[英]Load keras model h5 unknown metrics
我已经训练了一个 keras CNN 监控如下指标:
METRICS = [
TruePositives(name='tp'),
FalsePositives(name='fp'),
TrueNegatives(name='tn'),
FalseNegatives(name='fn'),
BinaryAccuracy(name='accuracy'),
Precision(name='precision'),
Recall(name='recall'),
AUC(name='auc'),
]
然后是 model.compile:
model.compile(optimizer='nadam', loss='binary_crossentropy',
metrics=METRICS)
它工作得很好,我保存了我的 h5 model (model.h5)。
现在我已经下载了 model 并且我想在导入 model 的其他脚本中使用它:
from keras.models import load_model
model = load_model('model.h5')
model.predict(....)
但在运行期间编译器返回:
ValueError: Unknown metric function: {'class_name': 'TruePositives', 'config': {'name': 'tp', 'dtype': 'float32', 'thresholds': None}}
我应该如何处理这个问题?
先感谢您
当您有自定义指标时,您需要遵循稍微不同的方法。
custom_objects
并compile = False
我在这里展示了方法
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%
看起来您正在玩 tensorflow 教程。 我也使用了这些确切的指标并且遇到了同样的问题。 对我有用的是使用compile = False
加载 model,然后使用自定义指标对其进行编译。 然后您应该能够按预期使用model.predict(....)
。
import keras
model = keras.models.load_model('model.h5', compile = False)
METRICS = [
keras.metrics.TruePositives(name='tp'),
keras.metrics.FalsePositives(name='fp'),
keras.metrics.TrueNegatives(name='tn'),
keras.metrics.FalseNegatives(name='fn'),
keras.metrics.BinaryAccuracy(name='accuracy'),
keras.metrics.Precision(name='precision'),
keras.metrics.Recall(name='recall'),
keras.metrics.AUC(name='auc'),
]
model.compile(optimizer = keras.optimizers.Adam(learning_rate=1e-4),
loss = 'binary_crossentropy',
metrics = METRICS
)
custom_objects['METRICS'] = METRICS
model = load_model('model.h5', custom_objects=custom_objects)
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