Tensorflow 实现RNN分类.zip

""" Dependencies: tensorflow: 1.1.0 matplotlib numpy """ import matplotlib.pyplot as plt import numpy as np import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data tf.set_random_seed(1) np.random.seed(1) # Hyper Parameters BATCH_SIZE = 64 TIME_STEP = 28 # rnn time step / image height INPUT_SIZE = 28 # rnn input size / image width LR = 0.01 # learning rate # data mnist = input_data.read_data_sets('./mnist', one_hot=True) # they has been normalized to range (0,1) test_x = mnist.test.images[:2000] test_y = mnist.test.labels[:2000] # plot one example print(mnist.train.images.shape) # (55000, 28 * 28) print(mnist.train.labels.shape) # (55000, 10) plt.imshow(mnist.train.images[0].reshape((28, 28)), cmap='gray') plt.title('%i' % np.argmax(mnist.train.labels[0])) plt.show() # tensorflow placeholders tf_x = tf.placeholder(tf.float32, [None, TIME_STEP * INPUT_SIZE]) # shape(batch, 784) image = tf.reshape(tf_x, [-1, TIME_STEP, INPUT_SIZE]) # (batch, height, width, channel) tf_y = tf.placeholder(tf.int32, [None, 10]) # input y # RNN rnn_cell = tf.nn.rnn_cell.LSTMCell(num_units=64) outputs, (h_c, h_n) = tf.nn.dynamic_rnn( rnn_cell, # cell you have chosen image, # input initial_state=None, # the initial hidden state dtype=tf.float32, # must given if set initial_state = None time_major=False, # False: (batch, time step, input); True: (time step, batch, input) ) output = tf.layers.dense(outputs[:, -1, :], 10) # output based on the last output step loss = tf.losses.softmax_cross_entropy(onehot_labels=tf_y, logits=output) # compute cost train_op = tf.train.AdamOptimizer(LR).minimize(loss) accuracy = tf.metrics.accuracy( # return (acc, update_op), and create 2 local variables labels=tf.argmax(tf_y, axis=1), predictions=tf.argmax(output, axis=1),)[1] sess = tf.Session() init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer()) # the local var is for accuracy_op sess.run(init_op) # initialize var in graph for step in range(1200): # training b_x, b_y = mnist.train.next_batch(BATCH_SIZE) _, loss_ = sess.run([train_op, loss], {tf_x: b_x, tf_y: b_y}) if step % 50 == 0: # testing accuracy_ = sess.run(accuracy, {tf_x: test_x, tf_y: test_y}) print('train loss: %.4f' % loss_, '| test accuracy: %.2f' % accuracy_) # print 10 predictions from test data test_output = sess.run(output, {tf_x: test_x[:10]}) pred_y = np.argmax(test_output, 1) print(pred_y, 'prediction number') print(np.argmax(test_y[:10], 1), 'real number')