学习了基本理论后，进行keras实战练习手写数字识别，主要目的是了解Keras相关API的使用，并且对深度学习再深入理解下。深度学习的主要特点是特征的自动提取。我都不需要设置权重的初始化值。它会自行修正。
为了再理解下卷积核对特征图片的影响，所以实现了一个工程，期望打印出过程中的特征图片。打印出来后，貌似没看出什么东东。后来了解了卷积神经网络的缺点就是物理含义不明确（也就说，我们并不知道没个卷积层到底提取到的是什么特征，而且神经网络本身就是一种难以解释的“黑箱模型”）
python源码及数据集都在github上
https://github.com/AppleCai/Keras_cnn_mytest.git
网络模型如下：
model_my.png
打印的参数如下：
Layer (type) Output Shape Param #
Dense_1_my (Conv2D) (None, 32, 28, 28) 832
pool_1_my (MaxPooling2D) (None, 32, 14, 14) 0
Dense_2_my (Conv2D) (None, 64, 14, 14) 51264
max_pooling2d_1 (MaxPooling2 (None, 64, 7, 7) 0
flatten_1 (Flatten) (None, 3136) 0
dense_1 (Dense) (None, 1024) 3212288
activation_1 (Activation) (None, 1024) 0
dense10 (Dense) (None, 10) 10250
softmax (Activation) (None, 10) 0
参数如何算？
Dense_1_my参数个数计算55321+32=832
Dense_2_my参数个数计算556432+64=51264
其中5是卷积核的size。
尝试提取了2层如下效果。
python代码
import os
import numpy as np
import cv2
np.random.seed(1337) # for reproducibility
from keras.models import Sequential,Model,load_model
from keras.layers import Dense, Activation, Convolution2D, MaxPooling2D, Flatten,Input,Conv2D
from keras.optimizers import Adam
import random
#工程1，训练出模型
i,j=0,0
img_rows, img_cols = 28, 28
data = np.zeros([8000, 1,img_rows, img_cols])
label = np.zeros([8000,10])
sum = 0
imgs = os.listdir("D:\pytorchpro\pro\mnist_digits_images")
print(len(os.listdir("D:\pytorchpro\pro\mnist_digits_images\\"+imgs[0])))
num = len(imgs)
for i in range(num):
path="D:\pytorchpro\pro\mnist_digits_images\\"+imgs[i]
pic=os.listdir(path)
for j, val in enumerate(pic):
data[j+sum, :, :,:] = cv2.resize(cv2.cvtColor(cv2.imread(path +"\\" +val), cv2.COLOR_BGR2GRAY), (img_rows, img_cols))/255
label[j+sum,i:i+1] = i
if (np.mod(j+sum, 100) == 0):
print('第', j+sum, '个训练图片正在装载')
sum += j+1 #每个i循环还要再加1的原因是，list的循环是从j=0开始的，所以要补加1个。
# print(data.shape)
#打散
index = [i for i in range(len(data))]
random.shuffle(index)
data = data[index]
label = label[index]
#分配数量
train_data = data[:7000]
train_labels = label[:7000]
validation_data = data[7000:]
validation_labels = label[7000:]
# print(train_data.shape)
# print(train_labels.shape)
# print(validation_data.shape)
# print(validation_labels.shape)
#创建模型
model = Sequential()
# Conv layer 2 output shape (32, 28, 28)
model.add(Conv2D(
batch_input_shape=(None, 1, 28, 28),
filters=32,
kernel_size=5,
strides=1,
padding='same', # Padding method
data_format='channels_first',
activation='relu',
name="Dense_1_my"
))
# Pooling layer 1 (max pooling) output shape (32, 14, 14)
model.add(MaxPooling2D(
pool_size=2,
strides=2,
padding='same', # Padding method
data_format='channels_first',
name="pool_1_my",
))
# Conv layer 2 output shape (64, 14, 14)
model.add(Convolution2D(64, 5, strides=1, padding='same', data_format='channels_first',activation='relu',name="Dense_2_my"))
# Pooling layer 2 (max pooling) output shape (64, 7, 7)
model.add(MaxPooling2D(2, 2, 'same', data_format='channels_first'))
# Fully connected layer 1 input shape (64 * 7 * 7) = (3136), output shape (1024)
model.add(Flatten())
model.add(Dense(1024))
model.add(Activation('relu'))
# Fully connected layer 2 to shape (10) for 10 classes
model.add(Dense(10,name="dense10"))
model.add(Activation('softmax',name="softmax"))
# Another way to define your optimizer
adam = Adam(lr=1e-4)
# We add metrics to get more results you want to see
model.compile(optimizer=adam,
loss='categorical_crossentropy',
metrics=['accuracy'])
print('Training ------------')
# Another way to train the model
model.fit(train_data, train_labels, epochs=5, batch_size=64,)
print('\nTesting ------------')
# Evaluate the model with the metrics we defined earlier
loss, accuracy = model.evaluate(validation_data, validation_labels)
print('\ntest loss: ', loss)
print('\ntest accuracy: ', accuracy)
model.summary()
from keras.utils.vis_utils import plot_model
plot_model(model, to_file='model_my.png',show_shapes=True)
model.save('my_model.h5')
del model
#工程2，查看部分特征图
image = cv2.cvtColor(cv2.imread('2.bmp', 1), cv2.COLOR_BGR2GRAY)
print(image.shape)
myimage = np.zeros([1, 1,28, 28])
myimage[0,0,:,:] = cv2.resize(image, (28, 28))/255
#可以修改想要导入的模块
model = load_model('my_model.h5')
dense1_layer_model = Model(inputs=model.input,outputs=model.get_layer('Dense_1_my').output)
out = dense1_layer_model.predict(myimage)
print (type(out.shape))
num = out.shape[1]
print(num)
image_conv=[]
out = out.reshape(32,28,28)
for i in range(num):
image_conv.append(out[i,:,:].reshape(28,28))
imgs = np.hstack(image_conv)
cv2.imshow("Dense_1_my", imgs)
pool1_layer_model = Model(inputs=model.input, outputs=model.get_layer('Dense_2_my').output)
out = pool1_layer_model.predict(myimage)
print(out.shape)
image_conv2=[]
out = out.reshape(64,14,14)
for i in range(64):
image_conv2.append(out[i,:,:].reshape(14,14))
imgs2 = np.hstack(image_conv2)
cv2.imshow("Dense_2_my", imgs2)
cv2.waitKey(0)