TensorFlow(四)——MNIST分類之CNN
阿新 • • 發佈：2018-12-09
import input_data import tensorflow as tf import numpy as np mnist = input_data.read_data_sets('data/', one_hot=True) trX, trY, teX, teY = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labels #-1 不考慮數量，28×28畫素，1為通道 trX = trX.reshape(-1, 28, 28, 1) teX = teX.reshape(-1, 28, 28, 1) X = tf.placeholder("float", [None, 28, 28, 1]) Y = tf.placeholder("float", [None, 10]) #3卷基層，3池化層，1全連線，1輸出層 def init_weights(shape): return tf.Variable(tf.random_normal(shape, stddev=0.01)) w = init_weights([3, 3, 1, 32]) w2 = init_weights([3, 3, 32, 64]) w3 = init_weights([3, 3, 64, 128]) w4 = init_weights([128 * 4 *4, 625]) w_o = init_weights([625, 10]) #定義模型函式 #X:輸入資料，w：權重，p_keep_conv,p_keep_hidden:dropout保留的神經元比例 def model(X, w, w2, w3, w4, w_o, p_keep_conv, p_keep_hidden): #第一層卷集和池化，然後dropout l1a = tf.nn.relu(tf.nn.conv2d(X, w, strides=[1, 1, 1, 1], padding='SAME')) l1 = tf.nn.max_pool(l1a, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') l1 = tf.nn.dropout(l1, p_keep_conv) l2a = tf.nn.relu(tf.nn.conv2d(l1, w2, strides=[1, 1, 1, 1], padding='SAME')) l2 = tf.nn.max_pool(l2a, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') l2 = tf.nn.dropout(l2, p_keep_conv) l3a = tf.nn.relu(tf.nn.conv2d(l2, w3, strides=[1, 1, 1, 1], padding='SAME')) l3 = tf.nn.max_pool(l3a, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') l3 = tf.reshape(l3, [-1, w4.get_shape().as_list()[0]]) l3 = tf.nn.dropout(l3, p_keep_conv) #全連線層 l4 = tf.nn.relu(tf.matmul(l3, w4)) l4 = tf.nn.dropout(l4, p_keep_hidden) #輸出層 pyx = tf.matmul(l4, w_o) return pyx p_keep_conv = tf.placeholder('float') p_keep_hidden = tf.placeholder('float') py_x = model(X, w, w2, w3, w4, w_o, p_keep_conv, p_keep_hidden) #定義損失函式 cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=py_x, labels=Y)) train_op = tf.train.RMSPropOptimizer(0.001, 0.9).minimize(cost) predict_op = tf.argmax(py_x, 1) batch_size = 128 test_size = 256 with tf.Session() as sess: tf.global_variables_initializer().run() for i in range(100): training_batch = zip(range(0, len(trX), batch_size), range(batch_size, len(trX)+1, batch_size)) for start, end in training_batch: sess.run(train_op, feed_dict={X: trX[start:end], Y: trY[start:end], p_keep_conv: 0.8, p_keep_hidden: 0.5}) test_indices = np.arange(len(teX)) np.random.shuffle(test_indices) test_indices = test_indices[0:test_size] print (i, np.mean(np.argmax(teY[test_indices], axis=1) == sess.run(predict_op, feed_dict={X: teX[test_indices], p_keep_conv: 1.0, p_keep_hidden: 1.0})))
結果：
0 0.93359375 1 0.9765625 2 0.9765625 3 0.9921875 4 0.984375 5 0.9921875 6 1.0 7 0.98828125 8 0.984375 9 0.98046875 10 0.99609375 11 0.98828125 12 1.0 13 0.98828125 14 0.9921875 15 0.99609375 16 0.9921875 17 0.99609375 18 0.99609375 19 0.98828125 20 0.9921875 21 1.0 22 0.9921875 23 0.9921875 24 0.98828125 25 0.99609375 26 0.99609375 27 0.98046875 28 0.98828125 29 0.9921875 30 1.0 31 1.0 32 0.99609375 33 0.98828125 34 0.984375 35 1.0 36 0.984375 37 0.99609375 38 1.0 39 0.99609375 40 0.9921875 41 0.97265625 42 1.0 43 0.9921875 44 0.99609375 45 0.984375 46 1.0 47 1.0 48 0.98828125 49 0.9765625 50 0.9921875 51 1.0 52 0.98828125 53 0.98828125 54 0.9921875 55 0.99609375 56 1.0 57 0.99609375 58 1.0 59 0.9921875 60 0.99609375 61 0.98828125 62 0.9921875 63 0.9921875 64 0.9921875 65 0.98828125 66 0.99609375 67 0.99609375 68 0.984375 69 1.0 70 0.98828125 71 0.98828125 72 0.99609375 73 1.0 74 1.0 75 0.99609375 76 0.98828125 77 0.9921875 78 0.98828125 79 0.9921875 80 1.0 81 0.99609375 82 0.99609375 83 0.98828125 84 0.984375 85 0.98828125 86 0.99609375 87 0.99609375 88 0.9921875 89 0.99609375 90 0.98828125 91 0.99609375 92 1.0 93 0.9921875 94 0.98046875 95 1.0 96 0.99609375 97 0.984375 98 0.9921875 99 0.99609375