机器学习进阶-案例实战-停车场车位识别-keras预测是否停车站有车

import numpy
import os
 
from keras import applications
from keras.preprocessing.image import ImageDataGenerator
from keras import optimizers
from keras.models import Sequential, Model
from keras.layers import Dropout, Flatten, Dense, GlobalAveragePooling2D
from keras import backend as k
from keras.callbacks import ModelCheckpoint, LearningRateScheduler, TensorBoard, EarlyStopping
from keras.models import Sequential
from keras.layers.normalization import BatchNormalization
from keras.layers.convolutional import Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras.initializers import TruncatedNormal
from keras.layers.core import Activation
from keras.layers.core import Flatten
from keras.layers.core import Dropout
from keras.layers.core import Dense
 
files_train = 0
files_validation = 0
 
cwd = os.getcwd()
folder = 'train_data/train'
for sub_folder in os.listdir(folder):
    path, dirs, files = next(os.walk(os.path.join(folder,sub_folder)))
    files_train += len(files)
 
folder = 'train_data/test'
for sub_folder in os.listdir(folder):
    path, dirs, files = next(os.walk(os.path.join(folder,sub_folder)))
    files_validation += len(files)
 
print(files_train,files_validation)
 
img_width, img_height = 48, 48
train_data_dir = "train_data/train"
validation_data_dir = "train_data/test"
nb_train_samples = files_train
nb_validation_samples = files_validation
batch_size = 32
epochs = 15
num_classes = 2
 
model = applications.VGG16(weights='imagenet', include_top=False, input_shape = (img_width, img_height, 3))
 
for layer in model.layers[:10]:
    layer.trainable = False
 
x = model.output
x = Flatten()(x)
predictions = Dense(num_classes, activation="softmax")(x)
 
model_final = Model(input = model.input, output = predictions)
 
model_final.compile(loss = "categorical_crossentropy",
                    optimizer = optimizers.SGD(lr=0.0001, momentum=0.9),
                    metrics=["accuracy"]) 
 
train_datagen = ImageDataGenerator(
rescale = 1./255,
horizontal_flip = True,
fill_mode = "nearest",
zoom_range = 0.1,
width_shift_range = 0.1,
height_shift_range=0.1,
rotation_range=5)
 
test_datagen = ImageDataGenerator(
rescale = 1./255,
horizontal_flip = True,
fill_mode = "nearest",
zoom_range = 0.1,
width_shift_range = 0.1,
height_shift_range=0.1,
rotation_range=5)
 
train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size = (img_height, img_width),
batch_size = batch_size,
class_mode = "categorical")
 
validation_generator = test_datagen.flow_from_directory(
validation_data_dir,
target_size = (img_height, img_width),
class_mode = "categorical")
 
checkpoint = ModelCheckpoint("car1.h5", monitor='val_acc', verbose=1, save_best_only=True, save_weights_only=False, mode='auto', period=1)
early = EarlyStopping(monitor='val_acc', min_delta=0, patience=10, verbose=1, mode='auto')
 
history_object = model_final.fit_generator(
train_generator,
samples_per_epoch = nb_train_samples,
epochs = epochs,
validation_data = validation_generator,
nb_val_samples = nb_validation_samples,
callbacks = [checkpoint, early])