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I'm training a neural network using Python Keras package. I care about the True Positive rate, so I added it to Callbacks and Metrics. Surprisingly, I'm getting different results using the same formula (Callbacks shows 81%, which is correct: I can see the same manually after I join Labels and Predictions; Metrics shows higher, around 86%). What is the matter? Any comment on the code is also appreciated

def sensitivity(y_true, y_pred):
 true_positives = K.sum(K.round(K.clip(y_true, 0, 1)) * K.round(K.clip(y_pred, 0, 1)))
 possible_positives = K.sum(y_true)
 return true_positives / (possible_positives + K.epsilon())
....
def calculate_rates(model, data, label):
 num_positive_prediction = np.sum(label)
 prediction = np.round(np.clip(model.predict(data, batch_size = 1024)[:,1], 0, 1))
 true_positive = np.sum(np.multiply(prediction, label)) / num_positive_prediction
 return(true_positive)
....
class TestCallback(keras.callbacks.Callback):
 def __init__(self, is_train, data, labels):
 self.data = data
 self.labels = labels[:,1]
 self.is_train = is_train

 def on_epoch_end(self, epoch, logs=):
 true_positive = calculate_rates(model, self.data, self.labels)
 if (epoch + 1) % 10 == 0 or epoch == 0:
 if self.is_train:
 print("Epoch: %d" % epoch + 1)
 print("Training Set:")
 else:
 print("Testing Set:")
 print("True Positive Rate: %4g" % true_positive)
....
model = keras.Sequential()
my_init = keras.initializers.RandomNormal(stddev=0.1)
model.add(keras.layers.Dense(units=200, activation='relu', input_dim=num_variables))
model.add(keras.layers.Dropout(dropout_rate))
model.add(keras.layers.Dense(units = 200, activation = 'relu', kernel_initializer=my_init, bias_initializer=my_init))
model.add(keras.layers.Dropout(dropout_rate))
model.add(keras.layers.Dense(units=num_outputs, activation='softmax', kernel_initializer=my_init, bias_initializer=my_init))
model.compile(loss='binary_crossentropy',
 optimizer=keras.optimizers.Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-8),
 metrics=[sensitivity]
 )
history = model.fit(train_data, train_labels, epochs=num_epochs, batch_size=1024, class_weight=0: 1, 1: weight,
 callbacks=[TestCallback(1, train_data, train_labels), TestCallback(0, test_data, test_labels)], verbose=1)