Francesco Gatti
45 lines
1.2 KiB
Python
45 lines
1.2 KiB
Python
import keras
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import numpy as np
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import pickle
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from keras.models import Sequential
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from keras.layers import Input, Dense, Activation, Flatten, Dropout, ELU
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from keras.layers.convolutional import Convolution2D, Convolution3D
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from keras.layers.pooling import MaxPooling2D, MaxPooling3D
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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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def dense_model(inp, out):
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model = Sequential()
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model.add(Dense(out, input_shape=(1, inp)))
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model.add(ELU())
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sgd = keras.optimizers.Adam(lr=1e-4, decay=1e-8)
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model.compile(optimizer=sgd, loss="mse")
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return model
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if __name__ == '__main__':
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model = dense_model(8, 2)
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wg = model.get_weights()
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w = np.squeeze(wg[0])
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w = np.array([ i[0] for i in w ] + [ i[1] for i in w ], dtype=np.float32)
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b = np.squeeze(wg[1])
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print "weigths: ", w
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print "bias: ", b
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w.tofile("dense.bin", format="f")
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b.tofile("dense.bias.bin", format="f")
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X = np.array([[[0,1,2,3,4,5,6,7]]], dtype=np.float32)
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i = np.squeeze(X[0][0])
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print "input: ", i
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i.tofile("input.bin", format="f")
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r = model.predict( X, batch_size=1)
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print "Result: ", r
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print "Result shape: ", np.shape(r)
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