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Copy pathconvert_pb_to_nn.py
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convert_pb_to_nn.py
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import os
from tensorflow.keras import models
import argparse
ROOT_DIR = os.path.abspath(__file__)
print(ROOT_DIR)
parser = argparse.ArgumentParser()
parser.add_argument('-p', type=str)
args = parser.parse_args()
model = models.load_model(args.p)
model.compile()
model.build()
layers_num = len(model.layers)
neurons = []
for layer in model.layers:
neurons.append(len(layer.weights[0][:, 0]))
neurons.append(len(model.layers[-1].weights[1].numpy()))
with open(os.path.join(args.p, 'saved_model.nn'), 'w') as f:
f.write(str(layers_num)+'\n')
f.write('0\n')
for neuron in neurons:
f.write(str(neuron) + ' ')
f.write('\n')
for _ in range(neurons[0]):
f.write('0.000000 ')
f.write('\n')
for n in range(1, len(neurons)):
biases = model.layers[n - 1].weights[1].numpy().flatten()
for i in range(neurons[n]):
f.write(f'{biases[i]:.6f} ')
f.write('\n')
for n in range(1, len(neurons)):
for i in range(neurons[n]):
f.write(f'{model.layers[n - 1].weights[0].numpy().flatten()[i]:.6f} ')
for n in range(1, len(neurons)):
weights = model.layers[n - 1].weights[0].numpy().flatten()
for i in range(len(weights)):
f.write(f'{weights[i]:.6f} ')
f.write('\n')
print(f'Done, file is: {os.path.join(args.p, "saved_model.nn")}')