Keras try save and load model error You are trying to load a weight file containing 16 layers into a model with 0 layers

Question

I am trying to fine tune and save a model in Keras and load it, but I am getting this error:
Value Error: You are trying to load a weight file containing 16 layers into a model with 0 layers.
I tried another model for number I made it save and load mode work without error when I tried to adopt vgg16, it gave that error
I want load model but can't load because of this error. Can anyone help?

import keras
from  keras.models import Sequential,load_model,model_from_json

from keras import backend as K
from keras.layers import Activation,Conv2D,MaxPooling2D,Dropout
from keras.layers.core import Dense,Flatten
from keras.optimizers import Adam
from keras.metrics import categorical_crossentropy
from keras.layers.normalization import BatchNormalization
from keras.layers.convolutional import *
from keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
import itertools
from sklearn.metrics import confusion_matrix

import numpy as np
train_path='dataset/train'
test_path='dataset/test'
valid_path='dataset/valid'
train_batches=ImageDataGenerator()
.flow_from_directory(train_path,batch_size=1,target_size=(224,224),classes= 
 ['dog','cat'])
valid_batches=ImageDataGenerator()
.flow_from_directory(valid_path,batch_size=4,target_size=(224,224),classes= 
['dog','cat'])
test_batches=ImageDataGenerator()
.flow_from_directory(test_path,target_size=(224,224),classes=['dog','cat'])

 vgg16_model=keras.applications.vgg16.VGG16();

vgg16_model.summary()

type(vgg16_model)

model=Sequential()
for layer in vgg16_model.layers[:-1]:
    model.add(layer)




for layer in model.layers:
    layer.trainable=False

 model.add(Dense(2,activation='softmax'))


 model.compile(Adam(lr=.0001),loss='categorical_crossentropy',metrics= 
 ['accuracy'])
model.fit_generator(train_batches,validation_data=valid_batches,epochs=1)


model.save('test.h5')
model.summary()
xx=load_model('test.h5')

Answer 1

I loaded the model in a different way looking around for a solution, I had same problem.. NOW to apply my trained model. finally i used VGG16 as model and using my h5 weights i´ve trained on my own and Great!

weights_model='C:/Anaconda/weightsnew2.h5'  # my already trained 
weights .h5
vgg=applications.vgg16.VGG16()
cnn=Sequential()
for capa in vgg.layers:
    cnn.add(capa)
cnn.layers.pop()
for layer in cnn.layers:
    layer.trainable=False
cnn.add(Dense(2,activation='softmax'))  

cnn.load_weights(weights_model)

def predict(file):
    x = load_img(file, target_size=(longitud, altura)) 
    x = img_to_array(x)                            
    x = np.expand_dims(x, axis=0)
    array = cnn.predict(x)     
    result = array[0]
    respuesta = np.argmax(result) 
    if respuesta == 0:
        print("Gato")
    elif respuesta == 1:
        print("Perro")

Answer 2

This seems to be a bug in Keras. I had a similar issue with a model using dropout in the first layer. Removing the dropout functionality from the input layer fixed this issue for me.

In your case, I suggest using a dense input layer specifying the input dimensions of your data first. Thus, adding the line

model.add(Dense(numberOfNeurons, activation='yourActivationFunction', input_dim=inputDimension))

should do the trick.