diff --git a/model.py b/model.py
index 08c0f8d..0aa51eb 100644
--- a/model.py
+++ b/model.py
@@ -6,54 +6,54 @@
 import tensorflow as tf
 
 def create_model():
-    tf.reset_default_graph()
+    tf.compat.v1.reset_default_graph()
     g=tf.Graph()
     with g.as_default():
-        synmg=tf.placeholder(dtype=tf.float64,shape=(None,2*n+1,2*n+1,2),name="Synteny_matrix_placeholder_Global")
-        synml=tf.placeholder(dtype=tf.float64,shape=(None,2*n+1,2*n+1,2),name="Synteny_matrix_placeholder_Local")
-        pfam=tf.placeholder(dtype=tf.float64,shape=(None,2*n+1,2*n+1,1),name="Pfam_matrix_placeholder")
-        bls=tf.placeholder(dtype=tf.float64,shape=(None,maxbl),name="Species_Branch_Length_Placeholder")
-        blhs=tf.placeholder(dtype=tf.float64,shape=(None,maxbl),name="Homology_Species_Branch_Length_Placeholder")
+        synmg=tf.compat.v1.placeholder(dtype=tf.float64,shape=(None,2*n+1,2*n+1,2),name="Synteny_matrix_placeholder_Global")
+        synml=tf.compat.v1.placeholder(dtype=tf.float64,shape=(None,2*n+1,2*n+1,2),name="Synteny_matrix_placeholder_Local")
+        pfam=tf.compat.v1.placeholder(dtype=tf.float64,shape=(None,2*n+1,2*n+1,1),name="Pfam_matrix_placeholder")
+        bls=tf.compat.v1.placeholder(dtype=tf.float64,shape=(None,maxbl),name="Species_Branch_Length_Placeholder")
+        blhs=tf.compat.v1.placeholder(dtype=tf.float64,shape=(None,maxbl),name="Homology_Species_Branch_Length_Placeholder")
         #gl=tf.placeholder(dtype=tf.float64,shape=(None,1),name="Mean_gene_length")
-        dps=tf.placeholder(dtype=tf.float64,shape=(None,1),name="mca_species_distance")
-        dphs=tf.placeholder(dtype=tf.float64,shape=(None,1),name="mca_homology_species_distance")
-        dis=tf.placeholder(dtype=tf.float64,shape=(None,1),name="total_distance")
-        lr=tf.placeholder(dtype=tf.float64,shape=(),name="learning_rate")
-        y=tf.placeholder(dtype=tf.int32,shape=(None),name="labels")
+        dps=tf.compat.v1.placeholder(dtype=tf.float64,shape=(None,1),name="mca_species_distance")
+        dphs=tf.compat.v1.placeholder(dtype=tf.float64,shape=(None,1),name="mca_homology_species_distance")
+        dis=tf.compat.v1.placeholder(dtype=tf.float64,shape=(None,1),name="total_distance")
+        lr=tf.compat.v1.placeholder(dtype=tf.float64,shape=(),name="learning_rate")
+        y=tf.compat.v1.placeholder(dtype=tf.int32,shape=(None),name="labels")
         
-        lrs=tf.summary.scalar("Learning_Rate",lr)
+        lrs=tf.compat.v1.summary.scalar("Learning_Rate",lr)
         
         x=tf.concat([dps,dps-dphs,dis],1,name="Create_train_vector")
 
         print(synmg,"\n",synml,"\n",bls,"\n",blhs,"\n",dps,"\n",dphs,"\n",dis,"\n",x)
 
-        reg_l2=tf.contrib.layers.l2_regularizer(0.001)
-        reg_l1 = tf.contrib.layers.l1_regularizer(scale=0.005, scope=None)
+        reg_l2=tf.keras.regularizers.l2(0.5 * (0.001))
+        reg_l1 = tf.keras.regularizers.l1(l=0.005)
 
         def get_variable_by_shape(shape,name):
-            f=tf.get_variable(name,shape=shape,initializer=tf.glorot_uniform_initializer(),dtype=tf.float64,regularizer=reg_l2)
+            f=tf.compat.v1.get_variable(name,shape=shape,initializer=tf.compat.v1.glorot_uniform_initializer(),dtype=tf.float64,regularizer=reg_l2)
             return f
 
         def create_synteny_aligner(name,synm):
-            with tf.variable_scope(name+"Synteny_Aligner",reuse=tf.AUTO_REUSE):
+            with tf.compat.v1.variable_scope(name+"Synteny_Aligner",reuse=tf.compat.v1.AUTO_REUSE):
 
                 fconv=get_variable_by_shape((2,2,2,dim),"fconv")
-                conv=tf.nn.conv2d(synm,fconv,(1,1,1,1),padding="VALID",name="Conv_aligner")
+                conv=tf.nn.conv2d(input=synm,filters=fconv,strides=(1,1,1,1),padding="VALID",name="Conv_aligner")
 
                 fconv_1=get_variable_by_shape((2,2,dim,dim*2),"fconv_1")
-                conv_1=tf.nn.conv2d(conv,fconv_1,(1,1,1,1),padding="VALID",name="Conv_aligner_1")
+                conv_1=tf.nn.conv2d(input=conv,filters=fconv_1,strides=(1,1,1,1),padding="VALID",name="Conv_aligner_1")
                 
                 fxconv=get_variable_by_shape((fl,2,dim*2),"fxconv")
                 x_conv=tf.reshape(synm,(-1,fl*fl,2))
-                x_conv=tf.nn.conv1d(x_conv,fxconv,stride=fl,padding="SAME",name="row_aligner")
+                x_conv=tf.nn.conv1d(input=x_conv,filters=fxconv,stride=fl,padding="SAME",name="row_aligner")
                 
-                y_conv=tf.reshape(tf.transpose(synm,(0,2,1,3)),(-1,fl*fl,2))
+                y_conv=tf.reshape(tf.transpose(a=synm,perm=(0,2,1,3)),(-1,fl*fl,2))
                 fyconv=get_variable_by_shape((fl,2,dim*2),"fyconv")
-                y_conv=tf.nn.conv1d(y_conv,fyconv,stride=fl,padding="SAME",name="column_aligner")
+                y_conv=tf.nn.conv1d(input=y_conv,filters=fyconv,stride=fl,padding="SAME",name="column_aligner")
                 
                 
                 wconv=get_variable_by_shape((fl,fl,2,dim*2*10),"wconv")
-                w_conv=tf.nn.conv2d(synm,wconv,(1,1,1,1),padding="VALID",name="Global_Aligner_1")
+                w_conv=tf.nn.conv2d(input=synm,filters=wconv,strides=(1,1,1,1),padding="VALID",name="Global_Aligner_1")
                 
                 conv_1=tf.reshape(conv_1,(-1,25,dim*2))
                 x_conv=tf.reshape(x_conv,(-1,fl,dim*2))
@@ -62,9 +62,9 @@ def create_synteny_aligner(name,synm):
                 conv_final=tf.concat([conv_1,x_conv,y_conv,w_conv],1,name="Concatenate_All_Alignments")
             return conv_final
 
-        with tf.variable_scope("Pfam",reuse=tf.AUTO_REUSE):
+        with tf.compat.v1.variable_scope("Pfam",reuse=tf.compat.v1.AUTO_REUSE):
             wconv_pfam=get_variable_by_shape((fl,fl,1,dim*2*10),"wconv_pfam")
-            w_conv_pfam=tf.nn.conv2d(pfam,wconv_pfam,(1,1,1,1),padding="VALID",name="Global_Aligner_pfam")
+            w_conv_pfam=tf.nn.conv2d(input=pfam,filters=wconv_pfam,strides=(1,1,1,1),padding="VALID",name="Global_Aligner_pfam")
             w_conv_pfam=tf.reshape(w_conv_pfam,(-1,10,dim*2))
 
         conv_final_g=create_synteny_aligner("Global_",synmg)
@@ -72,7 +72,7 @@ def create_synteny_aligner(name,synm):
         final=tf.concat([conv_final_g,conv_final_l,w_conv_pfam],1)
         #final=conv_final_l
 
-        with tf.variable_scope("Combine_Renormalize",reuse=tf.AUTO_REUSE):
+        with tf.compat.v1.variable_scope("Combine_Renormalize",reuse=tf.compat.v1.AUTO_REUSE):
             bl=tf.concat([bls,blhs],1)
             #bl=bls-blhs
             theta_bl=get_variable_by_shape((maxbl*2,1),"theta_bl")
@@ -86,7 +86,7 @@ def create_synteny_aligner(name,synm):
             final=final*theta_2
             print(final)
 
-        flat=tf.layers.flatten(final)
+        flat=tf.compat.v1.layers.flatten(final)
 
         zero=tf.constant(0.0,dtype=tf.float64)
         diff=dps-dphs
@@ -99,29 +99,29 @@ def create_synteny_aligner(name,synm):
         print(flat)
         #dense=tf.layers.dense(flat,2048,kernel_regularizer=reg_l2,bias_regularizer=reg_l2)
         #dense_2=tf.layers.dense(dense,1024,kernel_regularizer=reg_l2,bias_regularizer=reg_l2)
-        dense_3=tf.layers.dense(flat,512,kernel_regularizer=reg_l2,bias_regularizer=reg_l2)
+        dense_3=tf.compat.v1.layers.dense(flat,512,kernel_regularizer=reg_l2,bias_regularizer=reg_l2)
         
-        logits_pred=tf.layers.dense(dense_3,3,name="Predictions")
+        logits_pred=tf.compat.v1.layers.dense(dense_3,3,name="Predictions")
         print(logits_pred)
         entropy=tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits_pred,labels=y)
         print(entropy)
         
         #weights = tf.trainable_variables() # all vars of your graph
         #regl1 = tf.contrib.layers.apply_regularization(reg_l1, weights)
-        reg_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
+        reg_losses = tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.REGULARIZATION_LOSSES)
         reg_constant =0.00000001
-        loss=tf.reduce_mean(entropy)+reg_constant * sum(reg_losses)
+        loss=tf.reduce_mean(input_tensor=entropy)+reg_constant * sum(reg_losses)
         #loss=tf.reduce_mean(entropy)
-        optimizer=tf.train.RMSPropOptimizer(lr)
+        optimizer=tf.compat.v1.train.RMSPropOptimizer(lr)
         #optimizer=tf.train.AdamOptimizer()
-        losses=tf.summary.scalar("Loss",loss)
+        losses=tf.compat.v1.summary.scalar("Loss",loss)
         t_op=optimizer.minimize(loss)
-        acc=tf.math.in_top_k(tf.cast(logits_pred,tf.float32),y,1)
-        accuracy=tf.reduce_mean(tf.cast(acc,tf.float32))
-        accs=tf.summary.scalar("Accuracy",accuracy)
-        summary=tf.summary.merge_all()
-        init=tf.global_variables_initializer()
-        saver=tf.train.Saver()
+        acc=tf.math.in_top_k(predictions=tf.cast(logits_pred,tf.float32),targets=y,k=1)
+        accuracy=tf.reduce_mean(input_tensor=tf.cast(acc,tf.float32))
+        accs=tf.compat.v1.summary.scalar("Accuracy",accuracy)
+        summary=tf.compat.v1.summary.merge_all()
+        init=tf.compat.v1.global_variables_initializer()
+        saver=tf.compat.v1.train.Saver()
 
     for node in (synmg,synml,pfam,bls,blhs,dps,dphs,dis,lr,y):
         g.add_to_collection("input_nodes",node)