# Get the data.
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
# Convert class vectors to binary class matrices.
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
# Preprocess the images.
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
# Get the model and compile it.
img_input = keras.layers.Input(shape=(32, 32, 3))
model = MobileNet(input_tensor=img_input, classes=num_classes)
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
print("Training model.")
model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
validation_data=(x_test, y_test),
shuffle=True,
verbose=1)
# 神兽保佑
# BUG是不可能有BUG的!
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
from mobilenet import MobileNet
import numpy as np
np.random.seed(10)
from keras.datasets import cifar10
from keras.utils import np_utils
(x_img_train, y_label_train),(x_img_test, y_label_test) = cifar10.load_data()
x_img_train = x_img_train.astype('float')/255.0
x_img_test = x_img_test.astype('float')/255.0
y_label_train = np_utils.to_categorical(y_label_train)
y_label_test = np_utils.to_categorical(y_label_test)
model = MobileNet()
try:
model.load_weights("mobileV1-lite.h5")
print("模型加载成功！继续训练")
except:
print("模型加载失败！从头开始训练")
model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])
train_history = model.fit(x_img_train, y_label_train, validation_split=0.2, epochs=10, batch_size=128, verbose=2)
model.save_weights("mobileV1-lite.h5")
print("保存模型成功！")
import sys, os
sys.path.insert(0, os.path.abspath('..'))
from keras.callbacks import TensorBoard
import keras
from keras.datasets import cifar10
from mobilenet import MobileNet
batch_size = 128
num_classes = 10
epochs = 20
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
img_input = keras.layers.Input(shape=(32, 32, 3))
model = MobileNet(input_tensor=img_input, classes=num_classes)
model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])
model.summary()
model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
validation_data=(x_test, y_test),
shuffle=True,
verbose=1,
callbacks=[TensorBoard(log_dir='./runs/keras',write_grads=True)])
