How to reduce false positives and false negatives on train set in deep learning

I have training a deep neural network for classification task on my machine learning dataset.

On train as well as test set below are the observations:

For every true positive there are approx 3 false positive.

For approx 4 true negatives there is 1 false negatives

The data is scaled with standardscaler and then clipped between -5 and 5. Below are observations while training.

382/382 [==============================] - 3s 9ms/step - loss: 0.6897 - tp: 84096.0000 - fp: 244779.0000 - tn: 355888.0000 - fn: 97448.0000 - accuracy: 0.5625 - precision: 0.2557 - recall: 0.4632 - auc: 0.5407 - prc: 0.2722
val_loss: 0.6838 - val_tp: 19065.0000 - val_fp: 56533.0000 - val_tn: 91902.0000 - val_fn: 23829.0000 - val_accuracy: 0.5800 - val_precision: 0.2522 - val_recall: 0.4445 - val_auc: 0.5468 - val_prc: 0.2722

Can someone expert please help to let me know what can I do to minimise false classification on train as well as test set.

I am using imbalanced dataset with class_weight as shown in below code:-

METRICS = [
      keras.metrics.TruePositives(name='tp'),
      keras.metrics.FalsePositives(name='fp'),
      keras.metrics.TrueNegatives(name='tn'),
      keras.metrics.FalseNegatives(name='fn'), 
      keras.metrics.BinaryAccuracy(name='accuracy'),
      keras.metrics.Precision(name='precision'),
      keras.metrics.Recall(name='recall'),
      keras.metrics.AUC(name='auc'),
      keras.metrics.AUC(name='prc', curve='PR'), # precision-recall curve
]

pos = sum(y_train)
neg = y_train.shape[0] - pos
total = y_train.shape[0]

weight_for_0 = (1 / neg) * (total / 2.0)
weight_for_1 = (1 / pos) * (total / 2.0)
class_weight = {0: weight_for_0, 1: weight_for_1}

def make_model(size, layers, metrics=METRICS, output_bias=None):
    if output_bias is not None:
        output_bias = tf.keras.initializers.Constant(output_bias)
    model = keras.Sequential()
    model.add(keras.layers.Dense(size,input_shape=(window_length*indicators,)))
    model.add(keras.layers.Dropout(0.5))
    for i in range(layers-1):
        model.add(keras.layers.Dense(size))
        model.add(keras.layers.Dropout(0.5))
    model.add(keras.layers.Dense(1, activation = "sigmoid", bias_initializer=output_bias))
    model.compile(
        optimizer=keras.optimizers.Adam(learning_rate=0.0001),
        loss=tf.keras.losses.BinaryCrossentropy(),
        metrics=metrics)
    return model


EPOCHS = 100
BATCH_SIZE = 2048

early_stopping = tf.keras.callbacks.EarlyStopping(
    monitor='val_prc', 
    verbose=1,
    patience=10,
    mode='max',
    restore_best_weights=True)

model = make_model(size = size,layers=layers, output_bias=np.log(pos/neg))    
history = model.fit(X_train,y_train, batch_size=BATCH_SIZE, epochs=EPOCHS,callbacks=[early_stopping],validation_data=(X_test, y_test_pos)
                            ,class_weight=class_weight
                            )

Can somebody please help