1-2,图片数据建模流程范例

仅作示范，具体参数自己调

一，准备数据

训练集有airplane和automobile图片各5000张，测试集有airplane和automobile图片各1000张。

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) 
    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。

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()

三，训练模型

import datetime

logdir = "./data/keras_model/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
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)

四，评估模型

#%load_ext tensorboard
#%tensorboard --logdir ./data/keras_model

from tensorboard import notebook
notebook.list()

Known TensorBoard instances:
  - port 6006: logdir ./data/keras_model (started 0:12:09 ago; pid 4208)

#在tensorboard中查看模型
notebook.start("--logdir ./data/keras_model")

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)

20/20 [==============================] - 1s 40ms/step - loss: 1.9538e-11 - accuracy: 1.0000
1.9537624990086845e-11 1.0

五，使用模型

可以使用model.predict(ds_test)进行预测。

model.predict(ds_test)

array([[1.0360916e-19],
       [1.7873154e-16],
       [5.5106573e-23],
       ...,
       [6.4991871e-23],
       [1.1000750e-31],
       [2.0341410e-26]], dtype=float32)

for x,y in ds_test.take(1):
    print(model.predict_on_batch(x[0:20]))

tf.Tensor(
[[5.2837010e-23]
 [1.0252299e-24]
 [5.8508803e-26]
 [2.3309494e-22]
 [2.2657999e-16]
 [1.9107325e-17]
 [1.8045347e-20]
 [1.0879469e-17]
 [5.7537389e-25]
 [6.6131420e-19]
 [1.0352329e-14]
 [2.7486181e-20]
 [4.4809946e-27]
 [1.8120942e-21]
 [9.1887365e-32]
 [1.1202152e-24]
 [2.5798152e-26]
 [8.2470569e-21]
 [1.3405049e-20]
 [1.8362107e-15]], 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)