import os import sys import time import tensorflow as tf from tensorflow.python.ops import rnn, rnn_cell from tensorflow.examples.tutorials.mnist import input_data n_input = 28 # MNIST data input (img shape: 28*28) n_steps = 28 # timesteps n_hidden = 128 # hidden layer num of features n_classes = 10 # MNIST total classes (0-9 digits) def rnn_model(x, weights, biases): """Build a rnn model for image""" x = tf.transpose(x, [1, 0, 2]) x = tf.reshape(x, [-1, n_input]) x = tf.split(0, n_steps, x) lstm_cell = rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0) outputs, states = rnn.rnn(lstm_cell, x, dtype=tf.float32) return tf.matmul(outputs[-1], weights) + biases def predict(): """Predict unseen images""" """Step 0: load data and trained model""" mnist = input_data.read_data_sets("./data/", one_hot=True) checkpoint_dir = sys.argv[1] """Step 1: build the rnn model""" x = tf.placeholder("float", [None, n_steps, n_input]) y = tf.placeholder("float", [None, n_classes]) weights = tf.Variable(tf.random_normal([n_hidden, n_classes]), name='weights') biases = tf.Variable(tf.random_normal([n_classes]), name='biases') pred = rnn_model(x, weights, biases) correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) """Step 2: predict new images with the trained model""" with tf.Session() as sess: sess.run(tf.initialize_all_variables()) """Step 2.0: load the trained model""" checkpoint_file = tf.train.latest_checkpoint(checkpoint_dir + 'checkpoints') print('Loaded the trained model: {}'.format(checkpoint_file)) saver = tf.train.Saver() saver.restore(sess, checkpoint_file) """Step 2.1: predict new data""" test_len = 500 test_data = mnist.test.images[:test_len].reshape((-1, n_steps, n_input)) test_label = mnist.test.labels[:test_len] print("Testing Accuracy:", sess.run(accuracy, feed_dict={x: test_data, y: test_label})) if __name__ == '__main__': predict()