Convolutional (pyeddl.layers.conv)

Conv2D

2D convolution layer (spatial convolution over images).

Conv2DTranspose

Transposed convolution layer (sometimes called Deconvolution).

Conv2D

class pyeddl.layers.conv.Conv2D(filters, kernel_size, strides=(1, 1), padding='valid', data_format=None, dilation_rate=(1, 1), activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, **kwargs)[source]

2D convolution layer (spatial convolution over images).

This layer creates a convolution kernel that is convolved with the layer input to produce a tensor of outputs. 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 batch axis), e.g. input_shape=(128, 128, 3) for 128x128 RGB pictures in data_format=”channels_last”.

Args:
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” 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). 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. “linear” activation: a(x) = x).

use_bias: Boolean, whether the layer uses a bias vector. 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 is “channels_first” or 4D tensor with shape: (batch, rows, cols, channels) if data_format is “channels_last”.

Output shape:

4D tensor with shape: (batch, filters, new_rows, new_cols) if data_format is “channels_first” or 4D tensor with shape: (batch, new_rows, new_cols, filters) if data_format is “channels_last”. rows and cols values might have changed due to padding.

__init__(filters, kernel_size, strides=(1, 1), padding='valid', data_format=None, dilation_rate=(1, 1), activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, **kwargs)[source]

Initialize self. See help(type(self)) for accurate signature.

Conv2DTranspose

class pyeddl.layers.conv.Conv2DTranspose(filters, kernel_size, strides=(1, 1), padding='valid', output_padding=None, data_format=None, dilation_rate=(1, 1), activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, **kwargs)[source]

Transposed 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. When using this layer as the first layer in a model, provide the keyword argument input_shape (tuple of integers, does not include the batch axis), e.g. input_shape=(128, 128, 3) for 128x128 RGB pictures in data_format=”channels_last”.

Args:
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” 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

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. “linear” activation: a(x) = x).

use_bias: Boolean, whether the layer uses a bias vector. 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 is “channels_first” or 4D tensor with shape: (batch, rows, cols, channels) if data_format is “channels_last”.

Output shape:

4D tensor with shape: (batch, filters, new_rows, new_cols) if data_format is “channels_first” or 4D tensor with shape: (batch, new_rows, new_cols, filters) if data_format is “channels_last”. rows and cols values might have changed due to padding. If output_padding is specified: ``` new_rows = ((rows - 1) * strides[0] + kernel_size[0]

  • 2 * padding[0] + output_padding[0])

new_cols = ((cols - 1) * strides[1] + kernel_size[1]

  • 2 * padding[1] + output_padding[1])

```

References
__init__(filters, kernel_size, strides=(1, 1), padding='valid', output_padding=None, data_format=None, dilation_rate=(1, 1), activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, **kwargs)[source]

Initialize self. See help(type(self)) for accurate signature.