pCNN AE

sample generation changes

autoencoder.py

@@ -6,25 +6,25 @@
 
 class AE(object):
     def __init__(self, X):
-        self.fmap_out = 32
+        self.num_layers = 2
+        self.fmap_out = [8, 32]
         self.fmap_in = conf.channel
         self.fan_in = X
-        self.num_layers = 3
-        self.filter_size = 3
+        self.filter_size = 4
         self.W = []
         self.strides = [1, 1, 1, 1]
 
         for i in range(self.num_layers):
-            if i == self.num_layers -1 :
-                self.fmap_out = 10 
-            self.W.append(tf.Variable(tf.truncated_normal(shape=[self.filter_size, self.filter_size, self.fmap_in, self.fmap_out], stddev=0.1), name="W_%d"%i))
-            b = tf.Variable(tf.ones(shape=[self.fmap_out], dtype=tf.float32), name="encoder_b_%d"%i)
+            self.W.append(tf.Variable(tf.truncated_normal(shape=[self.filter_size, self.filter_size, self.fmap_in, self.fmap_out[i]], stddev=0.1), name="W_%d"%i))
+            b = tf.Variable(tf.ones(shape=[self.fmap_out[i]], dtype=tf.float32), name="encoder_b_%d"%i)
             en_conv = tf.nn.conv2d(self.fan_in, self.W[i], self.strides, padding='SAME', name="encoder_conv_%d"%i)
+            en_pool = tf.nn.max_pool(en_conv, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME', name="encoder_pool_%d"%i)
 
-            self.fan_in = tf.tanh(tf.add(en_conv, b))
-            self.fmap_in = self.fmap_out
+            self.fan_in = tf.tanh(tf.add(en_pool, b))
+            self.fmap_in = self.fmap_out[i]
 
-        self.fan_in = tf.reshape(self.fan_in, (-1, conf.img_width*conf.img_height*self.fmap_out))
+        op_shape = self.fan_in.get_shape()
+        self.fan_in = tf.reshape(self.fan_in, (-1, int(op_shape[1])*int(op_shape[2])*int(op_shape[3])))
 
     def decoder(self):
         self.W.reverse()

main.py

@@ -40,17 +40,16 @@ def train(conf, data):
                     data_dict[model.h] = batch_y
                 _, cost,_f = sess.run([optimizer, model.loss, model.fc2], feed_dict=data_dict)
             print "Epoch: %d, Cost: %f"%(i, cost)
-
-        saver.save(sess, conf.ckpt_file)
-        generate_samples(sess, X, model.h, model.pred_sample, conf, "sample")
-        generate_samples(sess, X, model.h, model.pred_argmax, conf, "argmax")
+            if (i+1)%10 == 0:
+                saver.save(sess, conf.ckpt_file)
+                generate_samples(sess, X, model.h, model.pred, conf, "argmax")
 
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument('--type', type=str, default='train')
     parser.add_argument('--data', type=str, default='mnist')
-    parser.add_argument('--layers', type=int, default=5)
+    parser.add_argument('--layers', type=int, default=12)
     parser.add_argument('--f_map', type=int, default=32)
     parser.add_argument('--epochs', type=int, default=50)
     parser.add_argument('--batch_size', type=int, default=100)

utils.py

@@ -8,7 +8,7 @@ def binarize(images):
     return (np.random.uniform(size=images.shape) < images).astype(np.float32)
 
 def generate_samples(sess, X, h, pred, conf, suff):
-    n_row, n_col = 5, 5
+    n_row, n_col = 10,10
     samples = np.zeros((n_row*n_col, conf.img_height, conf.img_width, conf.channel), dtype=np.float32)
     # TODO make it generic
     labels = one_hot(np.array([1,2,3,4,5]*5), conf.num_classes)
@@ -28,8 +28,8 @@ def generate_samples(sess, X, h, pred, conf, suff):
     save_images(samples, n_row, n_col, conf, suff)
 
 
-def generate_ae(sess, encoder_X, decoder_X, y, data, conf):
-    n_row, n_col = 5, 5
+def generate_ae(sess, encoder_X, decoder_X, y, data, conf, suff=''):
+    n_row, n_col = 1,1
     samples = np.zeros((n_row*n_col, conf.img_height, conf.img_width, conf.channel), dtype=np.float32)
     if conf.data == 'mnist':
         labels = binarize(data.train.next_batch(n_row*n_col)[0].reshape(n_row*n_col, conf.img_height, conf.img_width, conf.channel))
@@ -44,7 +44,9 @@ def generate_ae(sess, encoder_X, decoder_X, y, data, conf):
                     next_sample = binarize(next_sample)
                 samples[:, i, j, k] = next_sample[:, i, j, k]
 
-    save_images(samples, n_row, n_col, conf, '')
+        np.save('preds_'+suff+'.npy', samples)
+        print "height:%d"%i
+    save_images(samples, n_row, n_col, conf, suff)
 
 def save_images(samples, n_row, n_col, conf, suff):
     images = samples