def define_model(vocab_size, max_length, curr_shape):
    inputs1 = Input(shape=curr_shape)
    fe1 = Dropout(0.5)(inputs1)
    fe2 = Dense(256, activation='relu')(fe1)
    model = tf.keras.models.Sequential()
    inputs2 = Input(shape=(max_length,))
    se1 = Embedding(vocab_size, 256, mask_zero=True)(inputs2)
    se2 = Dropout(0.5)(se1)
    se3 = LSTM(256)(se2)
    decoder1 =Concatenate()([fe2, se3])
    decoder2 = Dense(256, activation='relu')(decoder1)
    outputs = Dense(vocab_size, activation='softmax')(decoder2)
    model = Model(inputs=[inputs1, inputs2], outputs=outputs)
    model.compile(loss='categorical_crossentropy', optimizer='Adam', metrics=['accuracy'])
    model.summary()
    return model

the model as follows
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_2 (InputLayer)            [(None, 49)]         0                                            
__________________________________________________________________________________________________
input_1 (InputLayer)            [(None, 1120)]       0                                            
__________________________________________________________________________________________________
embedding (Embedding)           (None, 49, 256)      6235648     input_2[0][0]                    
__________________________________________________________________________________________________
dropout (Dropout)               (None, 1120)         0           input_1[0][0]                    
__________________________________________________________________________________________________
dropout_1 (Dropout)             (None, 49, 256)      0           embedding[0][0]                  
__________________________________________________________________________________________________
dense (Dense)                   (None, 256)          286976      dropout[0][0]                    
__________________________________________________________________________________________________
lstm (LSTM)                     (None, 256)          525312      dropout_1[0][0]                  
__________________________________________________________________________________________________
concatenate (Concatenate)       (None, 512)          0           dense[0][0]                      
                                                                 lstm[0][0]                       
__________________________________________________________________________________________________
dense_1 (Dense)                 (None, 256)          131328      concatenate[0][0]                
__________________________________________________________________________________________________
dense_2 (Dense)                 (None, 24358)        6260006     dense_1[0][0]     

i set history = model.fit(train_generator, epochs=1,  steps_per_epoch=train_steps, verbose=1, callbacks=[checkpoint],  validation_data=val_generator, validation_steps=val_steps)
and got one sentence in model.predict() every time … how can i defined number of epochs or learning rate well to make model better. My dataset is COCO while training set is 82700 and testing is 40500 . The goal for the model is to make image captioning