【python实现卷积神经网络】开始训练

代码来源：https://github.com/eriklindernoren/ML-From-Scratch

卷积神经网络中卷积层Conv2D（带stride、padding）的具体实现：https://www.cnblogs.com/xiximayou/p/12706576.html

激活函数的实现（sigmoid、softmax、tanh、relu、leakyrelu、elu、selu、softplus）：https://www.cnblogs.com/xiximayou/p/12713081.html

损失函数定义（均方误差、交叉熵损失）：https://www.cnblogs.com/xiximayou/p/12713198.html

优化器的实现（SGD、Nesterov、Adagrad、Adadelta、RMSprop、Adam）：https://www.cnblogs.com/xiximayou/p/12713594.html

卷积层反向传播过程：https://www.cnblogs.com/xiximayou/p/12713930.html

全连接层实现：https://www.cnblogs.com/xiximayou/p/12720017.html

批量归一化层实现：https://www.cnblogs.com/xiximayou/p/12720211.html

池化层实现：https://www.cnblogs.com/xiximayou/p/12720324.html

padding2D实现：https://www.cnblogs.com/xiximayou/p/12720454.html

Flatten层实现：https://www.cnblogs.com/xiximayou/p/12720518.html

上采样层UpSampling2D实现：https://www.cnblogs.com/xiximayou/p/12720558.html

Dropout层实现：https://www.cnblogs.com/xiximayou/p/12720589.html

激活层实现：https://www.cnblogs.com/xiximayou/p/12720622.html

定义训练和测试过程：https://www.cnblogs.com/xiximayou/p/12725873.html

 

代码在mlfromscratch/examples/convolutional_neural_network.py 中：

from __future__ import print_function
from sklearn import datasets
import matplotlib.pyplot as plt
import math
import numpy as np

# Import helper functions
from mlfromscratch.deep_learning import NeuralNetwork
from mlfromscratch.utils import train_test_split, to_categorical, normalize
from mlfromscratch.utils import get_random_subsets, shuffle_data, Plot
from mlfromscratch.utils.data_operation import accuracy_score
from mlfromscratch.deep_learning.optimizers import StochasticGradientDescent, Adam, RMSprop, Adagrad, Adadelta
from mlfromscratch.deep_learning.loss_functions import CrossEntropy
from mlfromscratch.utils.misc import bar_widgets
from mlfromscratch.deep_learning.layers import Dense, Dropout, Conv2D, Flatten, Activation, MaxPooling2D
from mlfromscratch.deep_learning.layers import AveragePooling2D, ZeroPadding2D, BatchNormalization, RNN



def main():

    #----------
    # Conv Net
    #----------

    optimizer = Adam()

    data = datasets.load_digits()
    X = data.data
    y = data.target

    # Convert to one-hot encoding
    y = to_categorical(y.astype("int"))

    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, seed=1)

    # Reshape X to (n_samples, channels, height, width)
    X_train = X_train.reshape((-1,1,8,8))
    X_test = X_test.reshape((-1,1,8,8))

    clf = NeuralNetwork(optimizer=optimizer,
                        loss=CrossEntropy,
                        validation_data=(X_test, y_test))

    clf.add(Conv2D(n_filters=16, filter_shape=(3,3), stride=1, input_shape=(1,8,8), padding='same'))
    clf.add(Activation('relu'))
    clf.add(Dropout(0.25))
    clf.add(BatchNormalization())
    clf.add(Conv2D(n_filters=32, filter_shape=(3,3), stride=1, padding='same'))
    clf.add(Activation('relu'))
    clf.add(Dropout(0.25))
    clf.add(BatchNormalization())
    clf.add(Flatten())
    clf.add(Dense(256))
    clf.add(Activation('relu'))
    clf.add(Dropout(0.4))
    clf.add(BatchNormalization())
    clf.add(Dense(10))
    clf.add(Activation('softmax'))

    print ()
    clf.summary(name="ConvNet")

    train_err, val_err = clf.fit(X_train, y_train, n_epochs=50, batch_size=256)

    # Training and validation error plot
    n = len(train_err)
    training, = plt.plot(range(n), train_err, label="Training Error")
    validation, = plt.plot(range(n), val_err, label="Validation Error")
    plt.legend(handles=[training, validation])
    plt.title("Error Plot")
    plt.ylabel('Error')
    plt.xlabel('Iterations')
    plt.show()

    _, accuracy = clf.test_on_batch(X_test, y_test)
    print ("Accuracy:", accuracy)


    y_pred = np.argmax(clf.predict(X_test), axis=1)
    X_test = X_test.reshape(-1, 8*8)
    # Reduce dimension to 2D using PCA and plot the results
    Plot().plot_in_2d(X_test, y_pred, title="Convolutional Neural Network", accuracy=accuracy, legend_labels=range(10))

if __name__ == "__main__":
    main()

我们还是一步步进行分析：

1、优化器使用Adam()

2、数据集使用的是sklearn.datasets中的手写数字，其部分数据如下：

(1797, 64)
(1797,)
[[ 0.  0.  5. 13.  9.  1.  0.  0.  0.  0. 13. 15. 10. 15.  5.  0.  0.  3.
  15.  2.  0. 11.  8.  0.  0.  4. 12.  0.  0.  8.  8.  0.  0.  5.  8.  0.
   0.  9.  8.  0.  0.  4. 11.  0.  1. 12.  7.  0.  0.  2. 14.  5. 10. 12.
   0.  0.  0.  0.  6. 13. 10.  0.  0.  0.]
 [ 0.  0.  0. 12. 13.  5.  0.  0.  0.  0.  0. 11. 16.  9.  0.  0.  0.  0.
   3. 15. 16.  6.  0.  0.  0.  7. 15. 16. 16.  2.  0.  0.  0.  0.  1. 16.
  16.  3.  0.  0.  0.  0.  1. 16. 16.  6.  0.  0.  0.  0.  1. 16. 16.  6.
   0.  0.  0.  0.  0. 11. 16. 10.  0.  0.]
 [ 0.  0.  0.  4. 15. 12.  0.  0.  0.  0.  3. 16. 15. 14.  0.  0.  0.  0.
   8. 13.  8. 16.  0.  0.  0.  0.  1.  6. 15. 11.  0.  0.  0.  1.  8. 13.
  15.  1.  0.  0.  0.  9. 16. 16.  5.  0.  0.  0.  0.  3. 13. 16. 16. 11.
   5.  0.  0.  0.  0.  3. 11. 16.  9.  0.]
 [ 0.  0.  7. 15. 13.  1.  0.  0.  0.  8. 13.  6. 15.  4.  0.  0.  0.  2.
   1. 13. 13.  0.  0.  0.  0.  0.  2. 15. 11.  1.  0.  0.  0.  0.  0.  1.
  12. 12.  1.  0.  0.  0.  0.  0.  1. 10.  8.  0.  0.  0.  8.  4.  5. 14.
   9.  0.  0.  0.  7. 13. 13.  9.  0.  0.]
 [ 0.  0.  0.  1. 11.  0.  0.  0.  0.  0.  0.  7.  8.  0.  0.  0.  0.  0.
   1. 13.  6.  2.  2.  0.  0.  0.  7. 15.  0.  9.  8.  0.  0.  5. 16. 10.
   0. 16.  6.  0.  0.  4. 15. 16. 13. 16.  1.  0.  0.  0.  0.  3. 15. 10.
   0.  0.  0.  0.  0.  2. 16.  4.  0.  0.]
 [ 0.  0. 12. 10.  0.  0.  0.  0.  0.  0. 14. 16. 16. 14.  0.  0.  0.  0.
  13. 16. 15. 10.  1.  0.  0.  0. 11. 16. 16.  7.  0.  0.  0.  0.  0.  4.
   7. 16.  7.  0.  0.  0.  0.  0.  4. 16.  9.  0.  0.  0.  5.  4. 12. 16.
   4.  0.  0.  0.  9. 16. 16. 10.  0.  0.]
 [ 0.  0.  0. 12. 13.  0.  0.  0.  0.  0.  5. 16.  8.  0.  0.  0.  0.  0.
  13. 16.  3.  0.  0.  0.  0.  0. 14. 13.  0.  0.  0.  0.  0.  0. 15. 12.
   7.  2.  0.  0.  0.  0. 13. 16. 13. 16.  3.  0.  0.  0.  7. 16. 11. 15.
   8.  0.  0.  0.  1.  9. 15. 11.  3.  0.]
 [ 0.  0.  7.  8. 13. 16. 15.  1.  0.  0.  7.  7.  4. 11. 12.  0.  0.  0.
   0.  0.  8. 13.  1.  0.  0.  4.  8.  8. 15. 15.  6.  0.  0.  2. 11. 15.
  15.  4.  0.  0.  0.  0.  0. 16.  5.  0.  0.  0.  0.  0.  9. 15.  1.  0.
   0.  0.  0.  0. 13.  5.  0.  0.  0.  0.]
 [ 0.  0.  9. 14.  8.  1.  0.  0.  0.  0. 12. 14. 14. 12.  0.  0.  0.  0.
   9. 10.  0. 15.  4.  0.  0.  0.  3. 16. 12. 14.  2.  0.  0.  0.  4. 16.
  16.  2.  0.  0.  0.  3. 16.  8. 10. 13.  2.  0.  0.  1. 15.  1.  3. 16.
   8.  0.  0.  0. 11. 16. 15. 11.  1.  0.]
 [ 0.  0. 11. 12.  0.  0.  0.  0.  0.  2. 16. 16. 16. 13.  0.  0.  0.  3.
  16. 12. 10. 14.  0.  0.  0.  1. 16.  1. 12. 15.  0.  0.  0.  0. 13. 16.
   9. 15.  2.  0.  0.  0.  0.  3.  0.  9. 11.  0.  0.  0.  0.  0.  9. 15.
   4.  0.  0.  0.  9. 12. 13.  3.  0.  0.]]
[0 1 2 3 4 5 6 7 8 9]

3、接着有一个to_categorical()函数，在mlfromscratch.utils下的data_manipulation.py中：

def to_categorical(x, n_col=None):
    """ One-hot encoding of nominal values """
    if not n_col:
        n_col = np.amax(x) + 1
    one_hot = np.zeros((x.shape[0], n_col))
    one_hot[np.arange(x.shape[0]), x] = 1
    return one_hot

用于将标签转换为one-hot编码。

4、划分训练集和测试集：train_test_split()，在mlfromscratch.utils下的data_manipulation.py中：

def train_test_split(X, y, test_size=0.5, shuffle=True, seed=None):
    """ Split the data into train and test sets """
    if shuffle:
        X, y = shuffle_data(X, y, seed)
    # Split the training data from test data in the ratio specified in
    # test_size
    split_i = len(y) - int(len(y) // (1 / test_size))
    X_train, X_test = X[:split_i], X[split_i:]
    y_train, y_test = y[:split_i], y[split_i:]

    return X_train, X_test, y_train, y_test

5、由于卷积神经网络的输入是[batchsize,channel,wheight,width]的维度，因此要将原始数据进行转换，即将(1797,64)转换为(1797,1,8,8)格式的数据。这里batchsize就是样本的数量。

6、定义卷积神经网络的训练和测试过程：包括优化器、损失函数、测试数据

7、定义模型结构

8、输出模型每层的类型、参数数量以及输出大小

9、将数据输入到模型中，设置epochs的大小以及batch_size的大小

10、计算训练和测试的错误，并绘制成图

11、计算准确率

12、绘制测试集中每一类预测的结果，这里有一个plot_in_2d()函数，位于mlfromscratch.utils下的misc.py中

 # Plot the dataset X and the corresponding labels y in 2D using PCA.
    def plot_in_2d(self, X, y=None, title=None, accuracy=None, legend_labels=None):
        X_transformed = self._transform(X, dim=2)
        x1 = X_transformed[:, 0]
        x2 = X_transformed[:, 1]
        class_distr = []

        y = np.array(y).astype(int)

        colors = [self.cmap(i) for i in np.linspace(0, 1, len(np.unique(y)))]

        # Plot the different class distributions
        for i, l in enumerate(np.unique(y)):
            _x1 = x1[y == l]
            _x2 = x2[y == l]
            _y = y[y == l]
            class_distr.append(plt.scatter(_x1, _x2, color=colors[i]))

        # Plot legend
        if not legend_labels is None: 
            plt.legend(class_distr, legend_labels, loc=1)

        # Plot title
        if title:
            if accuracy:
                perc = 100 * accuracy
                plt.suptitle(title)
                plt.title("Accuracy: %.1f%%" % perc, fontsize=10)
            else:
                plt.title(title)

        # Axis labels
        plt.xlabel('Principal Component 1')
        plt.ylabel('Principal Component 2')

        plt.show()

接下来就可以实际进行操作了，我是在谷歌colab中，首先使用：

!git clone https://github.com/eriklindernoren/ML-From-Scratch.git

将相关代码复制下来。

然后进行安装：在ML-From-Scratch目录下输入：

!python setup.py install

最后输入：

 !python mlfromscratch/examples/convolutional_neural_network.py

最终结果：

+---------+
| ConvNet |
+---------+
Input Shape: (1, 8, 8)
+----------------------+------------+--------------+
| Layer Type           | Parameters | Output Shape |
+----------------------+------------+--------------+
| Conv2D               | 160        | (16, 8, 8)   |
| Activation (ReLU)    | 0          | (16, 8, 8)   |
| Dropout              | 0          | (16, 8, 8)   |
| BatchNormalization   | 2048       | (16, 8, 8)   |
| Conv2D               | 4640       | (32, 8, 8)   |
| Activation (ReLU)    | 0          | (32, 8, 8)   |
| Dropout              | 0          | (32, 8, 8)   |
| BatchNormalization   | 4096       | (32, 8, 8)   |
| Flatten              | 0          | (2048,)      |
| Dense                | 524544     | (256,)       |
| Activation (ReLU)    | 0          | (256,)       |
| Dropout              | 0          | (256,)       |
| BatchNormalization   | 512        | (256,)       |
| Dense                | 2570       | (10,)        |
| Activation (Softmax) | 0          | (10,)        |
+----------------------+------------+--------------+
Total Parameters: 538570

Training: 100% [------------------------------------------------] Time:  0:01:32

Accuracy: 0.9846796657381616

 

 

至此，结合代码一步一步看卷积神经网络的整个实现过程就完成了。通过结合代码的形式，可以加深对深度学习中卷积神经网络相关知识的理解。