From d58f0fd2fccfd72f1ac7d0bc3e8fd092e1d43795 Mon Sep 17 00:00:00 2001
From: nz11 <nz11@illinois.edu>
Date: Sun, 22 Nov 2020 17:39:48 -0600
Subject: [PATCH] Update alexnet_imagenet.py
---
llvm/projects/keras/src/alexnet_imagenet.py | 26 +++++++++++++--------
1 file changed, 16 insertions(+), 10 deletions(-)
diff --git a/llvm/projects/keras/src/alexnet_imagenet.py b/llvm/projects/keras/src/alexnet_imagenet.py
index 0e8425b825..1423b8c2e9 100644
--- a/llvm/projects/keras/src/alexnet_imagenet.py
+++ b/llvm/projects/keras/src/alexnet_imagenet.py
@@ -30,10 +30,10 @@ data_format = 'channels_first'
IMAGENET_DIR = '/home/nz11/ILSVRC2012/'
OUTPUT_DIR = 'data/alexnet_imagenet_tune/'
-WEIGHTS_PATH = 'data/weights.h5'
+WEIGHTS_PATH = 'data/alexnet_imagenet_tune/weights.h5'
NUM_CLASSES = 200
-IMAGES_PER_CLASS = 40
+IMAGES_PER_CLASS = 50
# VAL_SIZE = 100
@@ -183,6 +183,9 @@ 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))
+
@@ -233,16 +236,16 @@ model.compile(optimizer=keras.optimizers.Adam(lr=0.00001), loss='categorical_cro
if os.path.exists(WEIGHTS_PATH):
model.load_weights(WEIGHTS_PATH)
else:
- model.fit_generator(generate(), steps_per_epoch=1000, validation_data=(X_test, to_categorical(y_true, num_classes=1000)), epochs=2)
- K.set_value(model.optimizer.lr, 0.000001)
- model.fit_generator(generate(), steps_per_epoch=1000, validation_data=(X_test, to_categorical(y_true, num_classes=1000)), epochs=6)
- model.save_weights('data/weights.h5')
+ pass
+# model.fit_generator(generate(), steps_per_epoch=1000, validation_data=(X_test, to_categorical(y_true, num_classes=1000)), epochs=3)
+# K.set_value(model.optimizer.lr, 0.000001)
+# model.fit_generator(generate(), steps_per_epoch=1000, validation_data=(X_test, to_categorical(y_true, num_classes=1000)), epochs=3)
-translate_to_approxhpvm(model, OUTPUT_DIR, X_tune, y_tune, 1000)
+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)
+# # 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)
@@ -250,4 +253,7 @@ 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))
+
+model.save_weights(OUTPUT_DIR + '/weights.h5')
+
\ No newline at end of file
--
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