accuracy = 0 with tf.Session() as sess: sess.run(init) actual_steps = 0 runs = 0 index = 0 testlength = int(len(itest) / 70) saver.restore(sess, var_export_dir) for i in range((actual_steps - runs), steps): matches = tf.equal(tf.argmax(y_pred, 1), tf.argmax(y_true, 1)) test_x, test_y, index = nextImageBatch(itest, testlength, len(subfolders), index) acc = tf.reduce_mean(tf.cast(matches, tf.float32)) print("ACCURACY: ") get_acc = sess.run(acc, feed_dict={ x_image: test_x, y_true: test_y, hold_prob: 1.0 }) print(get_acc) test_saver.write(str(get_acc) + '\n') accuracy = accuracy + get_acc
#saver.restore(sess, var_export_dir) for i in range((actual_steps-runs), steps): # We will check time required to run each step using start_time and end_time train_time_saver = open('<PATH>/TempFM2/train_time.txt', 'a+') start_time = time.time() print('Saving step number'); step_saver.write(str(i) + '\n') step_saver.close() step_saver = open('<PATH>/TempFM2/steps.txt', 'a+') batch_x, batch_y, index = nextImageBatch(itrain, trainlength, len(subfolders), index) sess.run(train, feed_dict={x_image:batch_x, y_true:batch_y, pool_prob:0.75, hold_prob:0.5}) #print(trainlength) #print accuracy every few steps if (i+1)%10==0: i1 = 0 print("ON STEP {}".format(i)) matches = tf.equal(tf.argmax(y_pred, 1), tf.argmax(y_true, 1)) test_x, test_y, i1 = nextImageBatch(itest, testlength, len(subfolders), i1) acc = tf.reduce_mean(tf.cast(matches, tf.float32)) print("ACCURACY: ") accuracy = sess.run(acc, feed_dict={x_image:test_x, y_true:test_y, pool_prob:1.0, hold_prob:1.0}) print(accuracy)