HCIA-AI_深度学习_图像分类
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图像分类
4 图像分类
4.1 实验介绍
4.1.1 关于本实验
4.1.2 目标
- 加强对keras神经网络模型构建过程的理解
- 掌握加载预训练模型的方法
- 学习使用checkpoint功能
- 掌握如何使用训练好的模型进行预测
4.2 实验步骤
4.2.1 导入依赖包
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers,optimizers,datasets,Sequential
from tensorflow.keras.layers import Conv2D,Activation,MaxPooling2D,Dropout,Flatten,Dense
import os
import numpy as np
import matplotlib.pyplot as plt
4.2.2 数据预处理
# 下载数据集
(x_train, y_train),(x_test,y_test) = datasets.cifar10.load_data()
# 打印数据集尺寸
print(x_train.shape, y_train.shape, x_test.shape, y_test.shape)
print(y_train[0])
# 转换标签
num_classes = 10
y_train_onehot = keras.utils.to_categorical(y_train, num_classes)
y_test_onehot = keras.utils.to_categorical(y_test, num_classes)
y_train_onehot[0]
(50000, 32, 32, 3) (50000, 1) (10000, 32, 32, 3) (10000, 1)
[6]
array([0., 0., 0., 0., 0., 0., 1., 0., 0., 0.], dtype=float32)
# 生成图像标签列表
category_dict = 0:'airplane', 1:'automobile', 2:'bird', 3:'cat', 4:'deer',
5:'dog', 6:'frog', 7:'horse', 8:'ship', 9:'truck'
# 展示前9张图片和标签
plt.figure()
for i in range(9):
plt.subplot(3, 3, i+1)
plt.imshow(x_train[i])
plt.ylabel(category_dict[y_train[i][0]])
plt.show()
# 像素归一化
x_train = x_train.astype('float32')/255
x_test = x_test.astype('float32')/255
4.2.3 模型构建
def CNN_classification_model(input_size = x_train.shape[1:]):
model = Sequential()
# 前两个模块中有两个卷积一个池化
# 输入(32,32,3) W1=32 H1=32 D1=3
# 卷积层 卷积核个数K=32 卷积核大小 w=3 h=3 步长S=1 P=0
# 输出(30,30,32) W2=(W1-w+2P)/S+1=(32-3+0)/1+1=30 H2=30 D2=K=32
# 参数个数=K*(w*h*D1+1)=32*(3*3*3+1)=896
model.add(Conv2D(32, (3,3), padding='same',
input_shape=input_size))
model.add(Activation('relu'))
model.add(Conv2D(32, (3,3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=1))
model.add(Conv2D(64, (3,3), padding='same'))
model.add(Activation('relu'))
model.add(Conv2D(64, (3,3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
# 接入全连接之前要进行扁平化
model.add(Flatten())
model.add(Dense(128))
model.add(Activation('relu'))
model.add(Dropout(0.25))
model.add(Dense(num_classes))
model.add(Activation('softmax'))
opt = optimizers.Adam(lr=0.0001)
model.compile(loss='sparse_categorical_crossentropy', optimizer=opt,
metrics=['accuracy'])
return model
model = CNN_classification_model()
model.summary()
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 32, 32, 32) 896
_________________________________________________________________
activation (Activation) (None, 32, 32, 32) 0
_________________________________________________________________
conv2d_1 (Conv2D) (None, 30, 30, 32) 9248
_________________________________________________________________
activation_1 (Activation) (None, 30, 30, 32) 0
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 29, 29, 32) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 29, 29, 64) 18496
_________________________________________________________________
activation_2 (Activation) (None, 29, 29, 64) 0
_________________________________________________________________
conv2d_3 (Conv2D) (None, 27, 27, 64) 36928
_________________________________________________________________
activation_3 (Activation) (None, 27, 27, 64) 0
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 13, 13, 64) 0
_________________________________________________________________
flatten (Flatten) (None, 10816) 0
_________________________________________________________________
dense (Dense) (None, 128) 1384576
_________________________________________________________________
activation_4 (Activation) (None, 128) 0
_________________________________________________________________
dropout (Dropout) (None, 128) 0
_________________________________________________________________
dense_1 (Dense) (None, 10) 1290
_________________________________________________________________
activation_5 (Activation) (None, 10) 0
=================================================================
Total params: 1,451,434
Trainable params: 1,451,434
Non-trainable params: 0
_________________________________________________________________
4.2.4 模型训练
from tensorflow.keras.callbacks import ModelCheckpoint
model_name = 'final_cifar10.h5'
model_checkpoint = ModelCheckpoint(model_name, monitor='loss',
verbose=1, save_best_only=True)
# 加载预训练模型
trained_weights_path = 'cifar10_weights.h5'
if os.path.exists(trained_weights_path):
model.load_weights(trained_weights_path, by_name=True)
# 训练
model.fit(x_train,y_train, batch_size=32, epochs=10, callbacks=[model_checkpoint], verbose=1)
Epoch 1/10
1563/1563 [==============================] - ETA: 0s - loss: 1.6375 - accuracy: 0.4070
Epoch 00001: loss improved from inf to 1.63746, saving model to final_cifar10.h5
1563/1563 [==============================] - 187s 119ms/step - loss: 1.6375 - accuracy: 0.4070
Epoch 2/10
1563/1563 [==============================] - ETA: 0s - loss: 1.3156 - accuracy: 0.5308
Epoch 00002: loss improved from 1.63746 to 1.31557, saving model to final_cifar10.h5
1563/1563 [==============================] - 186s 119ms/step - loss: 1.3156 - accuracy: 0.5308
Epoch 3/10
1563/1563 [==============================] - ETA: 0s - loss: 1.1716 - accuracy: 0.5857
Epoch 00003: loss improved from 1.31557 to 1.17161, saving model to final_cifar10.h5
1563/1563 [==============================] - 188s 120ms/step - loss: 1.1716 - accuracy: 0.5857
Epoch 4/10
1563/1563 [==============================] - ETA: 0s - loss: 1.0639 - accuracy: 0.6249
Epoch 00004: loss improved from 1.17161 to 1.06385, saving model to final_cifar10.h5
1563/1563 [==============================] - 189s 121ms/step - loss: 1.0639 - accuracy: 0.6249
Epoch 5/10
1563/1563 [==============================] - ETA: 0s - loss: 0.9776 - accuracy: 0.6583
Epoch 00005: loss improved from 1.06385 to 0.97756, saving model to final_cifar10.h5
1563/1563 [==============================] - 188s 120ms/step - loss: 0.9776 - accuracy: 0.6583
Epoch 6/10
1563/1563 [==============================] - ETA: 0s - loss: 0.9035 - accuracy: 0.6839
Epoch 00006: loss improved from 0.97756 to 0.90353, saving model to final_cifar10.h5
1563/1563 [==============================] - 188s 120ms/step - loss: 0.9035 - accuracy: 0.6839
Epoch 7/10
1563/1563 [==============================] - ETA: 0s - loss: 0.8395 - accuracy: 0.7081
Epoch 00007: loss improved from 0.90353 to 0.83948, saving model to final_cifar10.h5
1563/1563 [==============================] - 199s 127ms/step - loss: 0.8395 - accuracy: 0.7081
Epoch 8/10
1563/1563 [==============================] - ETA: 0s - loss: 0.7853 - accuracy: 0.7267
Epoch 00008: loss improved from 0.83948 to 0.78526, saving model to final_cifar10.h5
1563/1563 [==============================] - 201s 129ms/step - loss: 0.7853 - accuracy: 0.7267
Epoch 9/10
1563/1563 [==============================] - ETA: 0s - loss: 0.7296 - accuracy: 0.7460
Epoch 00009: loss improved from 0.78526 to 0.72955, saving model to final_cifar10.h5
1563/1563 [==============================] - 197s 126ms/step - loss: 0.7296 - accuracy: 0.7460
Epoch 10/10
1563/1563 [==============================] - ETA: 0s - loss: 0.6787 - accuracy: 0.7635
Epoch 00010: loss improved from 0.72955 to 0.67875, saving model to final_cifar10.h5
1563/1563 [==============================] - 190s 122ms/step - loss: 0.6787 - accuracy: 0.7635
<tensorflow.python.keras.callbacks.History at 0x1b20188e5e0>
4.2.5 模型评估
new_model = CNN_classification_model()
new_model.load_weights(model_name)
new_model.evaluate(x_test, y_test, verbose=1)
313/313 [==============================] - 9s 27ms/step - loss: 0.8307 - accuracy: 0.7131
[0.8307360410690308, 0.713100016117096]
# 预测一张图片
# 输出每一类别的输出结果
new_model.predict(x_test[0:1])
array([[2.0661957e-04, 5.8463629e-04, 4.0328883e-02, 8.1551665e-01,
5.9472341e-03, 8.8567801e-02, 3.8351275e-02, 3.9349191e-04,
9.8961936e-03, 2.0709026e-04]], dtype=float32)
# 输出预测结果
new_model.predict_classes(x_test[0:1])
WARNING:tensorflow:From <ipython-input-11-3680f3a0a3c8>:2: Sequential.predict_classes (from tensorflow.python.keras.engine.sequential) is deprecated and will be removed after 2021-01-01.
Instructions for updating:
Please use instead:* `np.argmax(model.predict(x), axis=-1)`, if your model does multi-class classification (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`, if your model does binary classification (e.g. if it uses a `sigmoid` last-layer activation).
array([3], dtype=int64)
# 输出4张图片和预测结果
pred_list = []
plt.figure()
for i in range(0, 4):
plt.subplot(2, 2, i+1)
plt.imshow(x_test[i])
pred = new_model.predict_classes(x_test[0:10])
pred_list.append(pred)
plt.title('pred:' + category_dict[pred[i]] + ' actual:' + category_dict[y_test[i][0]])
plt.axis('off')
plt.show()
4.3 总结
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