From 294369474ad72d4716476f7cccc4a66bd6140b7e Mon Sep 17 00:00:00 2001 From: HarshitGupta11 <50410275+HarshitGupta11@users.noreply.github.com> Date: Mon, 24 Jun 2019 14:57:36 +0530 Subject: [PATCH] Delete model.py --- model.py | 134 ------------------------------------------------------- 1 file changed, 134 deletions(-) delete mode 100644 model.py diff --git a/model.py b/model.py deleted file mode 100644 index 77df560..0000000 --- a/model.py +++ /dev/null @@ -1,134 +0,0 @@ -dim=10 -n=3 -fl=2*n+1 -import tensorflow as tf - -def create_model(): - tf.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") - bls=tf.placeholder(dtype=tf.float64,shape=(None,21),name="Species_Branch_Length_Placeholder") - blhs=tf.placeholder(dtype=tf.float64,shape=(None,21),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_with_default(0.1,(),"learning_rate") - y=tf.placeholder(dtype=tf.int32,shape=(None),name="labels") - - #reshape the variables - #gl=tf.expand_dims(gl,-1) - #dps=tf.expand_dims(dps,-1) - #dphs=tf.expand_dims(dphs,-1) - #dis=tf.expand_dims(dis,-1) - - x=tf.concat([gl,dps-dphs,dis],1,name="Create_train_vector") - - print(synmg,"\n",synml,"\n",bls,"\n",blhs,"\n",gl,"\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) - - 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) - return f - - def create_synteny_aligner(name,synm): - with tf.variable_scope(name+"Synteny_Aligner",reuse=tf.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") - - 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") - print(conv) - print(conv_1) - - - #fxconv=get_variable_by_shape((1,5,2,dim),"fxconv") - 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") - print(x_conv) - - - #fyconv=get_variable_by_shape((5,1,2,dim),"fyconv") - #y_conv=tf.nn.conv2d(synm,fyconv,(1,1,1,1),padding="SAME",name="column_aligner") - y_conv=tf.reshape(tf.transpose(synm,(0,2,1,3)),(-1,fl*fl,2)) - print(y_conv) - 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") - print(y_conv) - - - 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") - print(w_conv) - - conv_1=tf.reshape(conv_1,(-1,25,dim*2)) - print(conv) - x_conv=tf.reshape(x_conv,(-1,fl,dim*2)) - print(x_conv) - y_conv=tf.reshape(y_conv,(-1,fl,dim*2)) - print(y_conv) - w_conv=tf.reshape(w_conv,(-1,10,dim*2)) - print(wconv) - conv_final=tf.concat([conv_1,x_conv,y_conv,w_conv],1,name="Concatenate_All_Alignments") - print(conv_final) - return conv_final - - 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],1) - - with tf.variable_scope("Combine_Renormalize",reuse=tf.AUTO_REUSE): - bl=tf.concat([bls,blhs],1) - #bl=bls-blhs - theta_bl=get_variable_by_shape((21*2,1),"theta_bl") - theta_bl=tf.matmul(bl,theta_bl) - x=tf.concat([x,theta_bl],1) - theta=get_variable_by_shape((4,49*2),"theta") - bias=get_variable_by_shape((1,49*2),"b") - theta_2=tf.matmul(x,theta)+bias - print(theta_2) - theta_2=tf.reshape(theta_2,(-1,49*2,1)) - theta_2=tf.tile(theta_2,[1,1,dim*2]) - print(theta_2) - final=final*theta_2 - print(final) - - flat=tf.layers.flatten(final) - #flat_w=tf.layers.flatten(W) - #flat=tf.concat([flat,flat_w],1) - 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(dense_2,512,kernel_regularizer=reg_l2,bias_regularizer=reg_l2) - - logits_pred=tf.layers.dense(dense_3,4,name="Predictions") - 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_constant =0.00000001 - loss=tf.reduce_mean(entropy)+reg_constant * sum(reg_losses) - #loss=tf.reduce_mean(entropy) - optimizer=tf.train.RMSPropOptimizer(lr) - #optimizer=tf.train.AdamOptimizer() - 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)) - init=tf.global_variables_initializer() - saver=tf.train.Saver() - - for node in (synmg,synml,bls,blhs,gl,dps,dphs,dis,lr,y): - g.add_to_collection("input_nodes",node) - - for node in (loss,t_op,accuracy,init): - g.add_to_collection("output_nodes",node) - - return g,saver