CNN：Convolutional Neural Networks
输入层→隐藏层（卷积层→池化层→全链接层）→输出层（Softmax层）
本文中，前两个卷积层由Convolution-ReLU-maxpool操作组成，后两层为全链接层。
1. 加载必要的编程库，开始计算图会话
import matplotlib.pyplot as plt import numpy as np import tensorflow as tf from tensorflow.contrib.learn.python.learn.datasets.minist import read_data_sets sess = tf.session()
2. 加载数据集，转化图像为28×28的数组
data_dir = 'temp' mnist = read_data_sets(data_dir) train_xdata = np.array([np.reshape(x, (28, 28)) for x in mnist.train.images]) test_xdata = np.array([np.reshape(x, (28, 28)) for x in mnist.test.images]) train_labels = mnist.train.labels test_labels = mnist.test.labels
3. 设置模型参数
图像为灰度图，深度为1，颜色通道数为3
# Set model parameters batch_size = 100 learning_rate = 0.005 evaluation_size = 500 image_width = train_xdata[0].shape[0] image_height = train_xdata[0].shape[1] target_size = max(train_labels) + 1 num_channels = 1 # greyscale = 1 channel generations = 500 eval_every = 5 conv1_features = 25 conv2_features = 50 max_pool_size1 = 2 # NxN window for 1st max pool layer max_pool_size2 = 2 # NxN window for 2nd max pool layer fully_connected_size1 = 100
4. 为数据集声明占位符
# Declare model placeholders x_input_shape = (batch_size, image_width, image_height, num_channels) x_input = tf.placeholder(tf.float32, shape=x_input_shape) y_target = tf.placeholder(tf.int32, shape=(batch_size)) eval_input_shape = (evaluation_size, image_width, image_height, num_channels) eval_input = tf.placeholder(tf.float32, shape=eval_input_shape) eval_target = tf.placeholder(tf.int32, shape=(evaluation_size))
5. 声明卷积层的权重和偏置
# Declare model parameters conv1_weight = tf.Variable(tf.truncated_normal([4, 4, num_channels, conv1_features], stddev=0.1, dtype=tf.float32)) conv1_bias = tf.Variable(tf.zeros([conv1_features], dtype=tf.float32)) conv2_weight = tf.Variable(tf.truncated_normal([4, 4, conv1_features, conv2_features], stddev=0.1, dtype=tf.float32)) conv2_bias = tf.Variable(tf.zeros([conv2_features], dtype=tf.float32))
6. 声明全链接层的权重和偏置
# fully connected variables resulting_width = image_width // (max_pool_size1 * max_pool_size2) resulting_height = image_height // (max_pool_size1 * max_pool_size2) full1_input_size = resulting_width * resulting_height * conv2_features full1_weight = tf.Variable(tf.truncated_normal([full1_input_size, fully_connected_size1], stddev=0.1, dtype=tf.float32)) full1_bias = tf.Variable(tf.truncated_normal([fully_connected_size1], stddev=0.1, dtype=tf.float32)) full2_weight = tf.Variable(tf.truncated_normal([fully_connected_size1, target_size], stddev=0.1, dtype=tf.float32)) full2_bias = tf.Variable(tf.truncated_normal([target_size], stddev=0.1, dtype=tf.float32))
7. 声明算法模型
# Initialize Model Operations def my_conv_net(input_data): # First Conv-ReLU-MaxPool Layer conv1 = tf.nn.conv2d(input_data, conv1_weight, strides=[1, 1, 1, 1], padding='SAME') relu1 = tf.nn.relu(tf.nn.bias_add(conv1, conv1_bias)) max_pool1 = tf.nn.max_pool(relu1, ksize=[1, max_pool_size1, max_pool_size1, 1], strides=[1, max_pool_size1, max_pool_size1, 1], padding='SAME') # Second Conv-ReLU-MaxPool Layer conv2 = tf.nn.conv2d(max_pool1, conv2_weight, strides=[1, 1, 1, 1], padding='SAME') relu2 = tf.nn.relu(tf.nn.bias_add(conv2, conv2_bias)) max_pool2 = tf.nn.max_pool(relu2, ksize=[1, max_pool_size2, max_pool_size2, 1], strides=[1, max_pool_size2, max_pool_size2, 1], padding='SAME') # Transform Output into a 1xN layer for next fully connected layer final_conv_shape = max_pool2.get_shape().as_list() final_shape = final_conv_shape[1] * final_conv_shape[2] * final_conv_shape[3] flat_output = tf.reshape(max_pool2, [final_conv_shape[0], final_shape]) # First Fully Connected Layer fully_connected1 = tf.nn.relu(tf.add(tf.matmul(flat_output, full1_weight), full1_bias)) # Second Fully Connected Layer final_model_output = tf.add(tf.matmul(fully_connected1, full2_weight), full2_bias) return(final_model_output)
8. 声明训练模型
model_output = my_conv_net(x_input)
test_model_output = my_conv_net(eval_input)
9. Softmax函数
# Declare Loss Function (softmax cross entropy) loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(model_output, y_target))
10. 创建训练集和测试集的预测函数
# Create a prediction function prediction = tf.nn.softmax(model_output) test_prediction = tf.nn.softmax(test_model_output) # Create accuracy function def get_accuracy(logits, targets): batch_predictions = np.argmax(logits, axis=1) num_correct = np.sum(np.equal(batch_predictions, targets)) return(100. * num_correct/batch_predictions.shape[0])
11. 创建优化器函数，声明训练步长，初始化所有的模型变量
# Create an optimizer my_optimizer = tf.train.MomentumOptimizer(learning_rate, 0.9) train_step = my_optimizer.minimize(loss) # Initialize Variables init = tf.initialize_all_variables() sess.run(init)
12. 开始训练模型
# Start training loop train_loss = [] train_acc = [] test_acc = [] for i in range(generations): rand_index = np.random.choice(len(train_xdata), size=batch_size) rand_x = train_xdata[rand_index] rand_x = np.expand_dims(rand_x, 3) rand_y = train_labels[rand_index] train_dict = {x_input: rand_x, y_target: rand_y} sess.run(train_step, feed_dict=train_dict) temp_train_loss, temp_train_preds = sess.run([loss, prediction], feed_dict=train_dict) temp_train_acc = get_accuracy(temp_train_preds, rand_y) if (i+1) % eval_every == 0: eval_index = np.random.choice(len(test_xdata), size=evaluation_size) eval_x = test_xdata[eval_index] eval_x = np.expand_dims(eval_x, 3) eval_y = test_labels[eval_index] test_dict = {eval_input: eval_x, eval_target: eval_y} test_preds = sess.run(test_prediction, feed_dict=test_dict) temp_test_acc = get_accuracy(test_preds, eval_y) # Record and print results train_loss.append(temp_train_loss) train_acc.append(temp_train_acc) test_acc.append(temp_test_acc) acc_and_loss = [(i+1), temp_train_loss, temp_train_acc, temp_test_acc] acc_and_loss = [np.round(x,2) for x in acc_and_loss] print('Generation # {}. Train Loss: {:.2f}. Train Acc (Test Acc): {:.2f} ({:.2f})'.format(*acc_and_loss))
13. 使用Matplotlib模板绘制损失函数和准确度
# Matlotlib code to plot the loss and accuracies eval_indices = range(0, generations, eval_every) # Plot loss over time plt.plot(eval_indices, train_loss, 'k-') plt.title('Softmax Loss per Generation') plt.xlabel('Generation') plt.ylabel('Softmax Loss') plt.show() # Plot train and test accuracy plt.plot(eval_indices, train_acc, 'k-', label='Train Set Accuracy') plt.plot(eval_indices, test_acc, 'r--', label='Test Set Accuracy') plt.title('Train and Test Accuracy') plt.xlabel('Generation') plt.ylabel('Accuracy') plt.legend(loc='lower right') plt.show()
14. 打印最新结果中的六幅抽样图
# Plot some samples # Plot the 6 of the last batch results: actuals = rand_y[0:6] predictions = np.argmax(temp_train_preds,axis=1)[0:6] images = np.squeeze(rand_x[0:6]) Nrows = 2 Ncols = 3 for i in range(6): plt.subplot(Nrows, Ncols, i+1) plt.imshow(np.reshape(images[i], [28,28]), cmap='Greys_r') plt.title('Actual: ' + str(actuals[i]) + ' Pred: ' + str(predictions[i]), fontsize=10) frame = plt.gca() frame.axes.get_xaxis().set_visible(False) frame.axes.get_yaxis().set_visible(False)