Predict Method Gives Error for Created Model

I built a model for a NLP problem and tried to make predictions by using it. It gives an error for both LSTM and RNN:

ValueError: in user code:

    File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1621, in predict_function  *
        return step_function(self, iterator)
    File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1611, in step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1604, in run_step  **
        outputs = model.predict_step(data)
    File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1572, in predict_step
        return self(x, training=False)
    File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 67, in error_handler
        raise e.with_traceback(filtered_tb) from None
    File "/usr/local/lib/python3.7/dist-packages/keras/engine/input_spec.py", line 213, in assert_input_compatibility
        raise ValueError(f'Input {input_index} of layer "{layer_name}" '

    ValueError: Exception encountered when calling layer "sequential_33" (type Sequential).
    
    Input 0 of layer "gru_8" is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 300)
    
    Call arguments received:
      • inputs=tf.Tensor(shape=(None,), dtype=int32)
      • training=False
      • mask=None

Training phase was successful, I don't know what I am doing wrong. I can get model_weights but can't make predictions. Here is the code below.

model = Sequential()
model.add(layers.Embedding(vocab_size, embedding_dim, input_length=maxlen))
model.add(layers.GRU(64, return_sequences=True))
model.add(layers.GlobalMaxPool1D())
model.add(layers.Dropout(0.4))
model.add(layers.Dense(8, activation='relu'))
model.add(layers.Dropout(0.4))
model.add(layers.Dense(4, activation='relu'))
model.add(layers.Dropout(0.4))
model.add(layers.Dense(3,activation='softmax'))


model_path= "sentiment labelled sentences/generic sentiment models/w4/model{epoch:04d}.hdf5"
check=ModelCheckpoint(model_path, monitor='val_loss', verbose=0, save_best_only=False, save_weights_only=False, mode='auto',save_freq='epoch') #modeli her epoch sonunda kaydet
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])
model.get_weights()
history = model.fit(X_train, y_train,
                    epochs=15,
                    validation_data=(X_test, y_test),
                    batch_size=150, callbacks=[check])

model.predict(X_test[0])

Solution 1:

Answering my own question, I specified batch_size at predict method and it works now.

Solution 2:

first you should specify batch_size if you pass just one sample second you should reshape your sample to match input data shape, for example if you wanna just pass an image with size 2242243 you could reshape it before passing to models as follow: