title: An error occurs when predict with the same data as when performing train (expects 3 input(s), but it received 75 input tensors.)
body: <p>After training the model, I tried to make predictions, but an error occurred and I don't know how to fix it.</p>
  <p>The model was constructed using electra.</p>
  <p><strong>here is my model</strong></p>
  <pre><code>electra = TFElectraModel.from_pretrained(&quot;monologg/koelectra-base-v3-discriminator&quot;, from_pt=True)
  input_ids = tf.keras.Input(shape=(MAX_LEN,), name='input_ids', dtype=tf.int32)
  mask = tf.keras.Input(shape=(MAX_LEN,), name='attention_mask', dtype=tf.int32)
  token = tf.keras.Input(shape=(MAX_LEN,), name='token_type_ids', dtype=tf.int32)
  embeddings = electra(input_ids, attention_mask = mask, token_type_ids= token)[0]
  X = tf.keras.layers.GlobalMaxPool1D()(embeddings)
  X = tf.keras.layers.BatchNormalization()(X)
  X = tf.keras.layers.Dense(128, activation='relu')(X)
  X = tf.keras.layers.Dropout(0.1)(X)
  y = tf.keras.layers.Dense(3, activation='softmax', name='outputs')(X)
  model = tf.keras.Model(inputs=[input_ids, mask, token], outputs=y)
  model.layers[2].trainable=False
  model.summary()
  </code></pre>
  <p><strong>and here is summary</strong></p>
  <pre><code>__________________________________________________________________________________________________
  Layer (type)                    Output Shape         Param #     Connected to                     
  ==================================================================================================
  input_ids (InputLayer)          [(None, 25)]         0                                            
  __________________________________________________________________________________________________
  attention_mask (InputLayer)     [(None, 25)]         0                                            
  __________________________________________________________________________________________________
  token_type_ids (InputLayer)     [(None, 25)]         0                                            
  __________________________________________________________________________________________________
  tf_electra_model_4 (TFElectraMo TFBaseModelOutput(la 112330752   input_ids[0][0]                  
                                                                   attention_mask[0][0]             
                                                                   token_type_ids[0][0]             
  __________________________________________________________________________________________________
  global_max_pooling1d_6 (GlobalM (None, 768)          0           tf_electra_model_4[3][0]         
  __________________________________________________________________________________________________
  batch_normalization_7 (BatchNor (None, 768)          3072        global_max_pooling1d_6[0][0]     
  __________________________________________________________________________________________________
  dense_18 (Dense)                (None, 128)          98432       batch_normalization_7[0][0]      
  __________________________________________________________________________________________________
  dropout_390 (Dropout)           (None, 128)          0           dense_18[0][0]                   
  __________________________________________________________________________________________________
  outputs (Dense)                 (None, 3)            387         dropout_390[0][0]                
  ==================================================================================================
  Total params: 112,432,643
  Trainable params: 112,431,107
  Non-trainable params: 1,536
  __________________________________________________________________________________________________
  </code></pre>
  <p><strong>This is the code to make train data set.</strong></p>
  <pre><code>input_ids = []
  attention_masks = []
  token_type_ids = []
  train_data_labels = []
  
  for train_sent, train_label in tqdm(zip(train_data[&quot;content&quot;], train_data[&quot;label&quot;]), total=len(train_data)):
      try:
          input_id, attention_mask, token_type_id = Electra_tokenizer(train_sent, MAX_LEN)
          input_ids.append(input_id)
          attention_masks.append(attention_mask)
          token_type_ids.append(token_type_id)
          train_data_labels.append(train_label)
  
      except Exception as e:
          print(e)
          print(train_sent)
          pass
  
  train_input_ids = np.array(input_ids, dtype=int)
  train_attention_masks = np.array(attention_masks, dtype=int)
  train_type_ids = np.array(token_type_ids, dtype=int)
  intent_train_inputs = (train_input_ids, train_attention_masks, train_type_ids)
  intent_train_data_labels = np.asarray(train_data_labels, dtype=np.int32)
  </code></pre>
  <p><strong>this is train data set shape</strong></p>
  <pre><code>tf.Tensor([ 3 75 25], shape=(3,), dtype=int32)
  </code></pre>
  <p><strong>With this train data, the model train works fine but execute the following code to predict, an error occurs.</strong></p>
  <pre><code>sample_text = 'this is sample text'
  input_id, attention_mask, token_type_id = Electra_tokenizer(sample_text, MAX_LEN)
  sample_text = (input_id, attention_mask, token_type_id)
  model(sample_text) #or model.predict(sample_text)
  </code></pre>
  <p><strong>here is error</strong></p>
  <pre><code>Layer model_15 expects 3 input(s), but it received 75 input tensors. Inputs received: [&lt;tf.Tensor: shape=(), dtype=int32, numpy=2&gt;, &lt;tf.Tensor: ....
  </code></pre>
  <p>It's the same shape as when i train, but why do i get an error and ask for help on how to fix it.</p>
  <p>hope you have a great year ahead. Happy New Year.</p>