关键代码：
tflearn.DNN(net, checkpoint_path='model_resnet_cifar10',
max_checkpoints=10, tensorboard_verbose=0,
clip_gradients=0.)
snapshot_epoch=True, # Snapshot (save & evaluate) model every epoch.
我的demo：
def get_model(width, height, classes=40):
# TODO, modify model
network = input_data(shape=[None, width, height, 3]) # if RGB, 224,224,3
# Residual blocks
# 32 layers: n=5, 56 layers: n=9, 110 layers: n=18
n = 2
net = tflearn.conv_2d(network, 16, 3, regularizer='L2', weight_decay=0.0001)
net = tflearn.residual_block(net, n, 16)
net = tflearn.residual_block(net, 1, 32, downsample=True)
net = tflearn.residual_block(net, n-1, 32)
net = tflearn.residual_block(net, 1, 64, downsample=True)
net = tflearn.residual_block(net, n-1, 64)
net = tflearn.batch_normalization(net)
net = tflearn.activation(net, 'relu')
net = tflearn.global_avg_pool(net)
# Regression
net = tflearn.fully_connected(net, classes, activation='softmax')
#mom = tflearn.Momentum(0.1, lr_decay=0.1, decay_step=32000, staircase=True)
mom = tflearn.Momentum(0.01, lr_decay=0.1, decay_step=2000, staircase=True)
net = tflearn.regression(net, optimizer=mom,
loss='categorical_crossentropy')
# Training
model = tflearn.DNN(net, checkpoint_path='model_resnet_cifar10',
max_checkpoints=10, tensorboard_verbose=0,
clip_gradients=0.)
return modeldef main():
trainX, trainY = image_preloader("data/train", image_shape=(width, height, 3), mode='folder', categorical_labels=True, normalize=True)
testX, testY = image_preloader("data/test", image_shape=(width, height, 3), mode='folder', categorical_labels=True, normalize=True)
#trainX = trainX.reshape([-1, width, height, 1])
#testX = testX.reshape([-1, width, height, 1])
print("sample data:")
print(trainX[0])
print(trainY[0])
print(testX[-1])
print(testY[-1]) model = get_model(width, height, classes=3755) filename = 'tflearn_resnet/model.tflearn'
# try to load model and resume training
try:
#model.load(filename)
model.load("model_resnet_cifar10-195804")
print("Model loaded OK. Resume training!")
except:
pass early_stopping_cb = EarlyStoppingCallback(val_acc_thresh=0.94)
try:
model.fit(trainX, trainY, validation_set=(testX, testY), n_epoch=500, shuffle=True,
snapshot_epoch=True, # Snapshot (save & evaluate) model every epoch.
show_metric=True, batch_size=1024, callbacks=early_stopping_cb, run_id='cnn_handwrite')
except StopIteration as e:
print("OK, stop iterate!Good!") model.save(filename) del tf.get_collection_ref(tf.GraphKeys.TRAIN_OPS)[:]
filename = 'tflearn_resnet/model-infer.tflearn'
model.save(filename)
