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fireml-0.1.1
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مشاهده بیشترتوضیحات
fireml machine learning framework
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | fireml-0.1.1 |
| نام | fireml |
| نسخه کتابخانه | 0.1.1 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Anatoly Belikov |
| ایمیل نویسنده | awbelikov@gmail.com |
| آدرس صفحه اصلی | https://bitbucket.org/noSkill/fireml |
| آدرس اینترنتی | https://pypi.org/project/fireml/ |
| مجوز | - |
# README #
Caffe-like machine learning framework in python
## layers types
### ImageData
layer to read images. Possible sources: txt file with path labels on each line, cifar archive.
**image_data_param**:
source: string - path to cifar archive or txt file
batch_size: int - how many images to process in each iteration
shuffle: bool - images will be shuffled when sampling for a batch
new_height: int - new image height(can be same as original)
new_width: int - new image width(can be same as original)
new_labels: int - expected number of labels(txt file may contain multiple labels for each path)
example:
layer {
top: "data"
top: "label"
name: "data"
type: "ImageData"
image_data_param {
source: "../cifar/cifar-10-python.tar.gz"
source: "data.txt.3" # use cifar or txt file
batch_size: 65
shuffle: true
new_height: 32
new_width: 32
n_labels: 10
}
transform_param {
mean_value: 126 # r
mean_value: 123 # g
mean_value: 114 # b
mirror: true
scale: 0.02728125
standard_params {
var_average: 5000
mean_average: 5000
mean_per_channel: false
var_per_channel: false
}
}
include: { phase: TRAIN }
}
**transform_param**
Parameters for data preprocessing
**standard_params**
Parameters for preprocessor for data standardization.
To achieve zero mean and unit variance the preprocessor will subtract iterative
mean from each sample and divide the result by standard deviation.
standard_params {
var_average: 1
mean_average: 1
mean_per_channel: false
var_per_channel: true
}
var_average: int [default = 0] - use last var_average samples to compute variance and std
disabled if var_average == 0
mean_average: int [default = 0] - use last var_average samples to compute mean
disabled if mean_average == 0
mean_per_channel: bool [default = false] - subtract from each channel mean for that channel
var_per_channel: [default = false] - divide each channel by separate std value
### Convolution
Convolution of 2-3 d images(matrices)
**convolution_param**
num_output: int number of filters(output feature maps)
kernel_size: int size of receptive field of filters. Receptive field is kernel_size * kernel_size
stride: int - filter will be applied after stride pixels
weight_filler: see weight filler
example:
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
convolution_param {
num_output: 40
kernel_size: 3
stride: 2
weight_filler {
type: "xavier"
variance_norm: AVERAGE
}
}
}
### Pooling
Subsampling layer for max or average pooling
**pooling_param**
pool: MAX or AVE
kernel_size: int
subsampling window size
stride: int
perform pooling each *stride* pixels
example:
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
### Accuracy
Layer for computing accuracy
Accuracy of a classifier is defined as (true positive + true negative)/total
In multilabel classification example counts as correctly classified iff **all** outputs
are correct.
Example:
layer {
name: "accuracy"
type: "Accuracy"
bottom: "pool10"
bottom: "label"
top: "accuracy"
}
## weight_filler
Weigth filler parameters are common for all layers with weights
type: string
"xavier", "gaussian", "uniform"
mean: float
mean value for gaussian initialization
std: float
standard deviation for gaussian initialization
min: float
lower bound for uniform initialization
max: float
upper bound for uniform initialization
## Activation functions
### SeLU
Self-regularized linear unit:
example:
{
name: "relu_conv1"
type: "SeLU"
bottom: "conv1"
top: "conv1"
}
## Loss layers
### SigmoidCrossEntropyLoss
Layer that applies sigmoid elementwise, followed by cross-entropy log loss
-mean(sum(y * log(p(y)) + (1 - y) * log(1 - p(y))))
where p(y) - sigmoid transformation of layer's input, that is vector of independent probabilities for each class.
example:
layer {
name: "loss"
type: "SigmoidCrossEntropyLoss"
bottom: "pool1"
bottom: "label"
top: "loss"
include {
phase: TRAIN
}
}
## Maxout layer
Apply max operator for each *size* channels
size: int [default = 0] - take max over each *size* channels
lambda: int [default = 0.0] - apply probabilistic max if lambda != 0
layer {
name: "maxout_1"
type: "Maxout"
maxout_param {
lambda: 1
size: 2
}
bottom: "conv1"
top: "conv1"
}
نحوه نصب
نصب پکیج whl fireml-0.1.1:
pip install fireml-0.1.1.whl
نصب پکیج tar.gz fireml-0.1.1:
pip install fireml-0.1.1.tar.gz