Update vgg16_imagenet.py

llvm/projects/keras/src/vgg16_imagenet.py

@@ -32,7 +32,7 @@ IMAGENET_DIR = '/home/nz11/ILSVRC2012/'
 OUTPUT_DIR = 'data/vgg16_imagenet_tune/'
 
 NUM_CLASSES = 200
-IMAGES_PER_CLASS = 40
+IMAGES_PER_CLASS = 50
 # VAL_SIZE = 100
 
 
@@ -212,18 +212,22 @@ y_true = np.array(y_true)
 X_tune = np.array(X_tune)
 y_tune = np.array(y_tune)
 
+print ('tune size', len(X_tune))
+print ('test size', len(X_test))
 
 
-translate_to_approxhpvm(model, OUTPUT_DIR, X_tune, y_tune, 1000)
 
-# dumpCalibrationData2(OUTPUT_DIR + 'test_input_10K.bin', X_test, OUTPUT_DIR + 'test_labels_10K.bin', y_true)
-dumpCalibrationData2(OUTPUT_DIR + 'tune_input.bin', X_tune, OUTPUT_DIR + 'tune_labels.bin', y_tune)
-dumpCalibrationData2(OUTPUT_DIR + 'test_input.bin', X_test, OUTPUT_DIR + 'test_labels.bin', y_true)
+translate_to_approxhpvm(model, OUTPUT_DIR, X_tune, y_tune, 1000, dump_weights=False)
+
+# # dumpCalibrationData2(OUTPUT_DIR + 'test_input_10K.bin', X_test, OUTPUT_DIR + 'test_labels_10K.bin', y_true)
+# dumpCalibrationData2(OUTPUT_DIR + 'tune_input.bin', X_tune, OUTPUT_DIR + 'tune_labels.bin', y_tune)
+# dumpCalibrationData2(OUTPUT_DIR + 'test_input.bin', X_test, OUTPUT_DIR + 'test_labels.bin', y_true)
 
 
 
 pred = np.argmax(model.predict(X_test), axis=1)
 print ('val accuracy', np.sum(pred == y_true.ravel()) / len(X_test))
     
-pred = np.argmax(model.predict(X_tune), axis=1)
-print ('val accuracy', np.sum(pred == y_tune.ravel()) / len(X_tune))
+# pred = np.argmax(model.predict(X_tune), axis=1)
+# print ('val accuracy', np.sum(pred == y_tune.ravel()) / len(X_tune))
+