Dimension mismatch error in Keras with Tensorflow Backend - tensorflow

Question

I'm fine-tuning the last convolutional block of the VGG16 model alongside the top-level classifier by starting from a trained network, then re-training it on a new dataset using very small weight updates. I instantiated the convolutional base of VGG16 and loaded its weights, added the previously defined fully-connected model on top, and loaded its weights and freeze the layers of the VGG16 model up to the last convolutional block. Here is the code:
from keras import applications
from keras.preprocessing.image import ImageDataGenerator
from keras import optimizers
from keras.models import Sequential
from keras.layers import Dropout, Flatten, Dense
# path to the model weights files.
weights_path = '../keras/examples/vgg16_weights.h5'
top_model_weights_path = 'modelvgg16.h5'
# dimensions of our images.
img_width, img_height = 32, 32
train_data_dir = 'data7/train'
validation_data_dir = 'data7/validation'
nb_train_samples = 1600
nb_validation_samples = 400
epochs = 100
batch_size = 16
# build the VGG16 network
model = applications.VGG16(weights='imagenet', include_top=False, input_shape=(224,224,3))
print('Model loaded.')
# build a classifier model to put on top of the convolutional model
top_model = Sequential()
top_model.add(Flatten(input_shape=model.output_shape[1:]))
top_model.add(Dense(256, activation='relu'))
top_model.add(Dropout(0.5))
top_model.add(Dense(1, activation='sigmoid'))
# note that it is necessary to start with a fully-trained
# classifier, including the top classifier,
# in order to successfully do fine-tuning
top_model.load_weights(top_model_weights_path)
# add the model on top of the convolutional base
model.add(top_model)
# set the first 25 layers (up to the last conv block)
# to non-trainable (weights will not be updated)
for layer in model.layers[:15]:
layer.trainable = False
# compile the model with a SGD/momentum optimizer
# and a very slow learning rate.
model.compile(loss='binary_crossentropy',
optimizer=optimizers.SGD(lr=1e-4, momentum=0.9),
metrics=['accuracy'])
# prepare data augmentation configuration
train_datagen = ImageDataGenerator(
rescale=1. / 255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1. / 255)
train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size=(img_height, img_width),
batch_size=batch_size,
class_mode='binary')
validation_generator = test_datagen.flow_from_directory(
validation_data_dir,
target_size=(img_height, img_width),
batch_size=batch_size,
class_mode='binary')
# fine-tune the model
model.fit_generator(
train_generator,
samples_per_epoch=nb_train_samples,
epochs=epochs,
validation_data=validation_generator,
nb_val_samples=nb_validation_samples)
On executing the script I get:
ValueError: Dimension O in both shapes must be equal, but are 25088 and 512 for 'Assign_26' (op: 'Assign') with input shapes: [25088, 256], [512, 256].
The error pops up with this instruction:
top_model.load_weights(top_model_weights_path)
Kindly help with the modification of the code. Thanks in advance.