I am writing a custom training loop to train my model using Adam optimizer and custom-written Binary Crossentropy loss function instead of using the inbuilt Binary Crossentropy loss function. When I use my custom-written loss function, the loss of the model doesn’t seem to decrease steeply as it does in the case when I use the inbuilt Binary Crossentropy.
The following notebook shows my code and outputs.
model = tf.keras.Sequential([
    tf.keras.layers.Rescaling(1./255),
    tf.keras.layers.Conv2D(16, (3, 3), activation='relu'),
    tf.keras.layers.BatchNormalization(),
    tf.keras.layers.MaxPooling2D(2, 2),
    tf.keras.layers.Dropout(0.25),
    tf.keras.layers.Conv2D(32, (3, 3), activation='relu'),
    tf.keras.layers.BatchNormalization(),
    tf.keras.layers.MaxPooling2D(2, 2),
    tf.keras.layers.Dropout(0.25),
    tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
    tf.keras.layers.BatchNormalization(),
    tf.keras.layers.MaxPooling2D(2, 2),
    tf.keras.layers.Dropout(0.25),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(512, activation='relu'),
    tf.keras.layers.BatchNormalization(),
    tf.keras.layers.Dropout(0.5),
    tf.keras.layers.Dense(1, activation='sigmoid')
])

class BCE(tf.keras.losses.Loss):
#   @tf.function
  def call(self, y_true, y_pred):
    y_pred = tf.convert_to_tensor(y_pred)
    y_true = tf.cast(y_true, y_pred.dtype)
    bce = y_true * tf.math.log(y_pred + 1e-07) + (1 - y_true) * tf.math.log(1 - y_pred + 1e-07)
    return -K.mean(bce)

optimizer = tf.keras.optimizers.Adam()
loss_fn = BCE()

@tf.function
def train_step(x, y):
    with tf.GradientTape() as tape:
        logits = model(x, training=True)
        loss_value = loss_fn(y, logits)
    grads = tape.gradient(loss_value, model.trainable_weights)
    optimizer.apply_gradients(zip(grads, model.trainable_weights))
    return loss_value

epochs = 10
for epoch in range(epochs):
    print("\nStart of epoch %d" % (epoch,))
    loss_over_epoch = 0
    # Iterate over the batches of the dataset.
    for x_batch_train, y_batch_train in training_dataset:
        loss_over_epoch += train_step(x_batch_train, y_batch_train)
    
    print("Loss over the epoch: ", float(loss_over_epoch / len(training_dataset)))

Start of epoch 0
Loss over the epoch:  0.723311185836792

Start of epoch 1
Loss over the epoch:  0.700799822807312

Start of epoch 2
Loss over the epoch:  0.6997634172439575

Start of epoch 3
Loss over the epoch:  0.6988803148269653

Start of epoch 4
Loss over the epoch:  0.6978280544281006

Start of epoch 5
Loss over the epoch:  0.6973050832748413

Start of epoch 6
Loss over the epoch:  0.696485698223114

Start of epoch 7
Loss over the epoch:  0.6961873769760132

Start of epoch 8
Loss over the epoch:  0.6955927014350891

Start of epoch 9
Loss over the epoch:  0.6953248977661133

But if I use
loss_fn = tf.keras.losses.BinaryCrossentropy()

I get the following statistics
Start of epoch 0
Loss over the epoch:  0.7039625644683838

Start of epoch 1
Loss over the epoch:  0.6281335949897766

Start of epoch 2
Loss over the epoch:  0.5979914665222168

Start of epoch 3
Loss over the epoch:  0.5627148151397705

Start of epoch 4
Loss over the epoch:  0.5050505995750427

Start of epoch 5
Loss over the epoch:  0.4839542806148529

Start of epoch 6
Loss over the epoch:  0.47102412581443787

Start of epoch 7
Loss over the epoch:  0.4398611783981323

Start of epoch 8
Loss over the epoch:  0.41520529985427856

Start of epoch 9
Loss over the epoch:  0.40074270963668823

What is the mistake I am committing in my custom loss function?