding`

: one of`"valid"`

or`"same"`

(case-insensitive).`output_padding`

: An integer or tuple/list of 3 integers, specifying the amount of padding along the depth, height, and width. Can be a single integer to specify the same value for all spatial dimensions. The amount of output padding along a given dimension must be lower than the stride along that same dimension. If set to`None`

(default), the output shape is inferred.`data_format`

: A string, one of`channels_last`

(default) or`channels_first`

. The ordering of the dimensions in the inputs.`channels_last`

corresponds to inputs with shape`(batch, depth, height, width, channels)`

while`channels_first`

corresponds to inputs with shape`(batch, channels, depth, height, width)`

. It defaults to the`image_data_format`

value found in your Keras config file at`~/.keras/keras.json`

. If you never set it, then it will be "channels_last".`dilation_rate`

: an integer or tuple/list of 3 integers, specifying the dilation rate to use for dilated convolution. Can be a single integer to specify the same value for all spatial dimensions. Currently, specifying any`dilation_rate`

value != 1 is incompatible with specifying any stride value != 1.`activation`

: Activation function to use. If you don't specify anything, no activation is applied (`a(x) = x`

).`use_bias`

: Boolean, whether the layer uses a bias vector.`input_quantizer`

: Quantization function applied to the input of the layer.`kernel_quantizer`

: Quantization function applied to the`kernel`

weights matrix.`kernel_initializer`

: Initializer for the`kernel`

weights matrix.`bias_initializer`

: Initializer for the bias vector.`kernel_regularizer`

: Regularizer function applied to the`kernel`

weights matrix.`bias_regularizer`

: Regularizer function applied to the bias vector.`activity_regularizer`

: Regularizer function applied to the output of the layer (its "activation").`kernel_constraint`

: Constraint function applied to the kernel matrix.`bias_constraint`

: Constraint function applied to the bias vector.`metrics`

: An array of metrics to add to the layer. If`None`

the metrics set in`larq.metrics.scope`

are used. Currently only the`flip_ratio`

metric is available.

**Input shape**

5D tensor with shape: `(batch, channels, depth, rows, cols)`

if data_format='channels_first' or 5D tensor with shape: `(batch, depth, rows, cols, channels)`

if data_format='channels_last'.

**Output shape**

5D tensor with shape: `(batch, filters, new_depth, new_rows, new_cols)`

if data_format='channels_first' or 5D tensor with shape: `(batch, new_depth, new_rows, new_cols, filters)`

if data_format='channels_last'. `depth`

and `rows`

and `cols`

values might have changed due to padding.

**References**

## QuantLocallyConnected1D¶

QuantLocallyConnected1D(filters, kernel_size, strides=1, padding='valid', data_format=None, activation=None, use_bias=True, input_quantizer=None, kernel_quantizer=None, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, metrics=None, implementation=1, **kwargs)

The `QuantLocallyConnected1D`

layer works similarly to the `QuantConv1D`

layer, except that weights are unshared, that is, a different set of filters is applied at each different patch of the input. `input_quantizer`

and `kernel_quantizer`

are the element-wise quantization functions to use. If both quantization functions are `None`

this layer is equivalent to `LocallyConnected1D`

.

Example

# apply a unshared weight convolution 1d of length 3 to a sequence with # 10 timesteps, with 64 output filters model = Sequential() model.add(QuantLocallyConnected1D(64, 3, input_shape=(10, 32))) # now model.output_shape == (None, 8, 64) # add a new conv1d on top model.add(QuantLocallyConnected1D(32, 3)) # now model.output_shape == (None, 6, 32)

**Arguments**

`filters`

: Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).`kernel_size`

: An integer or tuple/list of a single integer, specifying the length of the 1D convolution window.`strides`

: An integer or tuple/list of a single integer, specifying the stride length of the convolution. Specifying any stride value != 1 is incompatible with specifying any`dilation_rate`

value != 1.`padding`

: Currently only supports`"valid"`

(case-insensitive).`"same"`

may be supported in the future.`data_format`

: A string, one of`channels_last`

(default) or`channels_first`

. The ordering of the dimensions in the inputs.`channels_last`

corresponds to inputs with shape`(batch, length, channels)`

while`channels_first`

corresponds to inputs with shape`(batch, channels, length)`

. It defaults to the`image_data_format`

value found in your Keras config file at`~/.keras/keras.json`

. If you never set it, then it will be "channels_last".`activation`

: Activation function to use. If you don't specify anything, no activation is applied (`a(x) = x`

).`use_bias`

: Boolean, whether the layer uses a bias vector.`input_quantizer`

: Quantization function applied to the input of the layer.`kernel_quantizer`

: Quantization function applied to the`kernel`

weights matrix.`kernel_initializer`

: Initializer for the`kernel`

weights matrix.`bias_initializer`

: Initializer for the bias vector.`kernel_regularizer`

: Regularizer function applied to the`kernel`

weights matrix.`bias_regularizer`

: Regularizer function applied to the bias vector.`activity_regularizer`

: Regularizer function applied to the output of the layer (its "activation").`kernel_constraint`

: Constraint function applied to the kernel matrix.`bias_constraint`

: Constraint function applied to the bias vector.`metrics`

: An array of metrics to add to the layer. If`None`

the metrics set in`larq.metrics.scope`

are used. Currently only the`flip_ratio`

metric is available.-
`implementation`

: implementation mode, either`1`

or`2`

.`1`

loops over input spatial locations to perform the forward pass. It is memory-efficient but performs a lot of (small) ops.`2`

stores layer weights in a dense but sparsely-populated 2D matrix and implements the forward pass as a single matrix-multiply. It uses a lot of RAM but performs few (large) ops.Depending on the inputs, layer parameters, hardware, and

`tf.executing_eagerly()`

one implementation can be dramatically faster (e.g. 50X) than another.It is recommended to benchmark both in the setting of interest to pick the most efficient one (in terms of speed and memory usage).

Following scenarios could benefit from setting

`implementation=2`

:- eager execution;
- inference;
- running on CPU;
- large amount of RAM available;
- small models (few filters, small kernel);
- using
`padding=same`

(only possible with`implementation=2`

).

**Input shape**

3D tensor with shape: `(batch_size, steps, input_dim)`

**Output shape**

3D tensor with shape: `(batch_size, new_steps, filters)`

`steps`

value might have changed due to padding or strides.

## QuantLocallyConnected2D¶

QuantLocallyConnected2D(filters, kernel_size, strides=(1, 1), padding='valid', data_format=None, activation=None, use_bias=True, input_quantizer=None, kernel_quantizer=None, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, metrics=None, implementation=1, **kwargs)

The `QuantLocallyConnected2D`

layer works similarly to the `QuantConv2D`

layer, except that weights are unshared, that is, a different set of filters is applied at each different patch of the input. `input_quantizer`

and `kernel_quantizer`

are the element-wise quantization functions to use. If both quantization functions are `None`

this layer is equivalent to `LocallyConnected2D`

.

Example

# apply a 3x3 unshared weights convolution with 64 output filters on a 32x32 image # with `data_format="channels_last"`: model = Sequential() model.add(QuantLocallyConnected2D(64, (3, 3), input_shape=(32, 32, 3))) # now model.output_shape == (None, 30, 30, 64) # notice that this layer will consume (30*30)*(3*3*3*64) + (30*30)*64 parameters # add a 3x3 unshared weights convolution on top, with 32 output filters: model.add(QuantLocallyConnected2D(32, (3, 3))) # now model.output_shape == (None, 28, 28, 32)

**Arguments**

`filters`

: Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).`kernel_size`

: An integer or tuple/list of 2 integers, specifying the width and height of the 2D convolution window. Can be a single integer to specify the same value for all spatial dimensions.`strides`

: An integer or tuple/list of 2 integers, specifying the strides of the convolution along the width and height. Can be a single integer to specify the same value for all spatial dimensions.`padding`

: Currently only support`"valid"`

(case-insensitive).`"same"`

will be supported in future.`data_format`

: A string, one of`channels_last`

(default) or`channels_first`

. The ordering of the dimensions in the inputs.`channels_last`

corresponds to inputs with shape`(batch, height, width, channels)`

while`channels_first`

corresponds to inputs with shape`(batch, channels, height, width)`

. It defaults to the`image_data_format`

value found in your Keras config file at`~/.keras/keras.json`

. If you never set it, then it will be "channels_last".`activation`

: Activation function to use. If you don't specify anything, no activation is applied (`a(x) = x`

).`use_bias`

: Boolean, whether the layer uses a bias vector.`input_quantizer`

: Quantization function applied to the input of the layer.`kernel_quantizer`

: Quantization function applied to the`kernel`

weights matrix.`kernel_initializer`

: Initializer for the`kernel`

weights matrix.`bias_initializer`

: Initializer for the bias vector.`kernel_regularizer`

: Regularizer function applied to the`kernel`

weights matrix.`bias_regularizer`

: Regularizer function applied to the bias vector.`activity_regularizer`

: Regularizer function applied to the output of the layer (its "activation").`kernel_constraint`

: Constraint function applied to the kernel matrix.`bias_constraint`

: Constraint function applied to the bias vector.`metrics`

: An array of metrics to add to the layer. If`None`

the metrics set in`larq.metrics.scope`

are used. Currently only the`flip_ratio`

metric is available.-
`implementation`

: implementation mode, either`1`

or`2`

.`1`

loops over input spatial locations to perform the forward pass. It is memory-efficient but performs a lot of (small) ops.`2`

stores layer weights in a dense but sparsely-populated 2D matrix and implements the forward pass as a single matrix-multiply. It uses a lot of RAM but performs few (large) ops.Depending on the inputs, layer parameters, hardware, and

`tf.executing_eagerly()`

one implementation can be dramatically faster (e.g. 50X) than another.It is recommended to benchmark both in the setting of interest to pick the most efficient one (in terms of speed and memory usage).

Following scenarios could benefit from setting

`implementation=2`

:- eager execution;
- inference;
- running on CPU;
- large amount of RAM available;
- small models (few filters, small kernel);
- using
`padding=same`

(only possible with`implementation=2`

).

**Input shape**

4D tensor with shape: `(samples, channels, rows, cols)`

if data_format='channels_first' or 4D tensor with shape: `(samples, rows, cols, channels)`

if data_format='channels_last'.

**Output shape**

4D tensor with shape: `(samples, filters, new_rows, new_cols)`

if data_format='channels_first' or 4D tensor with shape: `(samples, new_rows, new_cols, filters)`

if data_format='channels_last'. `rows`

and `cols`

values might have changed due to padding.