def test(sequences, path):
for gn, sequence in sequences.items():
res_path = os.path.join(path, "{}.{}".format(gn, 'txt'))
eq_res, eq_time = condition_of_character_equability(sequence, SEQUENCE_LENGTH, ALFAS)
write_test_results(eq_res, eq_time, res_path, 'equability')
ind_res, ind_time = condition_of_character_independence(sequence, SEQUENCE_LENGTH, ALFAS)
write_test_results(ind_res, ind_time, res_path, 'independence')
uni_res, uni_time = condition_of_character_uniformity(sequence, SEQUENCE_LENGTH, ALFAS, RS)
write_test_results(uni_res, uni_time, res_path, 'uniformity')
# optimizer
optimizer = tf.train.AdamOptimizer(0.01)
train = optimizer.minimize(loss)
# training loop
sess = tf.Session()
sess.run(tf.global_variables_initializer())
for i in range(1000):
sess.run(train, {x: x_train, y: y_train})
# Create dirs as necessary
if not os.path.exists(CHKP_DIR_NAME):
os.makedirs(CHKP_DIR_NAME)
# Save model's checkpoint files
tf.train.Saver().save(sess, os.path.join(CHKP_DIR_NAME, GRAPH_NAME + '.chkp'))
tf.summary.FileWriter(CHKP_DIR_NAME, sess.graph)
# Freeze graph
freeze_graph(chkp_dir=CHKP_DIR_NAME, out_dir=PB_DIR_NAME, graph_name=GRAPH_NAME, output_node_names=['outputx'])
# Output results for tests
test_inputs = read_test_results(in_dir=IN_DIR_NAME, file_name="tf.in")
test_results = []
for test_input in test_inputs:
test_results.append(sess.run(model, feed_dict={
x: test_input
}))
write_test_results(result_list=test_results, out_dir=OUT_DIR_NAME, file_name="tf.out")
