ELU implemented

tests/simple_dense.py

@@ -0,0 +1,44 @@
+import keras
+import numpy as np
+import pickle
+from keras.models import Sequential
+from keras.layers import Input, Dense, Activation, Flatten, Dropout, ELU
+from keras.layers.convolutional import Convolution2D, Convolution3D
+from keras.layers.pooling import MaxPooling2D, MaxPooling3D
+from keras.models import Sequential, Model
+from keras.layers import Cropping2D
+import keras.backend.tensorflow_backend as KTF
+
+
+def dense_model(inp, out):
+    model = Sequential()
+    model.add(Dense(out, input_shape=(1, inp)))
+    model.add(ELU())
+
+    sgd = keras.optimizers.Adam(lr=1e-4, decay=1e-8)
+    model.compile(optimizer=sgd, loss="mse")
+
+    return model
+
+
+if __name__ == '__main__':
+
+    model = dense_model(8, 2)
+    wg = model.get_weights()
+    w = np.squeeze(wg[0])
+    w = np.array([ i[0] for i in w ] + [ i[1] for i in w ], dtype=np.float32) 
+    b = np.squeeze(wg[1])
+    
+    print "weigths: ", w
+    print "bias: ", b
+    w.tofile("dense.bin", format="f")
+    b.tofile("dense.bias.bin", format="f")
+   
+    X = np.array([[[0,1,2,3,4,5,6,7]]], dtype=np.float32)
+    i = np.squeeze(X[0][0])
+    print "input: ", i    
+    i.tofile("input.bin", format="f")
+    
+    r = model.predict( X, batch_size=1)
+    print "Result: ", r
+    print "Result shape: ", np.shape(r) 

tests/test.cpp

@@ -3,10 +3,22 @@
 
 int main() {
 
-    tkDNN::dataDim_t dim(1, 1, 10, 10);
-    dim.print();
     tkDNN::Network net;
-    tkDNN::Dense d(&net, dim, 2, "ci", "lol");
+    tkDNN::dataDim_t dim(1, 8, 1, 1);
+    tkDNN::Dense      d(&net, dim, 2, "../tests/dense.bin", "../tests/dense.bias.bin");
+    tkDNN::Activation a(&net, d.output_dim, tkDNN::ACTIVATION_ELU);
+
+    value_type *data;
+    value_type *input_h;
+    readBinaryFile("../tests/input.bin", 8, &input_h, &data);
+
+    dim.print();
+    data = d.infer(dim, data);
+    dim.print();
+    data = a.infer(dim, data);
+    dim.print();
+
+    printDeviceVector(dim.tot(), data);
 
     return 0;
 }