ValueError: Error al verificar la entrada: se esperaba que la entrada_1 tuviera forma (Ninguna, 65563) pero que tenía una matriz con forma (374, 65536)

Tengo un autoencoder escrito en Keras que se muestra a continuación. Recibo el siguiente error y no estoy seguro de cómo resolverlo, ¿alguna idea?

ValueError: Error al verificar la entrada: se esperaba que la entrada_1 tuviera forma (Ninguna, 65563) pero que tenía una matriz con forma (374, 65536)

from keras.layers import Input, Dense, Flatten from keras.models import Model from keras.preprocessing.image import img_to_array import cv2 import numpy import os training_directory = '/training' validation_directory ='/validation' results_directory = '/results' training_images = [] validation_images = [] # the size of the encoded represenatation encoding_dimension = 784 # input placeholder input_image = Input(shape=(65563,)) # the encoded representation of the input encoded = Dense(encoding_dimension,activation='relu')(input_image) # reconstruction of the input (lossy) decoded = Dense(65563,activation='sigmoid')(encoded) # map the input image to its reconstruction autoencoder = Model(input_image,decoded) # encoder model # map an input image to its encoded representation encoder = Model(input_image,encoded) # decoder model # place holder fpr an encoded input encoded_input = Input(shape=(encoding_dimension,)) # retrieve the last layer of the autoencoder model decoder_layer = autoencoder.layers[-1] # create the decoder model decoder = Model(encoded_input,decoder_layer(encoded_input)) for root, dirs, files in os.walk(training_directory): for file in files: image = cv2.imread(root + '/' + file) image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) image = img_to_array(image) training_images.append(image) training_images = numpy.array(training_images) for root, dirs, files in os.walk(validation_directory): for file in files: image = cv2.imread(root + '/' + file) image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) image = img_to_array(image) validation_images.append(image) validation_images = numpy.array(validation_images) # reshape data training_images = training_images.reshape((len(training_images), numpy.prod(training_images.shape[1:]))) validation_images = validation_images.reshape((len(validation_images), numpy.prod(validation_images.shape[1:]))) autoencoder.compile(optimizer='adam',loss='binary_crossentropy') autoencoder.fit(training_images,epochs=10,batch_size=20,shuffle=True,validation_data=(validation_images)) encoded_images = encoder.predict(validation_images) decoded_images = decoder.predict(encoded_images) 

Gracias.

Escribir 65563 lugar de 65536 podría haber sido un error tipográfico que causó el problema.