TensorFlow基于CIFAR10数据集的卷积神经网络CNN实现

# coding: utf-8 # Python2 # from __future__ import print_function, division # import tensorflow as tf # import numpy as np # import matplotlib.pyplot as plt # import cPickle as pickle # import seaborn # Python3 import tensorflow as tf import numpy as np import matplotlib.pyplot as plt import _pickle as pickle import seaborn def unpickle(filename): '''解压数据''' # Python2 # with open(filename) as f: # d = pickle.load(f) # return d # Python3 with open(filename, 'rb') as f: d = pickle.load(f, encoding='latin1') return d def onehot(labels): '''one-hot 编码''' n_sample = len(labels) n_class = max(labels) + 1 onehot_labels = np.zeros((n_sample, n_class)) onehot_labels[np.arange(n_sample), labels] = 1 return onehot_labels # 训练数据集 data1 = unpickle('cifar10-dataset/data_batch_1') data2 = unpickle('cifar10-dataset/data_batch_2') data3 = unpickle('cifar10-dataset/data_batch_3') data4 = unpickle('cifar10-dataset/data_batch_4') data5 = unpickle('cifar10-dataset/data_batch_5') X_train = np.concatenate((data1['data'], data2['data'], data3['data'], data4['data'], data5['data']), axis=0) y_train = np.concatenate((data1['labels'], data2['labels'], data3['labels'], data4['labels'], data5['labels']), axis=0) y_train = onehot(y_train) # 测试数据集 test = unpickle('cifar10-dataset/test_batch') X_test = test['data'][:5000, :] y_test = onehot(test['labels'])[:5000, :] del test print('Training dataset shape:', X_train.shape) print('Training labels shape:', y_train.shape) print('Testing dataset shape:', X_test.shape) print('Testing labels shape:', y_test.shape) with tf.device('/cpu:0'): # 模型参数 learning_rate = 1e-3 training_iters = 500 batch_size = 50 display_step = 5 n_features = 3072 # 32*32*3 n_classes = 10 n_fc1 = 384 n_fc2 = 192 # 构建模型 x = tf.placeholder(tf.float32, [None, n_features]) y = tf.placeholder(tf.float32, [None, n_classes]) w = { 'conv1': tf.Variable(tf.truncated_normal([5, 5, 3, 64], stddev=5e-2)), 'conv2': tf.Variable(tf.truncated_normal([5, 5, 64, 64], stddev=0.1)), 'fc1': tf.Variable(tf.truncated_normal([8*8*64, n_fc1], stddev=0.04)), 'fc2': tf.Variable(tf.truncated_normal([n_fc1, n_fc2], stddev=0.1)), 'fc3': tf.Variable(tf.truncated_normal([n_fc2, n_classes], stddev=1/192.0)) } b = { 'conv1': tf.Variable(tf.constant(0.0, dtype=tf.float32, shape=[64])), 'conv2': tf.Variable(tf.constant(0.1, dtype=tf.float32, shape=[64])), 'fc1': tf.Variable(tf.constant(0.1, dtype=tf.float32, shape=[n_fc1])), 'fc2': tf.Variable(tf.constant(0.1, dtype=tf.float32, shape=[n_fc2])), 'fc3': tf.Variable(tf.constant(0.0, dtype=tf.float32, shape=[n_classes])) } x4d = tf.reshape(x, [-1, 32, 32, 3]) # 卷积层 1 conv1 = tf.nn.conv2d(x4d, w['conv1'], strides=[1, 1, 1, 1], padding='SAME') conv1 = tf.nn.bias_add(conv1, b['conv1']) conv1 = tf.nn.relu(conv1) # 池化层 1 pool1 = tf.nn.max_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME') # LRN层，Local Response Normalization norm1 = tf.nn.lrn(pool1, 4, bias=1.0, alpha=0.001/9.0, beta=0.75) # 卷积层 2 conv2 = tf.nn.conv2d(norm1, w['conv2'], strides=[1, 1, 1, 1], padding='SAME') conv2 = tf.nn.bias_add(conv2, b['conv2']) conv2 = tf.nn.relu(conv2) # LRN层，Local Response Normalization norm2 = tf.nn.lrn(conv2, 4, bias=1.0, alpha=0.001/9.0, beta=0.75) # 池化层 2 pool2 = tf.nn.max_pool(norm2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME') reshape = tf.reshape(pool2, [-1, 8*8*64]) dim = reshape.get_shape()[1].value fc1 = tf.add(tf.matmul(reshape, w['fc1']), b['fc1']) fc1 = tf.nn.relu(fc1) # 全连接层 2 fc2 = tf.add(tf.matmul(fc1, w['fc2']), b['fc2']) fc2 = tf.nn.relu(fc2) # 全连接层 3, 即分类层 fc3 = tf.add(tf.matmul(fc2, w['fc3']), b['fc3']) # 定义损失 cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(fc3, y)) optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # 评估模型 correct_pred = tf.equal(tf.argmax(fc3, 1), tf.argmax(y, 1)) accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) init = tf.global_variables_initializer() with tf.Session() as sess: sess.run(init) c = [] total_batch = int(X_train.shape[0] / batch_size) for i in range(training_iters): avg_cost = 0 for batch in range(total_batch): batch_x = X_train[batch*batch_size : (batch+1)*batch_size, :] batch_y = y_train[batch*batch_size : (batch+1)*batch_size, :] _, co = sess.run([optimizer, cost], feed_dict={x: batch_x, y: batch_y}) avg_cost += co c.append(avg_cost) if (i+1) % display_step == 0: print("Iter " + str(i+1) + ", Training Loss= " + "{:.6f}".format(avg_cost)) print("Optimization Finished!") # Test test_acc = sess.run(accuracy, feed_dict={x: X_test, y: y_test}) print("Testing Accuracy:", test_acc) plt.plot(c) plt.xlabel('Iter') plt.ylabel('Cost') plt.title('lr=%f, ti=%d, bs=%d, acc=%f' % (learning_rate, training_iters, batch_size, test_acc)) plt.tight_layout() plt.savefig('cnn-tf-cifar10-%s.png' % test_acc, dpi=200)