From ab17f3f128c0d462596ee0338fb3457e3c15bc81 Mon Sep 17 00:00:00 2001
From: Hashim Sharif <hsharif3@miranda.cs.illinois.edu>
Date: Tue, 2 Feb 2021 17:33:41 -0600
Subject: [PATCH] Fixing LeNet model
---
hpvm/projects/keras/src/lenet.py | 22 ++++++++++------------
1 file changed, 10 insertions(+), 12 deletions(-)
diff --git a/hpvm/projects/keras/src/lenet.py b/hpvm/projects/keras/src/lenet.py
index 4b58e65314..01c84719e6 100644
--- a/hpvm/projects/keras/src/lenet.py
+++ b/hpvm/projects/keras/src/lenet.py
@@ -26,7 +26,7 @@ class LeNet_MNIST(Benchmark):
def buildModel(self):
- # Network Compostion: 3 Conv Layers, 2 Dense Layers
+ # Network Compostion: 2 Conv Layers, 2 Dense Layers
model = Sequential()
# ConvLayer1
@@ -40,10 +40,10 @@ class LeNet_MNIST(Benchmark):
model.add(Flatten())
# DenseLayer1
- model.add(Dense(1024, activation='relu'))
+ model.add(Dense(1024, activation='tanh'))
# DenseLayer2
- model.add(Dense(self.num_classes, activation='relu'))
+ model.add(Dense(self.num_classes, activation='tanh'))
# Softmax Layer
model.add(Activation('softmax'))
@@ -55,18 +55,16 @@ class LeNet_MNIST(Benchmark):
test_labels = y_val
X_train = X_train.reshape(X_train.shape[0], 1, 28, 28)
- X_val = X_val.reshape(X_val.shape[0], 1, 28, 28)
-
-
X_train = X_train.astype('float32')
- X_val = X_val.astype('float32')
X_train /= 255
- X_val /= 255
- X_test = X_val[0:5000]
- y_test = y_val[0:5000]
- X_tuner = X_val[5000:]
- y_tuner = y_val[5000:]
+ X_test = np.fromfile(MODEL_PARAMS_DIR + '/lenet_mnist/test_input.bin', dtype=np.float32)
+ X_test = X_test.reshape((-1, 1, 28, 28))
+ y_test = np.fromfile(MODEL_PARAMS_DIR + '/lenet_mnist/test_labels.bin', dtype=np.uint32)
+
+ X_tuner = np.fromfile(MODEL_PARAMS_DIR + '/lenet_mnist/tune_input.bin', dtype=np.float32)
+ X_tuner = X_tuner.reshape((-1, 1, 28, 28))
+ y_tuner = np.fromfile(MODEL_PARAMS_DIR + '/lenet_mnist/tune_labels.bin', dtype=np.uint32)
return X_train, y_train, X_test, y_test, X_tuner, y_tuner
--
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