from networks import googlenet
import tensorflow as tf
import scipy.io as sio
import numpy as np
BATCH_SIZE = 50
def get_predict_labels():
inputs = tf.placeholder("float", [None, 64, 64, 1])
is_training = tf.placeholder("bool")
prediction, _ = googlenet(inputs, is_training)
predict_labels = tf.argmax(prediction, 1)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
data = sio.loadmat("../data/dataset.mat")
testdata = data["test"] / 127.5 - 1.0
testlabel = data["testlabels"]
saver.restore(sess, "../save_para/.\\model.ckpt")
nums_test = testlabel.shape[1]
PREDICT_LABELS = np.zeros([nums_test])
for i in range(nums_test // BATCH_SIZE):
PREDICT_LABELS[i * BATCH_SIZE:i * BATCH_SIZE + BATCH_SIZE] = sess.run(predict_labels, feed_dict={inputs: testdata[i * BATCH_SIZE:i * BATCH_SIZE + BATCH_SIZE], is_training: False})
PREDICT_LABELS[(nums_test // BATCH_SIZE - 1) * BATCH_SIZE + BATCH_SIZE:] = sess.run(predict_labels, feed_dict={inputs: testdata[(nums_test // BATCH_SIZE - 1) * BATCH_SIZE + BATCH_SIZE:], is_training: False})
np.savetxt("../data/predict_labels.txt", PREDICT_LABELS)
def construct_confusion_matrix(true_labels, predict_labels):
con_mat = np.zeros([30, 30])
for i in range(30):
indx_true_labels = np.where(true_labels == i)[0]
for j in range(30):
con_mat[i, j] = np.sum(predict_labels[indx_true_labels] == j) / indx_true_labels.shape[0]
return con_mat
if __name__ == "__main__":
data = sio.loadmat("../data/dataset.mat")
testlabels = data["testlabels"][0, :]
predictlabels = np.loadtxt("../data/predict_labels.txt")
con_mat = construct_confusion_matrix(testlabels, predictlabels)
sio.savemat("../data/con_mat.mat", {"cm": con_mat})
