InvalidArgumentError: असंगत आकार: [3,256,256,2] बनाम [3,150,150,2]

मैं चाहता हूँ करने के लिए एक मॉडल का निर्माण करने के लिए colorize छवि में, मैं करने की कोशिश की इस पर अमल कोड लेकिन मैं का सामना करना पड़ा असंगत आकार: [3,256,256,2] बनाम [3,150,150,2] त्रुटि ।

#CNN model

from keras.layers import Conv2D, Conv2DTranspose, UpSampling2D
from keras.layers import Activation, Dense, Dropout, Flatten, InputLayer
from tensorflow.keras.layers import (
    BatchNormalization, SeparableConv2D, MaxPooling2D, Activation, Flatten, Dropout, Dense
)
from keras.callbacks import TensorBoard, ModelCheckpoint
from keras.models import Sequential

model = Sequential()

#Input Layer
model.add(Conv2D(64, (3, 3), input_shape=(256, 256, 1), activation='relu', padding='same'))

#Hidden Layers
model.add(Conv2D(64, (3, 3), activation='relu', padding='same', strides=2))
model.add(Conv2D(128, (3, 3), activation='relu', padding='same'))
model.add(Conv2D(128, (3, 3), activation='relu', padding='same', strides=2))
model.add(Conv2D(256, (3, 3), activation='relu', padding='same'))
model.add(Conv2D(256, (3, 3), activation='relu', padding='same', strides=2))
model.add(Conv2D(512, (3, 3), activation='relu', padding='same'))
model.add(Conv2D(256, (3, 3), activation='relu', padding='same'))
model.add(Conv2D(128, (3, 3), activation='relu', padding='same'))
model.add(UpSampling2D((2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))
model.add(UpSampling2D((2, 2)))
model.add(Conv2D(32, (3, 3), activation='relu', padding='same'))
model.add(Conv2D(2, (3, 3), activation='tanh', padding='same'))
model.add(UpSampling2D((2, 2)))



#Compiling the CNN
model.compile(optimizer='rmsprop', loss='mse', metrics = ['accuracy'])
#model.compile(optimizer='rmsprop', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

त्रुटि तब होती है जब मैं चलाने के लिए कोड मॉडल फिट करने के लिए

# Image transformer
datagen = ImageDataGenerator(
        shear_range=0.2,
        zoom_range=0.2,
        rotation_range=20,
        horizontal_flip=True)

import matplotlib.pyplot as plt

# Image transformer
datagen = ImageDataGenerator(
        shear_range=0.2,
        zoom_range=0.2,
        rotation_range=20,
        horizontal_flip=True)

# Generate training data
batch_size = 10
def image_a_b_gen(batch_size):
    for batch in datagen.flow(Xtrain, batch_size=batch_size ):
        lab_batch = rgb2lab(batch)
        X_batch = lab_batch[:,:,:,0]
        Y_batch = lab_batch[:,:,:,1:] / 128
        yield (X_batch.reshape(X_batch.shape+(1,)), Y_batch)


        # Train model      
tensorboard = TensorBoard(log_dir="/output/beta_run")
trainedmodel = model.fit(image_a_b_gen(batch_size), callbacks=[tensorboard],epochs=100, steps_per_epoch=30)

त्रुटि संदेश:

 InvalidArgumentError                      Traceback (most recent call last)
    <ipython-input-112-7a987e785f95> in <module>
         29         # Train model
         30 tensorboard = TensorBoard(log_dir="/output/beta_run")
    ---> 31 trainedmodel = model.fit(image_a_b_gen(batch_size), callbacks=[tensorboard],epochs=100, steps_per_epoch=30)
         32 
         33 
    
    ~\anaconda3\lib\site-packages\keras\utils\traceback_utils.py in error_handler(*args, **kwargs)
         65     except Exception as e:  # pylint: disable=broad-except
         66       filtered_tb = _process_traceback_frames(e.__traceback__)
    ---> 67       raise e.with_traceback(filtered_tb) from None
         68     finally:
         69       del filtered_tb
    
    ~\anaconda3\lib\site-packages\tensorflow\python\eager\execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
         56   try:
         57     ctx.ensure_initialized()
    ---> 58     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
         59                                         inputs, attrs, num_outputs)
         60   except core._NotOkStatusException as e:
    
    InvalidArgumentError:  Incompatible shapes: [3,256,256,2] vs. [3,150,150,2]
         [[node gradient_tape/mean_squared_error/BroadcastGradientArgs
     (defined at C:\Users\HudaA\anaconda3\lib\site-packages\keras\optimizer_v2\optimizer_v2.py:464)
    ]] [Op:__inference_train_function_33345]

मैं करने की कोशिश की प्रिंट को संक्षेप में प्रस्तुत की परतों, लेकिन मैं नहीं मिल सकता है समस्या