我正在学习关于Keras的本教程，但我不知道如何在训练后正确地使用自定义层保存此模型并加载它。这里和这里都提到了这个问题，但显然这些解决方案都不适用于这个Keras示例。有人能给我指正确的方向吗？
p.S：这是代码的主要部分：
class CTCLayer(layers.Layer):
def __init__(self, name=None):
super().__init__(name=name)
self.loss_fn = keras.backend.ctc_batch_cost
def call(self, y_true, y_pred):
# Compute the training-time loss value and add it
# to the layer using `self.add_loss()`.
batch_len = tf.cast(tf.shape(y_true)[0], dtype="int64")
input_length = tf.cast(tf.shape(y_pred)[1], dtype="int64")
label_length = tf.cast(tf.shape(y_true)[1], dtype="int64")
input_length = input_length * tf.ones(shape=(batch_len, 1), dtype="int64")
label_length = label_length * tf.ones(shape=(batch_len, 1), dtype="int64")
loss = self.loss_fn(y_true, y_pred, input_length, label_length)
self.add_loss(loss)
# At test time, just return the computed predictions
return y_pred
def build_model():
# Inputs to the model
input_img = layers.Input(
shape=(img_width, img_height, 1), name="image", dtype="float32"
)
labels = layers.Input(name="label", shape=(None,), dtype="float32")
# First conv block
x = layers.Conv2D(
32,
(3, 3),
activation="relu",
kernel_initializer="he_normal",
padding="same",
name="Conv1",
)(input_img)
x = layers.MaxPooling2D((2, 2), name="pool1")(x)
# Second conv block
x = layers.Conv2D(
64,
(3, 3),
activation="relu",
kernel_initializer="he_normal",
padding="same",
name="Conv2",
)(x)
x = layers.MaxPooling2D((2, 2), name="pool2")(x)
# We have used two max pool with pool size and strides 2.
# Hence, downsampled feature maps are 4x smaller. The number of
# filters in the last layer is 64. Reshape accordingly before
# passing the output to the RNN part of the model
new_shape = ((img_width // 4), (img_height // 4) * 64)
x = layers.Reshape(target_shape=new_shape, name="reshape")(x)
x = layers.Dense(64, activation="relu", name="dense1")(x)
x = layers.Dropout(0.2)(x)
# RNNs
x = layers.Bidirectional(layers.LSTM(128, return_sequences=True, dropout=0.25))(x)
x = layers.Bidirectional(layers.LSTM(64, return_sequences=True, dropout=0.25))(x)
# Output layer
x = layers.Dense(len(characters) + 1, activation="softmax", name="dense2")(x)
# Add CTC layer for calculating CTC loss at each step
output = CTCLayer(name="ctc_loss")(labels, x)
# Define the model
model = keras.models.Model(
inputs=[input_img, labels], outputs=output, name="ocr_model_v1"
)
# Optimizer
opt = keras.optimizers.Adam()
# Compile the model and return
model.compile(optimizer=opt)
return model
# Get the model
model = build_model()
model.summary()class CTCLayer(layers.Layer):
def __init__(self, name=None):
super().__init__(name=name)
self.loss_fn = keras.backend.ctc_batch_cost
def call(self, y_true, y_pred):
# Compute the training-time loss value and add it
# to the layer using `self.add_loss()`.
batch_len = tf.cast(tf.shape(y_true)[0], dtype="int64")
input_length = tf.cast(tf.shape(y_pred)[1], dtype="int64")
label_length = tf.cast(tf.shape(y_true)[1], dtype="int64")
input_length = input_length * tf.ones(shape=(batch_len, 1), dtype="int64")
label_length = label_length * tf.ones(shape=(batch_len, 1), dtype="int64")
loss = self.loss_fn(y_true, y_pred, input_length, label_length)
self.add_loss(loss)
# At test time, just return the computed predictions
return y_pred
def build_model():
# Inputs to the model
input_img = layers.Input(
shape=(img_width, img_height, 1), name="image", dtype="float32"
)
labels = layers.Input(name="label", shape=(None,), dtype="float32")
# First conv block
x = layers.Conv2D(
32,
(3, 3),
activation="relu",
kernel_initializer="he_normal",
padding="same",
name="Conv1",
)(input_img)
x = layers.MaxPooling2D((2, 2), name="pool1")(x)
# Second conv block
x = layers.Conv2D(
64,
(3, 3),
activation="relu",
kernel_initializer="he_normal",
padding="same",
name="Conv2",
)(x)
x = layers.MaxPooling2D((2, 2), name="pool2")(x)
# We have used two max pool with pool size and strides 2.
# Hence, downsampled feature maps are 4x smaller. The number of
# filters in the last layer is 64. Reshape accordingly before
# passing the output to the RNN part of the model
new_shape = ((img_width // 4), (img_height // 4) * 64)
x = layers.Reshape(target_shape=new_shape, name="reshape")(x)
x = layers.Dense(64, activation="relu", name="dense1")(x)
x = layers.Dropout(0.2)(x)
# RNNs
x = layers.Bidirectional(layers.LSTM(128, return_sequences=True, dropout=0.25))(x)
x = layers.Bidirectional(layers.LSTM(64, return_sequences=True, dropout=0.25))(x)
# Output layer
x = layers.Dense(len(characters) + 1, activation="softmax", name="dense2")(x)
# Add CTC layer for calculating CTC loss at each step
output = CTCLayer(name="ctc_loss")(labels, x)
# Define the model
model = keras.models.Model(
inputs=[input_img, labels], outputs=output, name="ocr_model_v1"
)
# Optimizer
opt = keras.optimizers.Adam()
# Compile the model and return
model.compile(optimizer=opt)
return model
# Get the model
model = build_model()
model.summary()
epochs = 100
early_stopping_patience = 10
# Add early stopping
early_stopping = keras.callbacks.EarlyStopping(
monitor="val_loss", patience=early_stopping_patience, restore_best_weights=True
)
# Train the model
history = model.fit(
train_dataset,
validation_data=validation_dataset,
epochs=epochs,
callbacks=[early_stopping],
)
# Get the prediction model by extracting layers till the output layer
prediction_model = keras.models.Model(
model.get_layer(name="image").input, model.get_layer(name="dense2").output
)
prediction_model.summary()
问题实际上不在于Keras的保存方法。characters集合不一致并且不保持排序。在创建characters集合后添加以下代码以解决问题：
characters = sorted(list(characters))
@Amirhosein，在Horovod存储库中查看此函数：
序列化：https://github.com/horovod/horovod/blob/6f0bb9fae826167559501701d4a5a0380284b5f0/horovod/spark/keras/util.py#L115
反序列化：https://github.com/horovod/horovod/blob/6f0bb9fae826167559501701d4a5a0380284b5f0/horovod/spark/keras/remote.py#L267
反序列化的使用示例：https://github.com/horovod/horovod/blob/6f0bb9fae826167559501701d4a5a0380284b5f0/horovod/spark/keras/remote.py#L118
如果您使用自定义对象，如自定义度量或自定义损失函数，则需要使用custom_object_scope，如示例所示。
它使用了一个名为cloudpickle的包(https://pypi.org/project/cloudpickle/)将KerasModel转换为字符串，反之亦然。