mnist 自定义CNN模型与lenet
import numpy as np
import matplotlib.pyplot as plt
from tensorflow import keras
from keras.layers import Input, Dense, Dropout, Activation,Conv2D,MaxPool2D,Flatten
from keras.datasets import mnist
from keras.models import Model
from tensorflow.python.keras.utils.np_utils import to_categorical
from keras.callbacks import TensorBoard
%load_ext tensorboard
%tensorboard --logdir ‘./log/train‘
# 加载一次后，如果要重新加载，就需要使用reload方法
%reload_ext tensorboard
%tensorboard --logdir ‘./log/train‘
(x_train,y_train),(x_test,y_test) = mnist.load_data()
# 数据可视化
def image(data,row,col):
images=data[:col*row]
fig,axs=plt.subplots(row,col)
idx = 0
for i in range(row):
for j in range(col):
axs[i, j].imshow(images[idx, :, :], cmap=‘gray‘)
axs[i, j].axis(‘off‘)
idx += 1
plt.show()
image(x_train,5,5)
temp=np.unique(y_train)
y_train_numbers = []
for v in temp:
y_train_numbers.append(np.sum(y_train==v))
plt.title("The number of each digit in the train data")
plt.bar(temp,y_train_numbers)
plt.xlabel(‘digits‘, color=‘b‘)
plt.ylabel(‘numbers of digits‘, color=‘b‘)
for x, y in zip(range(len(y_train_numbers)), y_train_numbers):
plt.text(x + 0.05, y + 0.05, ‘%.2d‘ % y, ha=‘center‘, va=‘bottom‘)
plt.show()
# 数据标准化
def normalize(x):
x = x.astype(np.float32) / 255.0
return x
x_train = x_train.reshape(x_train.shape[0], 28, 28, 1).astype(‘float32‘)
x_test = x_test.reshape(x_test.shape[0], 28, 28, 1).astype(‘float32‘)
x_train = normalize(x_train)
x_test = normalize(x_test)
# 创建模型
(60000, 28, 28)
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Dropout, Flatten
%load_ext tensorboard
#使用tensorboard 扩展
%tensorboard --logdir logs
#定位tensorboard读取的文件目录
# logs是存放tensorboard文件的目录
def model_1():
# 创建模型，输入28*28*1个神经元，输出10个神经元
model = Sequential()
model.add(Conv2D(filters=8, kernel_size=(5, 5), padding=‘same‘,
input_shape=(28, 28, 1), activation=‘relu‘))
# 第一个池化层
model.add(MaxPooling2D(pool_size=(2, 2))) # 出来的特征图为14*14大小
# 第二个
卷积层 16个滤波器（卷积核），卷积窗口大小为5*5
# 经过第二个卷积层后，有16个feature map，每个feature map为14*14
model.add(Conv2D(filters=16, kernel_size=(5,5), padding=‘same‘,
activation=‘relu‘))
model.add(MaxPooling2D(pool_size=(2, 2))) # 出来的特征图为7*7大小
# 平坦层，把第二个池化层的输出扁平化为1维 16*7*7
model.add(Flatten())
# 第一个全连接层
model.add(Dense(100, activation=‘relu‘))
# Dropout，用来放弃一些权值，防止过拟合
model.add(Dropout(0.25))
# 第二个全连接层，由于是输出层，所以使用softmax做激活函数
model.add(Dense(10, activation=‘softmax‘))
# 打印模型
return model
model_demo1=model_1()
model_demo1.summary()
Model: "sequential_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_2 (Conv2D) (None, 28, 28, 8) 208
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 14, 14, 8) 0
_________________________________________________________________
conv2d_3 (Conv2D) (None, 14, 14, 16) 3216
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 7, 7, 16) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 784) 0
_________________________________________________________________
dense_2 (Dense) (None, 100) 78500
_________________________________________________________________
dropout_1 (Dropout) (None, 100) 0
_________________________________________________________________
dense_3 (Dense) (None, 10) 1010
=================================================================
Total params: 82,934
Trainable params: 82,934
Non-trainable params: 0
_________________________________________________________________
batch_size=200
# 使用tensorboard可视化训练过程
# logs是存放tensorboard文件的目录
model_demo1.compile(loss=‘sparse_categorical_crossentropy‘,
optimizer=‘adam‘, metrics=[‘acc‘])
from tensorflow.keras.callbacks import TensorBoard
tbCallBack = TensorBoard(log_dir=‘./log‘, histogram_freq=1,
write_graph=True,
write_grads=True,
batch_size=batch_size,
write_images=True)
WARNING:tensorflow:`write_grads` will be ignored in TensorFlow 2.0 for the `TensorBoard` Callback.
WARNING:tensorflow:`batch_size` is no longer needed in the `TensorBoard` Callback and will be ignored in TensorFlow 2.0.
# 开始训练
history = model_demo1.fit(x=x_train, y=y_train, validation_split=0.2,
epochs=1, batch_size=200, verbose=1
,validation_data=(x_test, y_test)
,callbacks=[tbCallBack])
240/240 [==============================] - 32s 132ms/step - loss: 0.4900 - acc: 0.8523 - val_loss: 0.1351 - val_acc: 0.9604
%load_ext tensorboard
%tensorboard --logdir ‘./log‘
#定位tensorboard读取的文件目录
# logs是存放tensorboard文件的目录
The tensorboard extension is already loaded. To reload it, use:
%reload_ext tensorboard
Reusing TensorBoard on port 6008 (pid 975), started 0:13:42 ago. (Use ‘!kill 975‘ to kill it.)
<IPython.core.display.Javascript object>
x_train.shape
(60000, 28, 28, 1)
import tensorflow as tf
# 使用LeNet网络
def model_LeNet():
model = tf.keras.Sequential()
#28*28->14*14
model.add(tf.keras.layers.MaxPool2D((2,2),input_shape=(28,28,1)))
#卷积层：16个卷积核，尺寸为5*5，
参数个数16*（5*5*6+1）=2416,
维度变为（10，10，16）
model.add(tf.keras.layers.Conv2D(16,(5,5),activation=‘relu‘))
#池化层，维度变化为（5，5，16）
model.add(tf.keras.layers.MaxPool2D((2,2)))
#拉直，维度（400，）
model.add(tf.keras.layers.Flatten())
#全连接层,参数量400*120+120=48120
model.add(tf.keras.layers.Dense(120,activation=‘relu‘))
#全连接层,参数个数84*120+84=10164
model.add(tf.keras.layers.Dense(84,activation=‘relu‘))
#全连接层,参数个数10*120+10=130,
model.add(tf.keras.layers.Dense(10,activation=‘softmax‘))
return model
model_demo2=model_LeNet()
model_demo2.compile(loss=‘sparse_categorical_crossentropy‘,
optimizer=‘adam‘, metrics=[‘acc‘])
from tensorflow.keras.callbacks import TensorBoard
tbCallBack = TensorBoard(log_dir=‘./log‘, histogram_freq=1,
write_graph=True,
write_grads=True,
batch_size=batch_size,
write_images=True)
# 开始训练
history = model_demo2.fit(x=x_train, y=y_train, validation_split=0.2,
epochs=1, batch_size=200, verbose=1
,validation_data=(x_test, y_test)
,callbacks=[tbCallBack])
WARNING:tensorflow:`write_grads` will be ignored in TensorFlow 2.0 for the `TensorBoard` Callback.
WARNING:tensorflow:`batch_size` is no longer needed in the `TensorBoard` Callback and will be ignored in TensorFlow 2.0.
240/240 [==============================] - 6s 24ms/step - loss: 0.4880 - acc: 0.8655 - val_loss: 0.1780 - val_acc: 0.9462
%load_ext tensorboard
%tensorboard --logdir ‘./log‘
#定位tensorboard读取的文件目录
# logs是存放tensorboard文件的目录
The tensorboard extension is already loaded. To reload it, use:
%reload_ext tensorboard
Reusing TensorBoard on port 6008 (pid 975), started 0:28:04 ago. (Use ‘!kill 975‘ to kill it.)
<IPython.core.display.Javascript object>
x_train[0].shape
(28, 28, 1)
def lenet():
# 输入需要为32*32
model=keras.Sequential()
model.add(keras.layers.Conv2D(6,(1,1),activation=‘relu‘,input_shape=(28,28,1)))
# 28*28*6
#池化层,没有参数，维度变化为（14，14，6）
model.add(tf.keras.layers.MaxPool2D((2,2)))
#卷积层：16个卷积核，尺寸为5*5，参数个数16*（5*5*6+1）=2416,维度变为（10，10，16）
model.add(tf.keras.layers.Conv2D(16,(5,5),activation=‘relu‘))
#池化层，维度变化为（5，5，16）
model.add(tf.keras.layers.MaxPool2D((2,2)))
#拉直，维度（400，）
model.add(tf.keras.layers.Flatten())
#全连接层,参数量400*120+120=48120
model.add(tf.keras.layers.Dense(120,activation=‘relu‘))
#全连接层,参数个数84*120+84=10164
model.add(tf.keras.layers.Dense(84,activation=‘relu‘))
#全连接层,参数个数10*120+10=130,
model.add(tf.keras.layers.Dense(10,activation=‘softmax‘))
model.summary()
return model
mnist 自定义CNN模型与lenet
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