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.)
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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.)
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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
原文：https://www.cnblogs.com/hufeng2021/p/14932293.html