Francesco Gatti
45 lines
1.5 KiB
Python
45 lines
1.5 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, Reshape
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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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from weights_exporter import *
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def dense_model():
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model = Sequential()
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model.add(Reshape((10, 10, 1), input_shape=(10, 10)))
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model.add(Convolution2D(2, (4, 4), subsample=(2, 2),
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bias_initializer='random_uniform'))
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model.add(ELU())
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model.add(Convolution2D(4, (2, 2), subsample=(1, 1),
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bias_initializer='random_uniform', activation="relu"))
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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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print "DATA FORMAT: ", keras.backend.image_data_format()
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model = dense_model()
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wg = model.get_weights()
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export_conv2d("conv0", wg[0], wg[1])
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export_conv2d("conv1", wg[2], wg[3])
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grid = np.random.rand(10,10)
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X = grid[None,:,:]
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i = np.array(grid.flatten(), dtype=np.float32)
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print i
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i.tofile("input.bin", format="f")
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print "Input: ", X
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r = model.predict( X, batch_size=1)
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print np.shape(r)
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print "Result: ", r
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print "Result shape: ", np.shape(r) |