better network model

tests/simple/test_model.py

@@ -0,0 +1,43 @@
+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")

tests/simple/test_simple.cpp

@@ -0,0 +1,47 @@
+#include<iostream>
+#include "tkdnn.h"
+
+const char *input_bin   = "../tests/test/input.bin";
+const char *c0_bin      = "../tests/test/layers/c0.bin";
+const char *c1_bin      = "../tests/test/layers/c1.bin";
+const char *d2_bin      = "../tests/test/layers/d2.bin";
+const char *output_bin   = "../tests/test/output.bin";
+
+int main() {
+
+    // Network layout
+    tkDNN::dataDim_t dim(1, 1, 10, 10, 1);
+    tkDNN::Network net(dim);
+    tkDNN::Conv2d     l0(&net, 2, 4, 4, 2, 2, 0, 0, c0_bin);
+    tkDNN::Activation l1(&net, CUDNN_ACTIVATION_RELU);
+    tkDNN::Conv2d     l2(&net, 4, 2, 2, 1, 1, 0, 0, c1_bin);
+    tkDNN::Activation l3(&net, CUDNN_ACTIVATION_RELU);
+    tkDNN::Flatten    l4(&net);
+    tkDNN::Dense      l5(&net, 4, d2_bin);
+    tkDNN::Activation l6(&net, CUDNN_ACTIVATION_RELU);
+
+    // Load input
+    value_type *data;
+    value_type *input_h;
+    readBinaryFile(input_bin, dim.tot(), &input_h, &data);
+
+    printDeviceVector(dim.tot(), data);
+    dim.print(); //print initial dimension
+    
+    TIMER_START
+    // Inference
+    data = net.infer(dim, data); dim.print();
+    TIMER_STOP
+
+    // Print result
+    std::cout<<"\n======= RESULT =======\n";
+    printDeviceVector(dim.tot(), data);
+
+    // Print real test
+    std::cout<<"\n==== CHECK RESULT ====\n";
+    value_type *out;
+    value_type *out_h;
+    readBinaryFile(output_bin, dim.tot(), &out_h, &out);
+    printDeviceVector(dim.tot(), out);
+    return 0;
+}