fix

dataflow/dataset/mnist.py

@@ -16,7 +16,7 @@ class Mnist(object):
             train_or_test: string either 'train' or 'test'
         """
         if dir is None:
-            dir = os.path.join(os.path.dirname(__file__), 'mnist')
+            dir = os.path.join(os.path.dirname(__file__), 'mnist_data')
         self.dataset = input_data.read_data_sets(dir)
         self.train_or_test = train_or_test
 
@@ -28,5 +28,7 @@ class Mnist(object):
             yield (img, label)
 
 if __name__ == '__main__':
-    ds = Mnist()
-    ds.get_data()
+    ds = Mnist('train')
+    for (img, label) in ds.get_data():
+        from IPython import embed; embed()
+

example_mnist.py

@@ -4,10 +4,10 @@
 # Author: Yuxin Wu <ppwwyyxx@gmail.com>
 
 
-# HACK protobuf
-#import sys
-#import os
-#sys.path.insert(0, os.path.expanduser('~/.local/lib/python2.7/site-packages'))
+# prefer protobuf in user-namespace
+import sys
+import os
+sys.path.insert(0, os.path.expanduser('~/.local/lib/python2.7/site-packages'))
 
 import tensorflow as tf
 import numpy as np
@@ -35,21 +35,23 @@ def get_model(input, label):
         cost: scalar variable
     """
     input = tf.reshape(input, [-1, IMAGE_SIZE, IMAGE_SIZE, 1])
-    conv0 = Conv2D('conv0', input, out_channel=5, kernel_shape=3,
+    conv0 = Conv2D('conv0', input, out_channel=20, kernel_shape=5,
                   padding='valid')
     pool0 = tf.nn.max_pool(conv0, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1],
                            padding='SAME')
-    conv1 = Conv2D('conv1', pool0, out_channel=10, kernel_shape=4,
+    conv1 = Conv2D('conv1', pool0, out_channel=40, kernel_shape=3,
                   padding='valid')
     pool1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1],
                            padding='SAME')
+    conv2 = Conv2D('conv2', pool0, out_channel=40, kernel_shape=3,
+                  padding='valid')
 
-    feature = batch_flatten(pool1)
+    feature = batch_flatten(conv2)
 
     fc0 = FullyConnected('fc0', feature, 512)
     fc0 = tf.nn.relu(fc0)
-    fc2 = FullyConnected('lr', fc1, out_dim=10)
-    prob = tf.nn.softmax(fc2, name='output')
+    fc1 = FullyConnected('lr', fc0, out_dim=10)
+    prob = tf.nn.softmax(fc1, name='output')
 
     logprob = tf.log(prob)
     y = one_hot(label, NUM_CLASS)
@@ -82,8 +84,9 @@ def main():
             ext.init()
 
         summary_op = tf.merge_all_summaries()
-
-        sess = tf.Session()
+        config = tf.ConfigProto()
+        config.device_count['GPU'] = 1
+        sess = tf.Session(config=config)
         sess.run(tf.initialize_all_variables())
         summary_writer = tf.train.SummaryWriter(LOG_DIR, graph_def=sess.graph_def)