diff --git a/pixelcnn_tf.py b/pixelcnn_tf.py
new file mode 100644
index 0000000..bbb9897
--- /dev/null
+++ b/pixelcnn_tf.py
@@ -0,0 +1,90 @@
+import tensorflow as tf
+import numpy as np
+
+LAYERS = 1
+F_MAP = 32
+FILTER_SIZE = 7
+CHANNEL = 1
+
+def get_weights(shape):
+    # TODO set init bounds
+    return tf.Variable(tf.truncated_normal(shape=shape, stddev=0.1))
+
+def get_bias(shape):
+    return tf.Variable(tf.constant(shape=shape, value=0.1, dtype=tf.float32))
+
+def conv(x, W):
+    # TODO check strides
+    return tf.nn.conv2d(x, W, strides=[1,1,1,1], padding='SAME')
+
+def gated():
+    #TODO for gating y = tanh(W1*X) <element-wise product> sigmoid(W2*X)
+    # also check from figure about splitting 2p feature maps into p
+    return None
+
+X = tf.placeholder(tf.float32, shape=[None, 784])
+X_image = tf.reshape(X, [-1, 28, 28, CHANNEL])
+v_stack_in, h_stack_in = X_image, X_image
+
+class Conv():
+    def __init__(self, W_shape, b_shape, fan_in, gated=True):
+        self.W_f = get_weights(W_shape)
+        self.b_f = get_bias(b_shape)
+        self.W_g = get_weights(W_shape)
+        self.b_g = get_bias(b_shape)
+        
+        conv_f = conv(fan_in, self.W_f)
+        conv_g = conv(fan_in, self.W_g)
+        
+        self.fan_out = tf.mul(tf.tanh(conv_f + self.b_f), tf.sigmoid(conv_g + self.b_g))
+
+    def output(self):
+        return self.fan_out 
+
+for i in range(LAYERS):
+    FILTER_SIZE = 3 if i > 0 else FILTER_SIZE
+    CHANNEL = F_MAP if i > 0 else CHANNEL
+    i = str(i)
+    with tf.name_scope("v_stack"+i):
+        v_stack = Conv([FILTER_SIZE, FILTER_SIZE, CHANNEL, F_MAP], [F_MAP], v_stack_in).output()
+        '''
+        v_W = get_weights([FILTER_SIZE, FILTER_SIZE, CHANNEL, F_MAP])
+        v_b = get_bias([F_MAP])
+        print v_stack_in.get_shape(), v_W.get_shape()
+        v_stack = conv(v_stack_in, v_W)
+        #TODO gating
+        v_stack_gate = tf.nn.relu(v_stack + v_b)
+        '''
+        v_stack_in = v_stack
+        print "v_stack", v_stack.get_shape()
+    '''
+    with tf.name_scope("v_stack_1"+i):
+        v_W_1 = get_weights([1, 1, F_MAP, F_MAP])
+        v_b_1 = get_bias([F_MAP])
+        v_stack_1 = tf.nn.relu(conv(v_stack, v_W_1) + v_b_1)
+        print "v_stack_1", v_stack_1.get_shape()
+        
+        
+    #TODO masking
+    with tf.name_scope("h_stack"+i):
+        h_W = get_weights([1, FILTER_SIZE, CHANNEL, F_MAP])
+        h_b = get_bias([F_MAP])
+        h_stack = conv(h_stack_in, h_W)
+        #TODO gating
+        h_stack_gate = tf.nn.relu(h_stack + v_b)
+        print "h_stack", h_stack.get_shape()
+
+    with tf.name_scope("h_stack_1"+i):
+        h_W_1 = get_weights([1, 1, F_MAP, F_MAP])
+        h_b_1 = get_bias([F_MAP])
+        # TODO replace i/p with gated o/p
+        h_stack_1 = tf.nn.relu(conv(h_stack_gate, h_W_1) + h_b_1) 
+        # TODO add residual conn.
+
+        h_stack_in = h_stack_1
+    '''
+
+sess = tf.Session()
+summary = tf.train.SummaryWriter('logs', sess.graph)
+#Combine and Quantize into 255
+