fixes for multi-channel

main.py

@@ -16,12 +16,11 @@ def train(conf, data):
     saver = tf.train.Saver(tf.trainable_variables())
 
     with tf.Session() as sess: 
+        sess.run(tf.initialize_all_variables())
         if os.path.exists(conf.ckpt_file):
             saver.restore(sess, conf.ckpt_file)
             print "Model Restored"
-        else:
-            sess.run(tf.initialize_all_variables())
-
+
         pointer = 0
         for i in range(conf.epochs):
             for j in range(conf.num_batches):
@@ -35,8 +34,8 @@ def train(conf, data):
 
             print "Epoch: %d, Cost: %f"%(i, cost)
 
-        generate_and_save(sess, model.X, model.pred, conf)
         saver.save(sess, conf.ckpt_file)
+        generate_and_save(sess, model.X, model.pred, conf)
 
 
 if __name__ == "__main__":

models.py

@@ -4,8 +4,7 @@
 class PixelCNN():
     def __init__(self, conf):
 
-        data_shape = [conf.batch_size, conf.img_height, conf.img_width, conf.channel]
-        self.X = tf.placeholder(tf.float32, shape=data_shape)
+        self.X = tf.placeholder(tf.float32, shape=[None, conf.img_height, conf.img_width, conf.channel])
         self.X_norm = self.X if conf.data == "mnist" else tf.div(self.X, 255.0)
         v_stack_in, h_stack_in = self.X_norm, self.X_norm
 
@@ -56,5 +55,5 @@ def __init__(self, conf):
             #self.loss = tf.reduce_mean(-tf.reduce_sum(X_ohehot * tf.log(self.fc2), reduction_indices=[1]))
 
             # NOTE or check without argmax
-            self.preds = tf.reshape(tf.argmax(tf.nn.softmax(self.fc2), dimension=tf.rank(self.fc2) - 1), data_shape)
+            self.pred = tf.reshape(tf.argmax(tf.nn.softmax(self.fc2), dimension=tf.rank(self.fc2) - 1), tf.shape(self.X))
 

utils.py

@@ -9,11 +9,13 @@ def binarize(images):
 
 def generate_and_save(sess, X, pred, conf):
     n_row, n_col = 5, 5
-    samples = np.zeros((n_row*n_col, conf.img_height, conf.img_width, 1), dtype=np.float32)
+    samples = np.zeros((n_row*n_col, conf.img_height, conf.img_width, conf.channel), dtype=np.float32)
     for i in xrange(conf.img_height):
         for j in xrange(conf.img_width):
-            for k in xrange(1):
-                next_sample = binarize(sess.run(pred, {X:samples}))
+            for k in xrange(conf.channel):
+                next_sample = sess.run(pred, {X:samples})
+                if data == "mnist":
+                    next_sample = binarize(next_sample)
                 samples[:, i, j, k] = next_sample[:, i, j, k]
     images = samples 
     images = images.reshape((n_row, n_col, conf.img_height, conf.img_width))