NNSVG inspired network structure drawer for deep learning
dnnsvg has intuitive interface which enables building svgs for common neural network architecture
svg = SVGBuilder(height=height, width=width) \
.add_layer(Convolution2D(in_channels=None, out_channels=96, ksize=11, stride=4)) \
.add_layer(MaxPooling(ksize=3, stride=2)) \
.add_layer(Convolution2D(in_channels=96, out_channels=256, ksize=5, stride=1, pad=2)) \
.add_layer(MaxPooling(ksize=3, stride=2)) \
.add_layer(Convolution2D(in_channels=256, out_channels=384, ksize=3, stride=1, pad=1)) \
.add_layer(Convolution2D(in_channels=384, out_channels=384, ksize=3, stride=1, pad=1)) \
.add_layer(Convolution2D(in_channels=384, out_channels=256, ksize=3, stride=1, pad=1)) \
.add_layer(MaxPooling(ksize=3, stride=2)) \
.add_layer(Reshape(output_shape=(1, 9216))) \
.add_layer(FullyConnected(output_shape=(1, 4096))) \
.add_layer(FullyConnected(output_shape=(1, 4096))) \
.add_layer(FullyConnected(output_shape=(1, 1000))) \
.build(input_tensor)
python setup.py installor if you prefer using pip
pip install .It is recommended to use develop instead of install option to reflect changes in the directory
python setup.py developor if you prefer using pip
pip install -e .
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