# 读完一个周期后重新开始
i = (i + 1) % n
yield (np.array(X_train), np.array(Y_train))
def loss(y_true, y_pred):
crossloss = K.binary_crossentropy(y_true, y_pred)
loss = 16 * K.sum(crossloss) / HEIGHT / WIDTH
return loss
if __name__ == "__main__":
log_dir = "logs/"
# 获取model
model = mobilenet_pspnet(n_classes=NCLASSES,
input_height=HEIGHT,
input_width=WIDTH)
# model.summary()
BASE_WEIGHT_PATH = ('https://github.com/fchollet/deep-learning-models/'
'releases/download/v0.6/')
model_name = 'mobilenet_%s_%d_tf_no_top.h5' % ('1_0', 224)
weight_path = BASE_WEIGHT_PATH + model_name
weights_path = keras.utils.get_file(model_name, weight_path)
print(weight_path)
model.load_weights(weights_path, by_name=True, skip_mismatch=True)
# model.summary()
# 打开数据集的txt
with open(r".\dataset2\train.txt", "r") as f:
lines = f.readlines()
#---------------------------------------------#
# 该部分用于查看网络结构
#---------------------------------------------#
from nets.pspnet import mobilenet_pspnet
if __name__ == "__main__":
model = mobilenet_pspnet(2, 576, 576)
model.summary()
