我需要添加一个Cropping2D层，其中左和右裁剪参数由之前层的输出动态确定。也就是说，left_crop和right_crop参数在编码时是未知的。但是，我似乎无法访问模型中之前Tensor的值。下面是我的代码：

input1 = Input(name='dirty', shape=(IMG_HEIGHT, None, 1), dtype='float32')
input2 = Input(name='x0', shape=(), dtype='int32')
input3 = Input(name='x1', shape=(), dtype='int32')
# Encoder
conv1 = Conv2D(48, kernel_size=(3, 3), activation='relu', padding='same', name='conv1')(input1)
pool1 = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), name='pool1')(conv1)
conv2 = Conv2D(64, kernel_size=(3, 3), activation='relu', padding='same', name='conv2')(pool1)
# Decoder
deconv2 = Conv2DTranspose(48, kernel_size=(3, 3), activation='relu', padding='same', name='deconv2')(conv2)
depool1 = UpSampling2D(size=(2, 2), name='depool1')(deconv2)
output1 = Conv2DTranspose(1, kernel_size=(3, 3), activation='relu', padding='same', name='clean')(depool1)
_, _, width, _ = K.int_shape(output1)
left = K.eval(input2)
right = width - K.eval(input3)
output2 = Cropping2D(name='clean_snippet', cropping=((0, 0), (left, right)))(output1)

这会产生下列错误：

Traceback (most recent call last):
  File "test.py", line 81, in <module>
    left = K.eval(input2)
  File "/Users/garnet/Library/Python/3.8/lib/python/site-packages/keras/backend.py", line 1632, in eval
    return get_value(to_dense(x))
  File "/Users/garnet/Library/Python/3.8/lib/python/site-packages/keras/backend.py", line 4208, in get_value
    return x.numpy()
AttributeError: 'KerasTensor' object has no attribute 'numpy'

我在Keras 2.10.0中使用TF 2.10.0。我在启用和不启用急切模式的情况下都试过了。我的问题是关于我上面代码中“HERE'S THE AREA IN QUESTION...”注解后面的四行。我如何访问以前的图层值，并将它们用作Cropping2D（）的 * 参数 *（而不是输入图层）。有什么想法吗？
以下是我的完整代码：

import tensorflow as tf
import cv2
import random
import os
import numpy as np
from tensorflow.keras import backend as K
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import SGD
from tensorflow.keras.layers import Conv2D, Conv2DTranspose, MaxPooling2D, Cropping2D, UpSampling2D, Input
from tensorflow.keras import losses
SNIPPET_WIDTH = 40
IMG_HEIGHT = 60
def get_data(paths):
    for path in paths:
        clean = cv2.imread(path.decode('utf-8'), cv2.IMREAD_GRAYSCALE)
        h, w = clean.shape
        dirty = cv2.blur(clean, (random.randint(1, 5), random.randint(1, 5)))
        x0 = random.randint(0, w - SNIPPET_WIDTH)
        x1 = x0 + SNIPPET_WIDTH
        y0 = 0
        y1 = h - 1
        clean_snippet = clean[y0:y1, x0:x1]
        dirty[y0:y1, x0:x1] = 0  # masked out region
        dirty = (256. - dirty.astype(np.float32)) / 255.
        dirty = tf.convert_to_tensor(np.expand_dims(dirty, axis=2))
        x0 = tf.convert_to_tensor(x0)
        x1 = tf.convert_to_tensor(x1)
        clean = (256. - clean.astype(np.float32)) / 255.
        clean = tf.convert_to_tensor(np.expand_dims(clean, axis=2))
        clean_snippet = (256. - clean_snippet.astype(np.float32)) / 255.
        clean_snippet = tf.convert_to_tensor(np.expand_dims(clean_snippet, axis=2))
        yield {'dirty': dirty, 'x0': x0, 'x1': x1}, {'clean': clean, 'clean_snippet': clean_snippet}
train_directory = 'data/training/'
files = os.listdir(train_directory)
paths = []
for f in files:
    filename = os.fsdecode(f)
    paths.append(train_directory + filename)
train_ds = tf.data.Dataset.from_generator(get_data, args=[paths], output_signature=(
    {
        'dirty': tf.TensorSpec(shape=(IMG_HEIGHT, None, 1), dtype=tf.float32),
        'x0': tf.TensorSpec(shape=(), dtype=tf.int32),
        'x1': tf.TensorSpec(shape=(), dtype=tf.int32)
    },
    {
        'clean': tf.TensorSpec(shape=(IMG_HEIGHT, None, 1), dtype=tf.float32),
        'clean_snippet': tf.TensorSpec(shape=(IMG_HEIGHT, None, 1), dtype=tf.float32)
    }
))
bucket_sizes = [400, 500, 600, 700, 800]
bucket_batch_sizes = [16, 16, 16, 16, 16, 16]
train_ds = train_ds.bucket_by_sequence_length(element_length_func=lambda x, y: tf.shape(y['clean'])[1],
                                              bucket_boundaries=bucket_sizes, bucket_batch_sizes=bucket_batch_sizes)
input1 = Input(name='dirty', shape=(IMG_HEIGHT, None, 1), dtype='float32')
input2 = Input(name='x0', shape=(), dtype='int32')
input3 = Input(name='x1', shape=(), dtype='int32')
# Encoder
conv1 = Conv2D(48, kernel_size=(3, 3), activation='relu', padding='same', name='conv1')(input1)
pool1 = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), name='pool1')(conv1)
conv2 = Conv2D(64, kernel_size=(3, 3), activation='relu', padding='same', name='conv2')(pool1)
# Decoder
deconv2 = Conv2DTranspose(48, kernel_size=(3, 3), activation='relu', padding='same', name='deconv2')(conv2)
depool1 = UpSampling2D(size=(2, 2), name='depool1')(deconv2)
output1 = Conv2DTranspose(1, kernel_size=(3, 3), activation='relu', padding='same', name='clean')(depool1)
# HERE'S THE AREA IN QUESTION...
_, _, width, _ = K.int_shape(output1)
left = K.eval(input2)
right = width - K.eval(input3)
output2 = Cropping2D(name='clean_snippet', cropping=((0, 0), (left, right)))(output1)
# ...END AREA IN QUESTION
model = Model(inputs=[input1, input2, input3], outputs=[output1, output2])
optimizer = SGD(lr=0.02, decay=1e-6, momentum=0.9, nesterov=True, clipnorm=5)
loss_fcns = {'clean': losses.MeanAbsoluteError(), 'clean_snippet': losses.MeanAbsoluteError()}
model.compile(loss=losses.MeanAbsoluteError(), optimizer=optimizer, metrics=['acc'])
model.fit(x=train_ds, y=None, epochs=1000, shuffle=True, verbose=1)