<p>当您有自定义指标时,您需要遵循稍微不同的方法</p>
<ol>
<li>创建模型,训练并保存模型</li>
<li>用<code>custom_objects</code>和<code>compile = False</code>加载模型</li>
<li>最后用自定义的_对象编译模型</li>
</ol>
<p>我在这里展示方法</p>
<pre><code>import tensorflow as tf
from tensorflow import keras
mnist = tf.keras.datasets.mnist
(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
# Custom Loss1 (for example)
#@tf.function()
def customLoss1(yTrue,yPred):
return tf.reduce_mean(yTrue-yPred)
# Custom Loss2 (for example)
#@tf.function()
def customLoss2(yTrue, yPred):
return tf.reduce_mean(tf.square(tf.subtract(yTrue,yPred)))
def create_model():
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(512, activation=tf.nn.relu),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy', customLoss1, customLoss2])
return model
# Create a basic model instance
model=create_model()
# Fit and evaluate model
model.fit(x_train, y_train, epochs=5)
loss, acc,loss1, loss2 = model.evaluate(x_test, y_test,verbose=1)
print("Original model, accuracy: {:5.2f}%".format(100*acc)) # Original model, accuracy: 98.11%
# saving the model
model.save('./Mymodel',save_format='tf')
# load the model
loaded_model = tf.keras.models.load_model('./Mymodel',custom_objects={'customLoss1':customLoss1,'customLoss2':customLoss2},compile=False)
# compile the model
loaded_model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy', customLoss1, customLoss2])
# loaded model also has same accuracy, metrics and loss
loss, acc,loss1, loss2 = loaded_model.evaluate(x_test, y_test,verbose=1)
print("Loaded model, accuracy: {:5.2f}%".format(100*acc)) #Loaded model, accuracy: 98.11%
</code></pre>