Question

我已经编写了代码并成功运行并保存了模型，但是如何检查数据

max_features = 1000
maxlen = 201
embedding_dims = 50
filters = 250
kernel_size = 3
hidden_dims = 250

print('Build model...')
model = Sequential()
# we start off with an efficient embedding layer which maps
# our vocab indices into embedding_dims dimensions
model.add(Embedding(max_features,
                    embedding_dims,
                    input_length=maxlen))
model.add(Dropout(0.2))


# we add a Convolution1D, which will learn filters
# word group filters of size filter_length:
model.add(Conv1D(filters,
                 kernel_size,
                 padding='valid',
                 activation='relu',
                 strides=1))
# we use max pooling:
model.add(GlobalMaxPooling1D())

# We add a vanilla hidden layer:
model.add(Dense(hidden_dims))
model.add(Dropout(0.2))
model.add(Activation('relu'))

# We project onto a single unit output layer, and squash it with a sigmoid:
model.add(Dense(11))
model.add(Activation('softmax'))


# We project onto a single unit output layer, and squash it with a sigmoid:
model.add(Dense(11))
model.add(Activation('softmax'))

model.compile(loss='categorical_crossentropy',
              optimizer='rmsprop',
              metrics=['accuracy'])






model.fit(x_train, y_train,
          batch_size=32,
          epochs=50,
          validation_data=(x_test, y_test))
model.save('/content/model.tflearn')



score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

Test loss: 0.22043222188949585
Test accuracy: 1.0

例如输入：model.predict（“某些文本”）输出：团队合作

我认为上述方法可以帮助我吗？我展示了一些例子，例如，我们需要通过数据功能创建一个功能，然后才能检查新数据。

有什么方法可以检查数据模型吗？

0 个答案: