Francesco Gatti
44 lines
1.5 KiB
Python
44 lines
1.5 KiB
Python
import keras
|
|
import numpy as np
|
|
from keras.models import Sequential
|
|
from keras.layers import Input, Dense, Activation, Flatten, Dropout, ELU, Reshape, Lambda
|
|
from keras.layers.convolutional import Convolution2D, Convolution3D
|
|
from keras.layers.pooling import MaxPooling2D, MaxPooling3D, AveragePooling3D
|
|
from keras.models import Sequential, Model
|
|
from keras.layers import Cropping2D
|
|
import keras.backend.tensorflow_backend as KTF
|
|
|
|
def dense_model():
|
|
model = Sequential()
|
|
|
|
model.add(Reshape((10, 10, 1), input_shape=(10, 10)))
|
|
model.add(Convolution2D(2, (4, 4), subsample=(2, 2),
|
|
bias_initializer='random_uniform', activation="relu"))
|
|
model.add(Convolution2D(4, (2, 2), subsample=(1, 1),
|
|
bias_initializer='random_uniform', activation="relu"))
|
|
model.add(Flatten())
|
|
model.add(Dense(4, bias_initializer='random_uniform', activation="relu"))
|
|
sgd = keras.optimizers.Adam(lr=1e-4, decay=1e-8)
|
|
model.compile(optimizer=sgd, loss="mse")
|
|
return model
|
|
|
|
|
|
if __name__ == '__main__':
|
|
print "DATA FORMAT: ", keras.backend.image_data_format()
|
|
|
|
model = dense_model()
|
|
model.save("net.h5")
|
|
|
|
grid = np.random.rand(10,10)
|
|
X = grid[None,:,:]
|
|
i = np.array(grid.flatten(), dtype=np.float32)
|
|
print i
|
|
i.tofile("input.bin", format="f")
|
|
print "Input: ", X
|
|
|
|
r = model.predict( X, batch_size=1)
|
|
print np.shape(r)
|
|
print "Result: ", r
|
|
print "Result shape: ", np.shape(r)
|
|
r.tofile("output.bin", format="f")
|