Seralizing a keras model with an embedding layer - tensorflow

Question

I've trained a model with pre-trained word embeddings like this:
embedding_matrix = np.zeros((vocab_size, 100))
for word, i in text_tokenizer.word_index.items():
embedding_vector = embeddings_index.get(word)
if embedding_vector is not None:
embedding_matrix[i] = embedding_vector
embedding_layer = Embedding(vocab_size,
100,
embeddings_initializer=Constant(embedding_matrix),
input_length=50,
trainable=False)
With the architecture looking like this:
sequence_input = Input(shape=(50,), dtype='int32')
embedded_sequences = embedding_layer(sequence_input)
text_cnn = Conv1D(filters=5, kernel_size=5, padding='same', activation='relu')(embedded_sequences)
text_lstm = LSTM(500, return_sequences=True)(embedded_sequences)
char_in = Input(shape=(50, 18, ))
char_cnn = Conv1D(filters=5, kernel_size=5, padding='same', activation='relu')(char_in)
char_cnn = GaussianNoise(0.40)(char_cnn)
char_lstm = LSTM(500, return_sequences=True)(char_in)
merged = concatenate([char_lstm, text_lstm])
merged_d1 = Dense(800, activation='relu')(merged)
merged_d1 = Dropout(0.5)(merged_d1)
text_class = Dense(len(y_unique), activation='softmax')(merged_d1)
model = Model([sequence_input,char_in], text_class)
When I go to convert the model to json, I get this error:
ValueError: can only convert an array of size 1 to a Python scalar
Similarly, if I use the model.save() function, it seems to save correctly, but when I go to load it, I get Type Error: Expected Float32.
My question is: is there something I am missing when trying to serialize this model? Do I need some sort of Lambda layer or something of the sorts?
Any help would be greatly appreciated!

Answer 1

You can use the weights argument in Embedding layer to provide initial weights.
embedding_layer = Embedding(vocab_size,
100,
weights=[embedding_matrix],
input_length=50,
trainable=False)
The weights should remain non-trainable after model saving/loading:
model.save('1.h5')
m = load_model('1.h5')
m.summary()
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_3 (InputLayer) (None, 50) 0
__________________________________________________________________________________________________
input_4 (InputLayer) (None, 50, 18) 0
__________________________________________________________________________________________________
embedding_1 (Embedding) (None, 50, 100) 1000000 input_3[0][0]
__________________________________________________________________________________________________
lstm_4 (LSTM) (None, 50, 500) 1038000 input_4[0][0]
__________________________________________________________________________________________________
lstm_3 (LSTM) (None, 50, 500) 1202000 embedding_1[0][0]
__________________________________________________________________________________________________
concatenate_2 (Concatenate) (None, 50, 1000) 0 lstm_4[0][0]
lstm_3[0][0]
__________________________________________________________________________________________________
dense_2 (Dense) (None, 50, 800) 800800 concatenate_2[0][0]
__________________________________________________________________________________________________
dropout_2 (Dropout) (None, 50, 800) 0 dense_2[0][0]
__________________________________________________________________________________________________
dense_3 (Dense) (None, 50, 15) 12015 dropout_2[0][0]
==================================================================================================
Total params: 4,052,815
Trainable params: 3,052,815
Non-trainable params: 1,000,000
__________________________________________________________________________________________________

Answer 2

I hope you are saving the model after compiling. Like:
model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
To save model, you can do:
from keras.models import load_model
model.save('model.h5')
model = load_model('model_detect1.h5')
model_json = model.to_json()
with open("model.json", "w") as json_file:
json_file.write(model_json)
To load model,
from keras.models import model_from_json
json_file = open('model.json', 'r')
model_json = json_file.read()
model = model_from_json(model_json)
model.load_weights("model.h5")

Answer 3

I tried multiple methods . The problem is when we work in the embedding layer, then pickle doesnt work, and is not able to save the data.
SO what you can do , when you have some layers like these:-
## Creating model
embedding_vector_features=100
model=Sequential()
model.add(Embedding(voc_size,embedding_vector_features,input_length=sent_length))
model.add(LSTM(100))
model.add(Dense(1,activation='sigmoid'))
model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])
print(model.summary())
then, u can use
h5 extension to d=save file, and then convert that to json, model converetd to model2 here
from tensorflow.keras.models import load_model
model.save('model.h5')
model = load_model('model.h5')
model_json = model.to_json()
with open("model.json", "w") as json_file:
json_file.write(model_json)
and this to load data:-
from tensorflow.keras.models import model_from_json
json_file = open('model.json', 'r')
model_json = json_file.read()
model2 = model_from_json(model_json)
model2.load_weights("model.h5")