except:
# Make our neural network
myChatModel = Sequential()
myChatModel.add(Dense(8, input_shape=[len(words)], activation='relu'))
myChatModel.add(Dense(len(labels), activation='softmax'))
# optimize the model
myChatModel.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
# train the model
myChatModel.fit(training, output, epochs=1000, batch_size=8)
# serialize model to yaml and save it to disk
model_yaml = myChatModel.to_yaml()
with open("chatbotmodel.yaml", "w") as y_file:
y_file.write(model_yaml)
# serialize weights to HDF5
myChatModel.save_weights("chatbotmodel.h5")
print("Saved model from disk")
def bag_of_words(s, words):
bag = [0 for _ in range(len(words))]
s_words = nltk.word_tokenize(s)
s_words = [stemmer.stem(word.lower()) for word in s_words]
for se in s_words:
addition_model.fit(input_data,
output_data,
batch_size=1,
epochs=100,
verbose=1)
# Modell wird gespeichert
addition_model.save("addition_model.h5")
# Und auch für TensorFlow.js!
# tfjs.converters.save_keras_model(addition_model, "./addition_model")
print("== Modell als JSON-Struktur ==")
pprint(addition_model.to_json())
pprint("== Modell als YAML-Struktur ==")
pprint(addition_model.to_yaml())
# Weights werden gespeichert
addition_model.save_weights("addition_weights.h5")
# Struktur des Modells wird als JSON gespeichert
json_str = addition_model.to_json()
with open("addition_model.json", "w") as json_file:
json_file.write(json_str)
# Modell wird neu geladen (vom .h5 Datei)
model = load_model('addition_model.h5')
result = model.predict([[[5,
5]]]) # Das Ergebnis müsste ungefähr bei 10 liegen
print("Ergebnis: {}".format(result))
