I'm new to machine learning and I'm building an RNN classifier for a problem similar to Name Entity Recognition (NER) but with only two tags.
I followed a tutorial to build the classifier, and now when fitting the model, I get a constant validation accuracy for all the epochs, and some part of me thinks this may be a mistake. Is it normal to have a constant val_accuracy ?
this is my model:
input = Input(shape=(66,))
word_embedding_size = 66
model = Embedding(input_dim=n_words, output_dim=word_embedding_size, input_length=66)(input)
model = Bidirectional(LSTM(units=word_embedding_size,
return_sequences=True,
dropout=0.5,
recurrent_dropout=0.5,
kernel_initializer=k.initializers.he_normal()))(model)
model = LSTM(units=word_embedding_size * 2,
return_sequences=True,
dropout=0.5,
recurrent_dropout=0.5,
kernel_initializer=k.initializers.he_normal())(model)
model = TimeDistributed(Dense(n_tags, activation="sigmoid"))(model)
out = model
model = Model(input, out)
adam = k.optimizers.Adam(lr=0.0005, beta_1=0.9, beta_2=0.999)
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.summary()
history = model.fit(X, np.array(Y), batch_size=256, epochs=10, validation_split=0.3, verbose=1)
and this is how the epoch look
Epoch 1/10
2/2 [==============================] - 2s 801ms/step - loss: 0.6990 - accuracy: 0.3123 - val_loss: 0.5732 - val_accuracy: 0.9675
Epoch 2/10
2/2 [==============================] - 1s 334ms/step - loss: 0.5552 - accuracy: 0.9713 - val_loss: 0.4202 - val_accuracy: 0.9675
Epoch 3/10
2/2 [==============================] - 1s 310ms/step - loss: 0.3997 - accuracy: 0.9723 - val_loss: 0.2377 - val_accuracy: 0.9675
Epoch 4/10
2/2 [==============================] - 1s 303ms/step - loss: 0.2260 - accuracy: 0.9723 - val_loss: 0.1168 - val_accuracy: 0.9675
Epoch 5/10
2/2 [==============================] - 1s 312ms/step - loss: 0.1126 - accuracy: 0.9723 - val_loss: 0.0851 - val_accuracy: 0.9675