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Save Keras model for production mode without TensorFlow

I want to save a trained Keras model, so that it can be used in the Django REST backend of an application. I did a lot of research, but it seems there isn't any way to use these models without TensorFlow installed.
So, what is the use of this storage? I don't want to install a heavy library like TensorFlow on the server. I tested saving with pickle and joblib, as well as Keras' own model.save().
Is there a way to load this model without installing TensorFlow and only with Keras itself?
This is a part of my code,
from keras.models import Sequential
from keras.layers import Dense, LSTM, Dropout
xtrain, ytrain = np.array(xtrain), np.array(ytrain)
ytrain = np.reshape(ytrain, (ytrain.shape[0], 1, 1))
model = Sequential()
model.add(LSTM(150, return_sequences=True, input_shape=(xtrain.shape[1], 1)))
model.add(LSTM(150, return_sequences=False))
model.add(Dense(25))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mean_squared_error')
model.fit(xtrain, ytrain, batch_size=1, epochs=7)
model.save('model.h5')
which normally works perfectly, but if I use the model elsewhere, I get this error:
ModuleNotFoundError: No module named 'tensorflow'

You do not need to use TensorFlow in production. You can use coefficient by replacing what random functions in your programming language.
Sample: Input array, time coefficients matrixes, and unboxed system inputs to output with feedback system in the box containers.
temp = tf.random.normal([10], 1, 0.2, tf.float32)
temp = np.asarray(temp) * np.asarray([ coefficient_0, coefficient_1, coefficient_2, coefficient_3, coefficient_4, coefficient_5, coefficient_6, coefficient_7, coefficient_8, coefficient_9 ]) #action = actions['up']
temp = tf.nn.softmax(temp)
action = int(np.argmin(temp))

How to convert to Keras code from MATLAB Deep learning model

I am making the binary sound classification model by Keras on Python3.7. I have been make the sound classification model on MATLAB however some specifically layer is not installed on MATLAB (ex. GRU). So I try to convert to Keras deep learning model from MATLAB deep learning model.
The original MATLAB code is shown bellow:
inputsize=[31,69]
layers = [ ...
sequenceInputLayer(inputsize(1))
bilstmLayer(200,'OutputMode','last')
fullyConnectedLayer(2)
softmaxLayer
classificationLayer
]
options = trainingOptions('adam', ...
'MaxEpochs',30, ...
'MiniBatchSize', 200, ...
'InitialLearnRate', 0.01, ...
'GradientThreshold', 1, ...
'ExecutionEnvironment',"auto",...
'plots','training-progress', ...
'Verbose',false);
This model get to the accuracy is 0.955.
The Keras code based on MATLAB code is shown below:
# traindatasize=(86400,31,69)
inputsize=(31,69)
batchsize=200
epochs=30
model = Sequential()
model.add(Bidirectional(LSTM(200, input_shape=inputsize)))
model.add(Dense(2, activation='softmax'))
model.compile(optimizer=RMSprop(), loss='binary_crossentropy', metrics=['accuracy'])
model.fit(traindata, trainlabel, batch_size=batchsize, epochs=epochs, verbose=1)
This model get to the accuracy is 0.444
I don't understand what is the effect.
The traindata used same data from STFT and normalize before train those model using standard deviation and mean average.
Please some comments.
Python 3.7 on Anaconda
Keras 2.2.4

I think that's because the MATLAB code uses the Adam optimizer for training, and you defined RMSprop instead in:
model.compile(optimizer=RMSprop(),loss='binary_crossentropy',metrics=['accuracy'])
instead, use:
from keras import optimizers
adam = optimizers.Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, amsgrad=False)
...
model.compile(optimizer=adam,loss='binary_crossentropy',metrics=['accuracy'])
check if this improves the answer.

keras load model error trying to load a weight file containing 17 layers into a model with 0 layers

I am currently working on vgg16 model with keras.
I fine tune vgg model with some of my layer.
After fitting my model (training), I save my model with model.save('name.h5').
It can be saved without problem.
However, when I try to reload the model with load_model function, it shows the error:
You are trying to load a weight file containing 17 layers into a model
with 0 layers
Did anyone meet this problem before?
My keras verion is 2.2.
Here is part of my code ...
from keras.models import load_model
vgg_model = VGG16(weights='imagenet',include_top=False,input_shape=(224,224,3))
global model_2
model_2 = Sequential()
for layer in vgg_model.layers:
model_2.add(layer)
for layer in model_2.layers:
layer.trainable= False
model_2.add(Flatten())
model_2.add(Dense(128, activation='relu'))
model_2.add(Dropout(0.5))
model_2.add(Dense(2, activation='softmax'))
model_2.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model_2.fit(x=X_train,y=y_train,batch_size=32,epochs=30,verbose=2)
model_2.save('name.h5')
del model_2
model_2 = load_model('name.h5')
Actually I do not delete the model and then load_model immediately,
just for showing my problem.

It seems that this problem is related with the input_shape parameter of the first layer. I had this problem with a wrapper layer (Bidirectional) which did not have an input_shape parameter set. In code:
model.add(Bidirectional(LSTM(units=units, input_shape=(None, feature_size)), merge_mode='concat'))
did not work for loading my old model because the input_shape is only defined for the LSTM layer not the outer one. Instead
model.add(Bidirectional(LSTM(units=units), input_shape=(None, feature_size), merge_mode='concat'))
worked because the wrapper Birectional layer now has an input_shape parameter. Maybe you should check if the VGG net input_shape parameter is set or not or you should add a single input_layer to your model with the correct input_shape parameter.

I spent 6 hours looking around for a solution.. to apply me trained model.
finally i tried VGG16 as model and using 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")

In case anyone is still wondering about this error:
I had the same Problem and spent days figuring out, whats causing it. I have a copy of my whole code and dataset on another system on which it worked. I noticed that it is something about the training, because without training my model, saving and loading was no problem.
The only difference between my systems was, that I was using tensorflow-gpu on my main system and for this reason, the tensorflow base version was a little bit lower (1.14.0 instead of 2.2.0). So all I had to do was using
model.fit_generator()
instead of
model.fit()
before saving it. And it works

How do I add a top dense layer to ResNet50 in Keras?

I read this very helpful Keras tutorial on transfer learning here:
https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html
I am thinking that this is probably very applicable to the fish data here, and started going down that route. I tried to follow the tutorial as much as I could. The code is a mess as I was just tyring to figure out how everything works, but it can be found here:
https://github.com/MrChristophRivera/ClassifiyingFish/blob/master/notebooks/Anthony/Resnet50%2BTransfer%20Learning%20Attempt.ipynb
For brevity, here are the steps I did here:
model = ResNet50(top_layer = False, weights="imagenet"
# I would resize the image to that of the standard input size of ResNet50.
datagen=ImageDataGenerator(1./255)
generator = datagen.flow_from_directory(
train_data_dir,
target_size=(img_width, img_height),
batch_size=32,
class_mode=None,
shuffle=False)
# predict on the training data
bottleneck_features_train = model.predict_generator(generator,
nb_train_samples)
print(bottleneck_features_train)
file_name = join(save_directory, 'tbottleneck_features_train.npy')
np.save(open(file_name, 'wb'), bottleneck_features_train)
# Then I would use this output to feed my top layer and train it. Let's
say I defined
# it like so:
top_model = Sequential()
# Skipping some layers for brevity
top_model.add(Dense(8, activation='relu')
top_model.fit(train_data, train_labels)
top_model.save_weights(top_model_weights_path).
At this time, I have the weights saved. The next step would be to add the top layer to ResNet50. The tutorial simply did it like so:
# VGG16 model defined via Sequential is called bottom_model.
bottom_model.add(top_model)
The problem is when I try to do that this fails because "model does not have property add". My guess is that ResNet50 was defined in a different way. At any rate, my question is: How can I add this top model with the loaded weights to the bottom model? Can anyone give helpful pointers?

Try:
input_to_model = Input(shape=shape_of_your_image)
base_model = model(input_to_model)
top_model = Flatten()(base_model)
top_model = Dense(8, activation='relu')
...
Your problem comes from the fact that Resnet50 is defined in a so called functional API. I would also advise you to use different activation function because having relu as an output activation might cause problems. Moreover - your model is not compiled.

Keras model load_weights for Neural Net

I'm using the Keras library to create a neural network in python. I have loaded the training data (txt file), initiated the network and "fit" the weights of the neural network. I have then written code to generate the output text. Here is the code:
#!/usr/bin/env python
# load the network weights
filename = "weights-improvement-19-2.0810.hdf5"
model.load_weights(filename)
model.compile(loss='categorical_crossentropy', optimizer='adam')
My problem is: on execution the following error is produced:
model.load_weights(filename)
NameError: name 'model' is not defined
I have added the following but the error still persists:
from keras.models import Sequential
from keras.models import load_model
Any help would be appreciated.

you need to first create the network object called model, compile it and only after call the model.load_weights(fname)
working example:
from keras.models import Sequential
from keras.layers import Dense, Activation
def build_model():
model = Sequential()
model.add(Dense(output_dim=64, input_dim=100))
model.add(Activation("relu"))
model.add(Dense(output_dim=10))
model.add(Activation("softmax"))
# you can either compile or not the model
model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
return model
model1 = build_model()
model1.save_weights('my_weights.model')
model2 = build_model()
model2.load_weights('my_weights.model')
# do stuff with model2 (e.g. predict())
Save & Load an Entire Model
in Keras we can save & load the entire model like this (more info here):
from keras.models import load_model
model1 = build_model()
model1.save('my_model.hdf5')
model2 = load_model('my_model.hdf5')
# do stuff with model2 (e.g. predict()