卷积神经网络————经典vggnet(三)

from tensorflow.keras.layers import Flatten,Dense,Dropout,Input
from tensorflow.keras.applications import VGG16
import matplotlib.pyplot as plt
from tensorflow.keras.models import Model
import csv
import os
import json
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import tensorflow as tf






base_model=VGG16(weights='imagenet',include_top=False,input_tensor=Input(shape=(224,224,3)))
#微调
head_model=base_model.output
head_model=Flatten(name="flatten")(head_model)
head_model = Dense(2048, activation="relu")(head_model)
head_model = Dropout(0.5)(head_model)
head_model=Dense(2048,activation='relu')(head_model)
head_model = Dense(5, activation="softmax")(head_model)
model=Model(base_model.input,head_model)
#冻结前面的5个卷积组，只训练自定义的全连接层
for layer in base_model.layers:
    layer.trainable=False

data_root = r"d:\下载的文件"
#data_root = os.path.abspath(os.path.join(os.getcwd(), "../.."))  # get data root path
image_path = os.path.join(data_root, "flower_photos")  # flower data set path
train_dir = os.path.join(image_path, "train")
validation_dir = os.path.join(image_path, "val")
assert os.path.exists(train_dir), "cannot find {}".format(train_dir)
assert os.path.exists(validation_dir), "cannot find {}".format(validation_dir)


# create direction for saving weights
if not os.path.exists("save_weights_pre_train"):
    os.makedirs("save_weights_pre_train")

im_height = 224
im_width = 224
batch_size = 32
epochs = 2

_R_MEAN = 123.68
_G_MEAN = 116.78
_B_MEAN = 103.94

def pre_function(img):
    # img = im.open('test.jpg')
    # img = np.array(img).astype(np.float32)
    img = img - [_R_MEAN, _G_MEAN, _B_MEAN]

    return img

# data generator with data augmentation
train_image_generator = ImageDataGenerator(horizontal_flip=True,
                                           preprocessing_function=pre_function)
validation_image_generator = ImageDataGenerator(preprocessing_function=pre_function)

train_data_gen = train_image_generator.flow_from_directory(directory=train_dir,
                                                           batch_size=batch_size,
                                                           shuffle=True,
                                                           target_size=(im_height, im_width),
                                                           class_mode='categorical')
total_train = train_data_gen.n

# get class dict
class_indices = train_data_gen.class_indices

# transform value and key of dict
inverse_dict = dict((val, key) for key, val in class_indices.items())
# write dict into json file
json_str = json.dumps(inverse_dict, indent=4)
with open('class_indices.json', 'w') as json_file:
    json_file.write(json_str)

val_data_gen = validation_image_generator.flow_from_directory(directory=validation_dir,
                                                              batch_size=batch_size,
                                                              shuffle=False,
                                                              target_size=(im_height, im_width),
                                                              class_mode='categorical')
total_val = val_data_gen.n
print("using {} images for training, {} images for validation.".format(total_train,
                                                                       total_val))

model.summary()

# using keras high level api for training
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001),
              loss=tf.keras.losses.CategoricalCrossentropy(from_logits=False),
              metrics=["accuracy"])
checkpoint_save_path = "./save_weights_pre_train/myVGG16.ckpt"
callbacks = [tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_save_path,
                                                save_best_only=True,
                                                save_weights_only=True,
                                                monitor='val_loss')]

# tensorflow2.1 recommend to using fit
history = model.fit(x=train_data_gen,
                    steps_per_epoch=total_train // batch_size,
                    epochs=epochs,
                    validation_data=val_data_gen,
                    validation_steps=total_val // batch_size,
                    callbacks=callbacks)
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']

file='./flower_history_pre_train.csv'

with open(file,'a',encoding='utf-8',newline='')as f:
    writer=csv.writer(f)
    writer.writerow(['acc','val_acc','loss','val_loss'])
    for i in range(len(acc)):
        writer.writerow([acc[i], val_acc[i], loss[i], val_loss[i]])





plt.subplot(1, 2, 1)
plt.plot(acc, label='Training Accuracy')
plt.plot(val_acc, label='Validation Accuracy')
plt.title('Training and Validation Accuracy')
plt.legend()

plt.subplot(1, 2, 2)
plt.plot(loss, label='Training Loss')
plt.plot(val_loss, label='Validation Loss')
plt.title('Training and Validation Loss')
plt.legend()
plt.show()

迁移学习的网络构造有点改变

标签：loss,val,vggnet,卷积,神经网络,train,path,model,data
来源： https://www.cnblogs.com/fengqing111/p/14764129.html