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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 use Tensorflow addons' metrics correctly in functional API?

I have an LSTM model to perform binary classification of human activities using multivariate smartphone sensor data. The two classes are imbalanced (1:50). Therefore I would like to use F1-score as a metric, but I saw that it was deprecated as a metric.
Before it was best practice to use a callback function for the metric to ensure it was applied on the whole dataset, however, recently the TensorFlow addons reintroduced the F1-Score.
I now have a problem to apply this score to my functional API. Here is the code I am currently running:
import tensorflow as tf
import tensorflow_addons as tfa
from tensorflow import kerasdef
create_model(n_neurons=150, learning_rate=0.01, activation="relu", loss="binary_crossentropy"):
#create input layer and assign to current output layer
input_ = keras.layers.Input(shape=(X_train.shape[1],X_train.shape[2]))
#add LSTM layer
lstm = keras.layers.LSTM(n_neurons, activation=activation)(input_)
#Output Layer
output = keras.layers.Dense(1, activation="sigmoid")(lstm)
#Create Model
model = keras.models.Model(inputs=[input_], outputs=[output])
#Add optimizer
optimizer=keras.optimizers.SGD(lr=learning_rate, clipvalue=0.5)
#Compile model
model.compile(loss=loss, optimizer=optimizer, metrics=[tfa.metrics.F1Score(num_classes=2, average="micro")])
print(model.summary())
return model
#Create the model
model = create_model()
#fit the model
history = model.fit(X_train,y_train,
epochs=300,
validation_data=(X_val, y_val))
If I use another value for the metric argument average (e.g., average=None or average="macro") then I get an error message when fitting the model:
ValueError: Dimension 0 in both shapes must be equal, but are 2 and 1. Shapes are [ 2 ] and [ 1 ]. for 'AssignAddVariableOp' (op: 'AssignAddVariableOp') with input shapes: [ ], [ 1 ].
And if I use the value average="micro" I am not getting the error, but the F1-score is 0 throughout the learning process, while my loss decreases.
I believe I am still doing something wrong here. Can anybody provide an explanation for me?

Updated answer: The crucial bit is to import tf.keras, not keras. Then you can use e.g. tf.keras.metrics.Precision or tfa.metrics.F1Score without problems. See also here.
Old answer:
The problem with tensorflow-addons is that the implementation of the current release (0.6.0) only counts exact matches, such that a comparison e.g. of 1 and 0.99 yields 0. Of course, this is practically useless in a neural network. This has been fixed in 0.7.0 (not yet released). You can install it as follows:
pip3 install --upgrade pip
pip3 install tfa-nightly
and then use a threshold (everything below the threshold is counted as 0, otherwise as 1):
tfa.metrics.FBetaScore(num_classes=2,average="micro",threshold=0.9)
See also https://github.com/tensorflow/addons/issues/490.
The problem with other values for average is discussed here: https://github.com/tensorflow/addons/issues/746.
Beware that there are two other problems that probably lead to useless results, see also https://github.com/tensorflow/addons/issues/818:
the model uses binary classification, but f1-score in tfa assumes categorical classification with one-hot encoding
f1-score is called at each batch step at validation.
These problems should not appear when using the Keras metrics.

In this example, I will show how to use Adamw optimizer and F1_score using Tensorflow.
lr_schedule = keras.optimizers.schedules.ExponentialDecay(
initial_learning_rate, decay_steps=100000, decay_rate=0.96, staircase=True
)
import tensorflow_addons as tfa
f1 = tfa.metrics.F1Score(num_classes=2, average=None)
model=(..)
model.compile(
loss="binary_crossentropy",
optimizer=tfa.optimizers.AdamW(learning_rate=lr_schedule,weight_decay = 0.0001),
metrics=["acc",'AUC',f1],
)

Keras - Image Captioning with attention mechanism

I have written the below code for image captioning in keras & it works fine.
image_model = Sequential()
image_model.add(Dense(EMBEDDING_DIM, input_shape=(2048,), activation='relu'))
image_model.add(RepeatVector(max_length))
lang_model = Sequential()
lang_model.add(Embedding(vocab_size,EMBEDDING_DIM , input_length=max_length))
lang_model.add(Bidirectional(LSTM(256,return_sequences=True)))
lang_model.add(Dropout(0.5))
lang_model.add(BatchNormalization())
lang_model.add(TimeDistributed(Dense(EMBEDDING_DIM)))
fin_model = Sequential()
fin_model.add(Merge([image_model, lang_model], mode='concat'))
#model.add(Concatenate([image_model, lang_model]))
fin_model.add(Dropout(0.5))
fin_model.add(BatchNormalization())
fin_model.add(Bidirectional(LSTM(1000,return_sequences=False)))
fin_model.add(Dense(vocab_size))
fin_model.add(Activation('softmax'))
print ("Model created!")
fin_model.compile(loss='categorical_crossentropy', optimizer='Adam', metrics=['accuracy'])
But I wanted to add attention mechanism here. No need of a bidirectional lstm, just a usual LSTM is also fine. But I don't see one useful blog that explains how to do this in keras. Since I am very new to deep learning & Keras is the only python library I know, any help is much appreciated.

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.