Deep House Top 1000, Vol. 1 - Armada Music
Deep House Ibiza
Deep House Essential
Armada Deep House Selection, Vol. 3 (The Finest Deep House Tunes)
Deep House (Denis Bravo & Dima Young Radio Edit)
Ultra Deep House
Deep House (Denis Bravo & Dima Young Remix)
Winterbreak (Tiësto's Deep House Remix)
Deep House Lounge (Chill Out Set)
Sexy Chill Electronic Music & Deep House Hits DJ: 2020 Nye Dance Party Workout Year Mix from Miami to Ibiza to Amsterdam


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Define input layer
tefla.core.layers.input (shape, name='inputs', outputs_collections=None, **unused)
Args
- shape: A
Tensor
, define the input shape e.g. for image input [batch_size, height, width, depth] - name: A optional score/name for this op
- outputs_collections: The collections to which the outputs are added.
Returns
A placeholder for the input
Add item to colelction
tefla.core.layers.register_to_collections (inputs, name=None, outputs_collections=None, **unused)
Args
- shape: A
Tensor
, define the input shape e.g. for image input [batch_size, height, width, depth] - name: A optional score/name for this op
- outputs_collections: The collections to which the outputs are added.
Returns
A placeholder for the input
Adds a fully connected layer
tefla.core.layers.fully_connected (x, n_output, is_training, reuse, trainable=True, w_init=
fully_connected
creates a variable called weights
, representing a fully
connected weight matrix, which is multiplied by the x
to produce a
Tensor
of hidden units. If a batch_norm
is provided (such as
batch_norm
), it is then applied. Otherwise, if batch_norm
is
None and a b_init
and use_bias
is provided then a biases
variable would be
created and added the hidden units. Finally, if activation
is not None
,
it is applied to the hidden units as well.
Note: that if x
have a rank greater than 2, then x
is flattened
prior to the initial matrix multiply by weights
.
Args
- x: A
Tensor
of with at least rank 2 and value for the last dimension, i.e.[batch_size, depth]
,[None, None, None, channels]
. - is_training: Bool, training or testing
- n_output: Integer or long, the number of output units in the layer.
- reuse: whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given.
- activation: activation function, set to None to skip it and maintain a linear activation.
- batch_norm: normalization function to use. If
-
batch_norm
isTrue
then google original implementation is used and if another function is provided then it is applied. default set to None for no normalizer function - batch_norm_args: normalization function parameters.
- w_init: An initializer for the weights.
- w_regularizer: Optional regularizer for the weights.
- b_init: An initializer for the biases. If None skip biases.
- outputs_collections: The collections to which the outputs are added.
- trainable: If
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(see tf.Variable). - name: Optional name or scope for variable_scope/name_scope.
- use_bias: Whether to add bias or not
Returns
The 2-D Tensor
variable representing the result of the series of operations.
e.g: 2-D Tensor
[batch, n_output].
Adds a 2D convolutional layer
tefla.core.layers.conv2d (x, n_output_channels, is_training, reuse, trainable=True, filter_size= (3, 3), stride= (1, 1), padding='SAME', w_init=
convolutional layer
creates a variable called weights
, representing a conv
weight matrix, which is multiplied by the x
to produce a
Tensor
of hidden units. If a batch_norm
is provided (such as
batch_norm
), it is then applied. Otherwise, if batch_norm
is
None and a b_init
and use_bias
is provided then a biases
variable would be
created and added the hidden units. Finally, if activation
is not None
,
it is applied to the hidden units as well.
Note: that if x
have a rank 4
Args
- x: A 4-D
Tensor
of with at least rank 2 and value for the last dimension, i.e.[batch_size, in_height, in_width, depth]
, - is_training: Bool, training or testing
- n_output: Integer or long, the number of output units in the layer.
- reuse: whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given.
- filter_size: a int or list/tuple of 2 positive integers specifying the spatial dimensions of of the filters.
- stride: a int or tuple/list of 2 positive integers specifying the stride at which to compute output.
- padding: one of
"VALID"
or"SAME"
. - activation: activation function, set to None to skip it and maintain a linear activation.
- batch_norm: normalization function to use. If
batch_norm
isTrue
then google original implementation is used and if another function is provided then it is applied. default set to None for no normalizer function - batch_norm_args: normalization function parameters.
- w_init: An initializer for the weights.
- w_regularizer: Optional regularizer for the weights.
- untie_biases: spatial dimensions wise baises
- b_init: An initializer for the biases. If None skip biases.
- outputs_collections: The collections to which the outputs are added.
- trainable: If
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(see tf.Variable). - name: Optional name or scope for variable_scope/name_scope.
- use_bias: Whether to add bias or not
Returns
The 4-D Tensor
variable representing the result of the series of operations.
e.g.: 4-D Tensor
[batch, new_height, new_width, n_output].
Adds a 2D dilated convolutional layer
tefla.core.layers.dilated_conv2d (x, n_output_channels, is_training, reuse, trainable=True, filter_size= (3, 3), dilation=1, stride=1, padding='SAME', w_init=
also known as convolution with holes or atrous convolution.
If the rate parameter is equal to one, it performs regular 2-D convolution.
If the rate parameter
is greater than one, it performs convolution with holes, sampling the input
values every rate pixels in the height and width dimensions.
convolutional layer
creates a variable called weights
, representing a conv
weight matrix, which is multiplied by the x
to produce a
Tensor
of hidden units. If a batch_norm
is provided (such as
batch_norm
), it is then applied. Otherwise, if batch_norm
is
None and a b_init
and use_bias
is provided then a biases
variable would be
created and added the hidden units. Finally, if activation
is not None
,
it is applied to the hidden units as well.
Note: that if x
have a rank 4
Args
- x: A 4-D
Tensor
of with rank 4 and value for the last dimension, i.e.[batch_size, in_height, in_width, depth]
, - is_training: Bool, training or testing
- n_output: Integer or long, the number of output units in the layer.
- reuse: whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given.
- filter_size: a int or list/tuple of 2 positive integers specifying the spatial
- dimensions of of the filters.
- dilation: A positive int32. The stride with which we sample input values across the height and width dimensions. Equivalently, the rate by which we upsample the filter values by inserting zeros across the height and width dimensions. In the literature, the same parameter is sometimes called input stride/rate or dilation.
- padding: one of
"VALID"
or"SAME"
. - activation: activation function, set to None to skip it and maintain a linear activation.
- batch_norm: normalization function to use. If
batch_norm
isTrue
then google original implementation is used and if another function is provided then it is applied. default set to None for no normalizer function - batch_norm_args: normalization function parameters.
- w_init: An initializer for the weights.
- w_regularizer: Optional regularizer for the weights.
- untie_biases: spatial dimensions wise baises
- b_init: An initializer for the biases. If None skip biases.
- outputs_collections: The collections to which the outputs are added.
- trainable: If
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(see tf.Variable). - name: Optional name or scope for variable_scope/name_scope.
- use_bias: Whether to add bias or not
Returns
The 4-D Tensor
variable representing the result of the series of operations.
e.g.: 4-D Tensor
[batch, new_height, new_width, n_output].
Adds a 2D seperable convolutional layer
tefla.core.layers.separable_conv2d (x, n_output_channels, is_training, reuse, trainable=True, filter_size= (3, 3), stride= (1, 1), depth_multiplier=1, padding='SAME', w_init=
Performs a depthwise convolution that acts separately on channels followed by
a pointwise convolution that mixes channels. Note that this is separability between
dimensions [1, 2] and 3, not spatial separability between dimensions 1 and 2.
convolutional layer
creates two variable called depthwise_W
and pointwise_W
,
depthwise_W
is multiplied by x
to produce depthwise conolution, which is multiplied by
the pointwise_W
to produce a output Tensor
If a batch_norm
is provided (such as
batch_norm
), it is then applied. Otherwise, if batch_norm
is
None and a b_init
and use_bias
is provided then a biases
variable would be
created and added the hidden units. Finally, if activation
is not None
,
it is applied to the hidden units as well.
Note: that if x
have a rank 4
Args
- x: A 4-D
Tensor
of with rank 4 and value for the last dimension, i.e.[batch_size, in_height, in_width, depth]
, - is_training: Bool, training or testing
- n_output: Integer or long, the number of output units in the layer.
- reuse: whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given.
- filter_size: a int or list/tuple of 2 positive integers specifying the spatial
- stride: a int or tuple/list of 2 positive integers specifying the stride at which to compute output.
- dimensions of of the filters.
- depth_multiplier: A positive int32. 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
- padding: one of
"VALID"
or"SAME"
. - activation: activation function, set to None to skip it and maintain a linear activation.
- batch_norm: normalization function to use. If
batch_norm
isTrue
then google original implementation is used and if another function is provided then it is applied. default set to None for no normalizer function - batch_norm_args: normalization function parameters.
- w_init: An initializer for the weights.
- w_regularizer: Optional regularizer for the weights.
- untie_biases: spatial dimensions wise baises
- b_init: An initializer for the biases. If None skip biases.
- outputs_collections: The collections to which the outputs are added.
- trainable: If
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(see tf.Variable). - name: Optional name or scope for variable_scope/name_scope.
- use_bias: Whether to add bias or not
Returns
The 4-D Tensor
variable representing the result of the series of operations.
e.g.: 4-D Tensor
[batch, new_height, new_width, n_output].
Adds a 2D sdepthwise convolutional layer
tefla.core.layers.depthwise_conv2d (x, depth_multiplier, is_training, reuse, trainable=True, filter_size= (3, 3), stride= (1, 1), padding='SAME', w_init=
Given an input tensor of shape [batch, in_height, in_width, in_channels] and a filter
tensor of shape [filter_height, filter_width, in_channels, channel_multiplier] containing
in_channels convolutional filters of depth 1, depthwise_conv2d applies a different filter
to each input channel (expanding from 1 channel to channel_multiplier channels for each),
then concatenates the results together. The output has in_channels * channel_multiplier channels.
If a batch_norm
is provided (such as
batch_norm
), it is then applied. Otherwise, if batch_norm
is
None and a b_init
and use_bias
is provided then a biases
variable would be
created and added the hidden units. Finally, if activation
is not None
,
it is applied to the hidden units as well.
Note: that if x
have a rank 4
Args
- x: A 4-D
Tensor
of with rank 4 and value for the last dimension, i.e.[batch_size, in_height, in_width, depth]
, - is_training: Bool, training or testing
- reuse: whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given.
- filter_size: a int or list/tuple of 2 positive integers specifying the spatial dimensions of of the filters.
- stride: a int or tuple/list of 2 positive integers specifying the stride at which to compute output.
- depth_multiplier: A positive int32. 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
- padding: one of
"VALID"
or"SAME"
. - activation: activation function, set to None to skip it and maintain a linear activation.
- batch_norm: normalization function to use. If
batch_norm
isTrue
then google original implementation is used and if another function is provided then it is applied. default set to None for no normalizer function - batch_norm_args: normalization function parameters.
- w_init: An initializer for the weights.
- w_regularizer: Optional regularizer for the weights.
- untie_biases: spatial dimensions wise baises
- b_init: An initializer for the biases. If None skip biases.
- outputs_collections: The collections to which the outputs are added.
- trainable: If
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(see tf.Variable). - name: Optional name or scope for variable_scope/name_scope.
- use_bias: Whether to add bias or not
Returns
The tensor variable representing the result of the series of operations.
e.g.: 4-D Tensor
[batch, new_height, new_width, n_output].
Adds a 3D convolutional layer
tefla.core.layers.conv3d (x, n_output_channels, is_training, reuse, trainable=True, filter_size= (3, 3, 3), stride= (1, 1, 1), padding='SAME', w_init=
convolutional layer
creates a variable called weights
, representing a conv
weight matrix, which is multiplied by the x
to produce a
Tensor
of hidden units. If a batch_norm
is provided (such as
batch_norm
), it is then applied. Otherwise, if batch_norm
is
None and a b_init
and use_bias
is provided then a biases
variable would be
created and added the hidden units. Finally, if activation
is not None
,
it is applied to the hidden units as well.
Note: that if x
have a rank 5
Args
- x: A 5-D
Tensor
of with at least rank 2 and value for the last dimension, i.e.[batch_size, in_depth, in_height, in_width, depth]
, - is_training: Bool, training or testing
- n_output: Integer or long, the number of output units in the layer.
- reuse: whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given.
- filter_size: a int, or list/tuple of 3 positive integers specifying the spatial dimensions of of the filters.
- stride: a int, or tuple/list of 3 positive integers specifying the stride at which to compute output.
- padding: one of
"VALID"
or"SAME"
. - activation: activation function, set to None to skip it and maintain a linear activation.
- batch_norm: normalization function to use. If
batch_norm
isTrue
then google original implementation is used and if another function is provided then it is applied. default set to None for no normalizer function - batch_norm_args: normalization function parameters.
- w_init: An initializer for the weights.
- w_regularizer: Optional regularizer for the weights.
- untie_biases: spatial dimensions wise baises
- b_init: An initializer for the biases. If None skip biases.
- outputs_collections: The collections to which the outputs are added.
- trainable: If
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(see tf.Variable). - name: Optional name or scope for variable_scope/name_scope.
- use_bias: Whether to add bias or not
Returns
The 5-D Tensor
variable representing the result of the series of operations.
e.g.: 5-D Tensor
[batch, new_depth, new_height, new_width, n_output].
Adds a 2D upsampling or deconvolutional layer
tefla.core.layers.upsample2d (input_, output_shape, is_training, reuse, trainable=True, filter_size= (5, 5), stride= (2, 2), w_init=
his operation is sometimes called "deconvolution" after Deconvolutional Networks,
but is actually the transpose (gradient) of conv2d rather than an actual deconvolution.
If a batch_norm
is provided (such as
batch_norm
), it is then applied. Otherwise, if batch_norm
is
None and a b_init
and use_bias
is provided then a biases
variable would be
created and added the hidden units. Finally, if activation
is not None
,
it is applied to the hidden units as well.
Note: that if x
have a rank 4
Args
- x: A 4-D
Tensor
of with at least rank 2 and value for the last dimension, i.e.[batch_size, in_height, in_width, depth]
, - is_training: Bool, training or testing
- output_shape: 4D tensor, the output shape
- reuse: whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given.
- filter_size: a int or list/tuple of 2 positive integers specifying the spatial dimensions of of the filters.
- stride: a int or tuple/list of 2 positive integers specifying the stride at which to compute output.
- padding: one of
"VALID"
or"SAME"
. - activation: activation function, set to None to skip it and maintain a linear activation.
- batch_norm: normalization function to use. If
batch_norm
isTrue
then google original implementation is used and if another function is provided then it is applied. default set to None for no normalizer function - batch_norm_args: normalization function parameters.
- w_init: An initializer for the weights.
- w_regularizer: Optional regularizer for the weights.
- b_init: An initializer for the biases. If None skip biases.
- outputs_collections: The collections to which the outputs are added.
- trainable: If
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(see tf.Variable). - name: Optional name or scope for variable_scope/name_scope.
- use_bias: Whether to add bias or not
Returns
The tensor variable representing the result of the series of operations.
e.g.: 4-D Tensor
[batch, new_height, new_width, n_output].
Adds a 3D upsampling or deconvolutional layer
tefla.core.layers.upsample3d (input_, output_shape, is_training, reuse, trainable=True, filter_size= (5, 5, 5), stride= (2, 2, 2), w_init=
his operation is sometimes called "deconvolution" after Deconvolutional Networks,
but is actually the transpose (gradient) of conv2d rather than an actual deconvolution.
If a batch_norm
is provided (such as
batch_norm
), it is then applied. Otherwise, if batch_norm
is
None and a b_init
and use_bias
is provided then a biases
variable would be
created and added the hidden units. Finally, if activation
is not None
,
it is applied to the hidden units as well.
Note: that if x
have a rank 5
Args
- x: A 5-D
Tensor
of with at least rank 2 and value for the last dimension, i.e.[batch_size, in_depth, in_height, in_width, depth]
, - is_training: Bool, training or testing
- output_shape: 5D tensor, the output shape
- reuse: whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given.
- filter_size: a int or list/tuple of 3 positive integers specifying the spatial dimensions of of the filters.
- stride: a int or tuple/list of 3 positive integers specifying the stride at which to compute output.
- padding: one of
"VALID"
or"SAME"
. - activation: activation function, set to None to skip it and maintain a linear activation.
- batch_norm: normalization function to use. If
batch_norm
isTrue
then google original implementation is used and if another function is provided then it is applied. default set to None for no normalizer function - batch_norm_args: normalization function parameters.
- w_init: An initializer for the weights.
- w_regularizer: Optional regularizer for the weights.
- b_init: An initializer for the biases. If None skip biases.
- outputs_collections: The collections to which the outputs are added.
- trainable: If
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(see tf.Variable). - name: Optional name or scope for variable_scope/name_scope.
- use_bias: Whether to add bias or not
Returns
The tensor variable representing the result of the series of operations.
e.g.: 5-D Tensor
[batch, new_depth, new_height, new_width, n_output].
Adds a 1D convolutional layer
tefla.core.layers.conv1d (x, n_output_channels, is_training, reuse, trainable=True, filter_size=3, stride=1, padding='SAME', w_init=
convolutional layer
creates a variable called weights
, representing a conv
weight matrix, which is multiplied by the x
to produce a
Tensor
of hidden units. If a batch_norm
is provided (such as
batch_norm
), it is then applied. Otherwise, if batch_norm
is
None and a b_init
and use_bias
is provided then a biases
variable would be
created and added the hidden units. Finally, if activation
is not None
,
it is applied to the hidden units as well.
Note: that if x
have a rank 4
Args
- x: A 3-D
Tensor
of with at least rank 2 and value for the last dimension, i.e.[batch_size, in_width, depth]
, - is_training: Bool, training or testing
- n_output: Integer or long, the number of output units in the layer.
- reuse: whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given.
- filter_size: a `int specifying the spatial dimensions of of the filters.
- stride: a
int
specifying the stride at which to compute output. - padding: one of
"VALID"
or"SAME"
. - activation: activation function, set to None to skip it and maintain a linear activation.
- batch_norm: normalization function to use. If
batch_norm
isTrue
then google original implementation is used and if another function is provided then it is applied. default set to None for no normalizer function - batch_norm_args: normalization function parameters.
- w_init: An initializer for the weights.
- w_regularizer: Optional regularizer for the weights.
- untie_biases: spatial dimensions wise baises
- b_init: An initializer for the biases. If None skip biases.
- outputs_collections: The collections to which the outputs are added.
- trainable: If
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(see tf.Variable). - name: Optional name or scope for variable_scope/name_scope.
- use_bias: Whether to add bias or not
Returns
The 3-D Tensor
variable representing the result of the series of operations.
e.g.: 3-D Tensor
[batch, new_width, n_output].
Max Pooling 1D
tefla.core.layers.max_pool_1d (x, filter_size=3, stride=2, padding='SAME', name='maxpool1d', outputs_collections=None, **unused)
Args
- x: a 3-D
Tensor
[batch_size, steps, in_channels]. - kernel_size:
int
orlist of int
. Pooling kernel size. - strides:
int
orlist of int
. Strides of conv operation. Default: same as kernel_size. - padding:
str
from"same", "valid"
. Padding algo to use. Default: 'same'. - name: A name for this layer (optional). Default: 'maxpool1d'.
Returns
3-D Tensor [batch, pooled steps, in_channels].
Avg Pooling 1D
tefla.core.layers.avg_pool_1d (x, filter_size=3, stride=2, padding='SAME', name='avgpool1d', outputs_collections=None, **unused)
Args
- x: a 3-D
Tensor
[batch_size, steps, in_channels]. - kernel_size:
int
orlist of int
. Pooling kernel size. - strides:
int
orlist of int
. Strides of conv operation. Default: same as kernel_size. - padding:
str
from"same", "valid"
. Padding algo to use. Default: 'same'. - name: A name for this layer (optional). Default: 'avgpool1d'.
Returns
3-D Tensor [batch, pooled steps, in_channels].
Adds a 2D highway convolutional layer
tefla.core.layers.highway_conv2d (x, n_output, is_training, reuse, trainable=True, filter_size= (3, 3), stride= (1, 1), padding='SAME', w_init=
https://arxiv.org/abs/1505.00387
If a batch_norm
is provided (such as
batch_norm
), it is then applied. Otherwise, if batch_norm
is
None and a b_init
and use_bias
is provided then a biases
variable would be
created and added the hidden units. Finally, if activation
is not None
,
it is applied to the hidden units as well.
Note: that if x
have a rank 4
Args
- x: A 4-D
Tensor
of with at least rank 2 and value for the last dimension, i.e.[batch_size, in_height, in_width, depth]
, - is_training: Bool, training or testing
- n_output: Integer or long, the number of output units in the layer.
- reuse: whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given.
- filter_size: a int or list/ tuple of 2 positive integers specifying the spatial dimensions of of the filters.
- stride: a int or tuple/list of 2 positive integers specifying the stride at which to compute output.
- padding: one of
"VALID"
or"SAME"
. - activation: activation function, set to None to skip it and maintain a linear activation.
- batch_norm: normalization function to use. If
batch_norm
isTrue
then google original implementation is used and if another function is provided then it is applied. default set to None for no normalizer function - batch_norm_args: normalization function parameters.
- w_init: An initializer for the weights.
- w_regularizer: Optional regularizer for the weights.
- untie_biases: spatial dimensions wise baises
- b_init: An initializer for the biases. If None skip biases.
- outputs_collections: The collections to which the outputs are added.
- trainable: If
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(see tf.Variable). - name: Optional name or scope for variable_scope/name_scope.
- use_bias: Whether to add bias or not
Returns
The Tensor
variable representing the result of the series of operations.
e.g.: 4-D Tensor
[batch, new_height, new_width, n_output].
Adds a fully connected highway layer
tefla.core.layers.highway_fc2d (x, n_output, is_training, reuse, trainable=True, filter_size= (3, 3), w_init=
https://arxiv.org/abs/1505.00387
If a batch_norm
is provided (such as
batch_norm
), it is then applied. Otherwise, if batch_norm
is
None and a b_init
and use_bias
is provided then a biases
variable would be
created and added the hidden units. Finally, if activation
is not None
,
it is applied to the hidden units as well.
Note: that if x
have a rank greater than 2, then x
is flattened
prior to the initial matrix multiply by weights
.
Args
- x: A 2-D/4-D
Tensor
of with at least rank 2 and value for the last dimension, i.e.[batch_size, depth]
,[None, None, None, channels]
. - is_training: Bool, training or testing
- n_output: Integer or long, the number of output units in the layer.
- reuse: whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given.
- activation: activation function, set to None to skip it and maintain a linear activation.
- batch_norm: normalization function to use. If
batch_norm
isTrue
then google original implementation is used and if another function is provided then it is applied. default set to None for no normalizer function - batch_norm_args: normalization function parameters.
- w_init: An initializer for the weights.
- w_regularizer: Optional regularizer for the weights.
- b_init: An initializer for the biases. If None skip biases.
- outputs_collections: The collections to which the outputs are added.
- trainable: If
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(see tf.Variable). - name: Optional name or scope for variable_scope/name_scope.
- use_bias: Whether to add bias or not
Returns
The 2-D Tensor
variable representing the result of the series of operations.
e.g.: 2-D Tensor
[batch_size, n_output]
Max pooling layer
tefla.core.layers.max_pool (x, filter_size= (3, 3), stride= (2, 2), padding='SAME', name='pool', outputs_collections=None, **unused)
Args
- x: A 4-D 'Tensor
of shape
[batch_size, height, width, channels]` - filter_size: A int or list/tuple of length 2: [kernel_height, kernel_width] of the pooling kernel over which the op is computed. Can be an int if both values are the same.
- stride: A int or list/tuple of length 2: [stride_height, stride_width].
- padding: The padding method, either 'VALID' or 'SAME'.
- outputs_collections: The collections to which the outputs are added.
- name: Optional scope/name for name_scope.
Returns
A Tensor
representing the results of the pooling operation.
e.g.: 4-D Tensor
[batch, new_height, new_width, channels].
Max pooling layer
tefla.core.layers.max_pool_3d (x, filter_size= (3, 3, 3), stride= (2, 2, 2), padding='SAME', name='pool', outputs_collections=None, **unused)
Args
- x: A 5-D 'Tensor
of shape
[batch_size, depth, height, width, channels]` - filter_size: A int or list/tuple of length 3: [kernel_depth, kernel_height, kernel_width] of the pooling kernel over which the op is computed. Can be an int if both values are the same.
- stride: A int or list/tuple of length 3: [stride_depth, stride_height, stride_width].
- padding: The padding method, either 'VALID' or 'SAME'.
- outputs_collections: The collections to which the outputs are added.
- name: Optional scope/name for name_scope.
Returns
A Tensor
representing the results of the pooling operation.
e.g.: 5-D Tensor
[batch, new_depth, new_height, new_width, channels].
Fractional pooling layer
tefla.core.layers.fractional_pool (x, pooling_ratio=[1.0, 1.44, 1.73, 1.0], pseudo_random=None, determinastic=None, overlapping=None, name='fractional_pool', seed=None, seed2=None, type='avg', outputs_collections=None, **unused)
Args
- x: A 4-D
Tensor
of shape[batch_size, height, width, channels]
- pooling_ratio: A list of floats that has length >= 4. Pooling ratio for each dimension of value, currently only supports row and col dimension and should be >= 1.0. For example, a valid pooling ratio looks like [1.0, 1.44, 1.73, 1.0]. The first and last elements must be 1.0 because we don't allow pooling on batch and channels dimensions. 1.44 and 1.73 are pooling ratio on height and width dimensions respectively.
- pseudo_random: An optional bool. Defaults to False. When set to True, generates the pooling sequence in a pseudorandom fashion, otherwise, in a random fashion. Check paper Benjamin Graham, Fractional Max-Pooling for difference between pseudorandom and random.
- overlapping: An optional bool. Defaults to False. When set to True, it means when pooling, the values at the boundary of adjacent pooling cells are used by both cells. For example: index 0 1 2 3 4 value 20 5 16 3 7; If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice. The result would be [41/3, 26/3] for fractional avg pooling.
- deterministic: An optional bool. Defaults to False. When set to True, a fixed pooling region will be used when iterating over a FractionalAvgPool node in the computation graph. Mainly used in unit test to make FractionalAvgPool deterministic.
- seed: An optional int. Defaults to 0. If either seed or seed2 are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed.
- seed2: An optional int. Defaults to 0. An second seed to avoid seed collision.
- outputs_collections: The collections to which the outputs are added.
- type: avg or max pool
- name: Optional scope/name for name_scope.
Returns
A 4-D Tensor
representing the results of the pooling operation.
e.g.: 4-D Tensor
[batch, new_height, new_width, channels].
RMS pooling layer
tefla.core.layers.rms_pool_2d (x, filter_size= (3, 3), stride= (2, 2), padding='SAME', name='pool', epsilon=1e-12, outputs_collections=None, **unused)
Args
- x: A 4-D
Tensor
of shape[batch_size, height, width, channels]
- filter_size: A int or list/tuple of length 2: [kernel_height, kernel_width] of the pooling kernel over which the op is computed. Can be an int if both values are the same.
- stride: A int or list/tuple of length 2: [stride_height, stride_width].
- padding: The padding method, either 'VALID' or 'SAME'.
- outputs_collections: The collections to which the outputs are added.
- name: Optional scope/name for name_scope.
- epsilon: prevents divide by zero
Returns
A 4-D Tensor
representing the results of the pooling operation.
e.g.: 4-D Tensor
[batch, new_height, new_width, channels].
RMS pooling layer
tefla.core.layers.rms_pool_3d (x, filter_size= (3, 3, 3), stride= (2, 2, 2), padding='SAME', name='pool', epsilon=1e-12, outputs_collections=None, **unused)
Args
- x: A 5-D
Tensor
of shape[batch_size, depth, height, width, channels]
- filter_size: A int or list/tuple of length 3: [kernel_depth, kernel_height, kernel_width] of the pooling kernel over which the op is computed. Can be an int if both values are the same.
- stride: A int or list/tuple of length 3: [stride_depth, stride_height, stride_width].
- padding: The padding method, either 'VALID' or 'SAME'.
- outputs_collections: The collections to which the outputs are added.
- name: Optional scope/name for name_scope.
- epsilon: prevents divide by zero
Returns
A 5-D Tensor
representing the results of the pooling operation.
e.g.: 5-D Tensor
[batch, new_height, new_width, channels].
Avg pooling layer
tefla.core.layers.avg_pool_3d (x, filter_size= (3, 3, 3), stride= (2, 2, 2), padding='SAME', name=None, outputs_collections=None, **unused)
Args
- x: A 4-D
Tensor
of shape[batch_size, depth, height, width, channels]
- filter_size: A int or list/tuple of length 3: [kernel_depth, kernel_height, kernel_width] of the pooling kernel over which the op is computed. Can be an int if both values are the same.
- stride: A int or list/tuple of length 3: [stride_depth, stride_height, stride_width].
- padding: The padding method, either 'VALID' or 'SAME'.
- outputs_collections: The collections to which the outputs are added.
- name: Optional scope/name for name_scope.
Returns
A 5-D Tensor
representing the results of the pooling operation.
e.g.: 5-D Tensor
[batch, new_depth, new_height, new_width, channels].
Avg pooling layer
tefla.core.layers.avg_pool_2d (x, filter_size= (3, 3), stride= (2, 2), padding='SAME', name=None, outputs_collections=None, **unused)
Args
- x: A 4-D
Tensor
of shape[batch_size, height, width, channels]
- filter_size: A int or list/tuple of length 2: [kernel_height, kernel_width] of the pooling kernel over which the op is computed. Can be an int if both values are the same.
- stride: A int or list/tuple of length 2: [stride_height, stride_width].
- padding: The padding method, either 'VALID' or 'SAME'.
- outputs_collections: The collections to which the outputs are added.
- name: Optional scope/name for name_scope.
Returns
A 4-D Tensor
representing the results of the pooling operation.
e.g.: 4-D Tensor
[batch, new_height, new_width, channels].
Gloabl pooling layer
tefla.core.layers.global_avg_pool (x, name='global_avg_pool', outputs_collections=None, **unused)
Args
- x: A 4-D
Tensor
of shape[batch_size, height, width, channels]
- outputs_collections: The collections to which the outputs are added.
- name: Optional scope/name for name_scope.
Returns
A 4-D Tensor
representing the results of the pooling operation.
e.g.: 4-D Tensor
[batch, 1, 1, channels].
Gloabl max pooling layer
tefla.core.layers.global_max_pool (x, name='global_max_pool', outputs_collections=None, **unused)
Args
- x: A 4-D
Tensor
of shape[batch_size, height, width, channels]
- outputs_collections: The collections to which the outputs are added.
- name: Optional scope/name for name_scope.
Returns
A 4-D Tensor
representing the results of the pooling operation.
e.g.: 4-D Tensor
[batch, 1, 1, channels].
Feature max pooling layer
tefla.core.layers.feature_max_pool_1d (x, stride=2, name='feature_max_pool_1d', outputs_collections=None, **unused)
Args
- x: A 2-D tensor of shape
[batch_size, channels]
- stride: A int.
- outputs_collections: The collections to which the outputs are added.
- name: Optional scope/name for name_scope.
Returns
A 2-D Tensor
representing the results of the pooling operation.
e.g.: 2-D Tensor
[batch_size, new_channels]
Feature max pooling layer
tefla.core.layers.feature_max_pool_2d (x, stride=2, name='feature_max_pool_2d', outputs_collections=None, **unused)
Args
- x: A 4-D tensor of shape
[batch_size, height, width, channels]
- stride: A int.
- outputs_collections: The collections to which the outputs are added.
- name: Optional scope/name for name_scope.
Returns
A 4-D Tensor
representing the results of the pooling operation.
e.g.: 4-D Tensor
[batch_size, height, width, new_channels]
Adds a Batch Normalization layer from http://arxiv.org/abs/1502.03167
tefla.core.layers.batch_norm_tf (x, name='bn', scale=False, updates_collections=None, **kwargs)
"Batch Normalization: Accelerating Deep Network Training by Reducing
Internal Covariate Shift", Sergey Ioffe, Christian Szegedy
Can be used as a normalizer function for conv2d and fully_connected.
Note: When is_training is True the moving_mean and moving_variance need to be
updated, by default the update_ops are placed in tf.GraphKeys.UPDATE_OPS
so
they need to be added as a dependency to the train_op
, example:
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
if update_ops:
updates = tf.group(*update_ops)
total_loss = control_flow_ops.with_dependencies([updates], total_loss)
One can set updates_collections=None to force the updates in place, but that
can have speed penalty, specially in distributed settings.
Args
- x: a
Tensor
with 2 or more dimensions, where the first dimension hasbatch_size
. The normalization is over all but the last dimension ifdata_format
isNHWC
and the second dimension ifdata_format
isNCHW
. - decay: decay for the moving average. Reasonable values for
decay
are close to 1.0, typically in the multiple-nines range: 0.999, 0.99, 0.9, etc. Lowerdecay
value (recommend tryingdecay
=0.9) if model experiences reasonably good training performance but poor validation and/or test performance. Try zero_debias_moving_mean=True for improved stability. - center: If True, subtract
beta
. If False,beta
is ignored. - scale: If True, multiply by
gamma
. If False,gamma
is not used. When the next layer is linear (also e.g.nn.relu
), this can be disabled since the scaling can be done by the next layer. - epsilon: small float added to variance to avoid dividing by zero.
- activation_fn: activation function, default set to None to skip it and maintain a linear activation.
- param_initializers: optional initializers for beta, gamma, moving mean and moving variance.
- updates_collections: collections to collect the update ops for computation. The updates_ops need to be executed with the train_op. If None, a control dependency would be added to make sure the updates are computed in place.
- is_training: whether or not the layer is in training mode. In training mode
it would accumulate the statistics of the moments into
moving_mean
andmoving_variance
using an exponential moving average with the givendecay
. When it is not in training mode then it would use the values of themoving_mean
and themoving_variance
. - reuse: whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given. outputs_collections: collections to add the outputs.
- trainable: If
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(seetf.Variable
). - batch_weights: An optional tensor of shape
[batch_size]
, containing a frequency weight for each batch item. If present, then the batch normalization uses weighted mean and variance. (This can be used to correct for bias in training example selection.) - fused: Use nn.fused_batch_norm if True, nn.batch_normalization otherwise.
- name: Optional scope/name for
variable_scope
.
Returns
A Tensor
representing the output of the operation.
Adds a Batch Normalization layer from http://arxiv.org/abs/1502.03167
tefla.core.layers.batch_norm_lasagne (x, is_training, reuse, trainable=True, decay=0.9, epsilon=0.0001, name='bn', updates_collections='update_ops', outputs_collections=None)
Instead of storing and updating moving variance, this layer store and
update moving inverse standard deviation
"Batch Normalization: Accelerating Deep Network Training by Reducin Internal Covariate Shift"
Sergey Ioffe, Christian Szegedy
Can be used as a normalizer function for conv2d and fully_connected.
Note: When is_training is True the moving_mean and moving_variance need to be
updated, by default the update_ops are placed in tf.GraphKeys.UPDATE_OPS
so
they need to be added as a dependency to the train_op
, example:
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
if update_ops:
updates = tf.group(*update_ops)
total_loss = control_flow_ops.with_dependencies([updates], total_loss)
One can set updates_collections=None to force the updates in place, but that
can have speed penalty, specially in distributed settings.
Args
- x: a tensor with 2 or more dimensions, where the first dimension has
batch_size
. The normalization is over all but the last dimension ifdata_format
isNHWC
and the second dimension ifdata_format
isNCHW
. - decay: decay for the moving average. Reasonable values for
decay
are close to 1.0, typically in the multiple-nines range: 0.999, 0.99, 0.9, etc. Lowerdecay
value (recommend tryingdecay
=0.9) if model experiences reasonably good training performance but poor validation and/or test performance. Try zero_debias_moving_mean=True for improved stability. - epsilon: small float added to variance to avoid dividing by zero.
- updates_collections: collections to collect the update ops for computation. The updates_ops need to be executed with the train_op. If None, a control dependency would be added to make sure the updates are computed in place.
- is_training: whether or not the layer is in training mode. In training mode
it would accumulate the statistics of the moments into
moving_mean
andmoving_variance
using an exponential moving average with the givendecay
. When it is not in training mode then it would use the values of themoving_mean
and themoving_variance
. - reuse: whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given.
- outputs_collections: collections to add the outputs.
- trainable: If
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(seetf.Variable
). - name: Optional scope/name for
variable_scope
.
Returns
A Tensor
representing the output of the operation.
Prametric rectifier linear layer
tefla.core.layers.prelu (x, reuse, alpha_init=0.2, trainable=True, name='prelu', outputs_collections=None)
Args
- x: a
Tensor
with typefloat
,double
,int32
,int64
,uint8
, int16, or
int8`. - reuse: whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given.
- alpha_init: initalization value for alpha
- trainable: a bool, training or fixed value
- name: a optional scope/name of the layer
- outputs_collections: The collections to which the outputs are added.
Returns
A Tensor
representing the results of the prelu activation operation.
Computes relu
tefla.core.layers.relu (x, name='relu', outputs_collections=None, **unused)
Args
- x: a
Tensor
with typefloat
,double
,int32
,int64
,uint8
, int16, or
int8`. - name: a optional scope/name of the layer
- outputs_collections: The collections to which the outputs are added.
Returns
A Tensor
representing the results of the activation operation.
Rectifier linear relu6 layer
tefla.core.layers.relu6 (x, name='relu6', outputs_collections=None, **unused)
Args
- x: a
Tensor
with typefloat
,double
,int32
,int64
,uint8
, int16, or
int8`. - name: a optional scope/name of the layer
- outputs_collections: The collections to which the outputs are added.
Returns
A Tensor
representing the results of the relu6 activation operation.
Softplus layer
tefla.core.layers.softplus (x, name='softplus', outputs_collections=None, **unused) Computes softplus: log(exp(x) + 1).
Args
- x: a
Tensor
with typefloat
,double
,int32
,int64
,uint8
, int16, or
int8`. - name: a optional scope/name of the layer
- outputs_collections: The collections to which the outputs are added.
Returns
A Tensor
representing the results of the activation operation.
Softsign layer
tefla.core.layers.softsign (x, name='softsign', outputs_collections=None, **unused) Computes softsign: x / (abs(x) + 1).
Args
- x: a
Tensor
with typefloat
,double
,int32
,int64
,uint8
, int16, or
int8`. - name: a optional scope/name of the layer
- outputs_collections: The collections to which the outputs are added.
Returns
A Tensor
representing the results of the activation operation.
Computes Concatenated ReLU
tefla.core.layers.crelu (x, name='crelu', outputs_collections=None, **unused) Concatenates a ReLU which selects only the positive part of the activation with a ReLU which selects only the negative part of the activation. Note that at as a result this non-linearity doubles the depth of the activations. Source: https://arxiv.org/abs/1603.05201
Args
- x: a
Tensor
with typefloat
,double
,int32
,int64
,uint8
, int16, or
int8`. - name: a optional scope/name of the layer
- outputs_collections: The collections to which the outputs are added.
Returns
A Tensor
representing the results of the activation operation.
Computes exponential linear: exp(features) - 1 if < 0, features otherwise
tefla.core.layers.elu (x, name='elu', outputs_collections=None, **unused) See "Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)"
Args
- x: a
Tensor
with typefloat
,double
,int32
,int64
,uint8
, int16, or
int8`. - name: a optional scope/name of the layer
- outputs_collections: The collections to which the outputs are added.
Returns
A Tensor
representing the results of the activation operation.
like concatenated ReLU (http://arxiv.org/abs/1603.05201), but then with ELU
tefla.core.layers.concat_elu (x, name='concat_elu', outputs_collections=None, **unused)
Args
- x: a
Tensor
with typefloat
,double
,int32
,int64
,uint8
, int16, or
int8`. - name: a optional scope/name of the layer
- outputs_collections: The collections to which the outputs are added.
Returns
A Tensor
representing the results of the activation operation.
Computes leaky relu
tefla.core.layers.leaky_relu (x, alpha=0.01, name='leaky_relu', outputs_collections=None, **unused)
Args
- x: a
Tensor
with typefloat
,double
,int32
,int64
,uint8
, int16, or
int8`. - aplha: the conatant fro scalling the activation
- name: a optional scope/name of the layer
- outputs_collections: The collections to which the outputs are added.
Returns
A Tensor
representing the results of the activation operation.
Computes reaky relu lasagne style
tefla.core.layers.lrelu (x, leak=0.2, name='lrelu', outputs_collections=None, **unused)
Args
- x: a
Tensor
with typefloat
,double
,int32
,int64
,uint8
, int16, or
int8`. - leak: the conatant fro scalling the activation
- name: a optional scope/name of the layer
- outputs_collections: The collections to which the outputs are added.
Returns
A Tensor
representing the results of the activation operation.
Computes maxout activation
tefla.core.layers.maxout (x, k=2, name='maxout', outputs_collections=None, **unused)
Args
- x: a
Tensor
with typefloat
,double
,int32
,int64
,uint8
, int16, or
int8`. - k: output channel splitting factor
- name: a optional scope/name of the layer
- outputs_collections: The collections to which the outputs are added.
Returns
A Tensor
representing the results of the activation operation.
Computes maxout activation
tefla.core.layers.offset_maxout (x, k=2, name='maxout', outputs_collections=None, **unused)
Args
- x: a
Tensor
with typefloat
,double
,int32
,int64
,uint8
, int16, or
int8`. - k: output channel splitting factor
- name: a optional scope/name of the layer
- outputs_collections: The collections to which the outputs are added.
Returns
A Tensor
representing the results of the activation operation.
Computes softmax activation
tefla.core.layers.softmax (x, name='softmax', outputs_collections=None, **unused)
Args
- x: a
Tensor
with typefloat
,double
,int32
,int64
,uint8
, int16, or
int8`. - name: a optional scope/name of the layer
- outputs_collections: The collections to which the outputs are added.
Returns
A Tensor
representing the results of the activation operation.
Computes the spatial softmax of a convolutional feature map
tefla.core.layers.spatial_softmax (features, reuse, temperature=None, name='spatial_softmax', trainable=True, outputs_collections=None, **unused) First computes the softmax over the spatial extent of each channel of a convolutional feature map. Then computes the expected 2D position of the points of maximal activation for each channel, resulting in a set of feature keypoints [x1, y1, ... xN, yN] for all N channels. Read more here: "Learning visual feature spaces for robotic manipulation with deep spatial autoencoders." Finn et. al, http://arxiv.org/abs/1509.06113.
Args
- features: A
Tensor
of size [batch_size, W, H, num_channels]; the convolutional feature map. - reuse: whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given.
- outputs_collections: The collections to which the outputs are added.
- temperature: Softmax temperature (optional). If None, a learnable temperature is created.
- name: A name for this operation (optional).
- trainable: If
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(seetf.Variable
).
Returns
feature_keypoints: A Tensor
with size [batch_size, num_channels * 2];
the expected 2D locations of each channel's feature keypoint (normalized
to the range (-1,1)). The inner dimension is arranged as
[x1, y1, ... xN, yN].
Computes selu
tefla.core.layers.selu (x, alpha=None, scale=None, name='selu', outputs_collections=None, **unused)
Args
- x: a
Tensor
with typefloat
,double
,int32
,int64
,uint8
, int16, or
int8`. - alpha: float, selu parameters calculated from fixed points
- scale: float, selu parameters calculated from fixed points
- name: a optional scope/name of the layer
- outputs_collections: The collections to which the outputs are added.
Returns
A Tensor
representing the results of the selu activation operation.
Dropout layer for self normalizing networks
tefla.core.layers.dropout_selu (x, is_training, drop_p=0.2, alpha=-1.7580993408473766, fixedPointMean=0.0, fixedPointVar=1.0, noise_shape=None, seed=None, name='dropout_selu', outputs_collections=None, **unused)
Args
- x: a
Tensor
. - is_training: a bool, training or validation
- drop_p: probability of droping unit
- fixedPointsMean: float, the mean used to calculate the selu parameters
- fixedPointsVar: float, the Variance used to calculate the selu parameters
- alpha: float, product of the two selu parameters
- name: a optional scope/name of the layer
- outputs_collections: The collections to which the outputs are added.
Returns
A Tensor
representing the results of the dropout operation.
Computes Gumbel Softmax
tefla.core.layers.gumbel_softmax (logits, temperature, hard=False) Sample from the Gumbel-Softmax distribution and optionally discretize. http://blog.evjang.com/2016/11/tutorial-categorical-variational.html https://arxiv.org/abs/1611.01144
Args
- logits: [batch_size, n_class] unnormalized log-probs
- temperature: non-negative scalar
- hard: if True, take argmax, but differentiate w.r.t. soft sample y
Returns
[batch_size, n_class] sample from the Gumbel-Softmax distribution. If hard=True, then the returned sample will be one-hot, otherwise it will be a probabilitiy distribution that sums to 1 across classes
Computes pixel wise softmax activation
tefla.core.layers.pixel_wise_softmax (inputs)
Args
- x: a
Tensor
with typefloat
,double
,int32
,int64
,uint8
, int16, or
int8`. - name: a optional scope/name of the layer
- outputs_collections: The collections to which the outputs are added.
Returns
A Tensor
representing the results of the activation operation.
Dropout layer
tefla.core.layers.dropout (x, is_training, drop_p=0.5, seed=None, name='dropout', outputs_collections=None, **unused)
Args
- x: a
Tensor
. - is_training: a bool, training or validation
- drop_p: probability of droping unit
- name: a optional scope/name of the layer
- outputs_collections: The collections to which the outputs are added.
Returns
A Tensor
representing the results of the dropout operation.
Repeat op
tefla.core.layers.repeat (x, repetitions, layer, num_outputs=None, name='Repeat', outputs_collections=None, args, *kwargs)
Args
- x: a
Tensor
. - repetitions: a int, number of times to apply the same operation
- layer: the layer function with arguments to repeat
- name: a optional scope/name of the layer
- outputs_collections: The collections to which the outputs are added.
Returns
A Tensor
representing the results of the repetition operation.
Merge op
tefla.core.layers.merge (tensors_list, mode, axis=1, name='merge', outputs_collections=None, **kwargs)
Args
- tensor_list: A list
Tensors
to merge - mode: str, available modes are ['concat', 'elemwise_sum', 'elemwise_mul', 'sum','mean', 'prod', 'max', 'min', 'and', 'or']
- name: a optional scope/name of the layer
- outputs_collections: The collections to which the outputs are added.
Returns
A Tensor
representing the results of the repetition operation.
Builds a stack of layers by applying layer repeatedly using stack_args
tefla.core.layers.stack (inputs, layer, stack_args, is_training, reuse, outputs_collections=None, **kwargs)
stack
allows you to repeatedly apply the same operation with different
arguments stack_args[i]
. For each application of the layer, stack
creates
a new scope appended with an increasing number. For example:
y = stack(x, fully_connected, [32, 64, 128], scope='fc')
# It is equivalent to:
x = fully_connected(x, 32, scope='fc/fc_1')
x = fully_connected(x, 64, scope='fc/fc_2')
y = fully_connected(x, 128, scope='fc/fc_3')
If the scope
argument is not given in kwargs
, it is set to
layer.__name__
, or layer.func.__name__
(for functools.partial
objects). If neither __name__
nor func.__name__
is available, the
layers are called with scope='stack'
.
Args
- inputs: A
Tensor
suitable for layer. - layer: A layer with arguments
(inputs, *args, **kwargs)
- stack_args: A list/tuple of parameters for each call of layer.
- outputs_collections: The collections to which the outputs are added.
- **kwargs: Extra kwargs for the layer.
Returns
a Tensor
result of applying the stacked layers.
Normalizes the given input across the specified dimension to unit length
tefla.core.layers.unit_norm (inputs, dim, epsilon=1e-07, scope=None)
Note that the rank of input
must be known.
Args
- inputs: A
Tensor
of arbitrary size. - dim: The dimension along which the input is normalized.
- epsilon: A small value to add to the inputs to avoid dividing by zero.
- scope: Optional scope for variable_scope.
Returns
The normalized Tensor
.
Concates two features maps
tefla.core.layers.crop_and_concat (inputs1, inputs2, name='crop_concat') concates different sizes feature maps cropping the larger map concatenation across output channels
Args
- inputs1: A
Tensor
- inputs2: A
Tensor
Returns
concated output tensor


Half-price food at West Bridgford restaurant and takeaway - for one day only
Make a note of the date
by Lynette PinchessIt's that time of year when cash is tight, what with all that Christmas present buying and festive nights out, so anything where you can save a few quid is not to be sniffed at.
A West Bridgford restaurant and takeaway's six-month anniversary, with the promise of cut-price food, is perfect timing.
Customers can bag themselves a half-price lunch, tea or post-drinks' bargain.
For one day only, Eva's Grill in Radcliffe Road will be serving pitta gyros for just £2.50 and shish wraps for £3.25.
Owner Julian Koraqe said: "We want to celebrate being open six months and say thank you to all our customers."
The deal will be available from 11.30am to 11.30pm, on Monday, December 2.
Since opening, the business has increased its menu. "We are strong in the market with rotisserie products like a whole lamb, offal, chicken and pork.
"We also offer Greek home-made feta cheese pies, spinach pies, ham pies, sausage, cakes and coffee," said Julian.


DEAR SIR/MADAM, REPLY TO<EMAIL_ADDRESS>IN ORDER TO TRANSFER OUT ( THIRTY FIVE POINT FIVE MILLION U.S.DOLLARS) FROM OUR BANK. I HAVE THE COURAGE TO LOOK FOR A REALIABLE AND HONEST PERSON WHO WILL BE CAPABLE FOR THIS IMPORTANT BUSINESS. BELIEVING THAT YOU WILL NEVER LET ME DOWN EITHER NOW OR IN FUTURE. I AM MR DONALD MBEKI, THE AUDITOR AND COMPUTING MANAGER OF OUR BANK. THERE IS AN ACCOUNT OPENED IN OUR BANK IN 1980 AND SINCE 1990 TILL DATE, NOBODY HAS OPERATED ON THIS ACCOUNT AGAIN. AFTER GOING THROUGH SOME OLD FILES IN THE RECORDS I DISCOVERED THAT IF I DO NOT REMITT THIS MONEY OUT URGENTELY IT WILL BE FORFEITED FOR NOTHING. THE OWNER OF THIS ACCOUNT IS REV'D FR. CLAYTON REIDFIELD A MISSIONARY AND HE DIED SINCE 1990. NO OTHER PERSON KNOWS ABOUT THIS ACCOUNT OR ANYTHING CONCERNING IT, THE ACCOUNT HAS NO OTHER BENEFICIARY AND MY INVESTIGATION PROVED TO ME AS WELL THAT HIS CHURCH DOES NOT KNOW ANYTHING ABOUT THIS ACCOUNT AND THE AMOUNT INVLOVED IS(U.S.$35.5,000,000.00).  I KNOW THAT THIS MESSAGE WILL COME TO YOU AS ASURPRISE AS WE DON'T KNOW OURSELVES BEFORE, BUT BE SURE THAT IT IS REAL AND A GENUINE BUSINESS.  SO PLEASE REPLY URGENTLY. WHEN THE TRANSFER IS APPROVED AND PAYMENT SCHEDULE IS ALLOCATED OVERSEAS,THROUGH THE OFFSHORE PAYING DELEGATE FOR FINAL CLEARANCE AND SIGNING OF THE PAYMENT REALEASE FORM BY THE BENEFICIARY, I WANT US TO SEE AT THE OVERSEA PAYING CLEARANCE OFFICE FACE TO FACE OR SIGNING OF THE ORIGINAL BINDDING AGREEMENT TO BIND US TOGETHER SO THAT WE CAN RECEIVE THIS MONEY INTO A FOREIGN ACCOUNT OR ANY ACCOUNT OF YOUR CHOICE WHERE THE FUND WILL BE REMITTED. I AM CONTACTING YOU BECAUSE OF THE NEED TO INVOLVE A FOREIGNER WITH A FOREIGN ACCOUNT AS THE REAL BENEFICIARY. I NEED YOUR CO-OPERATION TO MAKE THIS WORK FINE, BECAUSE THE MANAGEMENT IS READY TO APPROVE THIS PAYMENT TO ANY FOREIGNER WHO HAS THE CORRECT INFORMATION TO THIS ACCOUNT. WHICH I WILL GIVE TO YOU WHEN SURE OF YOUR CAPABILITY TO HANDLE SUCH AMOUNT IN STRICT CONFIDENCE AND TRUST, ACCORDING TO MY INSTRUCTIONS AND MY ADIVICE FOR OUR MUTUAL BENEFIT BECAUSE I DON'T WANT TO MAKE ANY MISTAKE, I NEED YOUR STRONG ASSURANCE AND TRUST.  WITH ASSURANCE THAT THIS MONEY WILL BE INTACT PENDING MY PHYSICAL ARRIVAL TO YOUR COUNTRY FOR THE SHARING AND OTHER INVESTMENT. AT THE CONCLUSION OF THIS BUSINESS, YOU WILL BE GIVEN 30% OF THE TOTAL AMOUNT, 60% WILL BE FOR ME, WHILE 10% WILL BE FOR EXPENSES , BOTH PARTIES MIGHT HAVE INCURED DURING THE PROCESS OF THIS TRANSACTION. I LOOK FORWARD TO YOUR EARLIEST REPLY THROUGH MY PRIVATE E- MAIL ADDRESS<EMAIL_ADDRESS>YOURS TRULY. MR DONALD MBEKI -------------------------- Posílejte SMS pøes internet zdarma a bez reklamy. Pouze s TISCALI. Více na http://www.tiscali.cz


Account
Account Code
Organisational Unit
Account Status
{{option.name}}
Manager Contact
{{option.employee_name}}
Region
{{option.name}}
Location
Comments
default text
CSM Contact
Sales Contact
Account Contact
Account LoB
Services
Go to Services
Select a service
No Of Resources Needed
No Of Resources Allocated
Request Date
SOW Signed Date
Contract Type
{{option.name}}
SOW Status
{{option.name}}
Service Status
{{option.name}}
Currency
{{option.name}}
Start Date
End Date
Comments
Anticipated INR Value
Actual INR Value
Anticipated USD Value
Actual USD Value
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Magit-QuickStart
March 9, 2020
Magit is an interface to the version control system Git, implemented as an Emacs package. Magit aspires to be a complete Git porcelain. While we cannot (yet) claim that Magit wraps and improves upon each and every Git command, it is complete enough to allow even experienced Git users to perform almost all of their daily version control tasks directly from within Emacs. While many fine Git clients exist, only Magit and Git itself deserve to be called porcelains.
Quick Clojure Review
March 1, 2020
Clojure is a functional, symbiotic, and homoiconic programming language. - Functional: where functions are first-class citizens and mutating state is frowned upon - Symbiotic: the language is intended to be run atop a host. environment - Homoiconic: “code is data” — this helps facilitate a macro system for rewriting the language.
Free Monads Are Simple
April 13, 2015
I recently gave a talk at the Advanced Scala meetup in London on free monads. Despite the name of the group, I think that free monads are eminently simple as well as being extremely useful. Let me explain.


Govt Needs Stronger Anti-Corruption Measures: Danish
The Afghan government needs more financial controls in its organizations, Danish said.
by TOLOnews.comSarwar Danish, Afghanistan’s second vice president, addressed a gathering on Monday-- International Anti-Corruption Day-- and said: “Despite some achievements, we have not been completely successful in fighting against corruption.”
“The government needs to appoint “clean” people as leaders in every organization in order to fight corruption,” Danish said, adding: “Outside the government we also need strong organizations to fight against corruption.”
“We need strong financial controls in government organizations – especially in financial departments -- to stop corruption,” Danish said.
He believes that the media “can also play a key role in fighting against corruption with investigative reporting and disclosing information. They are doing it but we need more.”
“Registration of a government employee’s property--and monitoring--are very important,” he said. “We need to ask government officials about where they earn their property. If we do this the corruption will end at the government organizations.”
The government must improve the living conditions of government employees if it wants to stop corruption, he added.
In the meantime, UNAMA in a statement on Monday said that the UN "reiterates its support" for Afghanistan’s initiatives to fight corruption, and acknowledges steps made to "challenge impunity."
“The UN welcomes the legislative, policy and institutional improvements that strengthen Afghanistan’s framework to fight corruption,” said Tadamichi Yamamoto, the UN Secretary-General’s Special Representative for Afghanistan.
“However, corruption continues to affect Afghan citizens’ daily lives and erode public confidence in government institutions; it is therefore crucial to focus now on implementing the laws and holding to account those who break them to demonstrate resolve to counter corruption,” he said.
The statement also said that the UN acknowledges the "difficult security situation in which judges and prosecutors operate" and condemns targeted killing of justice officials.
“Outstanding arrest warrants and summonses related to corruption investigations, along with corresponding travel bans, are yet to be enforced. And many convicts have yet to be arrested to serve their sentences,” according to the statement.


An initiative to fight against the Heinous crimes against Women happening in the society
We are here to help women against crimes and to aware about laws for women


This visualization displays data from the Enlearn Fall Trial. The goal of this visualization is to show correlations between student learning gains and behaviors such as the number of exercise and assessment problems solved and time on-task. Scatterplots of learning gains and six important variables are shown in the main panel. Below the scatterplots, a bar chart shows student performance on individual items in the currently selected test.
We display bar chart data for four different tests: the Unit 2 pre-test, the Unit 2 post-test, the Unit 4 pre-test, and the Unit 4 post-test. To view data for a different test, select the appropriate button below.
The data is divided by one of two variables: the type of test that the student received (version A or B) or the condition that the student was in (Tablet or Paper). To view data divided by a different variable, select the appropriate button below.


You don't need to leave &productName; to work with your favorite shell. Just choose Tools | Open Terminal on the main menu, and enjoy using the embedded local terminal.


A Quick Primer on Dat
Dat is a peer-to-peer protocol for publishing files. It uses cryptography to sign and verify all information. Like BitTorrent, users can “seed” data to help keep it online and contribute bandwidth.
Each “dat” is given a public key address which looks like this:
dat://43dfc9f23fdded8cc7c01c71c0702a0529130af0258e7fb30bf5a0a3f73d69b3/
The public key is a 64-character hex string, but DNS may be used to provide a human-readable shortname:
Dat is a highly flexible protocol. It can sync many kinds of data, including event logs and key-value databases. However, the most common usecase is to share filesets which are called “hyperdrives.”
Applications like the Beaker Browser use Dat hyperdrives as websites. Users can browse to the hyperdrives and view the HTML/CSS/JS just like any HTTP website. Because anybody can create a hyperdrive by simply creating a new public key, users on the Dat Web can freely create and publish their own websites.


New Features
- Tabli now supports a limited form of tab de-duplication as an opt-in feature. To enable, open "Preferences..." and select "Automatically close duplicate tabs"
- By popular demand, Tabli now supports automatic synchronization of saved windows and tabs across computers logged in to the same Chrome profile.
- To improve performance, by default Tabli now only reloads saved tabs when re-opening saved windows. There is a "Preferences..." option to revert to the previous (slower) behavior of restoring all tabs.
...plus numerous internal upgrades, performance enhancements and minor bug fixes.


We try to keep it as simple as possible, having advanced features optional and hidden.
The stack provides many features. This page will attempt to expose them.
Modularity and flexibility
BlueBanquise can be seen as a set of Ansible roles, which ensure full modularity.
Even elements of the core can be replaced by others.
The Ansible inventories structure used can handle a wide range of cluster and network architectures, and allows adding more variables as desired.
Even elements of the core can be replaced by others.
The Ansible inventories structure used can handle a wide range of cluster and network architectures, and allows adding more variables as desired.
Modern
The whole stack is based on python 3.6+ and Ansible, for better long term investment.The PXE stack is Legacy or EFI compatible.
A focus is made on reducing scripting to the minimum.
Technical details
Supported OS and platform
In its current state, stack can deploy on mixed Linux distributions and platform (in the same cluster), including:- Centos/RHEL 7.X and 8.X, with all features available.
- OpenSuse Leap 15.1, with limited features (client only).
Tested platform were x86_64 for Centos/RHEL/OpenSuse Leap, but stack should be able to deploy on arm64.
This ability to handle multiple Linux distributions and architectures at the same time, allows to easily handle exotic hardware or customer needs.
Data
All stack data are stored in plain text files, in YAML format, using Ansible inventory folders structure.No binaries + no databases = fully editable and tunable with minimal knowledge.
Roles
The stack provides many Ansible roles, seen as independent modules. Roles are nearly all autonomous from each other's, to be easily switched with others.The following roles are available:
Core
- hosts_file: generate the /etc/hosts file
- nic: configure network interfaces for static configuration
- firewall: configure firewall
- dhcp_server: generate dhcp server configuration
- dns_server/dns_client: generate dns server or client configuration
- log_server/log_client: generate rsyslog server or client configuration
- pxe_stack: deploy and generate whole PXE stack
- Tftp based on atftp (with verbosity on by default)
- Http based on Apache for all repositories and images
- iPXE custom ROMs with menu, to handle all exotic hardware
- EFI / Legacy
- PXE / iPXE native ROM
- CD or USB boot to PXE when no native PXE available (or stupidly made BIOS/EFI)
- repositories_server/repositories_client: generate server or client configuration
- nfs_server/nfs_client: generate nfs server or client configuration
- set_hostname: set hostname of the target
- ssh_master: generate management ssh configuration
- ssh_slave: add ssh public keys on targets
- time: generate server or client time configuration
- ansible: install ansible package
- display_tuning: add some tuning for bash and screen
- conman: generate conman configuration, for ipmi consols logging
Advanced Core
- advanced_dhcp_server: generate advanced dhcp server configuration, for more complexe clusters
Addons
- clone: allows to clone systems with clonezilla
- diskless: allows to boot remote hosts in diskless mode (nfs or livenet)
- prometheus_server/prometheus_client: generate a prometheus configuration for monitoring
- slurm: generate server and client slurm job scheduler configuration
- openldap_server/openldap_client: generate a very unsecure and simple ldap configuration
- ofed/ofed_sm: install OFED for infiniband support
- clustershell: generate clustershells groups
- report: generate a small report on the stack inventory, for debugging purposes
- users_basic: add users on hosts, using a very basic useradd
Specific advanced features
The stack also as few native mechanisms under the hood. These features are a need for us, so we added them.Multi Icebergs
This feature is sometime called multi islands in HPC or Cloud.BlueBanquise can split cluster into independent and autonomous groups of equipment. These new subclusters are called icebergs, each iceberg being managed by a group of management nodes and isolated from the others (but can be reached through interconnect if exist and asked).
This feature is mainly used:
- When there is a need to separated parts of the cluster (our case, to provide dedicated small sub-clusters to some customers).
- When there is a need to run an old cluster aside a new one keeping both in production.
- When there is a need to scale to very large number of hosts.


RT @darinrs: Starting week off right! @NewRelic Ruby agent release: resque-pool gem support & environment-based configuration: https://t.co…
9/9/2013, 11:37:11 AM
Favs: 0
Retweets: 0
link--- layout: tweet ---
RT @darinrs: Starting week off right! @NewRelic Ruby agent release: resque-pool gem support & environment-based configuration: https://t.co…
9/9/2013, 11:37:11 AM
Favs: 0
Retweets: 0
link


We examine mices!
- scale distributed portals
- matrix viral action-items
- synthesize integrated networks
- scale one-to-one communities
- incentivize robust infomediaries
- envisioneer end-to-end models
- matrix user-centric infrastructures
- revolutionize web-enabled bandwidth
- streamline cross-platform networks
- facilitate open-source supply-chains
- monetize seamless relationships
- transform holistic initiatives
- visualize viral infomediaries
- revolutionize value-added e-markets


{% extends 'base.html' %} {% block content %}
{{profile.user.username}}
{{profile.user.email}}
Posts {{posts | length}}
{{profile.user_bio}}
You have no projects posted


| Positions | Rating | Native | Name |
1 |
907 |
Dale Steyn |
|
2 |
853 |
Ryan Harris |
|
3 |
851 |
Rangana Herath |
|
4 |
844 |
Mitchell Johnson |
|
5 |
806 |
James Anderson |
|
6 |
785 |
Vernon Philander |
|
7 |
774 |
Stuart Broad |
|
8 |
768 |
Kemar Roach |
|
9 |
762 |
Trent Boult |
|
10 |
744 |
Tim Southee |
|
| For more information about rankings please visit:- http://www.icc-cricket.com/player-rankings/overview |
| Positions | Rating | Native | Name |
| 1 | 770 | Saeed Ajmal | |
| 2 | 753 | Sunil Narine | |
| 3 | 699 | Dale Steyn | |
| 4 | 680 | James Anderson | |
| 5 | 680 | Mitchell Johnson | |
| 6 | 680 | Shakib Al Hasan | |
| 7 | 647 | Mohammad Hafezz | |
| 8 | 641 | Bhuvneshwar Kumar | |
| 9 | 638 | Ravindra Jadeja | |
| 10 | 632 | Morne Morkel | |
| For more information about rankings please visit:- http://www.icc-cricket.com/player-rankings/overview |
Positions |
Rating |
Native |
Name |
1 |
770 |
Samuel Badree |
|
2 |
753 |
Sunil Narine |
|
3 |
699 |
Sachithra Senanayake |
|
4 |
680 |
Ravichandran Ashwin |
|
5 |
680 |
Saeed Ajmal |
|
6 |
680 |
Lasith Malinga |
|
7 |
647 |
Shahid Afridi |
|
8 |
641 |
Mitchell Starc |
|
9 |
638 |
Nuwan Kulaserkara |
|
10 |
632 |
Nathan McCullum |
|
For more information about rankings please visit:- |


health climate Health public change Climate Public Environ. adaptation policy Change review Int. Available essential impacts (accessed online: vulnerability assessment data research development services ./ijerph Res. collab: systems care core global {border-style decade risks local px;} #ffffff;} extreme functions literature need planning March human mitigation warning Research co-benefits diseases evaluation evidence example, heat practice surveillance Adaptation Global adaptive early identified including monitoring national related risk role weather CFES Ebi Hess challenges change, efforts key progress However, Lancet impact indicators model noted action air broader climate-related outcomes studies work ./s--- K.L. Program Resilience activities areas disease field implementation population populations ... Assessment Disease Policy approach cities communication countries developing hazards information particularly resilience response specific use (ii) (see CDC Change: J.J. Review World ago, assessments capacity context effects emissions government included infectious medical needed needs particular px; remains reviewed tools warming {font-family (i) (iii) Figure Glob. Impact Organization Perspect. Sci. Vulnerability agencies based city developed dynamic economic effectiveness enhanced example flood font-size framework gas greenhouse heat-related identify increasingly integrated major mental nature policies relevant service social used using years (e.g., Ann. Berry Centers Cities Control European Framework J.M. Luber Risk United Urban assurance available benefits carried challenge changing collaboration coordination double; emerging events factors future governments greater help identifying increased lack levels management modeling partnerships pollution potential programs range recommended sectors seen status suggests support systematic terms urban workforce Austin Balbus Bowen CHP Campbell-Lendrum Care Countdown Environmental Europe Ford Frumkin GHG J.A. January Manag. National Paris Prevention Rep. Rev. States USA University Watts actions analysis area border-color border-width build community complex concern contributed costs creating diagnosis emergency flooding focused fully funding goal governance health, heatwave importance index initiatives intervention interventions level limited linking model. new observed opportunity program provide reduce reduction regulations reviews solid; strategies substantial substantially suggest supply targeting uncertainty understanding vector-borne ways {font-style {font-weight (CDC) ./.. ./ehp. ./j.envint... Action American Assurance Brown Challenges Curr. Data Enforce English HIA Hayes Initiative Integrating J.D. June Meteorological Mitigation Network OECD P.M. Progress Report S.E. Schramm Systematic Tracking U.S. Woodward active adjustment appropriate areas. assessing assessment, below). bold case change. change: climate-health climate-sensitive compared complexity concepts conditions considered control courier; critical data, decade. development, effective em; energy enhance environmental exposure findings food frameworks health. impacts, improve include inform infrastructure iterative laws learning likely link making mapping multiple national-level number organizations plans play preparedness protect pt;} recommend reducing reflect resources results sector sub-national suggested sustainable systems, target time time, tool tools, variability vulnerable water weather-related working world


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Bridge status:
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Finds the difference between two polygons by clipping the second polygon from the first.
input Polygon feature
Polygon feature to difference from polygon1
a Polygon or MultiPolygon feature showing the area of polygon1
excluding the area of polygon2
(if empty returns null
)
Takes two Polygon and finds their intersection. If they share a border, returns the border; if they don't intersect, returns undefined.
the first polygon
the second polygon
returns a feature representing the point(s) they share (in case of a Point or MultiPoint), the borders they share (in case of a LineString or a MultiLineString), the area they share (in case of Polygon or MultiPolygon). If they do not share any point, returns null
.
Takes two or more Polygon and returns a combined polygon. If the input polygons are not contiguous, this function returns a MultiPolygon feature.
An array of polygons to combine
a combined Polygon or MultiPolygon feature
Generated using TypeDoc
Turf graphical boolean functions. http://turfjs.org/docs/


| then {promises} | R Documentation |
Use the then
function to access the eventual result of a promise (or, if the operation fails, the reason for that failure). Regardless of the state of the promise, the call to then
is non-blocking, that is, it returns immediately; so what it does not do is immediately return the result value of the promise. Instead, you pass logic you want to execute to then
, in the form of function callbacks (or formulas, see Details). If you provide an onFulfilled
callback, it will be called upon the promise's successful resolution, with a single argument value
: the result value. If you provide an onRejected
callback, it will be called if the operation fails, with a single argument reason
: the error that caused the failure.
then(promise, onFulfilled = NULL, onRejected = NULL) catch(promise, onRejected, tee = FALSE) finally(promise, onFinally)
promise |
A promise object. The object can be in any state. |
onFulfilled |
A function (or a formula–see Details) that will be
invoked if the promise value successfully resolves. When invoked, the
function will be called with a single argument: the resolved value.
Optionally, the function can take a second parameter |
onRejected |
A function taking the argument |
tee |
If |
onFinally |
A function with no arguments, to be called when the async operation either succeeds or fails. Usually used for freeing resources that were used during async operations. |
For convenience, the then()
, catch()
, and finally()
functions use
rlang::as_function()
to convert onFulfilled
, onRejected
, and
onFinally
arguments to functions. This means that you can use formulas to
create very compact anonymous functions, using .
to access the value (in
the case of onFulfilled
) or error (in the case of onRejected
).
The first parameter of then
is a promise; given the stated purpose of the
function, this should be no surprise. However, what may be surprising is that
the return value of then
is also a (newly created) promise. This new
promise waits for the original promise to be fulfilled or rejected, and for
onFulfilled
or onRejected
to be called. The result of (or error raised
by) calling onFulfilled
/onRejected
will be used to fulfill (reject) the
new promise.
promise_a <- get_data_frame_async() promise_b <- then(promise_a, onFulfilled = head)
In this example, assuming get_data_frame_async
returns a promise that
eventually resolves to a data frame, promise_b
will eventually resolve to
the first 10 or fewer rows of that data frame.
Note that the new promise is considered fulfilled or rejected based on
whether onFulfilled
/onRejected
returns a value or throws an error, not on
whether the original promise was fulfilled or rejected. In other words, it's
possible to turn failure to success and success to failure. Consider this
example, where we expect some_async_operation
to fail, and want to consider
it an error if it doesn't:
promise_c <- some_async_operation() promise_d <- then(promise_c, onFulfilled = function(value) { stop("That's strange, the operation didn't fail!") }, onRejected = function(reason) { # Great, the operation failed as expected NULL } )
Now, promise_d
will be rejected if promise_c
is fulfilled, and vice
versa.
Warning: Be very careful not to accidentally turn failure into success, if your error handling code is not the last item in a chain!
some_async_operation() %>% catch(function(reason) { warning("An error occurred: ", reason) }) %>% then(function() { message("I guess we succeeded...?") # No! })
In this example, the catch
callback does not itself throw an error, so the
subsequent then
call will consider its promise fulfilled!
For readability and convenience, we provide catch
and finally
functions.
The catch
function is equivalent to then
, but without the onFulfilled
argument. It is typically used at the end of a promise chain to perform error
handling/logging.
The finally
function is similar to then
, but takes a single no-argument
function (or formula) that will be executed upon completion of the promise,
regardless of whether the result is success or failure. It is typically used
at the end of a promise chain to perform cleanup tasks, like closing file
handles or database connections. Unlike then
and catch
, the return value
of finally
is ignored; however, if an error is thrown in finally
, that
error will be propagated forward into the returned promise.
onFulfilled
functions can optionally have a second parameter visible
,
which will be FALSE
if the result value is invisible.


If you want to run multiple Git commands (repeatedly) with parameters, you can list them in powershell.
A sample template is listed below along with a way to call a powershell script from another script.
If you want to run multiple Git commands (repeatedly) with parameters, you can list them in powershell.
A sample template is listed below along with a way to call a powershell script from another script.
| Action | Command | |
|---|---|---|
| Save template code to 'sample.ps1' |
# https://stackoverflow.com/questions/13738634/how-can-i-check-if-a-string-is-null-or-empty-in-powershell if ($args.count -eq 0) { Write-Host " command 'param1' 'param2'";exit 0; } $param1 = $args[0];if (($str1)[string]::IsNullOrEmpty($param1)) { throw "No param1 specified.";} $param2 = $args[1];if ([string]::IsNullOrEmpty($param2)) { throw "No param2 specified.";} cls; Write-Host "---------------------------------------"; Write-Host "command '$param1' acting with '$param2'."; Write-Host "."; #******************************************************** # git commands # git do_something $param1; git do_something $param2; git do_something_else; #******************************************************** Write-Host "---------------------------------------"; if (-NOT ([string]::IsNullOrEmpty($waitForKey))) { Write-Host "Press any key to continue..."; $Host.UI.RawUI.ReadKey("NoEcho,IncludeKeyDown"); } #******************************************************** | |
| Copy 'sample.ps1' to repository folder. | Using Windows Explorer | |
| Edit 'sample.ps1' as required. | Code editor of choice | |
| Navigate to repository folder | Using Windows Explorer | |
| Launch PowerShell | type 'powershell' in Windows Explorer address bar | |
| Optional - call 'sample.ps1' from another script |
$branch1 = 'master'; $branch2 = 'develop'; & .\sample.ps1 $branch1 $branch2 |


High school is an important step in your educational life, as it helps to prepare you for college or your future career, and provides opportunities for learning essential components of personal development, including self-discipline, time management, decision-making, and teamwork. Because students have different interests, abilities, and needs, the Chicago Public Schools offers a wide variety of schools and programs to ensure that you are able to find the right fit.
Some high schools have multiple types of program and you can look to apply to a specific program to suit your interests and career goals.
Charter schools are public schools open to all Chicago children. Charters are approved by the board of Education but operate independently from the Board and each other. Due to that Charters have more freedom to be more innovative.
CTE-College and Career Academies prepare students for college and career success. Students take college prep courses in the core areas of math, science, and English, and also participate in hands-on training experiences in their chosen industry. (For more information on this program, see the Glossary of this guide.) Note that the CTE-College and Career Academies at Simeon Career Academy and Westinghouse High School have a separate application process; contact the schools for details.
The International Baccalaureate Programmes are guided by the mission to develop inquiring, knowledgeable and caring young people who help create a better and more peaceful world through intercultural understanding and respect. IB programs encourage students across the world to become active, compassionate, and lifelong learners.
Magnet High Schools offer one or more specialty programs. They do not have neighborhood attendance boundaries (with some exception), and they accept students from throughout the city.
Neighborhood High Schools teach the general education curriculum and do not have application requirements. Some of these schools have specialized programs such as Early College Stem or are open to all Chicago students.
Military Academies prepare students for college and subsequent careers in a structured environment. Students wear uniforms, and take part in leadership opportunities and co-curricular activities, such as college trips and citywide competitions.
Selective Enrollment High Schools provide academically advanced students with a challenging college preparatory experience. Each of the Selective Enrollment High Schools offers a rigorous curriculum with mainly honors and Advanced Placement (AP) courses.


|
QuantLib: a free/open-source library for quantitative finance
Fully annotated sources - version 1.30
|
Normal distribution function. More...
#include <ql/math/distributions/normaldistribution.hpp>
Public Member Functions | |
| NormalDistribution (Real average=0.0, Real sigma=1.0) | |
| Real | operator() (Real x) const |
| Real | derivative (Real x) const |
Public Attributes | |
| QL_DEPRECATED typedef Real | argument_type |
| QL_DEPRECATED typedef Real | result_type |
Private Attributes | |
| Real | average_ |
| Real | sigma_ |
| Real | normalizationFactor_ |
| Real | denominator_ |
| Real | derNormalizationFactor_ |
Normal distribution function.
Given x, it returns its probability in a Gaussian normal distribution. It provides the first derivative too.
Definition at line 44 of file normaldistribution.hpp.
| NormalDistribution | ( | Real | average = 0.0 , |
| Real | sigma = 1.0 |
||
| ) |
Definition at line 334 of file normaldistribution.hpp.
Definition at line 347 of file normaldistribution.hpp.
| QL_DEPRECATED typedef Real argument_type |
auto
or decltype
instead. Deprecated in version 1.29. Definition at line 50 of file normaldistribution.hpp.
| QL_DEPRECATED typedef Real result_type |
auto
or decltype
instead. Deprecated in version 1.29. Definition at line 56 of file normaldistribution.hpp.
|
private |
Definition at line 64 of file normaldistribution.hpp.
|
private |
Definition at line 64 of file normaldistribution.hpp.
|
private |
Definition at line 64 of file normaldistribution.hpp.
|
private |
Definition at line 64 of file normaldistribution.hpp.
|
private |
Definition at line 65 of file normaldistribution.hpp.


Resume
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Skills
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- Graded and acted as a Teacher's Assistant for Analysis and Design of Engineering Systems, Graphical Communications, and Computer Programming.
- Worked 20+ hours each week grading and fielding questions for 50+ students per quarter.
- Conferred with professors to ensure expectations and course objectives were met.
General Motors | Facilities Engineering Intern | Jun 2021 - Aug 2021
- Established and coordinated the maintenance, safety procedures, service schedule, and supply of materials required to maintain machines and equipment in the prescribed condition.
- Estimated costs and prepared contracts for engineering, construction, and extraction projects.
- Studied industrial processes to determine where and how application of equipment could be made.
General Motors | Mechanical Engineering Intern | Jun 2020 - Aug 2020
- Researched, designed, evaluated, installed, and maintained mechanical products, equipment, systems, and processes to meet requirements, applying knowledge of engineering principles.
- Developed and tested models of alternate designs and processing methods to assess feasibility, operating condition effects, possible new applications, and necessity of modification.
- Created and interpreted blueprints, technical drawings, schematics, and computer-generated reports.
General Motors | Tool and Die Group Leader Intern | Jun 2019 - Aug 2019
- Performed personnel functions such as supervision of production workers, skilled tradesmen, and technicians.
- Investigated equipment failures and difficulties to diagnose faulty operation to make recommendations to maintenance crew.
- Conferred with engineers and other personnel to implement operation procedures, resolve system malfunctions, and provide technical information.
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Extracurricular
- Delta Delta Delta - President Mar 2020 - May 2021
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Involved in designing and usability, passionate about designing digital experiences that leave a positive impact on people,
environment and the society. My portfolio includes user interface design, brand & identity design, illustration, and art & creative direction.
I've worked on web and product solutions for a range of startups, in a variety of industries.
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I'm passionate about creating better user experiences that are seamless and impactful with an interest in emerging technologies. I enjoy using my skill set to empower people to accomplish their goals by creating digital and user friendly user experiences that make life easier.
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Seeking out challenging and rewarding projects worldwide in my field of expertise for an organization of high repute and where I can acquire an opportunity, to learn and grow, and take my skill set to a whole new level.


I'm Kevin.
If you want to use these, please ask me. I'd love to hear from you, and I'll also gladly explain how to set them up.


GETIPNODEBYNAME
Section: Linux Programmer's Manual (3)
Updated: 2010-09-04
Index
Return to Main Contents
NAME
getipnodebyname, getipnodebyaddr, freehostent - get network
hostnames and addresses
SYNOPSIS
#include <sys/types.h>
#include <sys/socket.h>
#include <netdb.h>
struct hostent *getipnodebyname(const char *name, int af,
int flags, int *error_num);
struct hostent *getipnodebyaddr(const void *addr, size_t len,
int af, int *error_num);
void freehostent(struct hostent *ip);
DESCRIPTION
These functions are deprecated (and unavailable in glibc).
Use
getaddrinfo(3)
and
getnameinfo(3)
instead.
The
getipnodebyname()
and
getipnodebyaddr()
functions return the names and addresses of a network host.
These functions return a pointer to the
following structure:
struct hostent {
char *h_name;
char **h_aliases;
int h_addrtype;
int h_length;
char **h_addr_list;
};
These functions replace the
gethostbyname(3)
and
gethostbyaddr(3)
functions, which could access only the IPv4 network address family.
The
getipnodebyname()
and
getipnodebyaddr()
functions can access multiple network address families.
Unlike the
gethostby
functions,
these functions return pointers to dynamically allocated memory.
The
freehostent()
function is used to release the dynamically allocated memory
after the caller no longer needs the
hostent
structure.
getipnodebyname() arguments
The
getipnodebyname()
function
looks up network addresses for the host
specified by the
name
argument.
The
af
argument specifies one of the following values:
- AF_INET
-
The
name
argument points to a dotted-quad IPv4 address or a name
of an IPv4 network host.
- AF_INET6
-
The
name
argument points to a hexadecimal IPv6 address or a name
of an IPv6 network host.
The
flags
argument specifies additional options.
More than one option can be specified by bitwise OR-ing
them together.
flags
should be set to 0
if no options are desired.
- AI_V4MAPPED
-
This flag is used with
AF_INET6
to request a query for IPv4 addresses instead of
IPv6 addresses; the IPv4 addresses will
be mapped to IPv6 addresses.
- AI_ALL
-
This flag is used with
AI_V4MAPPED
to request a query for both IPv4 and IPv6 addresses.
Any IPv4 address found will be mapped to an IPv6 address.
- AI_ADDRCONFIG
-
This flag is used with
AF_INET6
to
further request that queries for IPv6 addresses should not be made unless
the system has at least one IPv6 address assigned to a network interface,
and that queries for IPv4 addresses should not be made unless the
system has at least one IPv4 address assigned to a network interface.
This flag may be used by itself or with the
AI_V4MAPPED
flag.
- AI_DEFAULT
-
This flag is equivalent to
(AI_ADDRCONFIG | AI_V4MAPPED).
getipnodebyaddr() arguments
The
getipnodebyaddr()
function
looks up the name of the host whose
network address is
specified by the
addr
argument.
The
af
argument specifies one of the following values:
- AF_INET
-
The
addr
argument points to a
struct in_addr
and
len
must be set to
sizeof(struct in_addr).
- AF_INET6
-
The
addr
argument points to a
struct in6_addr
and
len
must be set to
sizeof(struct in6_addr).
RETURN VALUE
NULL is returned if an error occurred, and
error_num
will contain an error code from the following list:
- HOST_NOT_FOUND
-
The hostname or network address was not found.
- NO_ADDRESS
-
The domain name server recognized the network address or name,
but no answer was returned.
This can happen if the network host has only IPv4 addresses and
a request has been made for IPv6 information only, or vice versa.
- NO_RECOVERY
-
The domain name server returned a permanent failure response.
- TRY_AGAIN
-
The domain name server returned a temporary failure response.
You might have better luck next time.
A successful query returns a pointer to a
hostent
structure that contains the following fields:
- h_name
-
This is the official name of this network host.
- h_aliases
-
This is an array of pointers to unofficial aliases for the same host.
The array is terminated by a null pointer.
- h_addrtype
-
This is a copy of the
af
argument to
getipnodebyname()
or
getipnodebyaddr().
h_addrtype
will always be
AF_INET
if the
af
argument was
AF_INET.
h_addrtype
will always be
AF_INET6
if the
af
argument was
AF_INET6.
- h_length
-
This field will be set to
sizeof(struct in_addr)
if
h_addrtype
is
AF_INET,
and to
sizeof(struct in6_addr)
if
h_addrtype
is
AF_INET6.
- h_addr_list
-
This is an array of one or more pointers to network address structures for the
network host.
The array is terminated by a null pointer.
CONFORMING TO
RFC 2553.
NOTES
These functions were present in glibc 2.1.91-95, but were
removed again.
Several UNIX-like systems support them, but all
call them deprecated.
SEE ALSO
getaddrinfo(3),
getnameinfo(3),
inet_ntop(3),
inet_pton(3)
Index
- NAME
-
- SYNOPSIS
-
- DESCRIPTION
-
- getipnodebyname() arguments
-
- getipnodebyaddr() arguments
-
- RETURN VALUE
-
- CONFORMING TO
-
- NOTES
-
- SEE ALSO
-
This document was created by
man2html,
using the manual pages.
Time: 02:55:10 GMT, September 18, 2014


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