我根据 Keras 代码示例中提供的内容创建了一个 CAPTCHA 模型。 但是当我加载模型时，会弹出一个错误。

我向你展示我在 Jupyter notebook 中编写的代码。

STEP1) 模型构建

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,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,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,name="Conv2",)(x)
    x = layers.MaxPooling2D((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,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,dropout=0.25))(x)

    # Output layer
    x = layers.Dense(
        len(char_to_num.get_vocabulary()) + 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()

STEP2) 训练模型

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],)

STEP3) 检查预测

# 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()

# A utility function to decode the output of the network
def decode_batch_predictions(pred):
    input_len = np.ones(pred.shape[0]) * pred.shape[1]
    # Use greedy search. For complex tasks,you can use beam search
    results = keras.backend.ctc_decode(pred,input_length=input_len,greedy=True)[0][0][
        :,:max_length
    ]
    # Iterate over the results and get back the text
    output_text = []
    for res in results:
        res = tf.strings.reduce_join(num_to_char(res)).numpy().decode("utf-8")
        output_text.append(res)
    return output_text


#  Let's check results on some validation samples
for batch in validation_dataset.take(1):
    batch_images = batch["image"]
    batch_labels = batch["label"]

    preds = prediction_model.predict(batch_images)
    pred_texts = decode_batch_predictions(preds)

    orig_texts = []
    for label in batch_labels:
        label = tf.strings.reduce_join(num_to_char(label)).numpy().decode("utf-8")
        orig_texts.append(label)

    _,ax = plt.subplots(4,4,figsize=(15,8))
    for i in range(len(pred_texts)):
        img = (batch_images[i,:,0] * 255).numpy().astype(np.uint8)
        img = img.T
        title = f"Prediction: {pred_texts[i]}"
        ax[i // 4,i % 4].imshow(img,cmap="gray")
        ax[i // 4,i % 4].set_title(title)
        ax[i // 4,i % 4].axis("off")
plt.show()

STEP4) 保存模型

model.save("ocr_model.h5")

STEP5) 加载模型

model = load_model('./ocr_model.h5',custom_objects={'CTCLayer':CTCLayer})

我收到以下错误消息。

TypeError: init() 得到了一个意外的关键字参数 'trainable'

我又试了一个这个代码。

model = load_model('./ocr_model.h5')

我收到以下错误消息。

值错误：未知层：CTCLayer。请确保将此对象传递给 custom_objects 参数。有关详细信息，请参阅 https://www.tensorflow.org/guide/keras/save_and_serialize#registering_the_custom_object。

如何使用存储模型？