①自制数据集，解决本领域应用
②数据增强，扩充数据集
③断点续训，存取模型
④参数提取，把参数存入文本
⑤acc/loss可视化，查看训练效果
⑥应用程序，给图识物
数据增强
image_gen_train = tf.keras.preprocessing.image.ImageDataGenerator(
rescale = 所有数据将乘以该数值
rotation_range = 随机旋转角度数范围
width_shift_range = 随机宽度偏移量
height_shift_range = 随机高度偏移量
水平翻转：horizontal_flip = 是否随机水平翻转
随机缩放：zoom_range = 随机缩放的范围 [1-n，1+n] )
例：
数据增强（增大数据量）
11
image_gen_train = ImageDataGenerator(
rescale=1. / 1., # 如为图像，分母为255时，可归至0～1
rotation_range=45, # 随机45度旋转
width_shift_range=.15, # 宽度偏移
height_shift_range=.15, # 高度偏移
horizontal_flip=False, # 水平翻转
zoom_range=0.5 # 将图像随机缩放阈量50％)
image_gen_train.fit(x_train)
image_gen_train.fit(x_t
实现对训练数据的增强
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
fashion = tf.keras.datasets.fashion_mnist
(x_train, y_train), (x_test, y_test) = fashion.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
x_train = x_train.reshape(x_train.shape[0], 28, 28, 1) # 给数据增加一个维度，使数据和网络结构匹配
image_gen_train = ImageDataGenerator(
rescale=1. / 1., # 如为图像，分母为255时，可归至0～1
rotation_range=45, # 随机45度旋转
width_shift_range=.15, # 宽度偏移
height_shift_range=.15, # 高度偏移
horizontal_flip=True, # 水平翻转
zoom_range=0.5 # 将图像随机缩放阈量50％
)
image_gen_train.fit(x_train)
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
metrics=['sparse_categorical_accuracy'])
model.fit(image_gen_train.flow(x_train, y_train, batch_size=32), epochs=5, validation_data=(x_test, y_test),
validation_freq=1)
model.summary()
读取保存模型
load_weights(路径文件名）
tf.keras.callbacks.ModelCheckpoint(
filepath=路径文件名,
save_weights_only=True/False,
save_best_only=True/False)
history = model.fit（ callbacks=[cp_callback] ）
保存模型：
保存模型 回调函数
import tensorflow as tf
import os
fashion = tf.keras.datasets.fashion_mnist
(x_train, y_train), (x_test, y_test) = fashion.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
metrics=['sparse_categorical_accuracy'])
checkpoint_save_path = "./checkpoint/fashion.ckpt"
if os.path.exists(checkpoint_save_path + '.index'):
print('-------------load the model-----------------')
model.load_weights(checkpoint_save_path)
cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_save_path,
save_weights_only=True,
save_best_only=True)
history = model.fit(x_train, y_train, batch_size=32, epochs=5, validation_data=(x_test, y_test), validation_freq=1,
callbacks=[cp_callback])
model.summary()
提取可训练参数
model.trainable_variables 返回模型中可训练的参数
设置print输出格式
np.set_printoptions(threshold=超过多少省略显示)
np.set_printoptions(threshold=np.inf) # np.inf表示无限大
print(model.trainable_variables)
file = open('./weights.txt', 'w')
for v in model.trainable_variables:
file.write(str(v.name) + '\n')
file.write(str(v.shape) + '\n')
file.write(str(v.numpy()) + '\n')
file.close()
acc曲线与loss曲线
history=model.fit(训练集数据, 训练集标签, batch_size=, epochs=,validation_split=用作测试数据的比例,validation_data=测试集,validation_freq=测试频率)
history：
训练集loss： loss
测试集loss： val_loss
训练集准确率： sparse_categorical_accuracy
测试集准确率： val_sparse_categorical_accuracy
# 显示训练集和验证集的acc和loss曲线
checkpoint_save_path = "./checkpoint/fashion.ckpt"
if os.path.exists(checkpoint_save_path + '.index'):
print('-------------load the model-----------------')
model.load_weights(checkpoint_save_path)
cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_save_path,
save_weights_only=True,
save_best_only=True)
history = model.fit(x_train, y_train, batch_size=32, epochs=5, validation_data=(x_test, y_test), validation_freq=1,callbacks=[cp_callback])
acc = history.history['sparse_categorical_accuracy']
val_acc = history.history['val_sparse_categorical_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
plt.subplot(1, 2, 1)
plt.plot(acc, label='Training Accuracy') #训练集准确率
plt.plot(val_acc, label='Validation Accuracy') #测试集准确率
plt.title('Training and Validation Accuracy')
plt.legend()
plt.subplot(1, 2, 2)
plt.plot(loss, label='Training Loss') # 训练集 损失函数
plt.plot(val_loss, label='Validation Loss') #测试集损失函数
plt.title('Training and Validation Loss')
plt.legend()
plt.show()
predict(输入特征, batch_size=整数)
返回前向传播计算结果
复现模型
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dense(10, activation='softmax’)])
加载参数
model.load_weights(model_save_path)
预测结果
result = model.predict(x_predict)
from PIL import Image
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
type = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
model_save_path = './checkpoint/fashion.ckpt'
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dense(10, activation='softmax')
])
model.load_weights(model_save_path)
preNum = int(input("input the number of test pictures:"))
for i in range(preNum):
image_path = input("the path of test picture:")
img = Image.open(image_path)
image = plt.imread(image_path)
plt.set_cmap('gray')
plt.imshow(image)
img=img.resize((28,28),Image.ANTIALIAS)
img_arr = np.array(img.convert('L'))
img_arr = 255 - img_arr
img_arr=img_arr/255.0
x_predict = img_arr[tf.newaxis,...]
result = model.predict(x_predict)
pred=tf.argmax(result, axis=1)
print('\n')
print(type[int(pred)])
plt.pause(1)
plt.close()