keras-style deep network package for classification and prediction
Project description
deepfree
Keras-style deep network package for classification and prediction.
install
pip install --upgrade numpy h5py
pip install --upgrade deepfree
feature
fast learning
The main framework of the program relies on Model
in core._model
and Layer
in core._layer
, which can import directly through 'from deepfree import Model, Layer'
. You can quickly build and train the model by using them flexibly. In addition, the constructed DBN
and SAE
can be employed directly, which are inherited from Model
.
stacking blocks
By calling Model.add_layer(['a Layer of a list of Layer'])
, you can build the model like stack the blocks. There are a set of Layer
can be selected, such as phvariable
, maxpooling2d
,flatten
,concatenate
, Dense
, Conv2D
.
flexible setting
You can set the model's parameters listed in base._attribute
when first building model (DBN(para=...)
, SAE(para=...)
, Model(para=...)
) or training it (Model.training(para=...)
). If you do not set a value, the default value in base._attribute
will be applied.
results display
'loss & test accuracy - epoch'
curve and 'prediction - epoch'
curve will be generated automatically. Furthermore, real label -> predicted label
count result and t-SNE visualization
image can be obtained by calling Model.plot_label_cnt
and Model.plot_tSNE
, respectively.
example
A simple DNN can be constructed and trained as:
from deepfree import Model
from deepfree import phvariable,Dense
model = Model()
model.struct = [784, 100 ,10]
model.input = phvariable(model.struct[0])('input')
model.label = phvariable(model.struct[-1])('label')
for i in range(len(model.struct)-2):
model.add_layer(Dense(model.struct[i+1],
activation = model.next_hidden_activation(),
is_dropout = True))
model.add_layer(Dense(model.struct[-1], activation = model.output_func))
model.training(dataset = ...,data_path = ...)
plot
The running result can be find in 'result'
folder.
- loss & test accuracy - epoch curve:
- prediction - epoch curve:
- real label -> predicted label count result:
- t-SNE visualization:
blog
Project details
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