conv2d implementation

tests/simple_dense.py

@@ -2,7 +2,7 @@ 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 import Input, Dense, Activation, Flatten, Dropout, ELU, Reshape
 from keras.layers.convolutional import Convolution2D, Convolution3D
 from keras.layers.pooling import MaxPooling2D, MaxPooling3D
 from keras.models import Sequential, Model
@@ -12,33 +12,34 @@ from weights_exporter import *
 
 def dense_model():
     model = Sequential()
-    model.add(Dense(256, input_shape=(1, 512)))
+
+    model.add(Reshape((10, 10, 1), input_shape=(10, 10)))
+    model.add(Convolution2D(2, (4, 4), subsample=(2, 2), 
+                           bias_initializer='random_uniform'))
     model.add(ELU())
-    model.add(Dense(32))
-    model.add(ELU())
-    model.add(Dense(2))
-    
+    model.add(Convolution2D(4, (2, 2), subsample=(1, 1), 
+                           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()
     wg = model.get_weights()
+    export_conv2d("conv0", wg[0], wg[1])
+    export_conv2d("conv1", wg[2], wg[3])
 
-    export_dense("dense0", wg[0], wg[1])
-    export_dense("dense1", wg[2], wg[3])
-    export_dense("dense2", wg[4], wg[5])
-
-    model.set_weights(wg)
-   
-    X = np.random.rand(1, 512)
-    i = np.array(X, dtype=np.float32)
+    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    
 
-    print "Input: ", i
-    r = model.predict( X[None, :], batch_size=1)
-
+    r = model.predict( X, batch_size=1)
+    print np.shape(r)  
     print "Result: ", r
     print "Result shape: ", np.shape(r)