Convolutional Neural Networks (CNNs) are responsible for the major breakthroughs in image recognition made in the past few years. In this chapter we will cover:

  • Implementing a Simpler CNN
  • Implementing an Advanced CNN
  • Retraining Existing CNN models
  • Applying Stylenet/Neural-Style
  • Implementing DeepDream

熟悉基本操作后，转入Model的具体实践，以及可视化。

Ref: [Model] LeNet-5 by Keras

Let's look at how to achieve it by Tensorflow.

Log赏析：

Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.

Extracting temp/train-images-idx3-ubyte.gz

Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.

Extracting temp/train-labels-idx1-ubyte.gz

Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.

Extracting temp/t10k-images-idx3-ubyte.gz

Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.

Extracting temp/t10k-labels-idx1-ubyte.gz
Generation # 5. Train Loss: 2.28. Train Acc (Test Acc): 14.00 (14.00)

Generation # 10. Train Loss: 2.22. Train Acc (Test Acc): 14.00 (22.00)

Generation # 15. Train Loss: 2.11. Train Acc (Test Acc): 40.00 (33.40)

Generation # 20. Train Loss: 2.05. Train Acc (Test Acc): 48.00 (50.00)

Generation # 25. Train Loss: 1.93. Train Acc (Test Acc): 52.00 (57.00)

Generation # 30. Train Loss: 1.69. Train Acc (Test Acc): 64.00 (62.60)

Generation # 35. Train Loss: 1.43. Train Acc (Test Acc): 67.00 (64.40)

Generation # 40. Train Loss: 1.22. Train Acc (Test Acc): 63.00 (70.80)

Generation # 45. Train Loss: 0.87. Train Acc (Test Acc): 82.00 (76.80)

Generation # 50. Train Loss: 0.76. Train Acc (Test Acc): 80.00 (77.20)

Generation # 55. Train Loss: 0.66. Train Acc (Test Acc): 80.00 (75.40)

Generation # 60. Train Loss: 0.59. Train Acc (Test Acc): 81.00 (80.80)

Generation # 65. Train Loss: 0.55. Train Acc (Test Acc): 79.00 (85.60)

Generation # 70. Train Loss: 0.41. Train Acc (Test Acc): 85.00 (81.80)

Generation # 75. Train Loss: 0.57. Train Acc (Test Acc): 83.00 (85.20)

Generation # 80. Train Loss: 0.39. Train Acc (Test Acc): 90.00 (86.00)

Generation # 85. Train Loss: 0.39. Train Acc (Test Acc): 90.00 (86.00)

Generation # 90. Train Loss: 0.26. Train Acc (Test Acc): 92.00 (90.60)

Generation # 95. Train Loss: 0.32. Train Acc (Test Acc): 90.00 (87.60)

Generation # 100. Train Loss: 0.39. Train Acc (Test Acc): 89.00 (89.80)

Generation # 105. Train Loss: 0.49. Train Acc (Test Acc): 85.00 (90.00)

Generation # 110. Train Loss: 0.34. Train Acc (Test Acc): 88.00 (90.00)

Generation # 115. Train Loss: 0.24. Train Acc (Test Acc): 91.00 (89.20)

Generation # 120. Train Loss: 0.30. Train Acc (Test Acc): 92.00 (91.40)

Generation # 125. Train Loss: 0.29. Train Acc (Test Acc): 89.00 (91.60)

Generation # 130. Train Loss: 0.31. Train Acc (Test Acc): 93.00 (90.20)

Generation # 135. Train Loss: 0.41. Train Acc (Test Acc): 85.00 (91.40)

Generation # 140. Train Loss: 0.22. Train Acc (Test Acc): 94.00 (91.40)

Generation # 145. Train Loss: 0.39. Train Acc (Test Acc): 85.00 (92.60)

Generation # 150. Train Loss: 0.38. Train Acc (Test Acc): 93.00 (90.00)

Generation # 155. Train Loss: 0.17. Train Acc (Test Acc): 96.00 (91.60)

Generation # 160. Train Loss: 0.22. Train Acc (Test Acc): 93.00 (93.20)

Generation # 165. Train Loss: 0.15. Train Acc (Test Acc): 97.00 (92.00)

Generation # 170. Train Loss: 0.24. Train Acc (Test Acc): 92.00 (93.40)

Generation # 175. Train Loss: 0.21. Train Acc (Test Acc): 93.00 (92.40)

Generation # 180. Train Loss: 0.35. Train Acc (Test Acc): 90.00 (91.80)

Generation # 185. Train Loss: 0.15. Train Acc (Test Acc): 95.00 (93.80)

Generation # 190. Train Loss: 0.17. Train Acc (Test Acc): 96.00 (91.60)

Generation # 195. Train Loss: 0.26. Train Acc (Test Acc): 89.00 (92.20)

Generation # 200. Train Loss: 0.32. Train Acc (Test Acc): 91.00 (91.40)

Generation # 205. Train Loss: 0.20. Train Acc (Test Acc): 93.00 (93.60)

Generation # 210. Train Loss: 0.16. Train Acc (Test Acc): 97.00 (93.80)

Generation # 215. Train Loss: 0.18. Train Acc (Test Acc): 95.00 (91.60)

Generation # 220. Train Loss: 0.21. Train Acc (Test Acc): 96.00 (92.40)

Generation # 225. Train Loss: 0.23. Train Acc (Test Acc): 93.00 (94.80)

Generation # 230. Train Loss: 0.16. Train Acc (Test Acc): 97.00 (96.60)

Generation # 235. Train Loss: 0.19. Train Acc (Test Acc): 93.00 (94.80)

Generation # 240. Train Loss: 0.12. Train Acc (Test Acc): 97.00 (95.20)

Generation # 245. Train Loss: 0.16. Train Acc (Test Acc): 96.00 (92.20)

Generation # 250. Train Loss: 0.22. Train Acc (Test Acc): 92.00 (93.80)

Generation # 255. Train Loss: 0.22. Train Acc (Test Acc): 94.00 (95.00)

Generation # 260. Train Loss: 0.22. Train Acc (Test Acc): 90.00 (93.40)

Generation # 265. Train Loss: 0.23. Train Acc (Test Acc): 93.00 (94.40)

Generation # 270. Train Loss: 0.11. Train Acc (Test Acc): 96.00 (92.00)

Generation # 275. Train Loss: 0.15. Train Acc (Test Acc): 94.00 (93.20)

Generation # 280. Train Loss: 0.17. Train Acc (Test Acc): 97.00 (95.60)

Generation # 285. Train Loss: 0.25. Train Acc (Test Acc): 90.00 (95.20)

Generation # 290. Train Loss: 0.17. Train Acc (Test Acc): 95.00 (95.80)

Generation # 295. Train Loss: 0.20. Train Acc (Test Acc): 93.00 (96.40)

Generation # 300. Train Loss: 0.12. Train Acc (Test Acc): 96.00 (93.60)

Generation # 305. Train Loss: 0.15. Train Acc (Test Acc): 94.00 (94.20)

Generation # 310. Train Loss: 0.37. Train Acc (Test Acc): 88.00 (94.80)

Generation # 315. Train Loss: 0.19. Train Acc (Test Acc): 96.00 (93.40)

Generation # 320. Train Loss: 0.17. Train Acc (Test Acc): 95.00 (96.20)

Generation # 325. Train Loss: 0.16. Train Acc (Test Acc): 92.00 (93.80)

Generation # 330. Train Loss: 0.17. Train Acc (Test Acc): 96.00 (94.00)

Generation # 335. Train Loss: 0.14. Train Acc (Test Acc): 96.00 (95.20)

Generation # 340. Train Loss: 0.15. Train Acc (Test Acc): 96.00 (96.60)

Generation # 345. Train Loss: 0.15. Train Acc (Test Acc): 94.00 (95.60)

Generation # 350. Train Loss: 0.27. Train Acc (Test Acc): 91.00 (97.00)

Generation # 355. Train Loss: 0.11. Train Acc (Test Acc): 98.00 (94.60)

Generation # 360. Train Loss: 0.15. Train Acc (Test Acc): 95.00 (95.20)

Generation # 365. Train Loss: 0.08. Train Acc (Test Acc): 98.00 (96.40)

Generation # 370. Train Loss: 0.15. Train Acc (Test Acc): 94.00 (93.80)

Generation # 375. Train Loss: 0.21. Train Acc (Test Acc): 92.00 (96.60)

Generation # 380. Train Loss: 0.21. Train Acc (Test Acc): 96.00 (94.40)

Generation # 385. Train Loss: 0.07. Train Acc (Test Acc): 99.00 (95.40)

Generation # 390. Train Loss: 0.19. Train Acc (Test Acc): 94.00 (95.40)

Generation # 395. Train Loss: 0.12. Train Acc (Test Acc): 97.00 (94.40)

Generation # 400. Train Loss: 0.14. Train Acc (Test Acc): 96.00 (96.60)

Generation # 405. Train Loss: 0.17. Train Acc (Test Acc): 95.00 (96.60)

Generation # 410. Train Loss: 0.16. Train Acc (Test Acc): 93.00 (96.40)

Generation # 415. Train Loss: 0.18. Train Acc (Test Acc): 93.00 (95.60)

Generation # 420. Train Loss: 0.11. Train Acc (Test Acc): 95.00 (94.80)

Generation # 425. Train Loss: 0.22. Train Acc (Test Acc): 91.00 (95.20)

Generation # 430. Train Loss: 0.07. Train Acc (Test Acc): 98.00 (96.20)

Generation # 435. Train Loss: 0.11. Train Acc (Test Acc): 97.00 (95.80)

Generation # 440. Train Loss: 0.07. Train Acc (Test Acc): 97.00 (95.20)

Generation # 445. Train Loss: 0.15. Train Acc (Test Acc): 99.00 (97.80)

Generation # 450. Train Loss: 0.09. Train Acc (Test Acc): 98.00 (95.00)

Generation # 455. Train Loss: 0.07. Train Acc (Test Acc): 97.00 (95.80)

Generation # 460. Train Loss: 0.08. Train Acc (Test Acc): 98.00 (94.60)

Generation # 465. Train Loss: 0.07. Train Acc (Test Acc): 98.00 (95.40)

Generation # 470. Train Loss: 0.14. Train Acc (Test Acc): 98.00 (94.40)

Generation # 475. Train Loss: 0.24. Train Acc (Test Acc): 93.00 (96.40)

Generation # 480. Train Loss: 0.08. Train Acc (Test Acc): 99.00 (94.40)

Generation # 485. Train Loss: 0.16. Train Acc (Test Acc): 96.00 (96.40)

Generation # 490. Train Loss: 0.09. Train Acc (Test Acc): 95.00 (96.40)

Generation # 495. Train Loss: 0.13. Train Acc (Test Acc): 95.00 (96.20)

Generation # 500. Train Loss: 0.09. Train Acc (Test Acc): 99.00 (95.80)

Training...

Code解读：

  • 加载数据
# Introductory CNN Model: MNIST Digits

#---------------------------------------

#

# In this example, we will download the MNIST handwritten

# digits and create a simple CNN network to predict the

# digit category (0-9)

import matplotlib.pyplot as plt

import numpy as np

import tensorflow as tf

from tensorflow.contrib.learn.python.learn.datasets.mnist import read_data_sets

from tensorflow.python.framework import ops

ops.reset_default_graph()

# Start a graph session

sess = tf.Session()

# Load data

data_dir = 'temp'

mnist = read_data_sets(data_dir)

# Convert images into 28x28 (they are downloaded as 1x784)

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])

# Convert labels into one-hot encoded vectors

train_labels = mnist.train.labels

test_labels  = mnist.test.labels

train_xdata --> train_labels;  test_xdata --> test_labels

  • Set model parameters
# 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

Set model parameters

  • 构建Graph连接
# Declare model placeholders

x_input_shape = (batch_size, image_width, image_height, num_channels)　　　　　　 # (, 28, 28, 1)

x_input       = tf.placeholder(tf.float32, shape=x_input_shape)

y_target      = tf.placeholder(tf.int32,   shape=(batch_size))

# Test/Evaluation

eval_input_shape = (evaluation_size, image_width, image_height, num_channels)　　# (, 28, 28, 1)

eval_input       = tf.placeholder(tf.float32, shape=eval_input_shape)

eval_target      = tf.placeholder(tf.int32,   shape=(evaluation_size))

# Declare model parameters
# For one layer

conv1_weight = tf.Variable(tf.truncated_normal([4, 4, num_channels,   conv1_features], stddev=0.1, dtype=tf.float32))　　# conv1_features = 25 conv kernels
conv1_bias   = tf.Variable(tf.zeros([conv1_features], dtype=tf.float32))

# For another layer

conv2_weight = tf.Variable(tf.truncated_normal([4, 4, conv1_features, conv2_features], stddev=0.1, dtype=tf.float32))　　# conv2_features = 50 conv kernels
conv2_bias   = tf.Variable(tf.zeros([conv2_features], dtype=tf.float32))

# fully connected variables

resulting_width  = image_width  // (max_pool_size1 * max_pool_size2)

resulting_height = image_height // (max_pool_size1 * max_pool_size2)
# Pooling两次后缩小为1/4

# For one layer

full1_input_size = resulting_width * resulting_height * conv2_features　　# 50个缩小后的feature map？总size？简直的人海战术

full1_weight     = tf.Variable(tf.truncated_normal([full1_input_size, fully_connected_size1], stddev=0.1, dtype=tf.float32))　　# fully_connected_size1 = 100
full1_bias       = tf.Variable(tf.truncated_normal([                  fully_connected_size1], stddev=0.1, dtype=tf.float32))
# For another layer

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))
  • 构建Graph结构
# 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)

model_output      = my_conv_net(x_input)

test_model_output = my_conv_net(eval_input)
  • Loss and solver
# Declare Loss Function (softmax cross entropy)

loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(model_output, y_target))

# Create an optimizer

my_optimizer = tf.train.MomentumOptimizer(learning_rate, 0.9)

train_step   = my_optimizer.minimize(loss)

通过梯度下降减小loss，对于GD的优化方式采用了Momentum。

  • 数据训练
# 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])

# Initialize Variables

init = tf.initialize_all_variables()

sess.run(init)

# 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)　　# 首先，train_xdata是三维(样本th, h, w)

    rand_x = train_xdata[rand_index]

    rand_x = np.expand_dims(rand_x, 3)　　# 其次，这里增加了一维（第四维），是为了channel

    rand_y = train_labels[rand_index]

    train_dict = {x_input: rand_x, y_target: rand_y}　　# 毕竟，feed时，x_input已规定为四维(样本th, h, w, ch)，这是卷积api的定义
    # training...
    sess.run(train_step, feed_dict=train_dict)
    # for loss

    temp_train_loss  = sess.run(loss,      feed_dict=train_dict)
    # for accuracy
    temp_train_preds = sess.run(predicton, feed_dict=train_dict)
    temp_train_acc = get_accuracy(temp_train_preds, rand_y)

    
    # 每次都要执行，这是当然；但只需要每五次打印一下log就可以了
    # NB: 打印log的过程，其实也包含里TEST过程

    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 可见，主要记录了三个指标（test时可不需要关注loss）

        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))
  • 结果展示
# 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()

# 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)

NB: 这个比 tf.nn.conv2d 好用了许多！

conv1 = tf.layers.conv2d(X, 
                     convlayer_sizes[0],　　# 使用几个filter卷积出多少个map

                     kernel_size       =filter_shape,

                     padding           =padding,

                     activation        =tf.nn.relu,

                     bias_initializer  =tf.zeros_initializer(),

                     kernel_regularizer=tf.nn.l2_loss,

                     bias_regularizer  =tf.nn.l2_loss,

                     name              ="conv1")

运行效率也要高一些。

$ python train.py

Extracting data/mnist/train-images-idx3-ubyte.gz

Extracting data/mnist/train-labels-idx1-ubyte.gz

Extracting data/mnist/t10k-images-idx3-ubyte.gz

Extracting data/mnist/t10k-labels-idx1-ubyte.gz

Starting Training...

Epoch 0, Training Loss: 1.60813545597, Test accuracy: 0.938201121795, time: 5.14s, total time: 6.15s

Epoch 1, Training Loss: 1.52455213715, Test accuracy: 0.950721153846, time: 4.53s, total time: 11.64s

Epoch 2, Training Loss: 1.51011738271, Test accuracy: 0.964743589744, time: 4.59s, total time: 17.22s

Epoch 3, Training Loss: 1.50035703349, Test accuracy: 0.966746794872, time: 4.48s, total time: 22.65s

Epoch 4, Training Loss: 1.49455949921, Test accuracy: 0.965044070513, time: 4.53s, total time: 28.17s

Epoch 5, Training Loss: 1.49036714219, Test accuracy: 0.969851762821, time: 4.58s, total time: 33.72s

Epoch 6, Training Loss: 1.48730323059, Test accuracy: 0.973657852564, time: 4.46s, total time: 39.21s

Epoch 7, Training Loss: 1.48489333978, Test accuracy: 0.972155448718, time: 4.51s, total time: 44.72s

Epoch 8, Training Loss: 1.48286200987, Test accuracy: 0.975260416667, time: 4.52s, total time: 50.22s

Epoch 9, Training Loss: 1.48113634647, Test accuracy: 0.973657852564, time: 4.5s, total time: 55.69s

Epoch 10, Training Loss: 1.47969393491, Test accuracy: 0.974559294872, time: 4.56s, total time: 61.21s

Epoch 11, Training Loss: 1.47822354946, Test accuracy: 0.975360576923, time: 4.69s, total time: 66.84s

Epoch 12, Training Loss: 1.47717202571, Test accuracy: 0.975360576923, time: 4.61s, total time: 72.51s

Epoch 13, Training Loss: 1.47643559991, Test accuracy: 0.974859775641, time: 4.53s, total time: 77.97s

Epoch 14, Training Loss: 1.4753200641, Test accuracy: 0.977864583333, time: 4.66s, total time: 83.59s

Epoch 15, Training Loss: 1.47432387181, Test accuracy: 0.97796474359, time: 4.44s, total time: 88.93s

Epoch 16, Training Loss: 1.47376608154, Test accuracy: 0.978565705128, time: 4.52s, total time: 94.4s

Epoch 17, Training Loss: 1.47339749864, Test accuracy: 0.976362179487, time: 4.53s, total time: 99.92s

Epoch 18, Training Loss: 1.47282876363, Test accuracy: 0.979967948718, time: 4.61s, total time: 105.53s

Epoch 19, Training Loss: 1.47205684374, Test accuracy: 0.980268429487, time: 4.53s, total time: 110.99s

Total training time: 110.99s

Confusion Matrix:

[[ 972    1    1    0    1    2    8    2    5    3]

 [   0 1123    6    0    0    0    2    2    0    4]

 [   1    4 1006    3    1    2    1   14    5    1]

 [   0    1    2  993    0    5    1    4    4    5]

 [   0    0    2    0  970    0    4    1    1    6]

 [   2    3    0    5    0  877    1    0    2    5]

 [   2    0    3    0    1    2  936    0    1    0]

 [   1    1    6    4    1    1    0  999    4   10]

 [   2    2    6    4    2    3    5    3  951    5]

 [   0    0    0    1    6    0    0    3    1  970]]

Training Complete

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