我使用countvector获得注释中每个单词的向量，并将其用作神经网络的输入数据。但是，总有问题。代码和错误如下：

train_X = vectorizer.transform(train_dataframe['comment'])
valid_X = vectorizer.transform(valid_dataframe['comment'])
test_X = vectorizer.transform(test_dataframe['comment'])
print (train_X.shape)
print (valid_X.shape)
print (test_X.shape)

train_Y = train_dataframe['label'].to_numpy()
valid_Y = valid_dataframe['label'].to_numpy()

train_inputs=train_X
train_targets=train_Y
validation_inputs=valid_X
validation_targets=valid_Y
# Set the input and output sizes
input_size = 31124
output_size = 1
# Use same hidden layer size for both hidden layers. Not a necessity.
hidden_layer_size = 50

# define how the model will look like
model = tf.keras.Sequential([
    # tf.keras.layers.Dense is basically implementing: output = activation(dot(input,weight) + bias)
    # it takes several arguments,but the most important ones for us are the hidden_layer_size and the activation function
    tf.keras.layers.Dense(hidden_layer_size,activation='relu'),# 1st hidden layer
    tf.keras.layers.Dense(hidden_layer_size,# 2nd hidden layer
    # the final layer is no different,we just make sure to activate it with softmax
    tf.keras.layers.Dense(output_size,activation='sigmoid') # output layer
])

model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])

### Training
# That's where we train the model we have built.

# set the batch size
batch_size = 100

# set a maximum number of training epochs
max_epochs = 100


# fit the model
# note that this time the train,validation and test data are not iterable
model.fit(train_inputs,# train inputs
          train_targets,# train targets
          batch_size=batch_size,# batch size
          epochs=max_epochs,# epochs that we will train for (assuming early stopping doesn't kick in)
          validation_data=(validation_inputs,validation_targets),# validation data
          verbose = 2 # making sure we get enough information about the training process
          )  

test_loss,test_accuracy = model.evaluate(test_inputs,test_targets)
print('\nTest loss: {0:.2f}. Test accuracy: {1:.2f}%'.format(test_loss,test_accuracy*100.))

错误是：

 Please provide as model inputs either a single array or a list of arrays. You passed: x=  (0,1404)    1
  (0,4453) 2
  (0,6653) 1
  (0,8151) 1
  (0,11070)    1
  (0,14557)    1
  (1,817)  1
  (1,1134) 1
  (1,1813) 1
  (1,1827) 1
  (1,2151) 1
  (1,4505) 1
  (1,4647) 1
  (1,8244) 2
  (1,8296) 1
  (1,8332) 1
  (1,9109) 1
  (1,9611) 1
  (1,10080)    1
  (1,10791)    1
  (1,11821)    1
  (1,12714)    1
  (1,12760)    1
  (1,13665)    1
  (1,14349)    1
  : :
  (42423,16238)    1
  (42423,17253)    1
  (42423,18627)    1
  (42423,19322)    1
  (42423,19811)    1
  (42423,21232)    1
  (42423,23128)    1
  (42423,25572)    1
  (42423,25681)    1
  (42423,27132)    1
  (42423,27568)    2
  (42423,27580)    1
  (42423,27933)    1
  (42423,30921)    2
  (42424,932)  1
  (42424,4078) 1
  (42424,10791)    1
  (42424,10835)    1
  (42424,27628)    1
  (42424,27933)    1
  (42424,30220)    1
  (42425,1813) 1
  (42425,13868)    1
  (42425,27580)    1
  (42425,28749)    1