现在用10x750+10x250张图片做训练测试
总体有101x750+101x250
不知道这样测试出来的网络能不能直接套用在全部的数据上(因为电脑原因跑的比较慢才这样测试的)
用10类别测试的时候 acc比较低而且把待测图片放入测试文件中的时候会出现100%但指向另外类别的结果
import sys
import os
from keras.preprocessing.image import ImageDataGenerator
from keras import optimizers
from keras.models import Sequential
from keras.layers import Dropout, Flatten, Dense, Activation
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras import callbacks
import time
import tensorflow as tf
config = tf.ConfigProto(gpu_options=tf.GPUOptions(allow_growth=True))
sess = tf.Session(config=config)
start = time.time()
DEV = False
argvs = sys.argv
argc = len(argvs)
if argc > 1 and (argvs[1] == "--development" or argvs[1] == "-d"):
DEV = True
if DEV:
epochs = 2
else:
epochs = 10
train_data_path = 'data/train'
validation_data_path = 'data/test'
"""
Parameters
"""
img_width, img_height = 100, 100
batch_size = 32
samples_per_epoch = 7500
validation_steps = 490
nb_filters1 = 32
nb_filters2 = 64
conv1_size = 3
conv2_size = 2
pool_size = 2
classes_num = 10
lr = 0.0004
model = Sequential()
model.add(Convolution2D(
nb_filters1,
conv1_size,
conv1_size,
border_mode ="same",
input_shape=(img_width, img_height, 3)
))
model.add(Activation("relu"))
model.add(MaxPooling2D(
pool_size=(pool_size, pool_size)
))
model.add(Convolution2D(nb_filters2, conv2_size, conv2_size, border_mode ="same"))
model.add(Activation("relu"))
model.add(Convolution2D(nb_filters2, conv2_size, conv2_size, border_mode ="same"))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(pool_size, pool_size), dim_ordering='th'))
model.add(Flatten())
model.add(Dense(256))
model.add(Activation("relu"))
model.add(Dropout(0.5))
model.add(Dense(classes_num, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer=optimizers.RMSprop(lr=lr),
metrics=['accuracy'])
train_datagen = ImageDataGenerator(
rescale=1. / 255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1. / 255)
train_generator = train_datagen.flow_from_directory(
train_data_path,
target_size=(img_height, img_width),
batch_size=batch_size,
class_mode='categorical')
validation_generator = test_datagen.flow_from_directory(
validation_data_path,
target_size=(img_height, img_width),
batch_size=batch_size,
class_mode='categorical')
"""
Tensorboard log
"""
log_dir = './training101tf-log/'
tb_cb = callbacks.TensorBoard(log_dir=log_dir, histogram_freq=0)
cbks = [tb_cb]
model.fit_generator(
train_generator,
samples_per_epoch=samples_per_epoch,
epochs=epochs,
validation_data=validation_generator,
callbacks=cbks,
validation_steps=validation_steps)
target_dir = './models_training101/'
if not os.path.exists(target_dir):
os.mkdir(target_dir)
model.save('./models_training101/model.h5')
model.save_weights('./models_training101/weights.h5')
#Calculate execution time
end = time.time()
dur = end-start
if dur<60:
print("Execution Time:",dur,"seconds")
elif dur>60 and dur<3600:
dur=dur/60
print("Execution Time:",dur,"minutes")
else:
dur=dur/(60*60)
print("Execution Time:",dur,"hours")