python - TypeError:维度值必须是整数或无或具有 __index__ 方法,得到 TensorShape([None, 1])
问题描述
我正在尝试将我发现的使用 tflearn 的机器人教程转换为使用 keras。现在输出应该是模型预测的数据。但是,我收到错误:
TypeError:维度值必须是整数或无或具有 __index__ 方法,得到 TensorShape([None, 1])
我的代码:
with open("intents.json") as file:
data = json.load(file)
try:
with open("data.pickle", "rb") as f:
words, lables, training, output = pickle.load(f)
except:
words = []
labels = []
docs_x = []
docs_y = []
for intent in data["intents"]:
for pattern in intent["patterns"]:
wrds = nltk.word_tokenize(pattern)
words.extend(wrds)
docs_x.append(wrds)
docs_y.append(intent["tag"])
if intent["tag"] not in labels:
labels.append(intent["tag"])
words = [stemmer.stem(w.lower()) for w in words if w != "?"]
words = sorted(list(set(words)))
labels = sorted(labels)
training = []
output = []
out_empty = [0 for _ in range(len(labels))]
for x, doc in enumerate(docs_x):
bag = []
wrds = [stemmer.stem(w) for w in doc]
# marks if a word is present in the input (1=yes, 0=no)
for w in words:
if w in wrds:
bag.append(1)
else:
bag.append(0)
output_row = out_empty[:]
output_row[labels.index(docs_y[x])] = 1
training.append(bag)
output.append(output_row)
training = numpy.array(training)
output = numpy.array(output)
with open("data.pickle", "wb") as f:
pickle.dump((words, labels, training, output), f)
tf.keras.backend.clear_session()
model = tf.keras.Sequential()
model.add(keras.layers.Dense(len(training[0])))
model.add(keras.layers.Dense(8))
model.add(keras.layers.Dense(8))
model.add(keras.layers.Dense(len(output[0]), activation="softmax"))
try:
keras.models.load_model("model.tflearn")
except:
model.compile(optimizer="adam", loss="categorical_crossentropy", metrics="accuracy")
model.fit(training, output, epochs=1000, batch_size=8)
model.save("model.tflearn")
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:
for i, w in enumerate(words):
if w == se:
bag[i] = 1
return numpy.array(bag)
def chat():
print("Ready to chat! (type quit to stop)")
while True:
inp = input("You: ")
if inp.lower() == "quit":
break
results = model.predict([bag_of_words(inp, words)])
print(results)
chat()
使用调试模式时,它告诉我错误是行(在聊天()内部):
结果 = model.predict([bag_of_words(inp, words)])
解决方案
不确定您是否仍然对答案感兴趣,因为已经有一段时间了。但是,当我编写自定义 keras 层时,我遇到了同样的问题。当我对图层的输入预计是张量但我提供了一个列表(带有单个元素)时,我也发生了同样的错误。可能要换
results = model.predict([bag_of_words(inp, words)])
经过
results = model.predict(bag_of_words(inp, words))
将把戏
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