import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
from keras.callbacks import ModelCheckpoint, TensorBoard
from keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense
from keras.layers.normalization import BatchNormalization
from keras.models import Sequential, load_model
from keras.preprocessing.image import ImageDataGenerator
from sklearn.metrics import classification_report
from sklearn.preprocessing import LabelBinarizer
matplotlib.use("Agg")
def GNT_generator(path, bs, start, end, mode='train', aug=None):
idx = start - 1
# print(start, "a epoch")
while True:
labels = []
images = []
# count = 0
# print(idx, "start")
for count in range(0, bs):
try:
# count = count + 1
idx = idx + 1
# print(count, idx)
if idx == end:
# print("reach end")
idx = start
if mode == 'eval':
break
for dir_name in _label:
root_path = os.path.join(path, dir_name)
filename = os.path.join(root_path, str(idx) + '.png')
try:
im = Image.open(filename).resize([64, 64])
except IOError:
# print("Error: no file name %s" % filename)
continue
image = np.asarray(im).reshape((64, 64, 3))
label = dir_name
images.append(image)
labels.append(label)
except StopIteration:
# print("ERROR at", count, idx)
continue
# print(" a batchsize")
labels = lb.transform(np.array(labels))
images = np.array(images)
if aug is not None:
(images, labels) = next(aug.flow(images,
labels, batch_size=bs))
yield (images, labels)
TRAIN_PATH =r'G:\best\matlab\a'
TEST_PATH = r'G:\best\matlab\test'
BS = 1
_label = [dir_name for dir_name in os.listdir(TRAIN_PATH)]
number_of_classes = len(_label)
num_of_epochs = 12
STEP_TRAIN = 60
STEP_VALIDATION = 60
def build_model():
model = Sequential()
model.add(Conv2D(64, (3, 3),
activation='relu', padding='same', strides=(1, 1),
input_shape=(64, 64, 1)))
model.add(BatchNormalization(axis=-1))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
model.add(Conv2D(128, (3, 3),
activation='relu', padding='same', strides=(1, 1)))
model.add(BatchNormalization(axis=-1))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
model.add(Conv2D(256, (3, 3),
activation='relu', padding='same', strides=(1, 1)))
model.add(BatchNormalization(axis=-1))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
model.add(Flatten())
model.add(Dropout(0.5))
model.add(Dense(1024, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(number_of_classes, activation='softmax'))
# model.add(Conv2D(128, (3, 3), input_shape=(64, 64, 3)))
# model.add(BatchNormalization(axis=-1))
# model.add(Activation('relu'))
# model.add(Conv2D(64, (3, 3)))
# model.add(BatchNormalization(axis=-1))
# model.add(Activation('relu'))
# model.add(MaxPooling2D(pool_size=(2, 2)))
#
# model.add(Conv2D(64, (3, 3)))
# model.add(BatchNormalization(axis=-1))
# model.add(Activation('relu'))
# model.add(Conv2D(64, (3, 3)))
# model.add(BatchNormalization(axis=-1))
# model.add(Activation('relu'))
# model.add(MaxPooling2D(pool_size=(2, 2)))
#
# model.add(Flatten())
#
# # Fully connected layer
# model.add(Dense(1024))
# model.add(BatchNormalization())
# model.add(Activation('relu'))
# model.add(Dropout(0.4))
# model.add(Dense(number_of_classes))
#
# model.add(Activation('softmax'))
return model
save_path = r'G:\best\trained_best_weights_1.h5'
def training(train, test):
model = build_model()
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
checkpoint = ModelCheckpoint(save_path,
monitor='val_loss', verbose=1, save_best_only=True, period=1)
tensor_board = TensorBoard(log_dir=r'G:\best\CNN', write_graph=True, batch_size=BS)
model.summary()
if os.path.exists(save_path):
model = load_model(save_path)
# 若成功加载前面保存的参数，输出下列信息
print("checkpoint_loaded")
H = model.fit(
train,
steps_per_epoch=STEP_TRAIN,
validation_data=test,
validation_steps=STEP_VALIDATION,
verbose=2,
epochs=num_of_epochs,
callbacks=[checkpoint, tensor_board]
) # the more epoch the better
model.save('model.h5')
# test = GNT_generator(TEST_PATH, BS, 193, 241, mode='eval', aug=None)
N = num_of_epochs
plt.style.use("ggplot")
plt.figure()
plt.plot(np.arange(0, N), H.history["loss"], label="train_loss")
plt.plot(np.arange(0, N), H.history["val_loss"], label="val_loss")
plt.plot(np.arange(0, N), H.history["accuracy"], label="train_acc")
plt.plot(np.arange(0, N), H.history["val_accuracy"], label="val_acc")
plt.title("Training Loss and Accuracy on Dataset")
plt.xlabel("Epoch #")
plt.ylabel("Loss/Accuracy")
plt.legend(loc="lower left")
plt.savefig("plot.png")
def testing(test):
# load model
model = build_model()
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.load_weights(save_path)
predIdxs = model.predict_generator(test,
steps=(STEP_VALIDATION) + 1)
predIdxs = np.argmax(predIdxs, axis=1)
print("[INFO] evaluating network...")
print(classification_report(test.classes, predIdxs,
target_names=lb.classes_))
print(model.metrics)
if __name__ == "__main__":
lb = LabelBinarizer()
lb.fit(_label)
mlabels = lb.transform(_label)
train_datagen = ImageDataGenerator(rescale=1. / 255,
rotation_range=0,
width_shift_range=0.1,
height_shift_range=0.1)
trainGen = train_datagen.flow_from_directory(TRAIN_PATH,
target_size=(64, 64),
batch_size=BS,
classes=_label,
class_mode="categorical",
color_mode="grayscale")
STEP_TRAIN = trainGen.n // trainGen.batch_size
test_datagen = ImageDataGenerator(rescale=1. / 255)
testGen = test_datagen.flow_from_directory(TEST_PATH,
target_size=(64, 64),
batch_size=BS,
classes=_label,
class_mode="categorical",
color_mode="grayscale")
STEP_VALIDATION = testGen.n // testGen.batch_size
training(trainGen, testGen)
testing(testGen)
报错详情：无法读取到图像
