Francesco Gatti
50 lines
1.3 KiB
Python
50 lines
1.3 KiB
Python
import keras
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import numpy as np
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from keras.models import Sequential
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from keras.layers import Input, Dense, Activation, Flatten, Dropout, ELU, Reshape, Lambda, Conv1D
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from keras.layers.convolutional import Convolution2D, Convolution3D
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from keras.layers.pooling import MaxPooling2D, MaxPooling3D, AveragePooling3D
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from keras.models import Sequential, Model
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from keras.layers import Cropping2D
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import keras.backend.tensorflow_backend as KTF
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import struct
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from keras.models import Sequential, Model
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def bin_write(f, data):
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data = data.flatten()
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fmt = 'f'*len(data)
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bin = struct.pack(fmt, *data)
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f.write(bin)
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def create_model():
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x1 = Input((6, 16), name='x1')
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conv = Conv1D(4, 2)(x1)
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model = Model([x1], [conv])
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model.summary()
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return model
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if __name__ == '__main__':
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print ("DATA FORMAT: ", keras.backend.image_data_format())
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model = create_model()
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model.save("net.hdf5")
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x = np.random.rand(1,1,6,16)
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r = model.predict( x[0], batch_size=1)
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r = np.array([r])
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x = x.transpose(0, 3, 1, 2)
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r = r.transpose(0, 3, 1, 2)
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print("in: ", np.shape(x))
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print("out: ", np.shape(r))
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x = np.array(x.flatten(), dtype=np.float32)
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f = open("input.bin", mode='wb')
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bin_write(f, x)
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r = np.array(r.flatten(), dtype=np.float32)
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f = open("output.bin", mode='wb')
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bin_write(f, r)
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