mnitest-app
#coding:utf-8 import tensorflow as tf import numpy as np from PIL import Image import mnist_backward import mnist_forward def restore_model(testPicArr): with tf.Graph().as_default() as tg: x = tf.placeholder(tf.float32, [None, mnist_forward.INPUT_NODE]) y = mnist_forward.forward(x, None) preValue = tf.argmax(y, 1) variable_averages = tf.train.ExponentialMovingAverage(mnist_backward.MOVING_AVERAGE_DECAY) variables_to_restore = variable_averages.variables_to_restore() saver = tf.train.Saver(variables_to_restore) with tf.Session() as sess: ckpt = tf.train.get_checkpoint_state(mnist_backward.MODEL_SAVE_PATH) if ckpt and ckpt.model_checkpoint_path: saver.restore(sess, ckpt.model_checkpoint_path) preValue = sess.run(preValue, feed_dict={x:testPicArr}) return preValue else: print("No checkpoint file found") return -1 def pre_pic(picName): img = Image.open(picName) reIm = img.resize((28,28), Image.ANTIALIAS) im_arr = np.array(reIm.convert('L')) threshold = 50 for i in range(28): for j in range(28): im_arr[i][j] = 255 - im_arr[i][j] if (im_arr[i][j] < threshold): im_arr[i][j] = 0 else: im_arr[i][j] = 255 nm_arr = im_arr.reshape([1, 784]) nm_arr = nm_arr.astype(np.float32) img_ready = np.multiply(nm_arr, 1.0/255.0) return img_ready def application(): testNum = input("input the number of test pictures:") for i in range(testNum): testPic = raw_input("the path of test picture:") testPicArr = pre_pic(testPic) preValue = restore_model(testPicArr) print "The prediction number is:", preValue def main(): application() if __name__ == '__main__': main()