from pyeddl.layers.base import Layer
[docs]class Conv2D(Layer):
"""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.
"""
[docs] def __init__(self, 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):
super(Conv2D, self).__init__()