TensorFlow2构建模型-境界4

1.导入CIFAR-10数据集

CIFAR-10是由 Hinton 的学生 Alex Krizhevsky 和 Ilya Sutskever 整理的一个用于识别普适物体的小型数据集。一共包含 10 个类别的 RGB 彩色图片:飞机( a叩lane )、汽车( automobile )、鸟类( bird )、猫( cat )、鹿( deer )、狗( dog )、蛙类( frog )、马( horse )、船( ship )和卡车( truck )。图片的尺寸为 32×32,3个通道 ,数据集中一共有 50000 张训练圄片和 10000 张测试图片。 CIFAR-10数据集有3个版本,这里使用python版本。

1.1 导入需要的库

1.2 定义批量导入数据的函数

1.3 定义加载数据函数

1.4 加载数据

运行结果
loading data\cifar-10-batches-py\data_batch_1
loading data\cifar-10-batches-py\data_batch_2
loading data\cifar-10-batches-py\data_batch_3
loading data\cifar-10-batches-py\data_batch_4
loading data\cifar-10-batches-py\data_batch_5
finished loadding CIFAR-10 data

1.5 可视化加载数据

运行结果

2 .数据预处理并设置超参数

3.使用tf.data构建数据管道

4.构建模型

使用子类方法自定义模型结构的一般步骤:

5.训练模型

(1)实例化模型
model = MyCNN()
(2)查看模型的详细结构
model.model01().summary()
Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) [(None, 32, 32, 3)] 0
_________________________________________________________________
conv2d (Conv2D) (None, 32, 32, 32) 896
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 16, 16, 32) 0
_________________________________________________________________
conv2d_1 (Conv2D) (None, 16, 16, 64) 18496
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 8, 8, 64) 0
_________________________________________________________________
reshape (Reshape) (None, 4096) 0
_________________________________________________________________
dense (Dense) (None, 256) 1048832
_________________________________________________________________
dense_1 (Dense) (None, 10) 2570
_________________________________________________________________
tf_op_layer_Softmax (TensorF [(None, 10)] 0
=================================================================
Total params: 1,070,794
Trainable params: 1,070,794
Non-trainable params: 0
(3)编译及训练模型
model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])
epochs = 10
batch_size = 64
learning_rate = 0.0002

train_history = model.fit(x_train, y_train,
validation_split=0.2,
epochs=epochs,
#steps_per_epoch=100,
batch_size=batch_size,
verbose=1)
运行结果
Train on 40000 samples, validate on 10000 samples
Epoch 1/10
40000/40000 [==============================] - 5s 113us/sample - loss: 1.4482 - accuracy: 0.4811 - val_loss: 1.1808 - val_accuracy: 0.5889
Epoch 2/10
40000/40000 [==============================] - 2s 53us/sample - loss: 1.0530 - accuracy: 0.6291 - val_loss: 1.0052 - val_accuracy: 0.6466
Epoch 3/10
40000/40000 [==============================] - 2s 51us/sample - loss: 0.9031 - accuracy: 0.6814 - val_loss: 0.9358 - val_accuracy: 0.6751
Epoch 4/10
40000/40000 [==============================] - 2s 50us/sample - loss: 0.7926 - accuracy: 0.7207 - val_loss: 0.8919 - val_accuracy: 0.6909
Epoch 5/10
40000/40000 [==============================] - 2s 53us/sample - loss: 0.6966 - accuracy: 0.7573 - val_loss: 0.8932 - val_accuracy: 0.6904
Epoch 6/10
40000/40000 [==============================] - 2s 50us/sample - loss: 0.6029 - accuracy: 0.7889 - val_loss: 0.8699 - val_accuracy: 0.7036
Epoch 7/10
40000/40000 [==============================] - 2s 51us/sample - loss: 0.5131 - accuracy: 0.8210 - val_loss: 0.8832 - val_accuracy: 0.7092
Epoch 8/10
40000/40000 [==============================] - 2s 52us/sample - loss: 0.4263 - accuracy: 0.8533 - val_loss: 0.9517 - val_accuracy: 0.7028
Epoch 9/10
40000/40000 [==============================] - 2s 53us/sample - loss: 0.3407 - accuracy: 0.8815 - val_loss: 0.9970 - val_accuracy: 0.7065
Epoch 10/10
40000/40000 [==============================] - 2s 52us/sample - loss: 0.2693 - accuracy: 0.9078 - val_loss: 1.0540 - val_accuracy: 0.7090

6.可视化运行结果

7.测试模型

运行结果
10000/10000 - 1s - loss: 1.1505 - accuracy: 0.6936
test_loss: 1.1505295364379884
test_acc: 0.6936
metrics_names: ['loss', 'accuracy']

8.保存恢复整个模型

(1)保存模型参数及网络结构等
可以使用两种格式将整个模型保存到磁盘:TensorFlow SavedModel 格式和较早的 Keras H5 格式。 tensorflow官方推荐使用 SavedModel 格式。它是使用 model.save() 时的默认格式,这种保存方式适合Sequential, Functional Model, or Model subclass。

(2)恢复模型

(3)检查恢复模型的结构

运行结果
Model: "my_cnn_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_2 (Conv2D) multiple 896
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 multiple 0
_________________________________________________________________
conv2d_3 (Conv2D) multiple 18496
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 multiple 0
_________________________________________________________________
reshape_1 (Reshape) multiple 0
_________________________________________________________________
dense_2 (Dense) multiple 1048832
_________________________________________________________________
dense_3 (Dense) multiple 2570
=================================================================
Total params: 1,070,794
Trainable params: 1,070,794
Non-trainable params: 0
_______________________________
与原模型结构完全一致!

(4)基于恢复模型进行测试

运行结果
10000/10000 - 1s - loss: 1.1488 - accuracy: 0.6936
test_loss: 1.1488204129219055
test_acc: 0.6936
metrics_names: ['loss', 'accuracy']
模型精度与原模型完全一致!

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