Francesco Gatti
61 lines
2.4 KiB
Python
61 lines
2.4 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
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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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from weights_exporter import *
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def dense_model():
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model = Sequential()
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model.add(Reshape((100, 100, 4, 1), input_shape=(100, 100, 4)))
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model.add(Lambda(lambda x: 2*x - 1.,
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batch_input_shape=(1, 100, 100, 4), # 100by100by2
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output_shape=(100, 100, 4, 1))) # 100by100by2
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model.add(Convolution3D(16, kernel_size=(8, 8, 2), subsample=(4, 4, 1), border_mode="valid",
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bias_initializer="random_uniform"))
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model.add(ELU())
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model.add(AveragePooling3D(pool_size=(2, 2, 1)))
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model.add(Convolution3D(16, kernel_size=(4, 4, 2), subsample=(2, 2, 1), border_mode="valid",
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bias_initializer="random_uniform"))
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model.add(ELU())
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model.add(Convolution3D(24, kernel_size=(3, 3, 2), subsample=(1, 1, 1), border_mode="valid",
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bias_initializer="random_uniform"))
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model.add(ELU())
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model.add(Flatten())
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model.add(Dense(256, bias_initializer="random_uniform"))
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model.add(ELU())
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model.add(Dense(32, activation="relu", bias_initializer="random_uniform"))
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model.add(Dense(2, bias_initializer="random_uniform"))
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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_conv3d("conv0", wg[0], wg[1])
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export_conv3d("conv1", wg[2], wg[3])
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export_conv3d("conv2", wg[4], wg[5])
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export_dense ("dense3", wg[6], wg[7])
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export_dense ("dense4", wg[8], wg[9])
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export_dense ("dense5", wg[10], wg[11])
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grid = np.random.rand(100, 100,4)
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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) |