keras用vgg16作圖像分類

實際上我只是提供一個模版而已，代碼應該很容易看得懂，label是存在一個csv裏面的，圖片是在一個文件夾裏面的

沒GPU的就不用嘗試了，訓練一次要好久好久。。。

## import libaries
import pandas as pd
import numpy as np
from skimage import io
import os, sys
from tqdm import tqdm

## load data
train = pd.read_csv('./data/data/train.csv')
test = pd.read_csv('./data/data/test.csv')

def read_img(img_path):
    img = io.imread(img_path)
    return img

## set path for images
TRAIN_PATH = './data/data/train_img/'
TEST_PATH = './data/data/test_img/'


# load data
train_img, test_img = [],[]
for img_path in tqdm(train['image_id'].values):
    train_img.append(read_img(TRAIN_PATH + img_path + '.png'))

for img_path in tqdm(test['image_id'].values):
    test_img.append(read_img(TEST_PATH + img_path + '.png'))

# normalize images
x_train = np.array(train_img, np.float32) / 255.
x_test = np.array(test_img, np.float32) / 255.

# target variable - encoding numeric value
label_list = train['label'].tolist()
Y_train = {k:v+1 for v,k in enumerate(set(label_list))}
y_train = [Y_train[k] for k in label_list]   
y_train = np.array(y_train)


from keras import applications
from keras.models import Model
from keras import optimizers
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.layers.normalization import BatchNormalization
from keras.metrics import categorical_accuracy
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import EarlyStopping
from keras.utils import to_categorical
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import ModelCheckpoint

y_train = to_categorical(y_train)

#Transfer learning with Inception V3 
base_model = applications.VGG16(weights='imagenet', include_top=False, input_shape=(256, 256, 3))

## set model architechture 
add_model = Sequential()
add_model.add(Flatten(input_shape=base_model.output_shape[1:]))
add_model.add(Dense(256, activation='relu'))
add_model.add(Dense(y_train.shape[1], activation='softmax'))

model = Model(inputs=base_model.input, outputs=add_model(base_model.output))
model.compile(loss='categorical_crossentropy', optimizer=optimizers.SGD(lr=1e-4, momentum=0.9),
            metrics=['accuracy'])

model.summary()

batch_size = 128 # tune it
epochs = 30 # increase it
print ("Hello")
train_datagen = ImageDataGenerator(
        shear_range=0.2,
        zoom_range=0.2,
        rotation_range=30, 
        width_shift_range=0.1,
        height_shift_range=0.1, 
        horizontal_flip=True)
train_datagen.fit(x_train)

history = model.fit_generator(
    train_datagen.flow(x_train, y_train, batch_size=batch_size),
    steps_per_epoch=x_train.shape[0] // batch_size,
    epochs=epochs,
    callbacks=[ModelCheckpoint('VGG16-transferlearning2.model', monitor='val_acc', save_best_only=True)]
)

## predict test data
predictions = model.predict(x_test)


# get labels
predictions = np.argmax(predictions, axis=1)
rev_y = {v:k for k,v in Y_train.items()}
pred_labels = [rev_y[k] for k in predictions]

## make submission
sub = pd.DataFrame({'image_id':test.image_id, 'label':pred_labels})
sub.to_csv('sub_vgg2.csv', index=False) ## ~0.59