Keras: model.evaluate vs model.predict accuracy di

I am training a simple model in keras for NLP task with following code. Variable names are self explanatory for train, test and validation set. This dataset has 19 classes so final layer of the network has 19 outputs. Labels are also one-hot encoded.

nb_classes = 19
model1 = Sequential()
model1.add(Embedding(nb_words,
                     EMBEDDING_DIM,
                     weights=[embedding_matrix],
                     input_length=MAX_SEQUENCE_LENGTH,
                     trainable=False))
model1.add(LSTM(num_lstm, dropout=rate_drop_lstm, recurrent_dropout=rate_drop_lstm))
model1.add(Dropout(rate_drop_dense))
model1.add(BatchNormalization())
model1.add(Dense(num_dense, activation=act))
model1.add(Dropout(rate_drop_dense))
model1.add(BatchNormalization())

model1.add(Dense(nb_classes, activation = 'sigmoid'))


model1.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
#One hot encode all labels
ytrain_enc = np_utils.to_categorical(train_labels)
yval_enc = np_utils.to_categorical(val_labels)
ytestenc = np_utils.to_categorical(test_labels)

model1.fit(train_data, ytrain_enc,
             validation_data=(val_data, yval_enc),
             epochs=200,
             batch_size=384,
             shuffle=True,
             verbose=1)

After first epoch, this gives me these outputs.

Epoch 1/200
216632/216632 [==============================] - 2442s - loss: 0.1427 - acc: 0.9443 - val_loss: 0.0526 - val_acc: 0.9826

Then I evaluate my model on testing dataset and this also shows me accuracy around 0.98.

model1.evaluate(test_data, y = ytestenc, batch_size=384, verbose=1)

However, the labels are one-hot encoded, so I need prediction vector of classes so that I can generate confusion matrix etc. So I use,

PREDICTED_CLASSES = model1.predict_classes(test_data, batch_size=384, verbose=1)
temp = sum(test_labels == PREDICTED_CLASSES)
temp/len(test_labels)
0.83

This shows that total predicted classes were 83% accurate however model1.evaluate shows 98% accuracy!! What am I doing wrong here? Is my loss function okay with categorical class labels? Is my choice of sigmoid activation function for prediction layer okay? or there is difference in the way keras evaluates a model? Please suggest on what can be wrong. This is my first try to make a deep model so I don't have much understanding of what's wrong here.

I have found the problem. metrics=['accuracy'] calculates accuracy automatically from cost function. So using binary_crossentropy shows binary accuracy, not categorical accuracy. Using categorical_crossentropy automatically switches to categorical accuracy and now it is the same as calculated manually using model1.predict(). Yu-Yang was right to point out the cost function and activation function for multi-class problem.

P.S: One can get both categorical and binary accuracy by using metrics=['binary_accuracy', 'categorical_accuracy']