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# Copyright 2018 Analytics Zoo Authors.
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# http://www.apache.org/licenses/LICENSE-2.0
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import sys
from zoo.pipeline.api.keras2.base import ZooKeras2Layer
if sys.version >= '3':
long = int
unicode = str
[docs]class Conv1D(ZooKeras2Layer):
"""1D 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.
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"` or `"same"` (case-insensitive).
`"valid"` means "no padding".
`"same"` results in padding the input such that
the output has the same length as the original input.
(see [activations](../activations.md)).
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
# 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.
>>> conv1d = Conv1D(12, 4, input_shape=(3, 16))
creating: createZooKeras2Conv1D
"""
def __init__(self,
filters,
kernel_size,
strides=1,
padding="valid",
activation=None,
use_bias=True,
kernel_initializer="glorot_uniform",
bias_initializer="zero",
kernel_regularizer=None,
bias_regularizer=None,
input_shape=None, **kwargs):
super(Conv1D, self).__init__(None,
filters,
kernel_size,
strides,
padding,
activation,
use_bias,
kernel_initializer,
bias_initializer,
kernel_regularizer,
bias_regularizer,
list(input_shape) if input_shape else None,
**kwargs)
[docs]class Conv2D(ZooKeras2Layer):
"""2D 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. 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
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.
Specifying any stride value != 1 is incompatible with specifying
any `dilation_rate` value != 1.
padding: one of `"valid"` or `"same"` (case-insensitive).
Note that `"same"` is slightly inconsistent across backends with
`strides` != 1.
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. "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
# 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.
>>> conv2d = Conv2D(12, kernel_size=(2, 5), input_shape=(3, 16, 16))
creating: createZooKeras2Conv2D
"""
def __init__(self,
filters,
kernel_size,
strides=(1, 1),
padding="valid",
data_format="channels_first",
activation=None,
use_bias=True,
kernel_initializer="glorot_uniform",
bias_initializer="zero",
kernel_regularizer=None,
bias_regularizer=None,
input_shape=None, **kwargs):
super(Conv2D, self).__init__(None,
filters,
kernel_size,
strides,
padding,
data_format,
activation,
use_bias,
kernel_initializer,
bias_initializer,
kernel_regularizer,
bias_regularizer,
list(input_shape) if input_shape else None,
**kwargs)
[docs]class Cropping1D(ZooKeras2Layer):
"""
Cropping layer for 1D input (e.g. temporal sequence).
The input of this layer should be 3D.
When you use this layer as the first layer of a model, you need to provide the argument
input_shape (a shape tuple, does not include the batch dimension).
# Arguments
cropping: Int tuple of length 2. How many units should be trimmed off at the beginning and
end of the cropping dimension. Default is (1, 1).
input_shape: A shape tuple, not including batch.
name: String to set the name of the layer.
If not specified, its name will by default to be a generated string.
>>> cropping1d = Cropping1D(cropping=(1, 2), input_shape=(8, 8))
creating: createZooKeras2Cropping1D
"""
def __init__(self, cropping=(1, 1), input_shape=None, **kwargs):
super(Cropping1D, self).__init__(None,
cropping,
list(input_shape) if input_shape else None,
**kwargs)