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https://github.com/Priyatham-sai-chand/compara-deep-learning.git
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45 lines
2.5 KiB
Python
45 lines
2.5 KiB
Python
import tensorflow as tf
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import numpy as np
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from model import create_model
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def train(train_synteny_matrices_global,train_synteny_matrices_local,train_branch_length_species,train_branch_length_homology_species,train_mean_gene_length,train_dist_p_s,train_dist_p_hs,train_distance,train_labels):
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graph,saver=create_model()
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synmg,synml,bls,blhs,gl,dps,dphs,dis,lr,y=graph.get_collection("input_nodes")
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loss,t_op,accuracy,init=graph.get_collection("output_nodes")
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with tf.Session(graph=graph) as sess:
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sess.run(init)
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batch_size=64
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num_epochs=30
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learn=0.001
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for j in range(num_epochs):
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for i in range(50000//batch_size):
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feed_dict={
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synmg:train_synteny_matrices_global[i*batch_size:(i+1)*batch_size],
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synml:train_synteny_matrices_local[i*batch_size:(i+1)*batch_size],
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bls:train_branch_length_species[i*batch_size:(i+1)*batch_size],
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blhs:train_branch_length_homology_species[i*batch_size:(i+1)*batch_size],
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gl:train_mean_gene_length[i*batch_size:(i+1)*batch_size].reshape((batch_size,1)),
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dps:train_dist_p_s[i*batch_size:(i+1)*batch_size].reshape((batch_size,1)),
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dphs:train_dist_p_hs[i*batch_size:(i+1)*batch_size].reshape((batch_size,1)),
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dis:train_distance[i*batch_size:(i+1)*batch_size].reshape((batch_size,1)),
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lr:learn,
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y:train_labels[i*batch_size:(i+1)*batch_size]
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}
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sess.run(t_op,feed_dict=feed_dict)
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test_dict={
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synmg:train_synteny_matrices_global[-15000:],
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synml:train_synteny_matrices_local[-15000:],
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bls:train_branch_length_species[-15000:],
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blhs:train_branch_length_homology_species[-15000:],
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gl:train_mean_gene_length[-15000:].reshape((15000,1)),
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dps:train_dist_p_s[-15000:].reshape((15000,1)),
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dphs:train_dist_p_hs[-15000:].reshape((15000,1)),
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dis:train_distance[-15000:].reshape((15000,1)),
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y:train_labels[-15000:]
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}
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accuracy_test,loss_test=sess.run([accuracy,loss],feed_dict=test_dict)
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print("Epoch:{} Test Accuracy:{} Test Loss:{}".format(j+1,accuracy_test*100,loss_test))
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learn*=0.97
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saver.save(sess,"saved_models/model.ckpt")
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