works but it need cleaning

tests/simple/test_model.py

@@ -2,6 +2,7 @@ import keras
 import numpy as np
 from keras.models import Sequential
 from keras.layers import Input, Dense, Activation, Flatten, Dropout, ELU, Reshape, Lambda, Conv1D
+from keras.layers import Bidirectional, CuDNNLSTM
 from keras.layers.convolutional import Convolution2D, Convolution3D
 from keras.layers.pooling import MaxPooling2D, MaxPooling3D, AveragePooling3D
 from keras.models import Sequential, Model
@@ -17,9 +18,11 @@ def bin_write(f, data):
     f.write(bin)
 
 def create_model():
-    x1 = Input((6, 16), name='x1')
+    x1 = Input((3, 8), name='x1')
     conv = Conv1D(4, 2)(x1)
-    model = Model([x1], [conv])
+    lstm = Bidirectional(CuDNNLSTM(5, return_sequences=True))(conv)    
+    lstm2 = Bidirectional(CuDNNLSTM(5, return_sequences=False))(lstm)    
+    model = Model([x1], [lstm2])
     model.summary()
 
     return model
@@ -30,14 +33,16 @@ if __name__ == '__main__':
     model = create_model()
     model.save("net.hdf5")
 
-    x = np.random.rand(1,1,6,16)
+    np.random.seed(2)
+    x = np.random.rand(1,1,3,8)
     r = model.predict( x[0], batch_size=1)
-    r = np.array([r])
 
+    r = np.array([r])
     x =  x.transpose(0, 3, 1, 2)
-    r =  r.transpose(0, 3, 1, 2)
+    #r =  r.transpose(0, 3, 1, 2)
     print("in: ", np.shape(x))
     print("out: ", np.shape(r))
+    print("output: ", r.tolist())
 
     x = np.array(x.flatten(), dtype=np.float32)
     f = open("input.bin", mode='wb')

tests/simple/test_simple.cpp

@@ -3,14 +3,20 @@
 
 const char *input_bin   = "../tests/simple/input.bin";
 const char *c0_bin      = "../tests/simple/layers/conv1d_1.bin";
+const char *l1_bin      = "../tests/simple/layers/bidirectional_1.bin";
+const char *l2_bin      = "../tests/simple/layers/bidirectional_2.bin";
 const char *output_bin  = "../tests/simple/output.bin";
 
 int main() {
 
     // Network layout
-    tk::dnn::dataDim_t dim(1, 16, 1, 6);
+    tk::dnn::dataDim_t dim(1, 8, 1, 3);
     tk::dnn::Network net(dim);
     tk::dnn::Conv2d     l0(&net, 4, 1, 2, 1, 1, 0, 0, c0_bin);
+    tk::dnn::LSTM       l1(&net, 5, true, l1_bin);
+    tk::dnn::LSTM       l2(&net, 5, false, l2_bin);
+
+    net.print();
 
     // Load input
     dnnType *data;