`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.

[source]

QuantDense¶

larq.layers.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,
    **kwargs
)

Just your regular densely-connected quantized NN layer.

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.

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).

[source]

QuantConv1D¶

larq.layers.QuantConv1D(
    filters,
    kernel_size,
    strides=1,
    padding="valid",
    pad_values=0.0,
    data_format="channels_last",
    dilation_rate=1,
    groups=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,
    **kwargs
)

1D quantized convolution layer (e.g. temporal convolution).

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.
pad_values: The pad value to use when padding="same".
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.
groups: A positive integer specifying the number of groups in which the input is split along the channel axis. Each group is convolved separately with filters / groups filters. The output is the concatenation of all the groups results along the channel axis. Input channels and filters must both be divisible by groups.
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.

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.

[source]

QuantConv2D¶

larq.layers.QuantConv2D(
    filters,
    kernel_size,
    strides=(1, 1),
    padding="valid",
    pad_values=0.0,
    data_format=None,
    dilation_rate=(1, 1),
    groups=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,
    **kwargs
)

2D quantized convolution layer (e.g. spatial convolution over images).

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).
pad_values: The pad value to use when padding="same".
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.
groups: A positive integer specifying the number of groups in which the input is split along the channel axis. Each group is convolved separately with filters / groups filters. The output is the concatenation of all the groups results along the channel axis. Input channels and filters must both be divisible by groups.
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.

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.

[source]

QuantConv3D¶

larq.layers.QuantConv3D(
    filters,
    kernel_size,
    strides=(1, 1, 1),
    padding="valid",
    pad_values=0.0,
    data_format=None,
    dilation_rate=(1, 1, 1),
    groups=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,
    **kwargs
)

3D convolution layer (e.g. spatial convolution over volumes).

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).
pad_values: The pad value to use when padding="same".
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 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.
groups: A positive integer specifying the number of groups in which the input is split along the channel axis. Each group is convolved separately with filters / groups filters. The output is the concatenation of all the groups results along the channel axis. Input channels and filters must both be divisible by groups.
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.

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.

[source]

QuantDepthwiseConv2D¶

larq.layers.QuantDepthwiseConv2D(
    kernel_size,
    strides=(1, 1),
    padding="valid",
    pad_values=0.0,
    depth_multiplier=1,
    data_format=None,
    dilation_rate=(1, 1),
    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,
    **kwargs
)

Quantized depthwise separable 2D convolution.

Depthwise Separable convolutions consists in performing just the first step in a depthwise spatial convolution (which acts on each input channel separately). The 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).
pad_values: The pad value to use when padding="same".
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'.
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 (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.

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.

[source]

QuantSeparableConv1D¶

larq.layers.QuantSeparableConv1D(
    filters,
    kernel_size,
    strides=1,
    padding="valid",
    pad_values=0.0,
    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,
    **kwargs
)

Depthwise separable 1D quantized convolution.

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).
pad_values: The pad value to use when padding="same".
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.
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.

[source]

QuantSeparableConv2D¶

larq.layers.QuantSeparableConv2D(
    filters,
    kernel_size,
    strides=(1, 1),
    padding="valid",
    pad_values=0.0,
    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,
    **kwargs
)

Depthwise separable 2D convolution.

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).
pad_values: The pad value to use when padding="same".
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.
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.`

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.

[source]

QuantConv2DTranspose¶

larq.layers.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,
    **kwargs
)

Transposed quantized convolution layer (sometimes called Deconvolution).

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.

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

[source]

QuantConv3DTranspose¶

larq.layers.QuantConv3DTranspose(
    filters,
    kernel_size,
    strides=(1, 1, 1),
    padding="valid",
    output_padding=None,
    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,
    **kwargs
)

Transposed quantized convolution layer (sometimes called Deconvolution).

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 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.

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

[source]

QuantLocallyConnected1D¶

larq.layers.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,
    implementation=1,
    **kwargs
)

Locally-connected quantized layer for 1D inputs.

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.
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.

[source]

QuantLocallyConnected2D¶

larq.layers.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,
    implementation=1,
    **kwargs
)

Locally-connected quantized layer for 2D inputs.

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.
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.

larq.layers¶

QuantDense¶

QuantConv1D¶

QuantConv2D¶

QuantConv3D¶

QuantDepthwiseConv2D¶

QuantSeparableConv1D¶

QuantSeparableConv2D¶

QuantConv2DTranspose¶

QuantConv3DTranspose¶

QuantLocallyConnected1D¶

QuantLocallyConnected2D¶

`larq.layers`¶