最近学校里课上完了，给了两周时间来复习考试。可能是因为突然一下从赶Due的紧张中解脱了出来，居然敢写写代码浪起来了。
上个月看到Google有一个ML Winter Camp，然后又想到自己为了赶Due写论文已经两个月没怎么写过代码了，于是深感惭愧，决定抽空玩一玩Tensorflow和之前一直想搞的Sounding Probe。
其实MNIST并不是特别适合用RNN来预测的，但是用来玩一玩还是没有问题的。经过一波强行优化，RNN的Accuracy在测试集上居然可以和CNN有的一拼了。
# encoding:utf-8 import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("mnist_data/", one_hot=True) # Hyper Parameters n_input = 28 n_steps = 28 n_hidden = 128 n_classes = 10 # Define Learning Rate, Training iterations and Batch Size learning_rate = 0.002 batch_size = 20 def weight_variable(shape): initial = tf.truncated_normal(shape, stddev=0.1) return tf.Variable(initial) def bias_variable(shape): initial = tf.constant(0.1, shape=shape) return tf.Variable(initial) # Define Placeholders with tf.name_scope('inputs'): x_ = tf.placeholder("float", [None, n_steps, n_input]) y_ = tf.placeholder("float", [None, n_classes]) # Time Sequence Transform with tf.name_scope('TS_Transform'): x_ts = tf.unstack(x_, n_steps, 1) # Define BasicLSTMCell with tf.name_scope("LSTM_layer"): lstm_cell = tf.nn.rnn_cell.LSTMCell(n_hidden, forget_bias=0.9) # Static RNN Layer outputs, states = tf.contrib.rnn.static_rnn(lstm_cell, x_ts, dtype=tf.float32) # Full Connection Layer with tf.name_scope("Full_connection_layer"): with tf.name_scope('weights'): w_fc1 = weight_variable([n_hidden, n_classes]) tf.summary.histogram('Full_connection_layer_2/weights', w_fc1) with tf.name_scope('biases'): b_fc1 = bias_variable([n_classes]) tf.summary.histogram('Full_connection_layer_2/biases', b_fc1) with tf.name_scope('prediction'): prediction = tf.matmul(outputs[-1], w_fc1) + b_fc1 # Cross Entropy with tf.name_scope('loss'): cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=prediction, labels=y_)) # Train with tf.name_scope('Train'): optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # Accuracy Test with tf.name_scope('Test'): correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(y_, 1)) accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) # Session with tf.Session() as sess: sess.run(tf.global_variables_initializer()) writer = tf.summary.FileWriter("logs/", sess.graph) for iter in range(5000): batch_x, batch_y = mnist.train.next_batch(batch_size) batch_x = batch_x.reshape((batch_size, n_steps, n_input)) sess.run(optimizer, feed_dict={x_: batch_x, y_: batch_y}) if iter % 100 == 0: acc = sess.run(accuracy, feed_dict={x_: batch_x, y_: batch_y}) loss = sess.run(cost, feed_dict={x_: batch_x, y_: batch_y}) print ("Iter " + str(iter) + ", Minibatch Loss= " + \ "{:.6f}".format(loss) + ", Training Accuracy= " + \ "{:.5f}".format(acc)) print (" Finished!") test_len = 1024 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}))