modular and experiment

models.py

@@ -25,13 +25,16 @@ def conv_op(x, W):
     return tf.nn.conv2d(x, W, strides=[1,1,1,1], padding='SAME')
 
 class PixelCNN():
-    def __init__(self, W_shape, b_shape, fan_in, gated=True, payload=None, mask=None, activation=True):
-        self.W_shape = W_shape
-        self.b_shape = b_shape
+    def __init__(self, W_shape, fan_in, gated=True, payload=None, mask=None, activation=True):
         self.fan_in = fan_in
+        in_dim = self.fan_in.get_shape()[-1]
+        self.W_shape = [W_shape[0], W_shape[1], in_dim, W_shape[2]]  
+        self.b_shape = W_shape[2]
+
         self.payload = payload
         self.mask = mask
         self.activation = activation
+
 
         if gated:
             self.gated_conv()
@@ -62,7 +65,6 @@ def simple_conv(self):
         else:
             self.fan_out = tf.add(conv, b)
 
-
     def output(self):
         return self.fan_out 
 

pixelcnn_tf.py

@@ -1,10 +1,13 @@
 # TODO
-# try changing 0.0 to np.random
+# kundan: concat payload instead of add
+#       : arch: without 1X1 in 1st layer and last 2 layers
+#       : replaces masking with n/2 filter
 # check network arch
-# upscale-downscale-q_level
 # autoencoder
 # cost on test set
-# make for imagenet data
+# make for imagenet data: upscale-downscale-q_level, mean pixel value if not mnist
+# stats
+# logger
 
 import tensorflow as tf
 import numpy as np
@@ -15,7 +18,7 @@
 from models import PixelCNN
 
 mnist = input_data.read_data_sets("data/")
-epochs = 50
+epochs = 1
 batch_size = 50
 grad_clip = 1
 
@@ -28,36 +31,35 @@
 FILTER_SIZE = 7
 
 X = tf.placeholder(tf.float32, shape=[None, img_height, img_width, channel])
-# TODO mean pixel value if not mnist
 v_stack_in, h_stack_in = X, X
 
+# TODO encapsulate
 for i in range(LAYERS):
     FILTER_SIZE = 3 if i > 0 else FILTER_SIZE
     in_dim = F_MAP if i > 0 else channel
     mask = 'b' if i > 0 else 'a'
     i = str(i)
-
     with tf.variable_scope("v_stack"+i):
-        v_stack = PixelCNN([FILTER_SIZE, FILTER_SIZE, in_dim, F_MAP], [F_MAP], v_stack_in, mask='a').output()
+        v_stack = PixelCNN([FILTER_SIZE, FILTER_SIZE, F_MAP], v_stack_in, mask=mask).output()
         v_stack_in = v_stack
 
     with tf.variable_scope("v_stack_1"+i):
-        v_stack_1 = PixelCNN([1, 1, F_MAP, F_MAP], [F_MAP], v_stack_in, gated=False, mask=mask).output()
-        
+        v_stack_1 = PixelCNN([1, 1, F_MAP], v_stack_in, gated=False, mask=mask).output()
+
     with tf.variable_scope("h_stack"+i):
-        h_stack = PixelCNN([1, FILTER_SIZE, in_dim, F_MAP], [F_MAP], h_stack_in, gated=True, payload=v_stack_1, mask=mask).output()
+        h_stack = PixelCNN([1, FILTER_SIZE, F_MAP], h_stack_in, payload=v_stack_1, mask=mask).output()
 
     with tf.variable_scope("h_stack_1"+i):
-        h_stack_1 = PixelCNN([1, 1, F_MAP, F_MAP], [F_MAP], h_stack, gated=False, mask=mask).output()
-        h_stack_1 += h_stack_in
+        h_stack_1 = PixelCNN([1, 1, F_MAP], h_stack, gated=False, mask=mask).output()
+        #h_stack_1 += h_stack_in # Residual connection
         h_stack_in = h_stack_1
 
 with tf.variable_scope("fc_1"):
-    fc1 = PixelCNN([1, 1, F_MAP, F_MAP],[F_MAP], h_stack_in, gated=False, mask='b').output()
+    fc1 = PixelCNN([1, 1, F_MAP], h_stack_in, gated=False, mask='b').output()
 
 # handle Imagenet differently
 with tf.variable_scope("fc_2"):
-    fc2 = PixelCNN([1, 1, F_MAP, 1],[1], fc1, gated=False, mask='b', activation=False).output()
+    fc2 = PixelCNN([1, 1, 1], fc1, gated=False, mask='b', activation=False).output()
 pred = tf.nn.sigmoid(fc2)
 
 loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(fc2, X))
@@ -97,7 +99,7 @@ def generate_and_save(sess):
                     .reshape([batch_size, img_height, img_width, 1]))
             _, cost = sess.run([optimizer, loss], feed_dict={X:batch_X})
 
-        print "Epoch: %d, Cost: %f"%(i, cost)
+            print "Epoch: %d, Cost: %f"%(i, cost)
         generate_and_save(sess)
 
     generate_and_save(sess)