Question

我已经使用NN训练了tf.keras，并用ModelCheckpoint将整个模型保存在.h5文件中。

但是，当我使用models.load_model还原它，然后使用方法fit对其进行再次训练时，它仅返回一个History对象，并且无所事事。

以下是培训的minimal example：

import numpy as np
import tensorflow as tf

# Creates dummy data
train_x = np.random.randint(10,size=40).reshape(-1,1)
train_y = np.random.randint(2,size=40).reshape(-1,1)
train_set = (train_x,train_y)
val_x = np.random.randint(10,size=20).reshape(-1,1)
val_y = np.random.randint(2,size=20).reshape(-1,1)
val_set = (val_x,val_y)

# Set Learning Rate Decay
import math
def step_decay(epoch):
    print('--- Epoch:',epoch)
    print(tf.keras.callbacks.History())
    init_lr = 0.001
    drop = 0.9
    epochs_drop = 1.0
    lr = init_lr*math.pow(drop,math.floor((1+epoch)/epochs_drop))
    return(lr)

lr_callback = tf.keras.callbacks.LearningRateScheduler(step_decay)

# Saves the whole model
cp_callback = tf.keras.callbacks.ModelCheckpoint('model.h5',
    save_weights_only=False,
    verbose=True)

# Creates the model
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(1,activation='relu',use_bias=False,input_dim=(1)))
model.add(tf.keras.layers.Dense(100,activation='relu',use_bias=False))
model.add(tf.keras.layers.Dense(1,activation='relu',use_bias=False))
model.compile(loss='mean_squared_error',
        optimizer=tf.keras.optimizers.Adam(),
        metrics=['accuracy'])


print('Learning Rate: ',tf.keras.backend.eval(model.optimizer.lr))
# Train the model
model.fit(x=train_set[0],y=train_set[1],epochs=2,steps_per_epoch=40,
            validation_data=val_set,validation_steps=20,
            callbacks=[lr_callback,cp_callback])
print('Learning Rate: ',tf.keras.backend.eval(model.optimizer.lr))

我当前用于再次加载的代码如下。

import numpy as np
import tensorflow as tf

# Creates dummy data
train_x = np.random.randint(10,size=40).reshape(-1,1)
train_y = np.random.randint(2,size=40).reshape(-1,1)
train_set = (train_x,train_y)
val_x = np.random.randint(10,size=20).reshape(-1,1)
val_y = np.random.randint(2,size=20).reshape(-1,1)
val_set = (val_x,val_y)

# Set Learning Rate Decay
import math
def step_decay(epoch):
    print('--- Epoch:',epoch)
    print(tf.keras.callbacks.History())
    init_lr = 0.001
    drop = 0.9
    epochs_drop = 1.0
    lr = init_lr*math.pow(drop,math.floor((1+epoch)/epochs_drop))
    return(lr)

lr_callback = tf.keras.callbacks.LearningRateScheduler(step_decay)

# Saves the whole model
cp_callback = tf.keras.callbacks.ModelCheckpoint('model.h5',
    save_weights_only=False,
    verbose=True)

# Load model
model = tf.keras.models.load_model('model.h5')

print('Learning Rate: ',tf.keras.backend.eval(model.optimizer.lr))
model.fit(x=train_set[0],y=train_set[1],epochs=2,steps_per_epoch=40,
            validation_data=val_set,validation_steps=20,initial_epoch=3,
            callbacks=[lr_callback,cp_callback])

正如您在运行时所观察到的那样，学习率得到了恢复，因此整个模型也得到了恢复，或者至少我是这样认为的。但是，在运行model.fit(...)之后，除了返回<tensorflow.python.keras.callbacks.History object at 0x7f11c81cb940>外，它什么也没做。知道如何让它再次训练吗？

编辑：我还尝试通过将compile的{{1}}属性设置为true来对其进行编译。

Answer 1

加载后是否尝试编译它？

我的模型的拟合方法在加载load_model后不执行训练

1 个答案: