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Made the model compatible with TensorFlow 2.0 #8

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76 changes: 38 additions & 38 deletions model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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))
Expand All @@ -62,17 +62,17 @@ 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)
conv_final_l=create_synteny_aligner("Local_",synml)
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")
Expand All @@ -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
Expand All @@ -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)
Expand Down