一，准备数据
cifar2数据集为cifar10数据集的子集，只包括前两种类别airplane和automobile。
训练集有airplane和automobile图片各5000张，测试集有airplane和automobile图片各1000张。
cifar2任务的目标是训练一个模型来对飞机airplane和机动车automobile两种图片进行分类。
我们准备的Cifar2数据集的文件结构如下所示。
在tensorflow中准备图片数据的常用方案有两种，第一种是使用tf.keras中的ImageDataGenerator工具构建图片数据生成器。
第二种是使用tf.data.Dataset搭配tf.image中的一些图片处理方法构建数据管道。
第二种方法是TensorFlow的原生方法，更加灵活，使用得当的话也可以获得更好的性能。
我们此处介绍第二种方法。
import tensorflow as tf from tensorflow.keras import datasets,layers,models BATCH_SIZE = 100 def load_image(img_path,size = (32,32)): label = tf.constant(1,tf.int8) if tf.strings.regex_full_match(img_path,".*automobile.*") \ else tf.constant(0,tf.int8) img = tf.io.read_file(img_path) img = tf.image.decode_jpeg(img) #注意此处为jpeg格式 img = tf.image.resize(img,size)/255.0 return(img,label)
#使用并行化预处理num_parallel_calls 和预存数据prefetch来提升性能 ds_train = tf.data.Dataset.list_files("./data/cifar2/train/*/*.jpg") \ .map(load_image, num_parallel_calls=tf.data.experimental.AUTOTUNE) \ .shuffle(buffer_size = 1000).batch(BATCH_SIZE) \ .prefetch(tf.data.experimental.AUTOTUNE) ds_test = tf.data.Dataset.list_files("./data/cifar2/test/*/*.jpg") \ .map(load_image, num_parallel_calls=tf.data.experimental.AUTOTUNE) \ .batch(BATCH_SIZE) \ .prefetch(tf.data.experimental.AUTOTUNE)
%matplotlib inline %config InlineBackend.figure_format = 'svg' #查看部分样本 from matplotlib import pyplot as plt plt.figure(figsize=(8,8)) for i,(img,label) in enumerate(ds_train.unbatch().take(9)): ax=plt.subplot(3,3,i+1) ax.imshow(img.numpy()) ax.set_title("label = %d"%label) ax.set_xticks([]) ax.set_yticks([]) plt.show()
for x,y in ds_train.take(1): print(x.shape,y.shape)
(100, 32, 32, 3) (100,)
二，定义模型
使用Keras接口有以下3种方式构建模型：使用Sequential按层顺序构建模型，使用函数式API构建任意结构模型，继承Model基类构建自定义模型。
此处选择使用函数式API构建模型。
tf.keras.backend.clear_session() #清空会话 inputs = layers.Input(shape=(32,32,3)) x = layers.Conv2D(32,kernel_size=(3,3))(inputs) x = layers.MaxPool2D()(x) x = layers.Conv2D(64,kernel_size=(5,5))(x) x = layers.MaxPool2D()(x) x = layers.Dropout(rate=0.1)(x) x = layers.Flatten()(x) x = layers.Dense(32,activation='relu')(x) outputs = layers.Dense(1,activation = 'sigmoid')(x) model = models.Model(inputs = inputs,outputs = outputs) model.summary()
Model: "model" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_1 (InputLayer) [(None, 32, 32, 3)] 0 _________________________________________________________________ conv2d (Conv2D) (None, 30, 30, 32) 896 _________________________________________________________________ max_pooling2d (MaxPooling2D) (None, 15, 15, 32) 0 _________________________________________________________________ conv2d_1 (Conv2D) (None, 11, 11, 64) 51264 _________________________________________________________________ max_pooling2d_1 (MaxPooling2 (None, 5, 5, 64) 0 _________________________________________________________________ dropout (Dropout) (None, 5, 5, 64) 0 _________________________________________________________________ flatten (Flatten) (None, 1600) 0 _________________________________________________________________ dense (Dense) (None, 32) 51232 _________________________________________________________________ dense_1 (Dense) (None, 1) 33 ================================================================= Total params: 103,425 Trainable params: 103,425 Non-trainable params: 0 _________________________________________________________________
三，训练模型
训练模型通常有3种方法，内置fit方法，内置train_on_batch方法，以及自定义训练循环。此处我们选择最常用也最简单的内置fit方法。
import datetime import os stamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") logdir = os.path.join('data', 'autograph', stamp) ## 在 Python3 下建议使用 pathlib 修正各操作系统的路径 # from pathlib import Path # stamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # logdir = str(Path('./data/autograph/' + stamp)) tensorboard_callback = tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1) model.compile( optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), loss=tf.keras.losses.binary_crossentropy, metrics=["accuracy"] ) history = model.fit(ds_train,epochs= 10,validation_data=ds_test, callbacks = [tensorboard_callback],workers = 4)
Train for 100 steps, validate for 20 steps Epoch 1/10 100/100 [==============================] - 16s 156ms/step - loss: 0.4830 - accuracy: 0.7697 - val_loss: 0.3396 - val_accuracy: 0.8475 Epoch 2/10 100/100 [==============================] - 14s 142ms/step - loss: 0.3437 - accuracy: 0.8469 - val_loss: 0.2997 - val_accuracy: 0.8680 Epoch 3/10 100/100 [==============================] - 13s 131ms/step - loss: 0.2871 - accuracy: 0.8777 - val_loss: 0.2390 - val_accuracy: 0.9015 Epoch 4/10 100/100 [==============================] - 12s 117ms/step - loss: 0.2410 - accuracy: 0.9040 - val_loss: 0.2005 - val_accuracy: 0.9195 Epoch 5/10 100/100 [==============================] - 13s 130ms/step - loss: 0.1992 - accuracy: 0.9213 - val_loss: 0.1949 - val_accuracy: 0.9180 Epoch 6/10 100/100 [==============================] - 14s 136ms/step - loss: 0.1737 - accuracy: 0.9323 - val_loss: 0.1723 - val_accuracy: 0.9275 Epoch 7/10 100/100 [==============================] - 14s 139ms/step - loss: 0.1531 - accuracy: 0.9412 - val_loss: 0.1670 - val_accuracy: 0.9310 Epoch 8/10 100/100 [==============================] - 13s 134ms/step - loss: 0.1299 - accuracy: 0.9525 - val_loss: 0.1553 - val_accuracy: 0.9340 Epoch 9/10 100/100 [==============================] - 14s 137ms/step - loss: 0.1158 - accuracy: 0.9556 - val_loss: 0.1581 - val_accuracy: 0.9340 Epoch 10/10 100/100 [==============================] - 14s 142ms/step - loss: 0.1006 - accuracy: 0.9617 - val_loss: 0.1614 - val_accuracy: 0.9345
四，评估模型
%load_ext tensorboard #%tensorboard --logdir ./data/autograph
from tensorboard import notebook notebook.list()
#在tensorboard中查看模型 notebook.start("--logdir ./data/autograph")
import pandas as pd dfhistory = pd.DataFrame(history.history) dfhistory.index = range(1,len(dfhistory) + 1) dfhistory.index.name = 'epoch' dfhistory
%matplotlib inline %config InlineBackend.figure_format = 'svg' import matplotlib.pyplot as plt def plot_metric(history, metric): train_metrics = history.history[metric] val_metrics = history.history['val_'+metric] epochs = range(1, len(train_metrics) + 1) plt.plot(epochs, train_metrics, 'bo--') plt.plot(epochs, val_metrics, 'ro-') plt.title('Training and validation '+ metric) plt.xlabel("Epochs") plt.ylabel(metric) plt.legend(["train_"+metric, 'val_'+metric]) plt.show()
plot_metric(history,"loss")
plot_metric(history,"accuracy")
#可以使用evaluate对数据进行评估 val_loss,val_accuracy = model.evaluate(ds_test,workers=4) print(val_loss,val_accuracy)
0.16139143370091916 0.9345
五，使用模型
可以使用model.predict(ds_test)进行预测。
也可以使用model.predict_on_batch(x_test)对一个批量进行预测。
model.predict(ds_test)
array([[9.9996173e-01], [9.5104784e-01], [2.8648047e-04], ..., [1.1484033e-03], [3.5589080e-02], [9.8537153e-01]], dtype=float32)
for x,y in ds_test.take(1): print(model.predict_on_batch(x[0:20]))
tf.Tensor( [[3.8065155e-05] [8.8236779e-01] [9.1433197e-01] [9.9921846e-01] [6.4052093e-01] [4.9970779e-03] [2.6735585e-04] [9.9842811e-01] [7.9198682e-01] [7.4823302e-01] [8.7208226e-03] [9.3951421e-03] [9.9790359e-01] [9.9998581e-01] [2.1642199e-05] [1.7915063e-02] [2.5839690e-02] [9.7538447e-01] [9.7393811e-01] [9.7333014e-01]], shape=(20, 1), dtype=float32)
六，保存模型
推荐使用TensorFlow原生方式保存模型。
# 保存权重，该方式仅仅保存权重张量 model.save_weights('./data/tf_model_weights.ckpt',save_format = "tf")
# 保存模型结构与模型参数到文件,该方式保存的模型具有跨平台性便于部署 model.save('./data/tf_model_savedmodel', save_format="tf") print('export saved model.') model_loaded = tf.keras.models.load_model('./data/tf_model_savedmodel') model_loaded.evaluate(ds_test)
[0.16139124035835267, 0.9345]
