I have the coco 2014 dataset and need to train it as training is around 82700 and testing is 40500. However, I got the same sentence with different values every time with model.pedict() as I used one epoch only. I tried to increase epochs
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]
Total params: 13,439,270
I set
history = model.fit(train_generator, epochs=100, steps_per_epoch=train_steps, verbose=1, callbacks=[checkpoint], validation_data=val_generator, validation_steps=val_steps,batch_size=64)
and got
Epoch 00001 loss: 4.6360 - accuracy: 0.2506 - val_loss: 4.1580 - val_accuracy: 0.2970
Epoch 00002 loss: 4.0904 - accuracy: 0.3026 - val_loss: 3.9843 - val_accuracy: 0.3134
Epoch 00003 loss: 3.9805 - accuracy: 0.3123 - val_loss: 3.9290 - val_accuracy: 0.3192
Epoch 00004 loss: 3.9422 - accuracy: 0.3169 - val_loss: 3.9061 - val_accuracy: 0.3223
Epoch 00005 loss: 3.9311 - accuracy: 0.3188 - val_loss: 3.8962 - val_accuracy: 0.3242
Epoch 00006 loss: 3.9335 - accuracy: 0.3196 - val_loss: 3.9165 - val_accuracy: 0.3229
Epoch 00006: val_loss did not improve from 3.89620
Epoch 00007 loss: 3.9437 - accuracy: 0.3196 - val_loss: 3.9297 - val_accuracy: 0.3241
Epoch 00007: val_loss did not improve from 3.89620
is there any wrong in the model or what exactly i can change for improve the results ?
Appreciating any help