README

2017-06-29 21:25:49 +02:00
parent 743c2dbfa4
commit 3d9148d1e0
2 changed files with 85 additions and 0 deletions
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+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
+from weights_exporter import *
+
+def dense_model():
+    model = Sequential()
+    model.add(Reshape((100, 100, 4, 1), input_shape=(100, 100, 4)))
+    model.add(Lambda(lambda x: 2*x - 1.,
+            batch_input_shape=(1, 100, 100, 4),         # 100by100by2
+            output_shape=(100, 100, 4, 1)))                         # 100by100by2
+    model.add(Convolution3D(16, kernel_size=(8, 8, 2), subsample=(4, 4, 1), border_mode="valid", 
+                bias_initializer="random_uniform"))
+    model.add(ELU())
+    model.add(AveragePooling3D(pool_size=(2, 2, 1)))
+    model.add(Convolution3D(16, kernel_size=(4, 4, 2), subsample=(2, 2, 1), border_mode="valid", 
+                bias_initializer="random_uniform"))
+    model.add(ELU())
+    model.add(Convolution3D(24, kernel_size=(3, 3, 2), subsample=(1, 1, 1), border_mode="valid", 
+                bias_initializer="random_uniform"))
+    model.add(ELU())
+    model.add(Flatten())
+    model.add(Dense(256, bias_initializer="random_uniform"))
+    model.add(ELU())
+    model.add(Dense(32, activation="relu", bias_initializer="random_uniform"))
+    model.add(Dense(2, bias_initializer="random_uniform"))
+
+    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()
+    wg = model.get_weights()
+    export_conv3d("conv0",  wg[0], wg[1])
+    export_conv3d("conv1",  wg[2], wg[3])
+    export_conv3d("conv2",  wg[4], wg[5])
+    export_dense ("dense3", wg[6], wg[7])
+    export_dense ("dense4", wg[8], wg[9])
+    export_dense ("dense5", wg[10], wg[11])
+
+    grid = np.random.rand(100, 100,4)
+    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)