Merge pull request ericjang#5 from iamgroot42/master

Changes for Tensorflow API1. thanks @iamgroot42 !

draw.py

@@ -42,8 +42,8 @@
 
 x = tf.placeholder(tf.float32,shape=(batch_size,img_size)) # input (batch_size * img_size)
 e=tf.random_normal((batch_size,z_size), mean=0, stddev=1) # Qsampler noise
-lstm_enc = tf.nn.rnn_cell.LSTMCell(enc_size, state_is_tuple=True) # encoder Op
-lstm_dec = tf.nn.rnn_cell.LSTMCell(dec_size, state_is_tuple=True) # decoder Op
+lstm_enc = tf.contrib.rnn.LSTMCell(enc_size, state_is_tuple=True) # encoder Op
+lstm_dec = tf.contrib.rnn.LSTMCell(dec_size, state_is_tuple=True) # decoder Op
 
 def linear(x,output_dim):
     """
@@ -73,7 +73,8 @@ def filterbank(gx, gy, sigma2,delta, N):
 def attn_window(scope,h_dec,N):
     with tf.variable_scope(scope,reuse=DO_SHARE):
         params=linear(h_dec,5)
-    gx_,gy_,log_sigma2,log_delta,log_gamma=tf.split(1,5,params)
+    # gx_,gy_,log_sigma2,log_delta,log_gamma=tf.split(1,5,params)
+    gx_,gy_,log_sigma2,log_delta,log_gamma=tf.split(params,5,1)
     gx=(A+1)/2*(gx_+1)
     gy=(B+1)/2*(gy_+1)
     sigma2=tf.exp(log_sigma2)
@@ -82,19 +83,19 @@ def attn_window(scope,h_dec,N):
 
 ## READ ## 
 def read_no_attn(x,x_hat,h_dec_prev):
-    return tf.concat(1,[x,x_hat])
+    return tf.concat([x,x_hat], 1)
 
 def read_attn(x,x_hat,h_dec_prev):
     Fx,Fy,gamma=attn_window("read",h_dec_prev,read_n)
     def filter_img(img,Fx,Fy,gamma,N):
         Fxt=tf.transpose(Fx,perm=[0,2,1])
         img=tf.reshape(img,[-1,B,A])
-        glimpse=tf.batch_matmul(Fy,tf.batch_matmul(img,Fxt))
+        glimpse=tf.matmul(Fy,tf.matmul(img,Fxt))
         glimpse=tf.reshape(glimpse,[-1,N*N])
         return glimpse*tf.reshape(gamma,[-1,1])
     x=filter_img(x,Fx,Fy,gamma,read_n) # batch x (read_n*read_n)
     x_hat=filter_img(x_hat,Fx,Fy,gamma,read_n)
-    return tf.concat(1,[x,x_hat]) # concat along feature axis
+    return tf.concat([x,x_hat], 1) # concat along feature axis
 
 read = read_attn if FLAGS.read_attn else read_no_attn
 
@@ -140,7 +141,7 @@ def write_attn(h_dec):
     w=tf.reshape(w,[batch_size,N,N])
     Fx,Fy,gamma=attn_window("write",h_dec,write_n)
     Fyt=tf.transpose(Fy,perm=[0,2,1])
-    wr=tf.batch_matmul(Fyt,tf.batch_matmul(w,Fx))
+    wr=tf.matmul(Fyt,tf.matmul(w,Fx))
     wr=tf.reshape(wr,[batch_size,B*A])
     #gamma=tf.tile(gamma,[1,B*A])
     return wr*tf.reshape(1.0/gamma,[-1,1])
@@ -163,7 +164,7 @@ def write_attn(h_dec):
     c_prev = tf.zeros((batch_size,img_size)) if t==0 else cs[t-1]
     x_hat=x-tf.sigmoid(c_prev) # error image
     r=read(x,x_hat,h_dec_prev)
-    h_enc,enc_state=encode(enc_state,tf.concat(1,[r,h_dec_prev]))
+    h_enc,enc_state=encode(enc_state,tf.concat([r,h_dec_prev], 1))
     z,mus[t],logsigmas[t],sigmas[t]=sampleQ(h_enc)
     h_dec,dec_state=decode(dec_state,z)
     cs[t]=c_prev+write(h_dec) # store results
@@ -217,7 +218,7 @@ def binary_crossentropy(t,o):
 sess=tf.InteractiveSession()
 
 saver = tf.train.Saver() # saves variables learned during training
-tf.initialize_all_variables().run()
+tf.global_variables_initializer().run()
 #saver.restore(sess, "/tmp/draw/drawmodel.ckpt") # to restore from model, uncomment this line
 
 for i in range(train_iters):
@@ -241,5 +242,3 @@ def binary_crossentropy(t,o):
 print("Model saved in file: %s" % saver.save(sess,ckpt_file))
 
 sess.close()
-
-print('Done drawing! Have a nice day! :)')