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")