Quiero construir un modelo para colorear la imagen, he probado a ejecutar este código pero me enfrenté Incompatible formas: [3,256,256,2] vs [3,150,150,2] error.
#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'])
el error se produce cuando ejecuto el código para ajustar el modelo
# 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)
Mensaje de Error:
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]
Traté de imprimir el resumen de las capas, pero no he podido encontrar el problema