I have an autoencoder that is supposed to create variation of the input text but this is the output: what went wrong. during evaluation it had an accuracy of 76%. Below is the code

TensorFlow General Discussion
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loaded_model = load_model(“vec_drug”)
loaded_layer = loaded_model.layers[0]
vocab = loaded_layer.get_vocabulary()

def get_word(v):

v = tf.convert_to_tensor(v)
transformed = ""
for each in tf.squeeze(v):
    transformed= transformed+ " "+ vocab[each]

return transformed

inputs = Input(shape=(max_len,))
encoder1 = Embedding(vocab_size, 128)(inputs)
encoder2 = LSTM(128)(encoder1)
encoder3 = RepeatVector(max_len)(encoder2)

decoder1 = LSTM(128, return_sequences=True)(encoder3)
outputs = TimeDistributed(Dense(vocab_size, activation=‘softmax’))(decoder1)

model = keras.Model(inputs=inputs, outputs=outputs)
model.load_weights(“weights-improvement-01-0.76.hdf5”)
model.compile(optimizer=“adam”, loss=“sparse_categorical_crossentropy”, metrics=[“accuracy”])

model.save(“drugs_model”, save_format=“tf”)
saved_model = load_model(“drugs_model”)

inputs = Input(shape=(1,), dtype=‘string’)
x = vectorize_text(inputs)
x = tf.reshape(x,[1,120])
output = saved_model(x)

end_model = keras.Model(inputs, output)
end_model.compile(loss=“sparse_categorical_crossentropy”, optimizer=“adam”, metrics=[‘accuracy’])
end_model.save(“full_model”, save_format=“tf”)

full_model = load_model(“full_model”)

def generate_text(seed_text, next_words, model,max_sequence_len,temp):

words = []
prediction = model.predict([seed_text])
for i in np.round(prediction[0],3):
    words.append(np.argmax(i))
    
words = np.array(words)
output_text = get_word(words)

return output_text

seed = “Do not use in larger or smaller amounts or for longer than recommended.”

created_text = generate_text(seed,5,end_model,20,1)

print(created_text)

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